Risk and Exposure Assessment to Support
the Review of the NC>2 Primary National
Ambient Air Quality Standard: Second Draft
Appendices
-------
EPA-452/P-08-004b
August 2008
Risk and Exposure Assessment to Support
the Review of the NC>2 Primary National
Ambient Air Quality Standard: Second Draft
Appendices
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina
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Appendix A. Supplement to the NO2 Air Quality Characterization
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i Table of Contents
2
3 Appendix A. Supplement to the N02 Air Quality Characterization i
4 A-1 Overview 1
5 A-2 AirQuality Data Screen 2
6 A-2.1 Introduction 2
7 A-2.2 Approach 2
8 A-2.3 Results 2
9 A-3 Selection of Locations 5
10 A-3.1 Introduction 5
11 A-3.2 Approach 5
12 A-3.3 Results 5
13 A-4 Ambient Monitoring Site Characteristics 7
14 A-4.1 Introduction 7
15 A-4.2 Approach 7
16 A-4.3 Summary Results 8
17 A-4.4 Detailed Monitoring Site Characteristics 10
18 A-5 Spatial and Temporal Air Quality Analyses 23
19 A-5.1 Introduction 23
20 A-5.2 Approach 23
21 A-5.3 Summary Results by Locations 24
22 A-5.4 Summary Results by Year 29
23 A-5.5 Detailed Results by Year and Location 34
24 A-6 Technical Memorandum on Regression Modeling 78
25 A-6.1 Summary 78
26 A-6.2 Data Used 78
27 A-6.3 Regression Models 79
28 A-6.4 Conclusion 90
29 A-6.5 Detailed Regression Model Predictions 91
30 A-7 Air Quality Simulations 97
31 A-7.1 Introduction 97
32 A-7.2 Approach 98
33 A-8 Method for Estimating On-Road Concentrations 107
34 A-8.1 Introduction 107
35 A-8.2 Derivation of On-Road Factors 108
36 A-8.3 Application of On-Road Factors 110
37 A-8.4 Interpretation of Estimated On-Road Concentrations 111
38 A-9 Supplemental Results Tables 113
39 A-9.1 Results Tables of Historic N02 Ambient Monitoring Data (1995-2000)
40 Adjusted to Just Meeting the Current Standard 113
41 A-9.2 Results Tables of Recent N02 Ambient Monitoring Data (2001-2006) As Is
42 and Just Meeting the Current and Alternative Standards 117
43 A-10 References 195
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i List of Tables
2
3 Table A-1. Example of ambient monitor years of operation, using the Boston CMSA.... 3
4 Table A-2. Counts of complete site-years of NC^ monitoring data 4
5 Table A-3. Locations selected for N02 Air Quality Characterization, associated
6 abbreviations, and values of selection criteria 6
7 Table A-4. Distribution of the distance of ambient monitors to the nearest major road in
8 selected locations 8
9 Table A-5. Distribution of the distance of ambient monitors to stationary sources with
10 NOX emissions >5 tons per year and within a 10 kilometers radius 9
11 Table A-6. Distribution of NOX emissions from stationary sources within 10 kilometers of
12 monitoring site, where emissions were >5 tons per year 9
13 Table A-7. Attributes of location-specific ambient monitors used for air quality
14 characterization and the distance to nearest major roadway 11
15 Table A-8. Distance of location-specific ambient monitors to stationary sources emitting
16 > 5 tons of NOX per year, within a 10 kilometer distance of monitoring site.... 17
17 Table A-9. Statistical test results for spatial comparisons of all location parameter
18 distributions 27
19 Table A-10. Statistical test results for spatial comparisons of within location parameter
20 distributions 28
21 Table A-11. Distribution of annual average NC^ ambient concentrations (ppb) by year,
22 Boston CMSA 35
23 Table A-12. Distribution of hourly NC^ ambient concentrations (ppb) by year, Boston
24 CMSA 35
25 Table A-13. Distribution of annual average NC^ ambient concentration (ppb) by
26 monitor, Boston CMSA set A, 1995-2006 36
27 Table A-14. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Boston
28 CMSA set A, 1995-2006 36
29 Table A-15. Distribution of annual average NC^ ambient concentration (ppb) by
30 monitor, Boston CMSA set B, 1995-2006 37
31 Table A-16. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Boston
32 CMSA set B, 1995-2006 37
33 Table A-17. Distribution of annual average NC^ ambient concentrations (ppb) by year,
34 Chicago CMSA 38
35 Table A-18. Distribution of hourly NC^ ambient concentrations (ppb) by year, Chicago
36 CMSA 38
37 Table A-19. Distribution of annual average NC^ ambient concentration (ppb) by
38 monitor, Chicago CMSA, 1995-2006 39
39 Table A-20. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Chicago
40 CMSA, 1995-2006 39
41 Table A-21. Distribution of annual average NC^ ambient concentrations (ppb) by year,
42 Cleveland CMSA 40
43 Table A-22. Distribution of hourly NC^ ambient concentrations (ppb) by year, Cleveland
44 CMSA 40
45 Table A-23. Distribution of annual average NC^ ambient concentration (ppb) by
46 monitor, Cleveland CMSA, 1995-2006 41
in
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1 Table A-24. Distribution of hourly N02 ambient concentration (ppb) by monitor,
2 Cleveland CMSA, 1995-2006 41
3 Table A-25. Temporal distribution of annual average NC^ ambient concentrations (ppb)
4 by year, Denver CMSA 42
5 Table A-26. Temporal distribution of hourly NC^ ambient concentrations (ppb) by year,
6 Denver CMSA 42
7 Table A-27. Distribution of annual average NC^ ambient concentration (ppb) by
8 monitor, Denver CMSA, 1995-2006 43
9 Table A-28. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Denver
10 CMSA, 1995-2006 43
11 Table A-29. Distribution of annual average NC^ ambient concentrations (ppb) by year,
12 Detroit CMSA 44
13 Table A-30. Distribution of hourly NC^ ambient concentrations (ppb) by year, Detroit
14 CMSA 44
15 Table A-31. Distribution of annual average NC^ ambient concentration (ppb) by
16 monitor, Detroit CMSA, 1995-2006 45
17 Table A-32. Distribution of annual average NC^ ambient concentration (ppb) by
18 monitor, Detroit CMSA, 1995-2006 45
19 Table A-33. Distribution of annual average NC^ ambient concentrations (ppb) by year,
20 Los Angeles CMSA 46
21 Table A-34. Distribution of hourly NC^ ambient concentrations (ppb) by year, Los
22 Angeles CMSA 46
23 Table A-35. Distribution of annual average NC^ ambient concentration (ppb) by
24 monitor, Los Angeles CMSA set A, 1995-2006 47
25 Table A-36. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Los
26 Angeles CMSA set A, 1995-2006 47
27 Table A-37. Distribution of annual average NC^ ambient concentration (ppb) by
28 monitor, Los Angeles CMSA set B, 1995-2006 48
29 Table A-38. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Los
30 Angeles CMSA set B, 1995-2006 48
31 Table A-39. Distribution of annual average NC^ ambient concentration (ppb) by
32 monitor, Los Angeles CMSA set C, 1995-2006 49
33 Table A-40. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Los
34 Angeles CMSA set C, 1995-2006 49
35 Table A-41. Distribution of annual average NC^ ambient concentrations (ppb) by year,
36 Miami CMSA 50
37 Table A-42. Distribution of hourly NC^ ambient concentrations (ppb) by year, Miami
38 CMSA 50
39 Table A-43. Distribution of annual average NC^ ambient concentration (ppb) by
40 monitor, Miami CMSA, 1995-2006 51
41 Table A-44. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Miami
42 CMSA, 1995-2006 51
43 Table A-45. Distribution of annual average NC^ ambient concentrations (ppb) by year,
44 New York CMSA 52
45 Table A-46. Distribution of hourly NC^ ambient concentrations (ppb) by year, New York
46 CMSA 52
IV
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1 Table A-47. Distribution of annual average N02 ambient concentration (ppb) by
2 monitor, New York CMSA set A, 1995-2006 53
3 Table A-48. Distribution of hourly NC^ ambient concentration (ppb) by monitor, New
4 York CMSA set A, 1995-2006 53
5 Table A-49. Distribution of annual average N02 ambient concentration (ppb) by
6 monitor, New York CMSA set B, 1995-2006 54
7 Table A-50. Distribution of hourly NC^ ambient concentration (ppb) by monitor, New
8 York CMSA set B, 1995-2006 54
9 Table A-51. Distribution of annual average N02 ambient concentrations (ppb) by year,
10 Philadelphia CMSA 55
11 Table A-52. Distribution of hourly N02 ambient concentrations (ppb) by year,
12 Philadelphia CMSA 55
13 Table A-53. Distribution of annual average N02 ambient concentration (ppb) by
14 monitor, Philadelphia CMSA, 1995-2006 56
15 Table A-54. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
16 Philadelphia CMSA, 1995-2006 56
17 Table A-55. Distribution of annual average N02 ambient concentrations (ppb) by year,
18 Washington DC CMSA 57
19 Table A-56. Distribution of hourly NC^ ambient concentrations (ppb) by year,
20 Washington DC CMSA 57
21 Table A-57. Distribution of annual average N02 ambient concentration (ppb) by
22 monitor, Washington DC CMSA set A, 1995-2006 58
23 Table A-58. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
24 Washington DC CMSA set A, 1995-2006 58
25 Table A-59. Distribution of annual average N02 ambient concentration (ppb) by
26 monitor, Washington DC CMSA set B, 1995-2006 59
27 Table A-60. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
28 Washington DC CMSA set B, 1995-2006 59
29 Table A-61. Distribution of annual average N02 ambient concentrations (ppb) by year,
30 Atlanta MSA 60
31 Table A-62. Distribution of hourly NC^ ambient concentrations (ppb) by year, Atlanta
32 MSA 60
33 Table A-63. Distribution of annual average N02 ambient concentration (ppb) by
34 monitor, Atlanta MSA, 1995-2006 61
35 Table A-64. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Atlanta
36 MSA, 1995-2006 61
37 Table A-65. Distribution of annual average N02 ambient concentrations (ppb) by year,
38 Colorado Springs MSA 62
39 Table A-66. Distribution of hourly NC^ ambient concentrations (ppb) by year, Colorado
40 Springs MSA 62
41 Table A-67. Distribution of annual average N02 ambient concentration (ppb) by
42 monitor, Colorado Springs MSA, 1995-2006 63
43 Table A-68. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
44 Colorado Springs MSA, 1995-2006 63
45 Table A-69. Distribution of annual average N02 ambient concentrations (ppb) by year,
46 El Paso MSA 64
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1 Table A-70. Distribution of hourly N02 ambient concentrations (ppb) by year, El Paso
2 MSA 64
3 Table A-71. Distribution of annual average N02 ambient concentration (ppb) by
4 monitor, El Paso MSA, 1995-2006 65
5 Table A-72. Distribution of hourly NC^ ambient concentration (ppb) by monitor, El Paso
6 MSA, 1995-2006 65
7 Table A-73. Distribution of annual average N02 ambient concentrations (ppb) by year,
8 Jacksonville MSA 66
9 Table A-74. Distribution of hourly NC^ ambient concentrations (ppb) by year,
10 Jacksonville MSA 66
11 Table A-75. Distribution of annual average N02 ambient concentration (ppb) by
12 monitor, Jacksonville MSA, 1995-2006 67
13 Table A-76. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
14 Jacksonville MSA, 1995-2006 67
15 Table A-77. Distribution of annual average N02 ambient concentrations (ppb) by year,
16 Las Vegas MSA 68
17 Table A-78. Distribution of hourly NC^ ambient concentrations (ppb) by year, Las
18 Vegas MSA 68
19 Table A-79. Distribution of annual average N02 ambient concentration (ppb) by
20 monitor, Las Vegas MSA, 1995-2006 69
21 Table A-80. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Las
22 Vegas MSA, 1995-2006 69
23 Table A-81. Distribution of annual average N02 ambient concentrations (ppb) by year,
24 Phoenix MSA 70
25 Table A-82. Distribution of hourly NC^ ambient concentrations (ppb) by year, Phoenix
26 MSA 70
27 Table A-83. Distribution of annual average N02 ambient concentration (ppb) by
28 monitor, Phoenix MSA, 1995-2006 71
29 Table A-84. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Phoenix
30 MSA, 1995-2006 71
31 Table A-85. Distribution of annual average N02 ambient concentrations (ppb) by year,
32 ProvoMSA 72
33 Table A-86. Distribution of hourly NC^ ambient concentrations (ppb) by year, Provo
34 MSA 72
35 Table A-87. Distribution of annual average N02 ambient concentration (ppb) by
36 monitor, Provo MSA, 1995-2006 73
37 Table A-88. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Provo
38 MSA, 1995-2006 73
39 Table A-89. Distribution of annual average N02 ambient concentrations (ppb) by year,
40 St. Louis MSA 74
41 Table A-90. Distribution of hourly NC^ ambient concentrations (ppb) by year, St. Louis
42 MSA 74
43 Table A-91. Distribution of annual average N02 ambient concentration (ppb) by
44 monitor, St. Louis MSA, 1995-2006 75
45 Table A-92. Distribution of hourly NC^ ambient concentration (ppb) by monitor, St.
46 Louis MSA, 1995-2006 75
VI
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1 Table A-93. Distribution of annual average N02 ambient concentrations (ppb) by year,
2 Other MS A/CMS A 76
3 Table A-94. Distribution of hourly NC^ ambient concentrations (ppb) by year, Other
4 MSA/CMSA 76
5 Table A-95. Distribution of annual average N02 ambient concentrations (ppb) by year,
6 Other Not MSA 77
7 Table A-96. Distribution of hourly N02 ambient concentrations (ppb) by year, Other Not
8 MSA 77
9 Table A-97. Goodness-of-fit statistics for eight generalized linear models 80
10 Table A-98. Parameters for Poisson exponential model stratified by location 83
11 Table A-99. Parameters for normal linear model stratified by location 84
12 Table A-100. As-is and current-standard scenario predictions for Poisson exponential
13 model, with separate coefficients for each location 88
14 Table A-101. As-is and current-standard scenario predictions for Normal linear model,
15 with separate coefficients for each location 89
16 Table A-102. Comparison of predicted exceedances of 150 ppb using McCurdy (1994)
17 for 1988-1992 data and the Poisson exponential and normal linear models for
18 1995-2006 data 90
19 Table A-103. Predictions for Poisson exponential model, with separate coefficients for
20 each location 91
21 Table A-104. Predictions for Normal linear model, with separate coefficients for each
22 location 94
23 Table A-105. Maximum annual average N02 concentrations and air quality adjustment
24 factors (F) to just meet the current standard, historic monitoring data 101
25 Table A-106. Maximum annual average N02 concentrations and air quality adjustment
26 factors (F) to just meet the current standard, recent monitoring data 102
27 Table A-107. Air quality adjustment factors (F) to just meet the alternative 1 -hour
28 standards, using recent monitoring data 103
29 Table A-108. Reviewed studies containing N02 concentrations at a distance from
30 roadways 108
31 Table 109. Number of exceedances of short-term (1-hour) potential health effect
32 benchmark levels in a year, 1995-2000 historic N02 air quality adjusted to just
33 meeting the current annual average standard (0.053 ppm) using monitors sited
34 >100 m of a major road 113
35 Table 110. Number of exceedances of short-term (1 -hour) potential health effect
36 benchmark levels in a year, 1995-2000 historic N02 air quality adjusted to just
37 meeting the current annual average standard (0.053 ppm) using monitors sited
38 <100 m of a major road 114
39 Table A-111. Number of exceedances of short-term (1 -hour) potential health effect
40 benchmark levels in a year on-roads, 1995-2000 historic N02 air quality
41 adjusted to just meeting the current annual average standard (0.053 ppm). 115
42 Table A-112. Estimated annual average N02 concentrations for monitors >100 m from
43 a major road following adjustment to just meeting the current and alternative
44 standards, 2001-2003 air quality 117
VII
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1 Table A-113. Estimated annual average N02 concentrations for monitors <100 m from
2 a major road following adjustment to just meeting the current and alternative
3 standards, 2001-2003 air quality 122
4 Table A-114. Estimated annual average N02 concentrations for monitors >100 m from
5 a major road following adjustment to just meeting the current and alternative
6 standards, 2004-2006 air quality 125
7 Table A-115. Estimated annual average N02 concentrations for monitors <100 m from
8 a major road following adjustment to just meeting the current and alternative
9 standards, 2004-2006 air quality 129
10 Table A-116. Estimated number of exceedances of 1-hour concentration levels (100,
11 150, and 200 ppb) for monitors >100 m from a major road following adjustment
12 to just meeting the current and alternative standards, 2001 -2003 air quality.
13 132
14 Table A-117. Estimated number of exceedances of 1 -hour concentration levels (200
15 and 250 ppb) for monitors >100 m from a major road following adjustment to
16 just meeting the current and alternative standards, 2001-2003 air quality. ..138
17 Table A-118. Estimated number of exceedances of 1 -hour concentration levels (100,
18 150, and 200 ppb) for monitors <100 m from a major road following
19 adjustment to just meeting the current and alternative standards, 2001 -2003
20 air quality 143
21 Table A-119. Estimated number of exceedances of 1 -hour concentration levels (250
22 and 300 ppb) for monitors <100 m from a major road following adjustment to
23 just meeting the current and alternative standards, 2001-2003 air quality. ..147
24 Table A-120. Estimated number of exceedances of 1 -hour concentration levels (100,
25 150, and 200 ppb) for monitors >100 m from a major road following adjustment
26 to just meeting the current and alternative standards, 2004-2006 air quality.
27 150
28 Table A-121. Estimated number of exceedances of 1 -hour concentration levels (250
29 and 300 ppb) for monitors >100 m from a major road following adjustment to
30 just meeting the current and alternative standards, 2004-2006 air quality. ..156
31 Table A-122. Estimated number of exceedances of 1 -hour concentration levels (100,
32 150, and 200 ppb) for monitors <100 m from a major road following
33 adjustment to just meeting the current and alternative standards, 2004-2006
34 air quality 160
35 Table A-123. Estimated number of exceedances of 1 -hour concentration levels (250
36 and 300 ppb) for monitors <100 m from a major road following adjustment to
37 just meeting the current and alternative standards, 2004-2006 air quality. ..164
38 Table A-124. Estimated annual average N02 concentrations on-roads following
39 adjustment to just meeting the current and alternative standards, 2001 -2003
40 air quality 167
41 Table A-125. Estimated annual average N02 concentrations on-roads following
42 adjustment to just meeting the current and alternative standards, 2004-2006
43 air quality 171
44 Table A-126. Estimated number of exceedances of 1 -hour concentration levels (100,
45 150, and 200 ppb) on-roads following adjustment to just meeting the current
46 and alternative standards, 2001-2003 air quality 175
VIM
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1 Table A-127. Estimated number of exceedances of 1 -hour concentration levels (250
2 and 300 ppb) on-roads following adjustment to just meeting the current and
3 alternative standards, 2001-2003 air quality 181
4 Table A-128. Estimated number of exceedances of 1 -hour concentration levels (100,
5 150, and 200 ppb) on-roads following adjustment to just meeting the current
6 and alternative standards, 2004-2006 air quality 185
7 Table A-129. Estimated number of exceedances of 1 -hour concentration levels (250
8 and 300 ppb) on-roads following adjustment to just meeting the current and
9 alternative standards, 2004-2006 air quality 191
10
IX
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i List of Figures
2
3 Figure A-1. Distributions of annual mean N02 ambient monitoring concentrations for
4 selected CMSA locations, years 1995-2006 25
5 Figure A-2. Distributions of annual mean N02 ambient monitoring concentrations for
6 selected MSA and grouped locations, years 1995-2006 25
7 Figure A-3. Distributions of hourly N02 ambient monitoring concentrations for selected
8 CMSA locations, years 1995-2006 26
9 Figure A-4. Distributions of hourly N02 ambient concentration for selected CMSA
10 locations, years 1995-2006 27
11 Figure A-5. Distributions of annual average N02 concentrations among 10 monitoring
12 sites in Philadelphia CMSA, years 1995-2006 28
13 Figure A-6. Distributions of annual mean N02 concentrations for all monitors, years
14 1995-2006 30
15 Figure A-7. Distributions of annual mean N02 concentrations for the Philadelphia
16 CMSA, years 1995-2006 31
17 Figure A-8. Distributions of hourly N02 concentrations in the Los Angeles CMSA, years
18 1995-2006 32
19 Figure A-9. Distributions of hourly N02 concentrations in the Jacksonville MSA, years
20 1995-2006, one monitor 33
21 Figure A-10. Distributions of annual average N02 concentrations in the Not MSA group
22 location, years 1995-2006 34
23 Figure A-11. Distribution of annual average N02 ambient concentrations (ppb) by year,
24 Boston CMSA 35
25 Figure A-12. Distribution of hourly N02 ambient concentrations (ppb) by year, Boston
26 CMSA 35
27 Figure A-13. Distribution of annual average N02 ambient concentration (ppb) by
28 monitor, Boston CMSA set A, 1995-2006 36
29 Figure A-14. Distribution of hourly N02 ambient concentration (ppb) by monitor, Boston
30 CMSA set A, 1995-2006 36
31 Figure A-15. Distribution of annual average N02 ambient concentration (ppb) by
32 monitor, Boston CMSA set B, 1995-2006 37
33 Figure A-16. Distribution of hourly N02 ambient concentration (ppb) by monitor, Boston
34 CMSA set B, 1995-2006 37
35 Figure A-17. Distribution of annual average N02 ambient concentrations (ppb) by year,
36 Chicago CMSA 38
37 Figure A-18. Distribution of hourly N02 ambient concentrations (ppb) by year, Chicago
38 CMSA 38
39 Figure A-19. Distribution of annual average N02 ambient concentration (ppb) by
40 monitor, Chicago CMSA, 1995-2006 39
41 Figure A-20. Distribution of hourly N02 ambient concentration (ppb) by monitor,
42 Chicago CMSA, 1995-2006 39
43 Figure A-21. Distribution of annual average N02 ambient concentrations (ppb) by year,
44 Cleveland CMSA 40
45 Figure A-22. Temporal distribution of hourly N02 ambient concentrations (ppb) by year,
46 Cleveland CMSA 40
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1 Figure A-23. Distribution of annual average NC^ ambient concentration (ppb) by
2 monitor, Cleveland CMSA, 1995-2006 41
3 Figure A-24. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
4 Cleveland CMSA, 1995-2006 41
5 Figure A-25. Distribution of annual average NC^ ambient concentrations (ppb) by year,
6 Denver CMSA 42
7 Figure A-26. Distribution of hourly NC^ ambient concentrations (ppb) by year, Denver
8 CMSA 42
9 Figure A-27. Distribution of annual average NC^ ambient concentration (ppb) by
10 monitor, Denver CMSA, 1995-2006 43
11 Figure A-28. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Denver
12 CMSA, 1995-2006 43
13 Figure A-29. Distribution of annual average NC^ ambient concentrations (ppb) by year,
14 Detroit CMSA 44
15 Figure A-30. Distribution of hourly NC^ ambient concentrations (ppb) by year, Detroit
16 CMSA 44
17 Figure A-31. Distribution of annual average NC^ ambient concentration (ppb) by
18 monitor, Detroit CMSA, 1995-2006 45
19 Figure A-32. Distribution of annual average NC^ ambient concentration (ppb) by
20 monitor, Detroit CMSA, 1995-2006 45
21 Figure A-33. Distribution of annual average NC^ ambient concentrations (ppb) by year,
22 Los Angeles CMSA 46
23 Figure A-34. Distribution of hourly NC^ ambient concentrations (ppb) by year, Los
24 Angeles CMSA 46
25 Figure A-35. Distribution of annual average NC^ ambient concentration (ppb) by
26 monitor, Los Angeles CMSA set A, 1995-2006 47
27 Figure A-36. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Los
28 Angeles CMSA set A, 1995-2006 47
29 Figure A-37. Distribution of annual average NC^ ambient concentration (ppb) by
30 monitor, Los Angeles CMSA set B 1995-2006 48
31 Figure A-38. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Los
32 Angeles CMSA set B, 1995-2006 48
33 Figure A-39. Distribution of annual average NC^ ambient concentration (ppb) by
34 monitor, Los Angeles CMSA set C 1995-2006 49
35 Figure A-40. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Los
36 Angeles CMSA set C 1995-2006 49
37 Figure A-41. Distribution of annual average NC^ ambient concentrations (ppb) by year,
38 Miami CMSA 50
39 Figure A-42. Distribution of hourly NC^ ambient concentrations (ppb) by year, Miami
40 CMSA 50
41 Figure A-43. Distribution of annual average NC^ ambient concentration (ppb) by
42 monitor, Miami CMSA, 1995-2006 51
43 Figure A-44. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Miami
44 CMSA, 1995-2006 51
45 Figure A-45. Distribution of annual average NC^ ambient concentrations (ppb) by year,
46 New York CMSA 52
XI
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1 Figure A-46. Distribution of hourly N02 ambient concentrations (ppb) by year, New York
2 CMSA 52
3 Figure A-47. Distribution of annual average N02 ambient concentration (ppb) by
4 monitor, New York CMSA set a, 1995-2006 53
5 Figure A-48. Distribution of hourly N02 ambient concentration (ppb) by monitor, New
6 York CMSA set a, 1995-2006 53
7 Figure A-49. Distribution of annual average N02 ambient concentration (ppb) by
8 monitor, New York CMSA set b, 1995-2006 54
9 Figure A-50. Distribution of hourly N02 ambient concentration (ppb) by monitor, New
10 York CMSA set b, 1995-2006 54
11 Figure A-51. Distribution of annual average N02 ambient concentrations (ppb) by year,
12 Philadelphia CMSA 55
13 Figure A-52. Distribution of hourly NC^ ambient concentrations (ppb) by year,
14 Philadelphia CMSA 55
15 Figure A-53. Distribution of annual average N02 ambient concentration (ppb) by
16 monitor, Philadelphia CMSA, 1995-2006 56
17 Figure A-54. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
18 Philadelphia CMSA, 1995-2006 56
19 Figure A-55. Distribution of annual average N02 ambient concentrations (ppb) by year,
20 Washington DC CMSA 57
21 Figure A-56. Distribution of hourly NC^ ambient concentrations (ppb) by year,
22 Washington DC CMSA 57
23 Figure A-57. Distribution of annual average N02 ambient concentration (ppb) by
24 monitor, Washington DC CMSA set A, 1995-2006 58
25 Figure A-58. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
26 Washington DC CMSA set A, 1995-2006 58
27 Figure A-59. Distribution of annual average N02 ambient concentration (ppb) by
28 monitor, Washington DC CMSA set B, 1995-2006 59
29 Figure A-60. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
30 Washington DC CMSA set B, 1995-2006 59
31 Figure A-61. Distribution of annual average N02 ambient concentrations (ppb) by year,
32 Atlanta MSA 60
33 Figure A-62. Distribution of hourly NC^ ambient concentrations (ppb) by year, Atlanta
34 MSA 60
35 Figure A-63. Distribution of annual average N02 ambient concentration (ppb) by
36 monitor, Atlanta MSA, 1995-2006 61
37 Figure A-64. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Atlanta
38 MSA, 1995-2006 61
39 Figure A-65. Distribution of annual average N02 ambient concentrations (ppb) by year,
40 Colorado Springs MSA 62
41 Figure A-66. Distribution of hourly NC^ ambient concentrations (ppb) by year, Colorado
42 Springs MSA 62
43 Figure A-67. Distribution of annual average N02 ambient concentration (ppb) by
44 monitor, Colorado Springs MSA, 1995-2006 63
45 Figure A-68. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
46 Colorado Springs MSA, 1995-2006 63
XII
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1 Figure A-69. Distribution of annual average N02 ambient concentrations (ppb) by year,
2 El Paso MSA 64
3 Figure A-70. Distribution of hourly N02 ambient concentrations (ppb) by year, El Paso
4 MSA 64
5 Figure A-71. Distribution of annual average N02 ambient concentration (ppb) by
6 monitor, El Paso MSA, 1995-2006 65
7 Figure A-72. Distribution of hourly NC^ ambient concentration (ppb) by monitor, El Paso
8 MSA, 1995-2006 65
9 Figure A-73. Distribution of annual average N02 ambient concentrations (ppb) by year,
10 Jacksonville MSA 66
11 Figure A-74. Distribution of hourly N02 ambient concentrations (ppb) by year,
12 Jacksonville MSA 66
13 Figure A-75. Distribution of annual average N02 ambient concentration (ppb) by
14 monitor, Jacksonville MSA, 1995-2006 67
15 Figure A-76. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
16 Jacksonville MSA, 1995-2006 67
17 Figure A-77. Distribution of annual average N02 ambient concentrations (ppb) by year,
18 Las Vegas MSA 68
19 Figure A-78. Distribution of hourly NC^ ambient concentrations (ppb) by year, Las
20 Vegas MSA 68
21 Figure A-79. Distribution of annual average N02 ambient concentration (ppb) by
22 monitor, Las Vegas MSA, 1995-2006 69
23 Figure A-80. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Las
24 Vegas MSA, 1995-2006 69
25 Figure A-81. Distribution of annual average N02 ambient concentrations (ppb) by year,
26 Phoenix MSA 70
27 Figure A-82. Distribution of hourly NC^ ambient concentrations (ppb) by year, Phoenix
28 MSA 70
29 Figure A-83. Distribution of annual average N02 ambient concentration (ppb) by
30 monitor, Phoenix MSA, 1995-2006 71
31 Figure A-84. Distribution of hourly NC^ ambient concentration (ppb) by monitor,
32 Phoenix MSA, 1995-2006 71
33 Figure A-85. Distribution of annual average N02 ambient concentrations (ppb) by year,
34 ProvoMSA 72
35 Figure A-86. Temporal distribution of hourly N02 ambient concentrations (ppb) by year,
36 ProvoMSA 72
37 Figure A-87. Distribution of annual average N02 ambient concentration (ppb) by
38 monitor, Provo MSA, 1995-2006 73
39 Figure A-88. Distribution of hourly NC^ ambient concentration (ppb) by monitor, Provo
40 MSA, 1995-2006 73
41 Figure A-89. Distribution of annual average N02 ambient concentrations (ppb) by year,
42 St. Louis MSA 74
43 Figure A-90. Temporal distribution of hourly N02 ambient concentrations (ppb) by year,
44 St. Louis MSA 74
45 Figure A-91. Distribution of annual average N02 ambient concentration (ppb) by
46 monitor, St. Louis MSA, 1995-2006 75
XIII
-------
1 Figure A-92. Distribution of hourly N02 ambient concentration (ppb) by monitor, St.
2 Louis MSA, 1995-2006 75
3 Figure A-93. Distribution of annual average N02 ambient concentrations (ppb) by year,
4 Other MSA/CMSA 76
5 Figure A-94. Distribution of hourly NC^ ambient concentrations (ppb) by year, Other
6 MSA/CMSA 76
7 Figure A-95. Distribution of annual average N02 ambient concentrations (ppb) by year,
8 Other Not MSA 77
9 Figure A-96. Distribution of hourly N02 ambient concentrations (ppb) by year, Other Not
10 MSA 77
11 Figure A-97. Exceedances of 150 ppb versus annual mean concentrations (ppb) for
12 CMSA locations 86
13 Figure A-98. Predicted and observed exceedances for CMSA locations using Poisson
14 exponential model 86
15 Figure A-99. Predicted and observed exceedances for CMSA locations using normal
16 linear model 87
17 Figure A-100. Trends in hourly and annual average N02 ambient monitoring
18 concentrations and their associated coefficients of variation (COV) for all
19 monitors, years 1995-2006 98
20 Figure A-101. Distribution of on-road factors (Cv/Cb or m) for two season groups 110
21
XIV
-------
i A-1 Overview
2
3 This appendix contains supplemental descriptions of the data and methods used in the NC>2
4 air quality characterization, as well as detailed results from the analyses performed. First,
5 ambient monitoring data form years 1995 through 2006 have been characterized based on siting
6 characteristics, proximity to stationary source emissions, and distance to roadways. Then,
7 ambient NC>2 concentration trends were evaluated considering the year of monitoring and
8 distribution of monitors within a location.
9 The primary output of the air quality characterization was the numbers of exceedances of
10 potential health effect benchmark levels identified in the Integrated Science Assessment. The
11 ambient NC>2 concentrations were evaluated for the numbers of exceedances of the selected
12 benchmarks in several locations and considering four scenarios. The first scenario considered as
13 is air quality as obtained from EPA's Air Quality System (US EPA, 2007a; 2007b). A second
14 scenario used a portion of the as is air quality to estimate on-road NO2 concentrations. A third
15 and fourth scenario followed in a similar manner, only these used air quality adjusted to just
16 meeting the current and potential alternative standards. Each of these scenarios, in addition to
17 the reasoning for the methods and data used, are described in detail in the sections that follow.
A-1
-------
i A-2 Air Quality Data Screen
2 A-2.1 Introduction
3 The current NC>2 standard of 53 ppb annual arithmetic average was set in 1971 and has been
4 retained since by subsequent reviews (i.e., 1985, 1995). Minor revisions to the standard made in
5 1985 included an explicit rounding convention, stated annual averages would be determined on a
6 calendar year basis, and indicated an explicit 75% completeness requirement for monitoring (60
7 FR 52874). Each of these components of the standard were considered in characterizing the air
8 quality monitoring data, beginning first with the selection of valid data.
9 A-2.2 Approach
10 NC>2 air quality data from years 1995 through 2006 and associated documentation were
11 downloaded from EPA's Air Quality System (US EPA, 2007a; 2007b). As of the date of the
12 analyses performed, hourly measurements for year 2006 were only available for January 1
13 through October 31, 2006. A site was defined by the state, county, site code, and parameter
14 occurrence code (POC), which gives a 10-digit monitor ID code. The POC identifies collocated
15 measurements at the same monitoring location, so that each measuring instrument is treated as a
16 different site. Typically there was only one POC at a given monitoring location.
17
18 As required by the NO2 NAAQS, a valid year of monitoring data is needed to calculate the
19 annual average concentration. A valid year at a monitoring site is comprised of 75% of valid
20 days in a year, with at least 18 hourly measurements for a valid day (thus at least 274 or 275
21 valid days depending on presence of a leap year, a minimum of 4,932 or 4,950 hours). This
22 served as a screening criterion for data to be used for analysis.
23
24 Site-years of data are the total numbers of years the collective monitors in a location were in
25 operation. For example, from years 1995-2006, the Boston CMS A had 27 total monitors in
26 operation, some of which did not contain sufficient numbers of monitoring values, while others
27 contained upwards of 11 years (Table A-l). Thus in summing the number of operating years,
28 this particular location contained a total of 105 site-years of data across the monitoring period.
29
30 In all of the subsequent analyses, where hourly values were missing they were treated as such.
31 Reported values of zero (0) concentration were also retained as is. For certain illustrations,
32 values of zero were substituted with 0.5 ppb, derived from one-half the lowest recorded 1-hour
33 concentration (1 ppb).
34 A-2.3 Results
35 Of a total of 5,243 site-years of data in the entire NO2 1-hour concentration database, 1,039
36 site-years did not meet the above criterion and were excluded from any further analyses. In
37 addition, since shorter term average concentrations are of interest, the remaining site-years of
38 data were further screened for 75% completeness on hourly measures in a year (i.e., containing a
39 minimum of 6,570 or 6,588, depending on presence of a leap year). Twenty-seven additional
40 site-years were excluded, resulting in 4,177 complete site-years in the analytical database. Table
A-2
-------
1
2
3
4
A-2 provides a summary of the site-years included in the analysis, relative to those excluded, by
location and by two site-year groupings.1 Location selection is defined in the Section A-1.2.
Table A-1. Example of ambient monitor years of operation, using the Boston CMSA.
Monitor ID
2303130021
2500510021
2500510051
2500900051
2500920061
2500940041
2500950051
2502100091
2502130031
2502500021
2502500211
2502500351
2502500361
2502500401
2502500411
2502500421
2502510031
2502700201
2502700231
3301100161
3301100191
3301100201
3301110111
3301500091
3301500131
3301500141
3301500151
Complete
Incomplete
Year of monitoring (1995-2006)
95
i
c
c
c
c
c
c
c
c
c
c
c
c
12
1
96
c
i
c
c
c
c
c
c
c
c
c
10
1
97
c
c
c
c
c
c
c
c
c
c
c
11
0
98
c
c
c
c
c
c
c
c
c
c
c
i
11
1
99
i
i
i
i
c
c
c
i
c
c
i
i
c
c
7
7
00
c
i
i
i
c
c
c
i
i
c
c
i
c
7
6
01
c
i
c
c
c
c
c
c
c
c
i
i
i
c
i
10
5
02
c
i
i
c
i
i
c
c
c
i
c
c
c
c
c
10
5
03
c
c
i
i
i
c
i
c
i
c
i
i
i
5
8
04
i
c
i
i
i
c
c
i
c
c
c
i
c
7
6
05
i
c
i
c
i
c
c
i
c
c
c
i
c
8
5
06
c
i
c
i
c
i
i
c
c
c
i
c
7
5
Totals
Complete
7
0
2
0
10
5
2
1
0
11
8
1
1
11
1
6
5
8
3
4
1
5
0
5
4
3
1
105
Incomplete
4
1
4
1
2
7
1
0
5
1
0
0
0
1
7
1
0
1
0
1
2
1
3
2
2
1
2
50
Notes:
c = met criteria for valid year of monitoring data.
i = did not met criteria for valid year of monitoring data.
14 of 18 named locations and the 2 grouped locations contained enough data to be considered valid for year 2006.
A-3
-------
1
2
Table A-2. Counts of complete site-years of NO2 monitoring data.
Location
Boston
Chicago
Cleveland
Denver
Detroit
Los Angeles
Miami
New York
Philadelphia
Washington
Atlanta
Colorado Springs
El Paso
Jacksonville
Las Vegas
Phoenix
Provo
St. Louis
Other CMSA
Not MSA
Total
Com
1995-2000
58
47
11
26
12
193
24
93
46
69
24
26
14
6
16
22
6
56
1135
200
Number of
Dlete
2001-2006
47
36
11
10
12
177
20
81
39
66
29
0
30
4
35
27
6
43
1177
243
4177
Site-Years
Incorr
1995-2000
16
20
2
10
4
16
1
12
6
21
5
4
11
0
4
8
0
3
249
112
iplete
2001-2006
34
22
2
4
1
19
4
24
8
18
1
4
0
2
9
25
0
9
235
141
1066
% Cor
1995-2000
78%
70%
85%
72%
75%
92%
96%
89%
88%
77%
83%
87%
56%
100%
80%
73%
100%
95%
82%
64%
iplete
2001-2006
58%
62%
85%
71%
92%
90%
83%
77%
83%
79%
97%
0%
100%
67%
80%
52%
100%
83%
83%
63%
80%
A-4
-------
i A-3 Selection of Locations
2 A-3.1 Introduction
3 The next step in this analysis was to identify similarities and differences in air quality among
4 locations for the purpose of either aggregating or segregating data using a combination of
5 descriptive statistics and health based criteria. Location in this context would include a
6 geographic area that encompasses more than a single air quality monitor (e.g., particular city,
7 consolidated metropolitan statistical area or CMSA).
8 A-3.2 Approach
9 Criteria were established for selecting sites with high annual means and/or frequent
10 exceedances of potential health effect benchmarks. Selected locations were those that had a
11 maximum annual mean NC>2 level at a particular monitor greater than or equal to 25.7 ppb, which
12 represents the 90th percentile across all locations and site-years, and/or had at least one reported
13 1-hour NC>2 level greater than or equal to 200 ppb, the lowest level of the potential health effect
14 benchmarks. A location in this context would include a geographic area that encompasses more
15 than a single air quality monitor (e.g., particular city, metropolitan statistical area (MSA), or
16 consolidated metropolitan statistical area or CMSA). First, all monitors were identified as either
17 belonging to a CMSA, a MSA, or neither. Then, locations of interest were identified through
18 statistical analysis of the ambient NC>2 air quality data for each site within a location.
19 A-3.3 Results
20 Fifteen locations met both selection criteria, that is, having at least one site-year annual mean
21 above 25.7 ppb and at least one exceedance of 200 ppb. Upon further analysis of the more recent
22 ambient data (2001-2006), four additional locations were observed to have met at least one of the
23 criteria (either high annual mean and/or at least one exceedance of 200 ppb). New Haven, CT,
24 while meeting the earlier criteria, did not have any recent exceedances of 200 ppb and contained
25 one of the lowest maximum concentration-to-mean ratios, therefore was not separated out as a
26 specific location. Thus, 14 locations were retained from the initial selection and 4 locations
27 selected from a second screening to provide additional geographical representation. In addition
28 to these 18 specific locations, the remaining sites were grouped into two broad location
29 groupings. The Other CMSA location contains all the other sites that are in MSAs or CMSAs but
30 are not in any of the 18 specified locations. The Not MSA location contains all the sites that are
31 not in an MSA or CMSA. The selected locations are summarized in Table A-3.
32
33 The final database for analysis included air quality data from a total of 205 monitors within
34 the named locations, 331 monitors in the Other CMSA group, and 92 monitors in the Not MSA
35 group. Again, the monitors that were retained contained the criteria for estimating a valid annual
36 average concentration described above.
A-5
-------
Table A-3. Locations selected for NO2 Air Quality Characterization, associated abbreviations, and values of selection criteria.
Location
Type1 Code Description Abbreviation
CMSA*
CMSA
CMSA*
CMSA*
CMSA*
CMSA*
CMSA
CMSA*
CMSA*
CMSA*
MSA*
MSA*
MSA*
MSA
MSA*
MSA*
MSA
MSA*
MSA/CMSA
-
1122
1602
1692
2082
2162
4472
4992
5602
6162
8872
0520
1720
2320
3600
4120
6200
6520
7040
-
-
Boston-Worcester-Lawrence, MA-NH-ME-CT
Chicago-Gary-Kenosha, IL-IN-WI
Cleveland-Akron, OH
Denver-Boulder-Greeley, CO
Detroit-Ann Arbor-Flint, Ml
Los Angeles-Riverside-Orange County, CA
Miami-Fort Lauderdale, FL
New York-Northern New Jersey-Long Island, NY-NJ-CT-PA
Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD
Washington-Baltimore, DC-MD-VA-WV
Atlanta, GA
Colorado Springs, CO
El Paso, TX
Jacksonville, FL
Las Vegas, NV-AZ
Phoenix-Mesa, AZ
Provo-Orem, UT
St, Louis, MO-IL
Other MSA/CMSA
Other Not MSA
Boston
Chicago
Cleveland
Denver
Detroit
Los Angeles
Miami
New York
Philadelphia
Washington DC
Atlanta
Colorado Springs
El Paso
Jacksonville
Las Vegas
Phoenix
Provo
St. Louis
Other CMSA
Not MSA
Maximum # of
Exceedances
of 200 ppb
1
0
1
2
12
5
3
3
3
2
1
69
2
2
11
37
0
8
10
2
Maximum
Annual Mean
(ppb)
31.1
33.6
28.1
36.8
25.9
50.6
16.8
42.2
34.00
27.2
26.6
34.8
35.1
15.9
27.1
40.5
28.9
27.2
31.9
19.7
1 CMSA is consolidated metropolitan statistical area; MSA is metropolitan statistical area according to the 1 999 Office of Management and Budget definitions (January 28, 2002
revision).
* Indicates locations that satisfied both the annual average and exceedance criteria.
A-6
-------
A -4 Ambient Monitoring Site Characteristics
A-4.1 Introduction
Siting of monitors is of particular importance, recognizing that proximity of local sources
could influence on measured NC>2 concentrations. As part of the risk and exposure scope and
methods document (US EPA, 2007c), both mobile and stationary sources (in particular power
generating utilities using fossil fuels) were indicated as significant contributors to nitrogen oxides
(NOX) emissions in the U.S. Analyses were performed to determine the distance of all location-
specific monitors to these source categories. In addition, emissions of NOX from stationary
sources within close proximity of the location-specific monitoring sites were estimated.
A-4.2 Approach
Major road distances to each monitor were calculated using GIS.2 Distances of monitoring
sites to stationary sources and those source's emissions were estimated using data within the 2002
National Emissions Inventory (NEI; US EPA, 2007d). The NEI database reports emissions of
NOX in tons per year (tpy) for 13 1,657 unique emission sources at various points of release. The
release locations were all taken from the latitude longitude values within the NEI. First, all NOX
emissions were summed for identical latitude and longitude entries while retaining source codes
for the emissions (e.g., Standard Industrial Code (SIC), or North American Industrial
Classification System (NAICS)). Therefore, any facility containing similar emission processes
were summed at the stack location, resulting in 40,855 observations. These data were then
screened for sources with emissions greater than 5 tpy, yielding 18,798 unique NOX emission
sources. Locations of these stationary source emissions were compared with ambient monitoring
locations using the following formula:
d = arccos(sin(toj ) x sin(to2 ) + cos(latl ) x cos(lat2 ) x cos(/o«2 -7oWj )) x r
where
d = distance (kilometers)
latj = latitude of a monitor (radians)
Iat2 = latitude of source emission (radians)
lon} = longitude of monitor (radians)
Iori2 = 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 180/7i. For each
monitor, source emissions with estimated distances within 10 km were retained.
2 Distances between monitors and major roads were first determined using a Tele-Atlas roads database in a GIS
application. For road-monitor pairs that showed particularly close distances, the values were fine-tuned using
GoogleEarth® to estimate the distance to road edge.
A-7
-------
A-4.3 Summary Results
Summary statistics for the monitoring site characteristics are presented in Tables A-4 through
A-6 for the selected locations. Detailed results for the distance to major roadways, the distance
and emissions from stationary sources for each ambient monitor are provided in section A-3.4,
Tables A-7 and A-8.
The distribution of the nearest distance of the ambient monitors to major roads for each of the
named locations is summarized in Table A-4. On average, most monitors are placed at a distance
of 50 meters or greater from a major road, however in locations with a large monitoring network
such as Boston, Chicago, or New York CMSA, there may be one or two monitors sited within
close proximity (<10 meters) of a road. Since there is potential for roadway emissions to affect
concentrations at monitors sited close to major roads, the ambient monitors were further
categorized based on the monitor distance from major roads. Two proximity bins were identified,
the first containing those monitors sited within 100 meters of a road (<100 m) and those located at
least 100 meters from a major road (>100 m).
Table A-4. Distribution of the distance of ambient monitors to the nearest major road in selected locations.
Location
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
n
4
21
12
4
6
7
3
7
1
10
43
4
26
10
7
1
13
16
Distance (m) of monitor to nearest major road
mean std min 2.5 50 97.5 max
488
101
158
114
196
166
382
282
144
244
155
57
145
247
190
353
126
129
283
93
212
90
103
260
39
266
286
150
45
130
199
177
123
104
134
7
2
2
79
18
339
33
1
1
15
6
45
7
5
14
134
7
2
2
79
18
339
33
1
2
15
6
45
7
5
14
505
70
93
134
180
65
393
128
181
89
55
119
167
141
97
83
809
337
738
187
386
748
415
718
914
522
103
508
630
433
421
338
809
337
738
187
386
748
415
718
914
570
103
508
630
433
421
338
1 n is the number of monitors operating in a particular location between 1 995 and 2006. The min, 2.5, med, 97.5, and max
represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for the distance in meters (m) to the
nearest major road. Monitors > 1km from road are not included.
Table A-5 contains a summary of the distance of stationary source emissions to monitors
within each named location. There were a number of sources emitting >5 tpy of NOX and located
within a 10 km radius for many of the monitors. On average though, most monitors are placed at
greater distances from stationary source emissions than roads with most sources at a distance of
greater than 5 km. Most of the stationary source emissions of NOX within a 10 km radius of
monitors were less than 50 tpy (Table A-6). Details regarding individual monitors are provided in
Table A-8.
A-8
-------
Table A-5. Distribution of the distance of ambient monitors to stationary sources with NOX emissions >5
tons per year and within a 10 kilometers radius.
Location
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
n1
9
595
394
19
66
140
87
126
20
18
523
11
736
382
59
7
253
160
Distance of monitor to NOX emission source (m)2
mean std min 2.5 50 97.5 max
6522
5333
6586
7092
6109
5655
6889
5694
5125
6700
6003
6184
6101
5837
6298
6558
6799
6173
3164
2603
2657
2439
2632
2593
2254
3185
2962
2184
2435
3151
2555
2474
2279
3664
2337
2425
656
142
411
956
782
910
321
119
708
3837
140
1323
103
231
833
1214
396
288
656
761
770
956
1034
1029
1963
1384
708
3837
1483
1323
1383
1299
1312
1214
1989
704
7327
5363
7277
7278
6340
5904
7549
6085
5720
7237
6165
7611
6467
5689
6355
8178
7120
6254
9847
9733
9834
9884
9847
9862
9974
9945
9558
9950
9801
9117
9818
9754
9803
9433
9863
9777
9847
9988
9994
9884
9933
9979
9997
9991
9558
9950
9991
9117
9983
9982
9890
9433
9990
9973
1 n is the number of sources emitting >5 tons per year (tpy) NOX within a 1 0 kilometer radius of a monitor in a particular location.
2 The min, 2.5, med, 97.5, and max represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for
the distance in meters (m) to the source emission.
Table A-6. Distribution of NOX emissions from stationary sources within 10 kilometers of monitoring site,
where emissions were >5 tons per year.
Location
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
n1
9
595
394
19
66
140
87
126
20
18
523
11
736
382
59
7
253
160
Emissions (tpy) of NOX from sources within 10 km of monitor 2
mean std min 2.5 50 97.5 max
709
128
204
702
387
252
251
117
201
483
70
24
284
154
85
60
167
320
1621
344
919
612
1091
1286
637
286
407
636
310
16
1024
408
234
38
1032
1254
22
5
5
126
5
5
5
5
5
18
5
8
5
5
5
7
5
6
22
5
5
126
5
5
6
5
5
18
5
8
6
5
5
7
5
6
35
10
10
284
19
15
24
31
31
84
12
22
31
29
14
83
16
34
4895
1155
2204
1476
4205
5404
2398
912
1642
1665
577
51
3676
1304
1049
102
848
6009
4895
3794
8985
1476
4205
9483
3762
1679
1642
1665
4256
51
9022
4968
1049
102
14231
10756
1 n is the number of sources emitting >5 tons per year (tpy) of NOX within a 1 0 kilometer radius of a monitor in a particular location.
2 The min, 2.5, med, 97.5, and max represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for
the source emissions.
A-9
-------
A-4.4 Detailed Monitoring Site Characteristics
Detailed physical attributes of each monitor used within the named locations (i.e., 18 specific
locations were defined; it does not include the broadly grouped locations of "Other CMS A" or Not
MSA). Each of these monitors met the criteria for containing a valid number of reported
concentrations and were used throughout the air quality characterization. Data provided include
monitor location and purpose, ground height and elevation above sea level, and distance to the
nearest major roadway (Table A-7). In addition, the distances and emissions of stationary sources
that emit > 5 tons NOX per year were calculated for each monitor (Table A-8)
A-10
-------
Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
ID
130890002
130893001
131210048
132230003
132470001
230313002
250051 005
250092006
250094004
250095005
250210009
250250002
250250021
250250035
250250036
250250040
250250041
250250042
250251 003
250270020
250270023
330110016
330110019
330110020
3301 50009
330150013
3301 5001 4
3301 5001 5
1 7031 0037
1 7031 0063
170310064
170310075
170310076
170313101
170313103
170314002
170314201
Latitude
33.69
33.85
33.78
33.93
33.59
43.08
42.06
42.47
42.79
42.76
42.32
42.35
42.38
42.33
42.33
42.35
42.32
42.33
42.40
42.27
42.27
42.99
43.00
43.00
43.08
43.00
43.08
43.08
41.98
41.88
41.79
41.96
41.75
41.97
41.97
41.86
42.14
Longitude
-84.29
-84.21
-84.40
-85.05
-84.07
-70.75
-71.15
-70.97
-70.81
-71.11
-71.13
-71.10
-71 .03
-71.12
-71.12
-71 .04
-70.97
-71 .08
-71 .03
-71 .80
-71 .79
-71 .46
-71 .47
-71 .47
-70.76
-71 .20
-70.75
-70.76
-87.67
-87.63
-87.60
-87.66
-87.71
-87.88
-87.88
-87.75
-87.80
Land Use
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
AGRICULTURAL
AGRICULTURAL
RESIDENTIAL
AGRICULTURAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
MOBILE
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
MOBILE
MOBILE
RESIDENTIAL
RESIDENTIAL
Location Type1
SUBURBAN
RURAL
URBAN AND CENTER CITY
RURAL
RURAL
SUBURBAN
RURAL
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
RURAL
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
RURAL
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
Objective2
POPULATION EXPOSURE
OTHER
HIGHEST CONCENTRATION
GENERAL/BACKGROUND
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
MAX OZONE
CONCENTRATION
POPULATION EXPOSURE
UNKNOWN
HIGHEST CONCENTRATION
HIGHEST CONCENTRATION
UNKNOWN
UNKNOWN
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
OTHER
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
HIGHEST CONCENTRATION
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
HIGHEST CONCENTRATION
HIGHEST CONCENTRATION
POPULATION EXPOSURE
POPULATION EXPOSURE
Monitor3
Ht (m) Elev (m)
5
5
5
4
5
-
4
5
4
-
4
5
4
-
-
4
6
5
4
3
4
5
-
5
3
1
2
4
-
3
15
15
4
3
4
4
8
308
0
290
417
219
40
61
52
1
0
0
6
6
0
0
0
10
6
59
145
145
75
61
61
3
0
4
3
183
181
180
180
186
197
195
184
198
Roadway4
Dist (m) Type
432
579
134
>1000
809
70
17
158
15
337
144
7
7
158
158
37
>1000
26
228
44
49
168
70
70
48
>1000
266
38
17
68
346
136
2
20
20
118
239
3
2
3
-
3
2
3
3
3
3
3
2
3
3
3
3
-
3
4
3
3
3
3
3
3
-
3
3
3
3
3
3
3
2
2
3
2
A-11
-------
Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.
Location
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Las Vegas
ID
170314201
170318003
171971011
1 80890022
180891016
390350043
390350060
390350066
390350070
080416001
080416004
08041 6005
080416006
080416009
080416011
080416013
080416018
080013001
080050003
080310002
080590006
080590008
080590009
08059001 0
260990009
261630016
261630019
481 41 0027
481 41 0028
481 41 0037
481 41 0044
481 41 0055
481 41 0057
481 41 0058
1 2031 0032
320030022
Latitude
42.14
41.63
41.22
41.61
41.60
41.46
41.49
41.46
41.46
38.63
38.92
38.76
38.92
38.64
38.85
38.81
38.81
39.84
39.66
39.75
39.91
39.88
39.86
39.90
42.73
42.36
42.43
31.76
31.75
31.77
31.77
31.75
31.66
31.89
30.36
36.39
Longitude
-87.80
-87.57
-88.19
-87.30
-87.33
-81 .58
-81 .68
-81 .58
-81 .59
-104.72
-104.81
-104.76
-105.00
-104.71
-104.83
-104.82
-104.75
-104.95
-105.00
-104.99
-105.19
-105.17
-105.20
-105.24
-82.79
-83.10
-83.00
-106.49
-106.40
-106.50
-106.46
-106.40
-106.30
-106.43
-81.64
-114.91
Land Use
RESIDENTIAL
RESIDENTIAL
AGRICULTURAL
INDUSTRIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
RESIDENTIAL
AGRICULTURAL
RESIDENTIAL
INDUSTRIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
AGRICULTURAL
COMMERCIAL
COMMERCIAL
INDUSTRIAL
INDUSTRIAL
INDUSTRIAL
AGRICULTURAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
Location Type1
SUBURBAN
SUBURBAN
RURAL
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
RURAL
URBAN AND CENTER CITY
URBAN AND CENTER CITY
RURAL
RURAL
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
RURAL
SUBURBAN
URBAN AND CENTER CITY
RURAL
RURAL
RURAL
RURAL
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
RURAL
Objective2
POPULATION EXPOSURE
POPULATION EXPOSURE
GENERAL/BACKGROUND
HIGHEST CONCENTRATION
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
POPULATION EXPOSURE
HIGHEST CONCENTRATION
HIGHEST CONCENTRATION
UNKNOWN
GENERAL/BACKGROUND
GENERAL/BACKGROUND
UNKNOWN
UNKNOWN
HIGHEST CONCENTRATION
POPULATION EXPOSURE
GENERAL/BACKGROUND
SOURCE ORIENTED
MAX OZONE
CONCENTRATION
MAX PRECURSOR
EMISSIONS IMPACT
UPWIND BACKGROUND
GENERAL/BACKGROUND
POPULATION EXPOSURE
UNKNOWN
SOURCE ORIENTED
Monitor3
Ht (m) Elev (m)
8
4
5
5
14
4
4
5
4
4
4
4
4
4
3
3
3
4
4
-
-
4
4
4
-
4
4
5
5
4
5
5
5
5
3
3.5
198
179
181
183
183
287
206
287
278
1673
1931
1747
2313
1707
1832
1823
1795
1559
1654
1589
1774
1715
1848
1877
189
191
192
1140
1126
1143
1128
0
0
0
7
0
Roadway4
Dist (m) Type
239
2
>1000
738
187
187
2
187
81
>1000
150
79
199
>1000
198
386
163
748
138
18
65
31
99
63
415
393
339
33
718
128
38
127
450
478
144
122
2
3
-
1
3
2
4
2
3
-
1
3
2
-
3
4
2
3
2
3
3
3
3
2
3
5
3
4
3
3
3
3
3
3
1
2
A-12
-------
Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.
Location
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
ID
320030023
320030073
320030078
320030539
320030557
320030563
320030601
320031019
320032002
060370002
060370016
060370030
060370113
060370206
060371002
060371103
060371201
060371301
060371601
060371701
060372005
060374002
060375001
060375005
060376002
060376012
060379002
060379033
060590001
060590007
060591003
060595001
060650012
060655001
060658001
060659001
06071 0001
Latitude
36.81
36.17
35.47
36.14
36.16
36.18
35.98
35.79
36.19
34.14
34.14
34.04
34.05
33.96
34.18
34.07
34.20
33.93
34.01
34.07
34.13
33.82
33.92
33.95
34.39
34.38
34.69
34.67
33.83
33.83
33.67
33.93
33.92
33.85
34.00
33.68
34.90
Longitude
-114.06
-115.33
-114.92
-115.09
-115.11
-115.10
-114.84
-115.36
-115.12
-117.92
-117.85
-118.22
-118.46
-117.84
-118.32
-118.23
-118.53
-118.21
-118.06
-117.75
-118.13
-118.19
-118.37
-118.43
-118.53
-118.53
-118.13
-118.13
-117.94
-117.94
-117.93
-117.95
-116.86
-116.54
-117.42
-117.33
-117.02
Land Use
RESIDENTIAL
RESIDENTIAL
DESERT
MOBILE
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
DESERT
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
MOBILE
COMMERCIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
Location Type1
RURAL
SUBURBAN
RURAL
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
RURAL
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
Objective2
POPULATION EXPOSURE
POPULATION EXPOSURE
REGIONAL TRANSPORT
POPULATION EXPOSURE
UNKNOWN
POPULATION EXPOSURE
POPULATION EXPOSURE
GENERAL/BACKGROUND
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
HIGHEST CONCENTRATION
UNKNOWN
UNKNOWN
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UPWIND BACKGROUND
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
Monitor3
Ht (m) Elev (m)
4
3.5
4
3.5
3
4
4
4
3.5
2
6
5
5
-
5
13
6
7
6
6
4
6
-
4
-
-
5
3
5
4
6
82
4
6
4
-
8
490
0
1094
533
567
570
0
950
0
183
275
65
91
300
168
87
226
27
75
270
250
6
21
21
375
397
725
725
45
10
0
82
677
171
250
1440
690
Roadway4
Dist (m) Type
303
515
25
11
1
254
52
914
240
329
300
50
190
>1000
58
55
206
29
78
15
385
1
10
149
2
143
61
146
225
225
202
570
432
75
133
522
64
3
2
3
3
3
3
3
3
3
3
3
3
3
-
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
1
3
3
4
3
A-13
-------
Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.
Location
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
ID
060710012
060710014
060710015
060710017
06071 0306
060711004
06071 2002
060711234
06071 4001
06071 9004
061 1 1 0005
061 1 1 0007
061111003
061111004
061 1 1 2002
061 1 1 2003
061113001
120110003
120110031
120118002
1 20860027
1 20864002
090010113
090019003
090090027
090091123
340030001
340030005
3401 3001 1
3401 3001 6
3401 31 003
3401 70006
340210005
34023001 1
340273001
340390004
Latitude
34.43
34.51
35.78
34.14
34.51
34.10
34.10
35.76
34.42
34.11
33.20
32.71
34.45
34.45
34.28
34.28
34.26
26.28
26.27
26.09
25.73
25.80
41.18
41.12
41.30
41.31
40.81
40.90
40.73
40.72
40.76
40.67
40.28
40.46
40.79
40.64
Longitude
-117.56
-117.33
-117.37
-116.06
-117.33
-117.63
-117.49
-117.40
-117.28
-117.27
-117.37
-117.15
-119.27
-119.23
-118.68
-119.31
-119.14
-80.28
-80.30
-80.11
-80.16
-80.21
-73.19
-73.34
-72.90
-72.92
-73.99
-74.03
-74.14
-74.15
-74.20
-74.13
-74.74
-74.43
-74.68
-74.21
Land Use
COMMERCIAL
RESIDENTIAL
INDUSTRIAL
MOBILE
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
DESERT
RESIDENTIAL
COMMERCIAL
UNKNOWN
COMMERCIAL
MOBILE
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
FOREST
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
INDUSTRIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
AGRICULTURAL
AGRICULTURAL
INDUSTRIAL
Location Type1
RURAL
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
RURAL
SUBURBAN
SUBURBAN
UNKNOWN
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
RURAL
RURAL
SUBURBAN
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
RURAL
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
RURAL
RURAL
SUBURBAN
Objective2
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UPWIND BACKGROUND
UNKNOWN
OTHER
UNKNOWN
HIGHEST CONCENTRATION
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
POPULATION EXPOSURE
HIGHEST CONCENTRATION
GENERAL/BACKGROUND
POPULATION EXPOSURE
HIGHEST CONCENTRATION
MAX PRECURSOR
EMISSIONS IMPACT
POPULATION EXPOSURE
POPULATION EXPOSURE
HIGHEST CONCENTRATION
HIGHEST CONCENTRATION
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
POPULATION EXPOSURE
UNKNOWN
POPULATION EXPOSURE
HIGHEST CONCENTRATION
POPULATION EXPOSURE
MAX OZONE
CONCENTRATION
POPULATION EXPOSURE
UNKNOWN
HIGHEST CONCENTRATION
Monitor3
Ht (m) Elev (m)
-
4
-
4
4
6
5
1
-
5
1
5
-
4
4
2
4
6
4
4
16
4
4
5
3.67
9
4
3
4
5
4
5
4
4
5
4
4100
876
498
607
913
369
381
545
1006
0
320
244
231
262
314
3
43
3
3
3
2
5
3
4
11
18
61
6
3
3
48.45
3
30
21
274
5.4
Roadway4
Dist (m) Type
30
18
42
64
38
349
81
>1000
111
169
63
89
18
56
471
90
307
22
103
>1000
15
87
8
508
237
14
82
172
232
6
25
266
442
298
227
37
3
3
3
3
3
2
3
-
3
3
3
3
2
3
1
1
3
3
4
-
3
3
3
4
1
2
3
5
1
1
3
3
1
3
3
4
A-14
-------
Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.
Location
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
ID
340390008
360050080
360050083
360050110
36047001 1
360590005
360610010
360610056
360810097
360810098
360810124
361030009
100031003
100031007
1 00032004
340070003
420170012
420450002
420910013
421 01 0004
421 01 0029
421 01 0047
0401 3001 9
0401 33002
0401 33003
0401 3301 0
0401 34005
0401 3401 1
0401 39997
490490002
171630010
291830010
291831002
291890001
291890004
291890006
291893001
Latitude
40.60
40.84
40.87
40.82
40.73
40.74
40.74
40.76
40.76
40.78
40.74
40.83
39.76
39.55
39.74
39.92
40.11
39.84
40.11
40.01
39.96
39.94
33.48
33.46
33.48
33.46
33.41
33.37
33.50
40.25
38.61
38.58
38.87
38.52
38.53
38.61
38.64
Longitude
-74.44
-73.92
-73.88
-73.90
-73.95
-73.59
-73.99
-73.97
-73.76
-73.85
-73.82
-73.06
-75.49
-75.73
-75.56
-75.10
-74.88
-75.37
-75.31
-75.10
-75.17
-75.17
-112.14
-112.05
-1 1 1 .92
-112.12
-1 1 1 .93
-112.62
-112.10
-1 1 1 .66
-90.16
-90.84
-90.23
-90.34
-90.38
-90.50
-90.35
Land Use
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
AGRICULTURAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
AGRICULTURAL
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
AGRICULTURAL
AGRICULTURAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
Location Type1
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
SUBURBAN
RURAL
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
RURAL
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
RURAL
RURAL
SUBURBAN
SUBURBAN
RURAL
SUBURBAN
Objective2
POPULATION EXPOSURE
HIGHEST CONCENTRATION
UNKNOWN
UNKNOWN
HIGHEST CONCENTRATION
HIGHEST CONCENTRATION
HIGHEST CONCENTRATION
HIGHEST CONCENTRATION
GENERAL/BACKGROUND
UNKNOWN
POPULATION EXPOSURE
UNKNOWN
POPULATION EXPOSURE
OTHER
UNKNOWN
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
POPULATION EXPOSURE
HIGHEST CONCENTRATION
POPULATION EXPOSURE
POPULATION EXPOSURE
HIGHEST CONCENTRATION
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
SOURCE ORIENTED
POPULATION EXPOSURE
UNKNOWN
POPULATION EXPOSURE
UNKNOWN
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
Monitor3
Ht (m) Elev (m)
4
15
15
-
6
5
38
10
12
8
-
-
-
-
-
5
2
2
4
7
11
11
4.3
9
5.8
4.2
4
4
-
4
4
3
4
4
4
4
4
0
15
24
0
9
27
38
15
0
6
8
0
65
20
0
7.6
12
3
53
22
25
21
333
339
368
325
352
258
346
1402
125
0
131
183
183
175
161
Roadway4
Dist (m) Type
99
122
132
76
171
32
55
62
197
9
150
116
189
144
82
405
393
413
630
45
103
66
401
141
78
7
259
12
433
353
18
340
31
161
95
97
5
3
3
5
3
3
3
3
3
3
3
3
2
2
3
3
3
3
3
1
3
3
2
3
3
3
3
3
3
3
2
4
3
3
2
2
3
1
A-15
-------
Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.
Location
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
ID
291895001
291897002
291897003
2951 00072
2951 00080
2951 00086
110010017
110010025
110010041
110010043
240053001
245100040
245100050
510130020
510590005
510590018
510591004
510591005
510595001
511071005
511530009
515100009
Latitude
38.77
38.73
38.72
38.62
38.68
38.67
38.90
38.98
38.90
38.92
39.31
39.30
39.32
38.86
38.89
38.74
38.87
38.84
38.93
39.02
38.86
38.81
Longitude
-90.29
-90.38
-90.37
-90.20
-90.25
-90.24
-77.05
-77.02
-76.95
-77.01
-76.47
-76.60
-76.58
-77.06
-77.47
-77.08
-77.14
-77.16
-77.20
-77.49
-77.64
-77.04
Land Use
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
AGRICULTURAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
Location Type1
SUBURBAN
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
SUBURBAN
URBAN AND CENTER CITY
URBAN AND CENTER CITY
URBAN AND CENTER CITY
RURAL
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
URBAN AND CENTER CITY
Objective2
UNKNOWN
UNKNOWN
HIGHEST CONCENTRATION
UNKNOWN
HIGHEST CONCENTRATION
HIGHEST CONCENTRATION
HIGHEST CONCENTRATION
POPULATION EXPOSURE
UNKNOWN
HIGHEST CONCENTRATION
MAX PRECURSOR
EMISSIONS IMPACT
HIGHEST CONCENTRATION
POPULATION EXPOSURE
UNKNOWN
POPULATION EXPOSURE
UNKNOWN
UNKNOWN
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
POPULATION EXPOSURE
UNKNOWN
Monitor3
Ht (m) Elev (m)
2
4
4
14
4
4
10
11
-
-
4.6
4.2
4
7
4
4
11
-
4
4
4
11
168
168
0
154
152
0
20
91
8
50
5
12
49
171
77
11
110
83.9
106
0
111
23
Roadway4
Dist (m) Type
421
59
112
43
116
133
54
106
141
278
186
14
338
80
315
54
84
50
18
75
196
83
3
3
3
4
3
3
3
3
4
3
3
3
2
3
5
3
5
3
5
3
2
3
Notes:
1 Land use indicates the prevalent land use within 1 /4 mile of that site.
2 Objective Indicates the reason for measuring air quality by the monitor.
3 Monitor probe height (Ht) and site elevation (Elev) above sea level are given in meters (m).
4 Distances (Dist) to roadway are given in meters (m). Major road types are defined as: 1 =primary limited access or interstate, 2=primary US and State highways, 3=Secondary State and County,
4=freeway ramp, 5=other ramps.
A-16
-------
Table A-8. Distance of location-specific ambient monitors to stationary sources emitting > 5 tons of NOX per year, within a 1 0 kilometer distance of monitoring
site.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
ID
1 30890002
1 30893001
1 31 21 0048
1 32230003
1 32470001
23031 3002
250051005
250092006
250094004
250095005
25021 0009
250250002
250250021
250250035
250250036
250250040
250250041
250250042
250251003
250270020
250270023
330110016
330110019
330110020
330150009
330150013
330150014
330150015
1 7031 0037
1 7031 0063
1 7031 0064
1 7031 0075
1 7031 0076
170313101
1 7031 31 03
n1
1
3
5
0
0
5
3
12
0
10
57
62
55
62
62
56
25
65
49
28
28
0
0
0
5
1
5
5
17
57
33
31
46
30
30
Distance (km) to Source emissions >5 tpy and within 10 km
mean
4.9
7.2
6.4
3.5
6.7
6.8
5.8
5.8
4.6
6.1
5.1
5.1
5.3
7.8
5.3
6.4
3.7
3.6
3.3
8.4
4.0
3.1
5.6
4.9
6.9
7.3
7.8
6.6
6.6
std
4.0
3.3
1.5
1.6
2.7
2.3
2.5
2.4
2.3
2.6
2.6
2.4
2.0
2.8
2.4
2.5
2.4
1.0
1.8
0.9
2.7
3.2
2.5
2.7
2.3
2.2
2.2
min
4.9
2.7
0.7
1.0
5.5
2.5
1.7
1.0
0.6
1.5
0.3
0.3
0.4
0.7
0.7
0.6
0.1
0.4
2.0
8.4
1.0
1.9
0.7
0.4
1.2
0.8
1.3
2.7
2.7
2.5
4.9
2.7
0.7
1.0
5.5
2.5
1.7
1.8
1.1
1.7
0.8
0.8
0.9
0.7
1.0
1.0
0.1
0.4
2.0
8.4
1.0
1.9
0.7
0.5
1.2
0.8
1.6
2.7
2.7
50
4.9
9.2
7.3
3.8
6.0
7.4
6.7
5.9
4.3
6.5
5.1
5.1
5.6
8.2
4.9
7.0
2.9
3.0
3.3
8.4
4.4
3.0
5.7
4.9
6.9
8.4
8.4
7.2
7.2
97.5
4.9
9.8
8.9
4.9
8.5
9.9
8.6
9.9
9.4
9.8
9.0
9.0
9.0
9.9
10.0
9.6
8.6
8.4
4.4
8.4
5.5
4.1
9.5
9.4
10.0
9.9
9.8
9.7
9.7
max
4.9
9.8
8.9
4.9
8.5
9.9
8.6
9.9
9.7
9.8
9.6
9.6
9.3
9.9
10.0
9.6
8.6
8.4
4.4
8.4
5.5
4.1
9.5
10.0
10.0
9.9
9.9
9.7
9.7
Emissions (tpy) of Sources within 10 km and >5 tpy
mean
34
34
1249
642
9
439
201
106
98
130
99
99
106
81
94
145
58
58
642
29
642
642
18
110
94
10
170
313
313
std
2
2106
769
4
1083
347
283
273
304
273
273
286
206
267
319
165
165
769
769
769
31
416
428
7
463
1638
1638
min
34
32
22
31
5
5
6
5
5
5
5
5
5
5
5
5
5
5
31
29
31
31
5
5
5
5
5
5
5
2.5
34
32
22
31
5
5
6
5
5
5
5
5
5
5
5
5
5
5
31
29
31
31
5
5
5
5
5
5
5
50
34
34
39
203
8
21
29
9
9
11
9
9
9
11
9
11
13
13
203
29
203
203
7
9
10
7
10
9
9
97.5
34
36
4895
1860
14
3794
923
1155
1155
1155
1155
1155
1155
957
1155
1155
868
868
1860
29
1860
1860
126
1677
2465
36
1677
8985
8985
max
34
36
4895
1860
14
3794
923
1419
1419
1419
1419
1419
1419
957
1419
1419
868
868
1860
29
1860
1860
126
2465
2465
36
2204
8985
8985
A-17
-------
Table A-8. Distance of location-specific ambient monitors to stationary sources emitting > 5 tons of NOX per year, within a 1 0 kilometer distance of monitoring
site.
Location
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
ID
1 7031 4002
1 7031 4201
1 7031 4201
1 7031 8003
171971011
1 80890022
180891016
390350043
390350060
390350066
390350070
08041 6001
08041 6004
08041 6005
08041 6006
08041 6009
080416011
080416013
080416018
08001 3001
080050003
08031 0002
080590006
080590008
080590009
080590010
260990009
261630016
261630019
481 41 0027
481 41 0028
481 41 0037
481 41 0044
481 41 0055
481 41 0057
481 41 0058
120310032
n1
63
7
7
63
1
8
8
5
4
5
5
4
10
9
0
4
14
14
11
34
19
52
9
9
10
7
4
51
32
22
24
15
25
24
0
16
20
Distance (km) to Source emissions >5 tpy and within 10 km
mean
6.7
6.5
6.5
7.3
4.0
5.1
4.7
8.1
4.1
8.0
7.6
5.1
5.9
7.5
5.2
5.0
6.3
6.9
5.3
6.7
5.3
5.9
6.2
6.5
5.5
4.9
7.4
6.3
8.1
2.2
8.7
5.9
2.8
8.8
5.1
std
2.6
1.5
1.5
2.0
3.8
2.4
1.9
2.4
1.9
1.8
4.4
2.2
2.1
4.3
2.3
2.9
1.7
1.8
3.7
2.5
2.1
2.0
3.2
3.1
3.2
2.1
2.2
1.6
1.9
2.6
1.2
1.8
0.4
3.0
min
0.5
4.0
4.0
1.7
4.0
0.8
2.1
5.2
1.0
5.2
5.5
0.8
3.5
3.3
1.0
2.0
2.1
4.3
1.6
1.0
0.9
2.7
3.7
2.5
1.1
0.3
1.3
2.6
1.5
0.9
0.1
4.4
1.6
8.4
0.7
2.5
0.5
4.0
4.0
2.3
4.0
0.8
2.1
5.2
1.0
5.2
5.5
0.8
3.5
3.3
1.0
2.0
2.1
4.3
1.6
1.0
0.9
2.7
3.7
2.5
1.1
0.3
2.0
2.6
1.5
0.9
0.1
4.4
1.6
8.4
0.7
50
7.2
6.6
6.6
8.0
4.0
4.1
4.1
8.3
4.4
8.3
7.3
5.1
5.6
8.1
5.3
5.8
6.9
7.1
4.7
9.1
5.8
6.3
6.1
7.0
5.6
5.7
7.9
6.5
8.6
1.6
9.4
5.6
2.2
8.6
5.7
97.5
9.8
9.0
9.0
9.6
4.0
9.4
7.6
9.9
6.4
9.8
9.7
9.1
9.8
9.5
9.3
9.6
9.9
9.6
9.5
10.0
9.7
8.6
10.0
9.9
9.2
7.7
9.8
10.0
9.3
9.3
10.0
9.5
9.6
9.5
9.6
max
9.9
9.0
9.0
9.7
4.0
9.4
7.6
9.9
6.4
9.8
9.7
9.1
9.8
9.5
9.3
9.6
9.9
9.6
9.5
10.0
9.8
8.6
10.0
9.9
9.2
7.7
9.9
10.0
9.3
9.3
10.0
9.5
9.6
9.5
9.6
Emissions (tpy) of Sources within 10 km and >5 tpy
mean
122
8
8
361
20
815
815
673
810
673
673
780
48
490
780
345
346
430
310
313
319
63
59
53
73
63
387
57
99
127
135
158
127
31
201
std
407
3
3
1201
1680
1680
664
681
664
664
1374
80
1393
1374
1113
1113
1254
1622
1233
1495
66
68
66
71
70
797
168
195
338
230
366
338
30
407
min
5
5
5
5
20
8
8
126
165
126
126
16
5
5
16
5
5
5
5
5
5
11
8
6
12
7
5
5
5
5
5
5
5
5
5
2.5
5
5
5
5
20
8
8
126
165
126
126
16
5
5
16
5
5
5
5
5
5
11
8
6
12
7
6
5
5
5
5
5
5
5
5
50
9
8
8
18
20
243
243
284
800
284
284
133
17
11
133
22
27
34
15
17
14
39
13
13
44
46
41
12
29
32
38
32
32
23
31
97.5
1677
14
14
6216
20
4936
4936
1476
1476
1476
1476
2835
267
4205
2835
4205
4205
4205
9483
5404
5404
182
182
182
182
152
3087
837
912
1679
912
1679
1679
106
1642
max
2465
14
14
7141
20
4936
4936
1476
1476
1476
1476
2835
267
4205
2835
4205
4205
4205
9483
5404
9483
182
182
182
182
152
3762
837
912
1679
912
1679
1679
106
1642
A-18
-------
Table A-8. Distance of location-specific ambient monitors to stationary sources emitting > 5 tons of NOX per year, within a 1 0 kilometer distance of monitoring
site.
Location
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
ID
320030022
320030023
320030073
320030078
320030539
320030557
320030563
320030601
320031019
320032002
060370002
060370016
060370030
060370113
060370206
060371002
060371103
060371201
060371301
060371601
060371701
060372005
060374002
060375001
060375005
060376002
060376012
060379002
060379033
060590001
060590007
060591003
060595001
060650012
060655001
060658001
060659001
n1
7
0
0
0
5
4
1
0
0
1
7
7
35
7
11
18
31
7
45
22
13
10
55
32
25
5
6
4
4
17
17
14
16
0
0
12
2
Distance (km) to Source emissions >5 tpy and within 10 km
mean
4.6
6.9
9.1
7.6
9.9
3.1
7.5
5.5
4.3
5.6
5.7
6.5
5.1
6.8
6.5
6.1
5.2
6.4
5.1
4.6
5.6
6.2
7.8
6.3
6.4
6.4
6.1
7.9
7.4
4.6
std
0.9
1.2
1.2
1.1
1.8
2.3
3.1
2.2
2.6
2.7
1.2
2.1
2.3
3.0
3.5
2.3
2.4
2.4
1.8
2.5
1.0
0.8
2.4
2.4
2.2
1.6
2.2
5.9
min
3.8
4.7
7.3
7.6
9.9
1.6
4.5
2.1
1.3
2.3
0.1
1.8
3.3
1.2
2.3
1.1
0.2
1.7
0.3
1.4
3.6
3.0
6.8
5.3
2.8
2.8
2.1
3.4
3.6
0.4
2.5
3.8
4.7
7.3
7.6
9.9
1.6
4.5
2.1
1.3
2.3
0.1
1.8
3.3
2.5
2.3
1.1
0.2
2.2
0.3
1.4
3.6
3.0
6.8
5.3
2.8
2.8
2.1
3.4
3.6
0.4
50
3.9
7.2
9.7
7.6
9.9
2.9
8.5
5.2
3.2
5.8
6.0
7.2
5.5
7.1
7.2
7.0
5.5
6.2
4.8
4.6
5.8
6.8
7.7
6.4
7.2
7.2
6.0
8.2
7.4
4.6
97.5
5.6
7.9
9.7
7.6
9.9
4.5
8.9
9.8
9.8
9.2
9.9
10.0
6.5
9.7
9.7
9.7
10.0
9.9
9.6
9.9
7.8
9.7
9.2
7.1
9.4
9.4
9.3
9.5
9.8
8.7
max
5.6
7.9
9.7
7.6
9.9
4.5
8.9
9.8
9.8
9.2
9.9
10.0
6.5
10.0
9.7
9.7
10.0
9.9
9.6
9.9
7.8
9.7
9.2
7.1
9.4
9.4
9.3
9.5
9.8
8.7
Emissions (tpy) of Sources within 10 km and >5 tpy
mean
175
816
807
84
84
10
12
23
15
32
47
18
10
22
28
22
12
76
205
224
29
26
22
22
14
14
65
19
119
11
std
222
760
877
4
8
27
10
31
59
21
4
24
33
20
8
159
754
850
20
19
28
28
12
12
116
26
358
9
min
30
18
18
84
84
5
5
5
5
6
6
5
6
5
5
5
5
5
6
6
8
8
6
6
5
5
5
6
5
5
2.5
30
18
18
84
84
5
5
5
5
6
6
5
6
5
5
5
5
5
6
6
8
8
6
6
5
5
5
6
5
5
50
77
851
772
84
84
9
9
11
13
20
24
10
10
12
12
16
9
16
21
21
18
18
9
9
8
8
10
9
10
11
97.5
650
1665
1665
84
84
16
29
115
36
109
215
86
15
86
115
70
30
744
4256
4256
54
54
64
64
46
46
434
109
1254
17
max
650
1665
1665
84
84
16
29
115
36
109
215
86
15
115
115
70
30
789
4256
4256
54
54
64
64
46
46
434
109
1254
17
A-19
-------
Table A-8. Distance of location-specific ambient monitors to stationary sources emitting > 5 tons of NOX per year, within a 1 0 kilometer distance of monitoring
site.
Location
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
ID
06071 0001
060710012
060710014
060710015
060710017
06071 0306
060711004
060711234
06071 2002
06071 4001
06071 9004
061 1 1 0005
061 1 1 0007
061111003
061111004
061 1 1 2002
061 1 1 2003
061113001
120110003
120110031
120118002
1 20860027
1 20864002
090010113
09001 9003
090090027
090091123
340030001
340030005
340130011
340130016
340131003
340170006
34021 0005
34023001 1
340273001
340390004
n1
3
0
3
3
0
3
19
2
20
1
8
5
20
0
0
4
3
7
0
0
0
3
8
7
3
5
6
48
18
43
44
32
42
8
20
1
46
Distance (km) to Source emissions >5 tpy and within 10 km
mean
6.9
6.0
4.4
6.1
7.3
1.6
5.7
6.5
5.8
6.9
4.7
6.6
5.5
5.1
4.1
7.0
4.4
6.3
2.7
3.3
6.5
6.8
5.4
5.5
6.4
6.9
5.4
6.1
8.5
6.3
std
1.9
2.6
4.6
2.6
1.7
0.4
2.2
2.5
2.5
2.2
1.0
1.3
2.3
4.2
2.6
3.1
2.0
1.0
2.8
2.2
2.9
2.9
2.8
2.0
2.5
1.7
2.8
2.4
min
5.3
3.5
1.7
3.6
4.3
1.3
2.0
6.5
1.5
3.1
1.7
5.2
4.1
1.9
1.6
1.3
1.4
4.0
1.3
1.2
2.9
0.1
0.7
0.1
2.1
1.1
3.2
1.0
8.5
0.7
2.5
5.3
3.5
1.7
3.6
4.3
1.3
2.0
6.5
1.5
3.1
1.7
5.2
4.1
1.9
1.6
1.3
1.4
4.0
1.3
1.2
2.9
0.1
0.8
1.0
2.1
1.6
3.2
1.0
8.5
0.9
50
6.5
5.9
1.8
5.7
7.4
1.6
5.8
6.5
5.7
7.7
4.2
6.8
5.6
5.9
1.8
7.8
3.4
7.4
2.7
2.4
6.3
7.4
5.8
6.3
6.8
7.7
5.5
7.0
8.5
6.6
97.5
9.0
8.6
9.7
8.9
9.8
1.9
9.6
6.5
9.0
9.6
9.3
7.5
6.7
7.4
8.9
9.1
8.8
7.5
3.9
8.9
9.8
10.0
9.4
9.4
9.3
9.5
7.3
9.5
8.5
9.6
max
9.0
8.6
9.7
8.9
9.8
1.9
9.6
6.5
9.0
9.6
9.3
7.5
6.7
7.4
8.9
9.1
8.8
7.5
3.9
8.9
9.9
10.0
9.5
9.6
9.3
9.5
7.3
9.5
8.5
9.7
Emissions (tpy) of Sources within 10 km and >5 tpy
mean
209
199
752
199
57
1122
44
577
171
68
25
63
18
35
31
22
538
127
280
234
468
53
273
267
77
369
115
95
20
134
std
321
327
1045
327
120
1168
65
438
118
20
113
4
51
19
15
711
179
484
447
1506
79
1372
1357
149
1420
244
175
341
min
10
6
12
6
5
296
5
577
5
8
5
5
14
5
14
8
48
12
14
7
6
6
5
5
5
5
8
6
20
5
2.5
10
6
12
6
5
296
5
577
5
8
5
5
14
5
14
8
48
12
14
7
7
6
5
5
5
6
8
6
20
6
50
38
15
296
15
18
1122
17
577
10
19
18
7
20
13
27
18
192
37
86
64
31
21
18
18
22
24
32
36
20
21
97.5
579
577
1948
577
492
1948
250
577
1254
278
76
232
22
146
51
51
1689
333
1144
1144
4440
307
640
640
640
2213
718
792
20
594
max
579
577
1948
577
492
1948
250
577
1254
278
76
232
22
146
51
51
1689
333
1144
1144
9022
307
9022
9022
640
9022
718
792
20
2213
A-20
-------
Table A-8. Distance of location-specific ambient monitors to stationary sources emitting > 5 tons of NOX per year, within a 1 0 kilometer distance of monitoring
site.
Location
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
St Louis
St Louis
St Louis
St Louis
St Louis
St Louis
St Louis
ID
340390008
360050080
360050083
360050110
36047001 1
360590005
360610010
36061 0056
36081 0097
36081 0098
36081 01 24
361 030009
100031003
100031007
1 00032004
340070003
420170012
420450002
420910013
421 01 0004
421 01 0029
421 01 0047
040130019
040133002
040133003
040133010
040134005
040134011
040139997
490490002
171630010
291830010
291831002
291 890001
291 890004
291 890006
291 893001
n1
12
54
37
55
56
7
52
54
11
48
24
3
39
11
32
69
10
30
12
32
74
73
11
6
10
10
11
1
10
7
48
1
9
10
6
8
16
Distance (km) to Source emissions >5 tpy and within 10 km
mean
7.2
6.4
6.0
5.9
5.9
6.3
5.9
5.4
6.3
7.1
7.0
3.8
5.5
9.2
4.8
7.7
4.1
4.8
5.1
5.9
5.7
5.2
6.8
4.1
6.7
5.0
5.8
6.4
8.5
6.6
7.0
1.7
7.5
7.7
8.9
7.0
7.3
std
2.1
2.3
2.8
2.2
2.7
3.4
2.5
2.6
2.1
2.3
2.6
3.2
2.5
0.6
1.9
2.3
2.3
2.6
2.5
2.5
2.1
2.1
2.2
2.3
1.4
0.9
2.9
1.2
3.7
2.8
2.1
1.3
1.5
1.7
2.0
min
3.2
1.8
1.6
2.1
0.7
1.9
0.3
0.3
2.9
1.6
2.1
2.0
1.6
8.0
0.7
1.8
1.2
0.2
1.4
1.0
1.1
0.6
4.2
1.3
4.1
3.5
0.8
6.4
5.6
1.2
1.3
1.7
4.3
6.2
6.9
4.2
3.4
2.5
3.2
1.8
1.6
2.6
1.5
1.9
1.4
1.4
2.9
2.8
2.1
2.0
1.6
8.0
0.7
2.0
1.2
0.2
1.4
1.0
1.8
0.8
4.2
1.3
4.1
3.5
0.8
6.4
5.6
1.2
1.9
1.7
4.3
6.2
6.9
4.2
3.4
50
8.0
6.4
6.3
5.7
5.7
8.1
6.1
5.5
6.9
7.8
8.0
2.0
6.2
9.3
4.7
8.5
4.2
5.4
4.3
5.6
5.6
4.8
6.7
4.1
6.6
4.9
7.0
6.4
8.7
8.2
8.0
1.7
7.7
7.4
9.8
7.9
7.6
97.5
10.0
9.9
9.9
9.6
9.7
9.8
9.6
9.9
9.5
9.8
10.0
7.6
9.7
9.8
8.4
10.0
9.4
9.5
8.8
9.9
9.7
9.6
9.8
6.9
9.0
6.6
9.4
6.4
9.9
9.4
9.8
1.7
9.9
9.8
10.0
8.7
9.6
max
10.0
9.9
9.9
9.9
10.0
9.8
9.8
10.0
9.5
9.8
10.0
7.6
9.7
9.8
8.4
10.0
9.4
9.5
8.8
9.9
9.7
9.7
9.8
6.9
9.0
6.6
9.4
6.4
9.9
9.4
9.9
1.7
9.9
9.8
10.0
8.7
9.6
Emissions (tpy) of Sources within 10 km and >5 tpy
mean
23
241
171
236
296
372
494
470
65
262
436
537
282
323
223
87
85
504
89
58
74
95
106
21
50
115
81
18
115
60
112
7821
1868
24
38
25
22
std
36
776
725
769
787
500
1453
1429
77
820
1136
759
481
494
403
196
96
1055
232
111
148
221
313
19
80
328
116
328
38
178
4704
20
37
34
43
min
5
6
6
6
7
7
5
7
13
6
8
40
5
6
5
5
11
5
5
5
5
5
5
5
9
5
6
18
5
7
5
7821
7
5
7
6
5
2.5
5
6
6
6
7
7
7
7
13
7
8
40
5
6
5
5
11
5
5
5
5
5
5
5
9
5
6
18
5
7
5
7821
7
5
7
6
5
50
10
29
21
29
42
223
50
50
26
31
26
161
62
63
45
24
57
73
12
20
19
19
10
15
24
10
38
18
10
83
17
7821
8
15
28
11
11
97.5
134
3676
4440
3676
3676
1451
4440
4440
246
3676
4440
1410
2058
1351
1312
477
275
4968
823
571
477
1033
1049
56
272
1049
350
18
1049
102
538
7821
14231
60
105
105
181
max
134
4440
4440
4440
4440
1451
9022
9022
246
4440
4440
1410
2058
1351
1312
1478
275
4968
823
571
1033
1478
1049
56
272
1049
350
18
1049
102
848
7821
14231
60
105
105
181
A-21
-------
Table A-8. Distance of location-specific ambient monitors to stationary sources emitting > 5 tons of NOX per year, within a 1 0 kilometer distance of monitoring
site.
Location
St Louis
St Louis
St Louis
St Louis
St Louis
St Louis
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
ID
291 895001
291 897002
291 897003
295100072
295100080
295100086
110010017
110010025
110010041
110010043
240053001
245100040
245100050
510130020
510590005
510590018
510591004
510591005
510595001
511071005
511530009
515100009
n1
11
16
16
46
31
35
13
6
10
12
11
26
24
14
2
6
10
8
4
5
0
9
Distance (km) to Source emissions >5 tpy and within 10 km
mean
7.5
5.7
6.2
6.3
6.9
6.7
5.4
6.4
6.1
5.0
7.5
5.0
6.2
6.2
4.9
8.4
7.4
6.3
6.5
7.1
7.0
std
1.7
1.8
2.0
2.5
2.2
2.3
2.4
1.0
2.4
3.2
2.1
2.5
2.1
2.6
4.8
0.4
1.6
2.0
2.8
2.3
2.4
min
4.3
2.0
2.5
0.7
0.4
1.7
2.9
4.8
0.6
0.3
2.6
0.3
2.4
1.5
1.4
8.0
3.7
4.6
3.2
4.5
1.1
2.5
4.3
2.0
2.5
2.0
0.4
1.7
2.9
4.8
0.6
0.3
2.6
0.3
2.4
1.5
1.4
8.0
3.7
4.6
3.2
4.5
1.1
50
7.7
5.4
6.0
6.5
7.3
6.6
4.5
6.5
6.1
4.6
7.9
4.9
6.0
5.4
4.9
8.4
7.8
5.5
6.8
6.5
7.9
97.5
9.7
9.7
9.6
9.9
10.0
9.9
9.7
7.6
9.8
9.8
9.7
9.5
10.0
9.8
8.3
9.2
9.3
9.4
9.2
9.6
8.8
max
9.7
9.7
9.6
9.9
10.0
9.9
9.7
7.6
9.8
9.8
9.7
9.5
10.0
9.8
8.3
9.2
9.3
9.4
9.2
9.6
8.8
Emissions (tpy) of Sources within 10 km and >5 tpy
mean
46
28
24
77
98
94
557
40
124
109
1034
122
129
558
13
1104
80
94
30
14
809
std
62
37
33
150
176
168
1643
35
137
129
3225
220
227
1579
7
2413
173
193
19
8
1959
min
5
5
5
5
5
5
11
11
11
11
6
6
6
11
8
9
14
14
17
8
14
2.5
5
5
5
5
5
5
11
11
11
11
6
6
6
11
8
9
14
14
17
8
14
50
15
15
15
16
17
17
34
26
66
46
45
56
56
46
13
13
19
19
22
12
156
97.5
181
143
143
508
848
848
6009
98
410
410
10756
1118
1118
6009
18
6009
571
571
58
27
6009
max
181
143
143
848
848
848
6009
98
410
410
10756
1118
1118
6009
18
6009
571
571
58
27
6009
n is the number of sources emitting >5 tons per year (tpy) NOX within a 1 0 kilometer radius of a monitor in a particular location.
2 The min, 2.5, med, 97.5, and max represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for the distance in meters (m) to the source emission.
A-22
-------
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 NC>2
concentrations collected from 1995 through 2006. The purpose is to present relevant information
on the air quality as it relates to both the current form of the standard (annual average
concentration) and the exposure concentration and duration associated with adverse health
effects (1-hour).
A-5.2 Approach
To evaluate variability inNO2 concentrations, temporal and spatial distributions of summary
statistics were computed in addition to use of statistical tests to compare distributions between
years and/or monitors and/or locations. For a given location, the variability within that location
is defined by the distribution of the annual summary statistics across years and monitors and by
the distribution of the hourly concentrations across hours and monitors. The summary statistics
were compiled into tables and used to construct figures for visual comparison and for statistical
analysis.
Boxplots were constructed to display the distribution across sites and years (or hours for the
hourly concentrations) for a single location. The box extends from the 25th to the 75th
percentile, with the median shown as the line inside the box. The whiskers extend from the box
to the 5th and 95th percentiles. The extreme values in the upper and lower tails beyond the 5th
and 95th percentiles are not shown to allow for similar scaling along the y-axis for the plotted
independent variables. The mean is plotted as a dot; typically it would appear inside the box,
however it will fall outside the box if the distribution is highly skewed. All concentrations are
shown in parts per billion (ppb).
Q-Q plots also display the distribution in the calculated air quality metrics across sites and
years (or hours for the hourly concentrations) for a single location. The Q-Q plot is used to
compare the observed cumulative distribution to a standard statistical distribution. In this case
the observed distributions are compared with a log-normal distribution, so that the vertical scale
is logarithmic. The horizontal scale is the quantile of a standard normal distribution, so that if
there are N observed values, then the kth highest value is plotted against the quantile probit(p),
where probit is the inverse of the standard normal distribution function, andp is the plotting
point. The plotting points were chosen asp = (k-3/8)/(N+l/4) for the annual statistics andp =
k/(N+l) for the hourly concentrations. If the distribution were exactly log-normal, then the
curve would be a straight line. The median value is the y-value when the normal quantile equals
zero. The slope of the line is related to the standard deviation of the logarithms, so that the higher
the slope, the higher the coefficient of variation (standard deviation divided by the mean for the
raw data, before taking logarithms).
In addition to the tabular and graphical comparisons of the summary statistics, the
distributions of each variable were compared using various statistical tests. An F-Statistic
comparison compares the mean values between locations using a one way analysis of variance
(ANOVA). This test assumed that for each location, the site-year or site-hour variables are
A-23
-------
normally distributed, with a mean that may vary with the location and a constant variance (i.e.,
the same for each location). Statistical significance was assigned for p-values less than or equal
to 0.05. The Kruskal-Wallis Statistics are non-parametric tests that are extensions of the more
familiar Wilcoxon tests to two or more groups. The analysis is valid if the difference between
the variable and the location median has the same distribution for each location. If so, this
procedure tests whether the location medians are equal. The test is also consistent under weaker
assumptions against more general alternatives. The Mood Statistic comparisons are 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. This test assumes that all the groups have the same median. Specifically,
suppose there is a total of N values, summing across all the locations to be compared. These N
values are ranked from 1 to N, and the jth highest value is given a score of (j - (N+l)/2}2. The
Mood statistic uses a one-way ANOVA statistic to compare the mean scores for each location.
Thus the Mood statistic compares the variability between the different locations assuming that
the medians are equal.
A-5.3 Summary Results by Locations
A summary of the important trends in NC>2 concentrations is reported in this section.
Detailed air quality results (i.e., by year and within-location) are presented in section A-5.4,
containing both tabular and graphic summaries of the spatial and temporal concentration
distributions.
A broad view of the NC>2 monitoring concentrations across locations is presented in Figures
A-l and A-2. In general there is variability in NC>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 annual mean concentrations was about 35%, however
locations such as Jacksonville or Provo had COVs as low as 6% while locations such as Las
Vegas and Not MSA contained COVs above 60%. Reasons for differing variability arise from
the size of the monitoring network in a location, level of the annual mean concentration,
underlying influence of temporal variability within particular locations, among others.
A-24
-------
Annual Mean
•15-
40-
20-
Uoston Chicago Cleveland Denver Detroit t.as Angeles Miami New York Philadelphia Washington
Location
Figure A-1. Distributions of annual mean NO2 ambient monitoring concentrations for selected CMSA
locations, years 1995-2006.
Annual Mean
40-
Atlanla Colorado F.l Piiso Jaeksonvjlle Las Vegas Phoenix Pnno Si Louis Other MS.\ Othr \on-MSA
Location
Figure A-2. Distributions of annual mean NO2 ambient monitoring concentrations for selected MSA and
grouped locations, years 1995-2006.
Differences in the distributions of hourly concentrations were of course consistent with that
observed for the annual mean concentrations, and as expected there were differences in the
A-25
-------
COVs across locations, ranging from about 60 to 120%. However, in comparing the 90 percent
intervals (from the 5th to the 95th percentiles) of hourly concentrations across locations, the
ranges are somewhat similar (for example see Figure 3 for the CMSA locations). This means
that the intervals for the annual mean differ more than that of the hourly concentrations between
locations likely due to the influence of high 1-hour NC>2 concentrations for certain locations.
Hourly C'oncs
fiO-
55-
50-
45-
Hosnm f'lucago Cleveland Denver Uctmit l.osAnyctcs Miami \c\v ^ ork Philadelphia Washington
Location
Figure A-3. Distributions3 of hourly NO2 ambient monitoring concentrations for selected CMSA locations,
years 1995-2006.
This presence of extreme NC>2 concentrations is best illustrated in Figure 4 using a Q-Q plot
that captures the full concentration distribution for each CMSA location. The Q-Q plots are
generally curved rather than straight, such that the distributions do not appear to be log-normal.
However, the annual mean and hourly concentration curves do tend to be approximately straight
and parallel for values above the median (normal quantile = 0) through the 3rd quantile,
suggesting that these upper tails of the distributions are approximately log-normal with
approximately the same coefficients of variation. Beyond the 3rd quantile though, each
distribution similarly and distinctly curves upwards, indicating a number of uncharacteristic NC>2
concentrations at each location when compared with the rest of their respective concentration
distributions.
3 The boxplots for hourly concentrations were created using a different procedure than for the annual
statistics, because of the large number of hourly values and the inability of the graphing procedure to
allow frequency weights. Therefore, the appropriate weighted percentiles and means were calculated
and plotted as shown, but the vertical lines composing the sides of the box were omitted.
A-26
-------
Hourly Cone
1000.0 =(
IOO.C
10.0
location ""Boston
-3
"Chicago
•"Cleveland "* Denver
0
Normal
Detroit Los Angeles Miami
3 4 5
"New York ""Philadelphia ""Washington
Zero values VICTC replaced by 0.?
Figure A-4. Distributions of hourly NO2 ambient concentration for selected CMSA locations, years 1995-
2006.
Distributions of each variable (annual means and hourly concentrations) were compared
between the different locations using statistical tests. The results in Table A-9 show statistically
significant differences between locations for both variables and all three summary statistics
(means, medians, and scales). This supports the previous observation that the distributions for the
different locations are dissimilar.
Table A-9. Statistical test results for spatial comparisons of all location parameter distributions.
Concentration
Parameter
Annual Mean
Hourly
Means Comparison
F Statistic p-value
148
330272
<0.0001
<0.0001
Central Values Comparison
Kruskal-Wallis p-value
1519
5414056
<0.0001
<0.0001
Scales Comparison
Mood p-value
729
1354075
<0.0001
<0.0001
The distributions of NC>2 concentrations within locations were also evaluated. As an
example, Figure A-5 illustrates the distribution of the annual mean NC>2 concentration at 10
monitoring sites within Philadelphia. The mean annual means vary from a minimum of 14.8 ppb
(site 1000310071) to a maximum of 30.5 ppb (site 4210100471). The range of within-site
variability can be attributed to the number of monitoring years available coupled with the
observed trends in temporal variability across the monitoring period (discussed below in Section
2.4.4).
Distributions of each variable (annual means and hourly NC>2 concentrations) within
locations (i.e., site distributions) were compared using statistical tests. The results in Table A-10
indicate statistically significant differences within locations for both variables and the central
A-27
-------
tendency statistics (means and medians), while scales were statistically significant for 38 out of
40 possible tests. This supports the previous observation that the distributions for the different
locations are dissimilar.
Annual Mean
.14-
28-
27-
26-
25-
24-
2.1-
1000310031 1000310071 1000120041 3400700032 4201700121 4204500021 4200100131 4210100043 4210100202 4210100471
Figure A-5. Distributions of annual average NO2 concentrations among 10 monitoring sites in
Philadelphia CMSA, years 1995-2006.
Table A-10. Statistical test results for spatial comparisons of within location parameter distributions.
Concentration
Parameter
Annual Mean
Location
Boston
Chicago
Cleveland
Denver
Detroit
Los Angeles
Miami
New York
Philadelphia
Washington DC
Atlanta
Colorado Springs
El Paso
Las Vegas
Means Comparison
F
Statistic p-value
47.3
123
12.1
85.3
13.2
49.0
111
106
48.9
48.6
119
8.7
36.0
137
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Central Values
Comparison
Kruskal-
Wallis p-value
96.5
76.7
15.4
32.0
13.1
325
36.2
163
68.8
104
45.2
18.8
31.6
45.4
<0.001
<0.001
0.002
<0.001
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.009
<0.001
<0.001
Scales Comparison
Mood p-value
79.9
68.5
7.5
23.0
7.8
240
29.9
151
33.0
71.2
28.6
8.7
35.3
35.2
<0.001
<0.001
0.058
0.001
0.020
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.273
<0.001
<0.001
A-28
-------
Concentration
Parameter
Hourly
Location
Phoenix
St. Louis
Other CMSA
Not MSA
Boston
Chicago
Cleveland
Denver
Detroit
Los Angeles
Miami
New York
Philadelphia
Washington
Atlanta
Colorado Springs
El Paso
Las Vegas
Phoenix
St. Louis
Other CMSA
Not MSA
Means Comparison
F
Statistic p-value
20.4
51.5
82.5
76.9
17884
11611
4191
25130
4125
27288
10669
20052
13759
14262
35917
5541
10503
22567
5626
14807
19557
17630
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Central Values
Comparison
Kruskal-
Wallis p-value
32.2
82.1
2152
424
312994
142034
14102
104800
10442
1050310
68580
404234
112129
223040
137022
48252
57694
136455
35645
178180
6306431
1580139
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
Scales Comparison
Mood p-value
23.6
69.0
1934
372
59896
37224
1985
2864
424
269190
43090
91104
4903
30974
17330
3921
18334
28972
6747
47842
2164452
491390
0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
A-5.4 Summary Results by Year
A broad view of the trend of NC>2 monitoring concentrations over time is presented in Figure
A-6. The annual mean concentrations were calculated for each monitor site within each year to
create a distribution of annual mean concentrations for each year. The distribution of annual
mean concentrations generally decreases with each increasing year. On average, mean annual
mean NC>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 consistent pattern in the decreasing concentrations
across each years distribution, the shape of each curve is similar indicating that while
concentrations have declined, the variability within each year is similar from year to year. The
variability within a given year is representing spatial differences in annual average
concentrations across the 20 locations.
A-29
-------
Annual Mean
100.0-
100-
0.1
-2
-I
0 I 2
Normal Quantilc
year - 1005- 19%- I007- 1008 1000 2000-2001-2002-2003-2004-2005-2006
Figure A-6. Distributions of annual mean NO2 concentrations for all monitors, years 1995-2006.
In general, temporal trends within a location were also consistent with the trends observed in
all monitors, particularly where the location's monitoring network was comprised of several
monitors. For example, Figure A-7 illustrates the temporal distributions of annual average NO2
concentration in the Philadelphia CMSA, each comprising between 4 and 8 monitors in operation
per year. Clearly NO2 concentrations are decreased with increasing calendar year of monitoring
with the lowest NO2 concentrations in the more recent years of monitoring. The pattern of
variability in NO2 concentration within a year at this location is also similar when comparing
across years based on similarities in the shape of each years respective curve.
A-30
-------
Q-Q Plot of Annual Mean
loc_type=CMSA loc_name=Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD CMSA
Annual Mean
100-
vear - 1995- 1996- 1997- 1998
Normal Quantile
1999 2000 - 2001 - 2002 - 2003 - 2004 -
2005 2006
Figure A-7. Distributions of annual mean NO2 concentrations for the Philadelphia CMSA, years 1995-
2006.
In general, temporal trends within a location considering the hourly concentration data were
consistent with the above, particularly where the monitoring network was comprised of several
monitors. For example, Figure A-8 illustrates the temporal distribution for hourly NOa
concentration in the Los Angeles CMSA, comprising between 26 and 36 monitors in operation
per year. NC>2 concentrations are decreased with increasing calendar year of monitoring with the
distribution of hourly concentrations lowest in the more recent years of monitoring. The pattern
of variability in NO2 concentration within a year at this location is also similar when comparing
across years based on similarities in the shape of each years respective curve.
A-31
-------
Q-Q Plot of Hourly Concentrations
loc_type=CMSA loc_ncime=Los Angeles-Riverside-Orange County, CA CMSA
Hourly Cone
lOOCO-
-2-10123
Ncrmal Quantile
year - IW5- 1996- 1997- 1998 1999 2000-2001 - 2002~ 2003- 2004-2005- 2006
Zero v»liKStt£rc
Figure A-8. Distributions of hourly NO2 concentrations in the Los Angeles CMSA, years 1995-2006.
These temporal trends were confirmed by statistical comparison tests. The means and
medians of the annual means and hourly concentrations compared across the different years were
statistically significant (all p<0.0001). A Mood test indicated that, for the annual means, the
scales were also significantly different (both the annual and hourly p<0.001). Note, however,
that the Mood test derivation assumes that the medians of the annual means are the same for each
year, whereas the plots and the Kruskall-Wallis test result implies that the medians are not the
same. As noted before, Figure A-8 indicates that the Q-Q curves for different years have similar
slopes but different intercepts, which implies that the annual means for different years have
different mean values but similar coefficients of variation. In fact the coefficients of variation of
the annual means are nearly identical for different years, ranging from 52 % to 55 %.
There were some exceptions to this temporal trend, particularly when considering the
distribution of hourly concentrations and where a given location had only few monitors per year.
Using Jacksonville as an example, Figure A-9 illustrates the same temporal trend in NC>2
concentrations as was observed above for much of the distribution, however distinctions are
noted at the upper tails of the distribution for two years of data, 2002 and 2004. For
Jacksonville, each years' hourly concentration distribution was based on only a single monitor.
Where few monitors exist in a given location, atypical variability in one or a few monitors from
year to year can greatly influence the distribution of short-term concentrations, particularly at the
upper percentiles.
The same follows for assignment of statistical significance to temporal trends within
locations. While annual average concentrations are observed to have declined over time within a
location, the number of sites were typically few thus limiting the power of the statistical tests.
A-32
-------
Only Los Angeles, El Paso, Phoenix, and Other CMSA were significant (p<0.05) for the central
tendency tests, while only Los Angeles and Other CMSA were significant (p<0.05) for scale
(data not shown). All hourly concentrations comparison tests for years within each location were
statistically significant (p<0.05) for all three test statistics (mean, median, scale).
Hourly Cone
1000.0-
Normal Quantile
vear - 1995- 1996- 1997" 1998 1999 2000^ 2002" 2003- 2004- 2005
Figure A-9. Distributions of hourly NO2 concentrations in the Jacksonville MSA, years 1995-2006, one
monitor.
There is very little difference in annual average concentrations across the 1995-2006
monitoring period for the grouped Not MSA location. While percentage-wise the reduction in
concentration is about 25%, on a concentration basis this amounts to a reduction of about 2 ppb
over the 11 year period (Figure A-10). When considering the last 5 years of data, the reduction
in annual average concentration was only 0.5 ppb. This could indicate that many of these
monitoring sites are affected less by local sources of NO2 (e.g., emissions from major roads and
stationary sources) compared with the other locations. Therefore, the areas that these monitors
represent may also be less likely to see significant benefit by changes in source emissions and/or
NO2 standard levels compared with the named CMSA/MSA locations.
A-33
-------
Annual Mean
20-
18-
17-
1
-------
I
Table A-11. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Boston CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
12
10
11
11
7
7
10
10
5
7
8
7
Mean
20
19
17
17
19
17
16
16
15
15
14
13
SD
7
8
8
8
9
8
8
7
6
6
6
6
cov
34
42
44
48
45
49
50
43
42
41
39
42
Min
6
6
6
6
6
5
7
5
9
7
7
8
p10
12
8
9
8
6
5
7
7
9
7
7
8
p20
14
11
11
10
9
10
8
10
10
9
10
9
p30
16
14
13
12
20
11
10
12
11
12
10
10
p40
21
17
15
15
20
11
12
13
11
12
11
10
p50
22
19
16
15
21
18
16
15
12
16
13
10
p60
22
21
19
19
21
20
20
19
17
16
15
15
p70
23
24
22
23
21
20
22
22
21
16
18
15
p80
23
26
22
23
27
22
24
24
22
17
19
19
p90
27
29
27
28
30
29
28
25
22
25
23
23
Max
31
31
30
31
30
29
30
25
22
25
23
23
1995 1996 1997 1998 1999
Figure A-11. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Boston CMSA.
Hourly Cor
Table A-12. Distribution of hourly NO2 ambient concentrations (ppb) by year, Boston
CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
99946
83541
90161
89710
54043
56196
82048
80472
41198
56831
66244
57681
Mean
20
19
17
17
19
16
16
16
15
15
14
13
SD
12
14
12
13
13
12
13
12
11
10
11
10
COV
62
72
72
75
70
76
77
75
75
71
75
74
Min
0
0
0
0
0
0
0
0
0
0
0
0
p10
5
3
3
3
3
2
2
2
3
3
3
3
p20
9
7
6
5
7
5
4
5
5
5
5
4
p30
12
10
9
8
10
7
7
7
7
7
7
6
p40
15
13
11
11
13
11
10
10
10
10
9
8
p50
18
16
15
15
17
14
14
14
13
12
12
11
p60
22
21
18
18
21
18
18
17
16
15
15
14
p70
26
25
23
23
25
22
22
21
19
19
18
17
p80
30
30
28
28
30
27
27
26
24
23
23
22
p90
36
38
35
35
37
34
34
32
31
29
29
28
Max
100
205
134
112
117
95
114
93
99
96
113
79
Figure A-12. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Boston CMSA.
A-35
-------
0 0
1 5
.1 1
0 0
555555555555
000000000000
991555555557
45000000001 0
0 0 0 0 0 0 0 0 0 0 0 0
Figure A-13. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Boston CMSAset A, 1995-2006.
Table A-13. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Boston CMSA set A, 1995-2006.
Monitor ID
2303130021
2500510051
2500920061
2500940041
2500950051
2502100091
2502500021
250250021 1
2502500351
2502500361
2502500401
250250041 1
2502500421
2502510031
2502700201
n
7
2
10
5
2
1
11
8
1
1
11
1
6
5
8
Mean
10
8
12
6
9
22
28
25
23
22
21
12
21
23
19
SD
1
1
3
0
1
3
3
2
3
2
1
cov
9
9
22
7
8
11
12
10
16
7
6
Min
9
8
9
6
9
22
23
21
23
22
16
12
17
21
17
p10
9
8
9
6
9
22
23
21
23
22
18
12
17
21
17
p20
9
8
10
6
9
22
25
22
23
22
20
12
19
21
18
p30
9
8
10
6
9
22
25
23
23
22
21
12
19
22
19
p40
9
8
11
6
9
22
29
27
23
22
21
12
19
22
19
p50
10
8
11
6
9
22
30
27
23
22
21
12
21
23
19
p60
10
9
13
6
10
22
30
27
23
22
22
12
22
23
19
p70
10
9
15
6
10
22
30
27
23
22
22
12
24
23
20
p80
10
9
15
7
10
22
31
28
23
22
23
12
24
24
20
p90
11
9
16
7
10
22
31
28
23
22
23
12
25
25
21
Max
11
9
16
7
10
22
31
28
23
22
23
12
25
25
21
Figure A-14. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Boston CMSA set A, 1995-2006.
Table A-14. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Boston CMSAset A, 1995-2006.
Monitor ID
2303130021
2500510051
2500920061
2500940041
2500950051
2502100091
2502500021
2502500211
2502500351
2502500361
2502500401
2502500411
2502500421
n
58123
16732
80761
41337
16228
8546
87534
63990
8539
8542
91196
8319
48078
Mean
10
8
12
6
9
22
28
25
23
22
21
12
21
SD
9
7
10
7
8
10
11
11
10
11
12
10
10
COV
94
81
80
108
91
46
40
45
47
49
59
89
48
Min
0
0
0
0
0
0
0
0
0
0
1
0
0
p10
1
2
3
0
2
9
14
13
10
9
7
2
9
p20
2
3
4
1
3
13
18
16
13
12
10
3
12
p30
4
4
6
2
4
15
21
18
16
15
13
5
15
p40
5
5
7
3
5
18
24
21
19
19
16
6
17
p50
7
6
9
4
6
21
27
24
21
21
19
8
20
p60
9
8
12
6
8
23
30
26
24
24
22
11
22
p70
12
10
15
7
11
27
33
30
27
28
26
15
25
p80
16
13
20
10
14
30
37
34
31
31
31
19
29
p90
23
18
27
16
22
35
43
40
37
36
38
27
35
Max
100
50
90
70
51
75
134
205
74
100
113
81
79
A-36
-------
Figure A-15. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Boston CMSA set B, 1995-2006.
Table A-15. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Boston CMSA set B, 1995-2006.
Monitor ID
2502700231
3301100161
3301100191
3301100201
3301500091
3301500131
3301500141
3301500151
n
3
4
1
5
5
4
3
1
Mean
15
16
11
12
12
6
8
13
SD
0
2
1
1
1
0
cov
3
15
8
12
10
6
Min
15
14
11
10
9
5
7
13
p10
15
14
11
10
9
5
7
13
p20
15
14
11
11
11
5
7
13
p30
15
15
11
11
12
5
7
13
p40
15
15
11
12
12
5
7
13
p50
15
15
11
12
12
6
7
13
p60
15
16
11
12
12
6
7
13
p70
16
16
11
12
12
6
8
13
p80
16
19
11
12
13
7
8
13
p90
16
19
11
13
13
7
8
13
Max
16
19
11
13
13
7
8
13
Table A-16. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Boston
CMSA setB, 1995-2006.
Monitor ID
2502510031
2502700201
2502700231
3301100161
3301100191
3301100201
3301500091
3301500131
3301500141
3301500151
n
40775
63836
24267
33436
8022
41325
40978
33536
25372
8599
Mean
23
19
15
16
11
12
12
6
8
13
SD
12
11
9
10
9
9
9
7
7
9
COV
54
59
58
64
81
75
77
118
94
75
Min
0
0
0
0
0
0
0
0
0
0
p10
9
6
5
6
2
3
2
0
1
3
p20
12
9
8
8
3
4
4
1
2
5
p30
14
11
10
9
5
6
6
2
3
6
p40
17
14
12
11
7
7
8
2
4
8
p50
20
17
14
13
9
9
10
3
5
10
p60
24
21
16
16
11
11
12
5
7
12
p70
28
24
19
19
14
14
15
7
9
16
p80
33
29
22
23
18
18
19
10
12
20
p90
40
35
27
29
24
25
25
15
17
27
Max
94
95
93
158
54
62
63
50
48
65
Figure A-16. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Boston CMSA set B, 1995-2006.
A-37
-------
Figure A-17. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Chicago CMSA.
ableA-17.
ear, Chicac
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
7
7
6
9
9
9
7
7
5
6
6
5
Distribution of annual average NO2 ambient concentrations (ppb) by
jo CMSA.
Mean
28
24
25
23
23
22
25
24
26
23
23
23
SD
3
8
8
7
7
7
5
6
5
6
5
6
cov
12
32
34
32
29
30
21
24
19
25
23
27
Min
23
9
9
9
10
9
18
17
20
16
17
16
p10
23
9
9
9
10
9
18
17
20
16
17
16
p20
24
21
23
17
17
18
18
19
21
18
18
17
p30
26
23
23
19
19
20
24
22
22
18
18
18
p40
26
23
24
23
22
21
24
22
25
20
20
20
p50
27
24
25
24
24
22
25
22
27
22
22
22
p60
29
28
27
25
24
23
28
23
29
24
24
25
p70
29
28
31
26
27
27
28
23
30
29
28
28
p80
30
31
31
31
31
29
28
30
31
29
28
29
p90
32
32
34
32
32
32
32
32
31
29
30
31
Max
32
32
34
32
32
32
32
32
31
29
30
31
Table A-18. Distribution of hourly NO2 ambient concentrations (ppb) by year, Chicago
CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
58998
59447
51443
76365
74985
75327
58268
58383
42406
49210
51043
42009
Mean
28
24
25
23
23
22
25
24
26
23
23
23
SD
14
14
15
14
14
14
13
14
14
13
13
13
COV
51
58
59
61
61
62
54
59
54
57
59
57
Min
0
0
0
0
0
0
0
0
0
0
0
0
p10
11
7
7
6
7
6
9
8
10
8
8
8
p20
15
11
11
10
10
10
13
12
14
11
11
11
p30
19
15
15
13
13
13
16
15
17
14
14
14
p40
22
18
19
17
17
17
20
18
21
18
17
17
p50
26
22
23
21
21
20
23
21
24
21
21
21
p60
29
26
27
25
25
24
27
25
28
25
24
25
p70
33
31
31
29
30
29
31
29
32
28
29
29
p80
38
36
36
34
35
34
36
34
37
33
34
34
p90
47
43
44
41
42
41
43
42
45
41
41
41
Max
113
127
113
112
113
108
114
149
122
101
106
137
Figure A-18. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Chicago CMSA.
A-38
-------
Figure A-19. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Chicago CMSA, 1995-2006.
Table A-19. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Chicago CMSA, 1995-2006.
Monitor ID
1703100371
1703100631
1703100641
1703100751
1703100761
1703131011
1703131031
1703140021
1703142011
1703142012
1703180031
1719710111
1808900221
1808910162
n
1
12
6
4
5
3
9
12
4
4
8
5
8
2
Mean
29
31
23
24
20
31
29
26
17
17
23
9
19
22
SD
1
2
0
2
1
1
2
1
1
1
1
1
2
cov
4
8
2
9
3
5
8
4
4
4
6
4
7
Min
29
29
21
23
18
30
28
22
17
16
22
9
18
21
p10
29
30
21
23
18
30
28
23
17
16
22
9
18
21
p20
29
31
22
23
19
30
28
24
17
16
22
9
18
21
p30
29
31
22
24
20
30
28
24
17
16
23
9
18
21
p40
29
31
23
24
20
31
29
26
17
16
23
9
19
21
p50
29
32
23
24
20
31
29
27
18
17
23
9
19
22
p60
29
32
24
24
21
31
30
27
18
17
24
9
19
23
p70
29
32
24
24
22
32
30
27
18
17
24
9
19
23
p80
29
32
24
25
22
32
31
27
18
17
24
10
20
23
p90
29
32
26
25
22
32
31
28
18
17
25
10
20
23
Max
29
34
26
25
22
32
31
28
18
17
25
10
20
23
Figure A-20. Distribution of hourly NO2 ambient concentration (ppb)
by monitor, Chicago CMSA, 1995-2006.
Table A-20. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Chicago CMSA, 1995-2006.
Monitor ID
1703100371
1703100631
1703100641
1703100751
1703100761
1703131011
1703131031
1703140021
1703142011
1703142012
1703180031
1719710111
1808900221
1808910162
n
8630
101935
52139
34028
42946
25141
75061
102779
32625
32552
68952
41227
63295
16574
Mean
29
31
23
24
20
31
29
26
17
17
23
9
19
22
SD
13
15
13
12
12
13
13
13
11
10
12
6
12
12
COV
44
46
57
52
59
41
44
51
64
62
53
69
66
56
Min
0
0
0
0
0
3
0
0
0
0
0
0
0
3
p10
15
13
8
10
7
16
14
11
5
6
9
3
4
9
p20
19
19
11
13
10
20
18
14
7
8
12
4
7
12
p30
22
23
15
16
12
23
22
17
10
10
15
5
10
14
p40
25
27
18
19
15
27
25
20
12
12
18
6
13
16
p50
28
30
21
22
18
30
28
24
15
14
21
8
17
19
p60
31
34
25
26
21
33
31
27
19
17
25
9
20
22
p70
35
38
29
29
25
37
35
31
22
20
29
11
25
26
p80
39
43
34
34
30
41
39
36
27
25
33
13
29
31
p90
47
51
41
41
37
48
47
44
33
31
40
18
36
39
Max
113
137
127
113
98
105
149
106
77
70
97
52
131
125
A-39
-------
Figure A-21. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Cleveland CMSA.
Table A-21. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Cleveland CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
2
2
1
2
2
2
2
2
2
1
2
2
Mean
24
23
28
24
21
20
21
20
20
22
19
16
SD
5
4
5
5
4
4
4
3
3
3
cov
19
19
0
22
26
19
17
18
15
0
17
17
Min
21
20
28
20
17
18
19
17
17
22
17
14
p10
21
20
28
20
17
18
19
17
17
22
17
14
p20
21
20
28
20
17
18
19
17
17
22
17
14
p30
21
20
28
20
17
18
19
17
17
22
17
14
p40
21
20
28
20
17
18
19
17
17
22
17
14
p50
24
23
28
24
21
20
21
20
20
22
19
16
p60
27
26
28
27
25
23
24
22
22
22
22
18
p70
27
26
28
27
25
23
24
22
22
22
22
18
p80
27
26
28
27
25
23
24
22
22
22
22
18
p90
27
26
28
27
25
23
24
22
22
22
22
18
Max
27
26
28
27
25
23
24
22
22
22
22
18
Table A-22. Distribution of hourly NO2 ambient concentrations (ppb) by year,
Cleveland CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
16042
16593
8300
16680
16743
16399
16566
16464
16948
8484
16558
16853
Mean
24
23
28
24
21
20
21
20
20
22
19
16
SD
13
12
17
13
12
11
12
11
11
11
12
10
COV
53
52
59
53
58
55
56
56
57
51
60
64
Min
2
1
0
0
0
0
0
1
0
0
0
0
p10
10
9
12
9
7
8
8
8
7
10
7
5
p20
13
13
15
13
10
10
10
10
10
13
9
8
p30
16
15
18
16
13
13
13
12
13
15
12
10
p40
19
18
21
19
16
16
16
15
15
18
14
12
p50
22
21
24
22
19
19
19
18
18
20
17
14
p60
25
24
28
25
22
22
22
21
20
23
20
16
p70
29
28
32
29
26
25
26
24
24
26
23
20
p80
34
32
38
33
30
30
30
28
28
30
28
24
p90
41
39
49
40
37
36
37
35
35
37
35
30
Max
108
148
253
89
86
74
103
88
90
83
85
175
Figure A-22. Temporal distribution of hourly NO2 ambient
concentrations (ppb) by year, Cleveland CMSA.
A-40
-------
f,|n: ("MS \ !nt- n.irno (T!i:-,,.-S.i!iJ-Aki.i-i
I
Table A-23. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Cleveland CMSA, 1995-2006.
Monitor ID
3903500431
3903500601
3903500661
3903500701
n
2
12
6
2
Mean
20
24
18
15
SD
1
3
1
2
cov
4
12
6
12
Min
20
18
17
14
p10
20
22
17
14
p20
20
22
17
14
p30
20
22
17
14
p40
20
22
17
14
p50
20
23
18
15
p60
21
25
18
17
p70
21
26
19
17
p80
21
27
19
17
p90
21
27
20
17
Max
21
28
20
17
Figure A-23. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Cleveland CMSA, 1995-2006.
Figure A-24. Distribution of hourly NO2 ambient concentration (ppb)
by monitor, Cleveland CMSA, 1995-2006.
Table A-24. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Cleveland CMSA, 1995-2006.
Monitor ID
3903500431
3903500601
3903500661
3903500701
n
16215
99696
50100
16619
Mean
20
24
18
15
SD
11
13
11
11
COV
54
53
60
70
Min
1
0
0
0
p10
8
10
7
5
p20
11
13
9
7
p30
13
16
11
9
p40
16
19
13
10
p50
18
22
15
13
p60
21
25
18
15
p70
24
28
22
18
p80
28
33
26
23
p90
35
40
33
30
Max
92
253
103
175
A-41
-------
Figure A-25. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Denver CMSA.
Figure A-26. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Denver CMSA.
Table A-25. Temporal distribution of annual average NO2 ambient concentrations
(ppb) by year, Denver CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
3
6
6
5
3
3
2
1
1
2
2
2
Mean
28
14
15
16
12
12
31
35
21
24
24
24
SD
6
11
11
13
6
3
8
4
5
8
cov
23
77
74
77
52
26
25
0
0
17
21
33
Min
23
6
6
7
8
9
26
35
21
21
20
18
p10
23
6
6
7
8
9
26
35
21
21
20
18
p20
23
7
8
7
8
9
26
35
21
21
20
18
p30
23
7
8
7
8
9
26
35
21
21
20
18
p40
26
8
9
8
9
10
26
35
21
21
20
18
p50
26
8
9
9
9
10
31
35
21
24
24
24
p60
26
9
9
16
9
10
37
35
21
27
28
29
p70
35
22
23
23
19
15
37
35
21
27
28
29
p80
35
22
23
29
19
15
37
35
21
27
28
29
p90
35
33
34
35
19
15
37
35
21
27
28
29
Max
35
33
34
35
19
15
37
35
21
27
28
29
Table A-26. Temporal distribution of hourly NO2 ambient concentrations (ppb) by
year, Denver CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
23204
46816
45049
40258
23164
24649
15204
7688
6989
15878
15467
13775
Mean
28
14
15
17
12
12
31
35
21
24
24
24
SD
17
15
15
17
13
13
17
13
17
15
16
15
COV
62
108
106
100
108
108
55
36
78
60
65
65
Min
0
0
0
0
0
0
0
0
0
0
0
0
p10
6
1
1
1
0
0
8
20
3
4
3
3
p20
11
2
3
3
2
1
15
24
5
10
8
7
p30
16
4
4
5
4
3
21
28
8
16
14
13
p40
22
6
6
7
6
5
27
31
13
20
19
19
p50
27
8
8
10
8
8
32
34
18
24
24
24
p60
32
11
12
15
10
10
36
38
25
28
29
28
p70
36
16
17
22
14
14
41
41
31
32
33
33
p80
41
25
26
31
21
19
45
45
37
37
38
38
p90
48
37
39
42
33
30
52
51
44
43
44
44
Max
286
137
141
148
96
141
157
159
136
115
114
169
A-42
-------
Figure A-27. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Denver CMSA, 1995-2006.
Figure A-28. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Denver CMSA, 1995-2006.
Table A-27. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Denver CMSA, 1995-2006.
Monitor ID
0800130011
0800500031
0803100021
0805900061
0805900081
0805900091
0805900101
n
11
1
9
3
4
3
5
Mean
21
26
33
7
9
9
7
SD
3
4
0
1
1
1
cov
14
11
6
7
8
19
Min
15
26
27
7
9
8
6
p10
18
26
27
7
9
8
6
p20
19
26
28
7
9
8
6
p30
20
26
29
7
9
8
6
p40
21
26
33
7
9
9
7
p50
21
26
34
7
9
9
7
p60
22
26
35
7
9
9
7
p70
23
26
35
8
9
9
8
p80
23
26
35
8
10
9
9
p90
23
26
37
8
10
9
9
Max
26
26
37
8
10
9
9
Table A-28. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Denver CMSA, 1995-2006.
Monitor ID
0800130011
0800500031
0803100021
0805900061
0805900081
0805900091
0805900101
n
83703
7790
68630
22077
32449
24368
39124
Mean
21
26
33
7
9
9
7
SD
17
15
15
8
9
9
8
COV
82
57
46
109
97
100
106
Min
0
0
0
0
0
0
0
p10
2
8
15
1
0
1
1
p20
4
12
20
1
2
2
2
p30
7
16
24
3
3
3
2
p40
11
20
28
4
5
5
4
p50
17
25
31
5
7
6
5
p60
25
29
35
6
9
8
6
p70
32
34
39
9
12
10
9
p80
38
39
44
12
15
14
12
p90
45
45
51
18
22
20
17
Max
239
176
286
66
68
88
98
A-43
-------
Figure A-29. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Detroit CMSA.
Figure A-30. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Detroit CMSA.
Table A-29. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Detroit CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
2
3
3
2
1
1
2
2
2
2
2
2
Mean
20
18
17
21
18
24
21
20
20
17
18
15
SD
2
5
8
3
3
1
2
3
2
1
cov
10
28
44
14
0
0
14
7
12
16
9
9
Min
19
12
13
19
18
24
19
19
19
15
17
14
p10
19
12
13
19
18
24
19
19
19
15
17
14
p20
19
12
13
19
18
24
19
19
19
15
17
14
p30
19
12
13
19
18
24
19
19
19
15
17
14
p40
19
20
13
19
18
24
19
19
19
15
17
14
p50
20
20
13
21
18
24
21
20
20
17
18
15
p60
22
20
13
23
18
24
23
21
22
19
20
16
p70
22
21
26
23
18
24
23
21
22
19
20
16
p80
22
21
26
23
18
24
23
21
22
19
20
16
p90
22
21
26
23
18
24
23
21
22
19
20
16
Max
22
21
26
23
18
24
23
21
22
19
20
16
Table A-30. Distribution of hourly NO2 ambient concentrations (ppb) by year, Detroit
CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
16629
23600
24117
14863
7110
8590
15154
16623
16569
14779
15827
17273
Mean
20
18
17
21
18
24
21
20
20
17
19
15
SD
12
13
16
14
13
13
13
15
13
11
12
10
COV
58
74
94
68
73
56
61
73
62
66
63
64
Min
0
0
0
0
0
0
0
0
0
0
0
0
p10
8
4
2
5
4
8
7
7
7
5
6
4
p20
10
7
5
9
7
12
9
10
9
7
8
6
p30
12
9
7
12
9
15
12
12
12
9
10
8
p40
15
12
10
15
12
19
15
15
15
12
13
10
p50
18
15
13
18
15
22
19
18
18
14
16
13
p60
21
18
16
22
19
26
23
22
21
17
19
16
p70
25
22
21
27
24
30
27
25
25
21
23
19
p80
29
27
26
31
29
35
32
30
30
26
28
23
p90
35
35
36
39
36
42
38
36
36
33
35
29
Max
117
167
322
136
104
128
194
443
139
78
84
58
A-44
-------
I
Table A-31. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Detroit CMSA, 1995-2006.
Monitor ID
2609900091
2616300161
2616300192
n
2
11
11
Mean
12
21
18
SD
0
3
3
cov
3
13
14
Min
12
16
13
p10
12
19
14
p20
12
20
15
p30
12
20
17
p40
12
21
18
p50
12
22
19
p60
13
22
19
p70
13
23
19
p80
13
23
19
p90
13
24
19
Max
13
26
21
Figure A-31. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Detroit CMSA, 1995-2006.
Figure A-32. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Detroit CMSA, 1995-2006.
Table A-32. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Detroit CMSA, 1995-2006.
Monitor ID
2609900091
2616300161
2616300192
n
16523
86487
88124
Mean
12
21
18
SD
9
13
13
COV
75
62
75
Min
0
0
0
p10
3
7
5
p20
5
10
7
p30
6
13
9
p40
8
16
12
p50
10
19
15
p60
12
23
18
p70
15
26
22
p80
19
31
27
p90
25
38
35
Max
322
244
443
A-45
-------
I
I
T
I
I
I
Figure A-33. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Los Angeles CMSA.
Figure A-34. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Los Angeles CMSA.
Table A-33. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Los Angeles CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
36
29
33
32
31
32
31
32
32
28
28
26
Mean
29
25
25
25
27
25
25
24
23
22
21
19
SD
13
12
12
11
12
11
11
9
9
7
7
7
cov
47
46
47
44
44
43
43
39
37
33
34
35
Min
5
4
4
4
5
4
4
5
5
5
5
5
p10
8
6
8
9
10
10
9
10
11
13
12
9
p20
18
15
14
16
18
16
17
16
15
15
14
13
p30
20
17
16
19
20
20
19
18
18
17
16
15
p40
23
21
20
21
23
22
24
22
21
20
19
17
p50
30
28
26
26
28
25
24
24
24
21
21
19
p60
37
31
29
33
32
28
27
25
26
24
22
20
p70
39
35
33
34
35
32
33
29
29
27
25
23
p80
45
38
34
36
39
36
36
33
31
30
27
25
p90
46
41
42
39
39
39
37
36
34
31
31
27
Max
46
42
43
43
51
44
41
40
35
34
31
30
Table A-34. Distribution of hourly NO2 ambient concentrations (ppb) by year, Los
Angeles CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
290519
232203
263050
257541
253401
26331 1
251895
258452
259935
225075
227769
184205
Mean
29
26
25
25
27
25
25
24
23
22
21
19
SD
22
19
19
19
20
18
18
17
17
15
14
14
COV
78
74
75
74
73
72
71
71
72
70
69
74
Min
0
0
0
0
0
0
0
0
0
0
0
0
p10
6
5
4
5
5
5
5
5
4
4
4
3
p20
9
8
7
8
8
8
8
8
7
7
7
6
p30
14
12
11
12
13
12
12
11
11
11
11
9
p40
19
17
16
17
18
17
17
16
15
15
15
12
p50
25
22
21
22
24
23
23
21
20
20
19
16
p60
30
28
27
28
30
28
28
26
25
25
23
20
p70
37
34
33
34
37
34
33
32
31
29
28
25
p80
45
40
40
40
43
40
39
38
37
35
33
31
p90
57
50
50
50
54
50
48
46
45
42
40
38
Max
239
250
200
255
307
214
251
262
163
157
136
107
A-46
-------
Figure A-35. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Los Angeles CMSA set A, 1995-
2006.
Table A-35. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Los Angeles CMSA set A, 1995-2006.
Monitor ID
0603700022
0603700161
0603700301
0603701131
0603702061
0603710022
0603711031
0603712012
0603713012
0603716012
0603717012
0603720051
0603740022
060375001 1
0603750051
n
12
12
1
12
1
11
11
12
12
10
12
12
11
9
2
Mean
33
28
38
24
45
38
36
26
37
37
39
32
30
27
13
SD
7
5
4
6
6
4
6
4
7
5
5
2
0
cov
22
17
18
16
16
17
16
11
17
15
16
9
1
Min
20
20
38
16
45
26
27
17
28
31
30
23
20
23
13
p10
25
22
38
17
45
29
27
20
30
33
31
24
24
23
13
p20
25
24
38
20
45
33
32
21
31
35
31
27
28
23
13
p30
29
26
38
23
45
35
33
24
31
35
35
29
29
24
13
p40
33
26
38
24
45
40
34
25
36
35
36
32
29
27
13
p50
33
27
38
25
45
41
37
26
38
36
40
33
30
28
13
p60
36
28
38
26
45
41
39
26
39
37
43
34
32
28
13
p70
36
29
38
28
45
41
39
28
41
38
43
35
33
29
13
p80
39
32
38
28
45
42
40
28
43
39
44
37
34
29
13
p90
41
33
38
28
45
45
43
31
43
42
46
37
34
30
13
Max
46
37
38
28
45
45
45
32
46
45
51
37
37
30
13
Figure A-36. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Los Angeles CMSA set A, 1995-2006.
Table A-36. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los
Angeles CMSA set A, 1995-2006.
Monitor ID
0603700022
0603700161
0603700301
0603701131
0603702061
0603710022
0603711031
0603712012
0603713012
0603716012
0603717012
0603720051
0603740022
0603750011
0603750051
n
97734
97838
6817
97124
7604
88656
88425
96922
97352
81411
98551
98151
88730
74014
15047
Mean
33
28
38
24
45
38
36
26
37
37
39
32
30
27
13
SD
20
18
17
16
25
19
19
16
17
18
18
17
17
19
15
COV
59
63
44
67
56
49
52
64
45
48
47
54
58
72
114
Min
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
p10
11
8
21
7
19
17
15
7
19
17
19
13
12
5
0
p20
16
13
25
9
25
23
20
11
24
23
25
18
16
9
1
p30
21
17
28
12
30
28
25
15
28
27
29
22
19
12
2
p40
26
21
32
16
34
32
30
19
31
31
33
26
23
17
4
p50
31
25
35
21
39
36
34
23
35
34
36
30
27
23
6
p60
35
29
38
26
45
41
38
28
39
38
40
34
31
30
10
p70
41
34
42
32
51
45
43
33
43
42
45
38
37
37
17
p80
47
40
48
37
60
52
49
38
48
48
52
44
43
43
26
p90
58
50
57
45
75
62
60
47
57
58
63
52
52
51
36
Max
223
196
160
201
208
262
239
163
250
225
184
225
208
178
91
A-47
-------
Figure A-37. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Los Angeles CMSA set B 1995-
2006.
Table A-37. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Los Angeles CMSA set B, 1995-2006.
Monitor ID
0603760021
0603760121
0603790021
0603790331
0605900015
0605900075
0605910031
0605950012
0606500121
0606550012
0606580012
060659001 1
0607100011
0607100121
0607100141
n
2
5
6
5
5
4
12
11
9
12
12
12
12
2
5
Mean
27
20
16
15
33
22
18
30
19
16
23
17
23
7
21
SD
4
1
2
0
3
2
3
6
3
3
4
2
1
0
1
cov
14
6
12
3
8
10
16
19
14
22
16
11
6
5
6
Min
25
18
14
15
29
20
12
21
15
9
17
14
20
7
20
p10
25
18
14
15
29
20
13
25
15
12
19
14
21
7
20
p20
25
19
15
15
31
20
16
25
16
13
21
15
22
7
20
p30
25
19
15
15
32
21
17
25
17
15
22
15
22
7
21
p40
25
19
16
15
32
21
18
27
18
16
22
17
22
7
21
p50
27
19
16
15
33
22
19
28
20
16
23
17
23
7
21
p60
30
20
16
15
33
24
19
33
20
16
24
17
24
7
22
p70
30
20
18
15
33
24
19
34
20
17
25
18
24
7
23
p80
30
21
18
15
35
24
20
35
22
18
26
18
24
7
23
p90
30
21
19
16
37
24
20
35
23
20
29
19
25
7
23
Max
30
21
19
16
37
24
23
39
23
21
30
20
25
7
23
Figure A-38. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Los Angeles CMSA set B, 1995-2006.
Table A-38. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los
Angeles CMSA set B, 1995-2006.
Monitor ID
0603760021
0603760121
0603790021
0603790331
0605900015
0605900075
0605910031
0605950012
0606500121
0606550012
0606580012
0606590011
0607100011
0607100121
0607100141
n
16534
39399
46871
40341
40987
33847
97546
88510
69857
95624
95642
95010
94741
14753
39719
Mean
27
20
16
15
33
22
18
30
19
16
23
17
23
7
21
SD
15
12
11
11
17
15
15
16
17
12
16
13
17
5
14
COV
57
61
69
73
53
70
85
54
91
73
67
75
76
69
67
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p10
10
4
5
5
14
5
4
12
3
4
6
4
5
2
7
p20
14
9
7
6
19
9
6
17
5
6
10
6
7
4
9
p30
18
12
9
7
22
10
7
20
7
8
13
8
9
4
11
p40
21
16
11
9
26
14
9
24
10
10
17
10
12
5
14
p50
25
19
13
11
30
20
12
27
13
12
21
13
18
6
17
p60
28
22
17
14
34
23
16
31
18
15
25
17
25
7
22
p70
32
25
21
18
38
30
23
35
25
19
30
22
33
8
27
p80
37
30
26
25
44
36
31
41
34
25
35
27
40
10
33
p90
46
36
32
32
55
42
40
50
43
33
44
34
48
14
41
Max
159
120
140
103
175
127
183
192
307
82
150
127
196
57
113
A-48
-------
T
_L
Figure A-39. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Los Angeles CMSA set C 1995-
2006.
Table A-39. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Los Angeles CMSA set C, 1995-2006.
Monitor ID
0607100151
0607100171
0607103061
0607110042
0607112341
0607120021
0607140011
0607190041
0611100051
0611100071
0611110031
0611110041
0611120021
0611120031
0611130011
n
2
3
7
11
9
12
3
12
7
9
1
7
12
9
12
Mean
7
6
21
36
5
34
17
31
4
16
10
8
18
10
13
SD
2
0
1
4
1
5
1
5
0
1
1
4
1
2
cov
28
4
5
12
12
13
4
16
8
9
7
20
8
16
Min
5
6
19
31
5
27
16
25
14
10
7
13
9
9
p10
5
6
19
31
5
27
16
26
14
10
7
14
9
10
p20
5
6
20
34
5
30
16
26
14
10
7
15
9
11
p30
5
6
21
34
5
31
16
26
15
10
8
15
9
11
p40
5
6
21
36
5
33
16
29
16
10
8
17
9
11
p50
7
6
22
36
5
36
16
31
16
10
8
19
10
13
p60
8
6
22
37
5
36
16
33
16
10
8
20
10
14
p70
8
7
22
38
6
36
18
34
17
10
8
20
11
14
p80
8
7
22
38
6
38
18
35
5
17
10
8
22
11
14
p90
8
7
22
39
6
38
18
38
5
19
10
8
22
11
15
Max
8
7
22
46
6
42
18
40
5
19
10
8
24
11
16
Figure A-40. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Los Angeles CMSA set C 1995-2006.
Table A-40. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los
Angeles CMSA set C, 1995-2006.
Monitor ID
0607100151
0607100171
0607103061
0607110042
0607112341
0607120021
0607140011
0607190041
0611100051
0611100071
0611110031
0611110041
0611120021
0611120031
0611130011
n
15531
23713
56831
88766
69325
95054
24587
97785
54034
73031
8240
56869
94238
70332
95263
Mean
7
6
21
36
5
34
17
31
4
16
10
8
18
10
13
SD
6
5
15
17
5
18
11
16
4
12
5
5
13
8
8
COV
82
84
70
48
103
54
68
51
89
74
52
66
70
85
65
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p10
2
2
5
17
1
12
6
12
0
4
4
3
4
1
4
p20
3
3
8
22
2
19
7
18
1
6
6
4
7
2
6
p30
3
3
11
26
2
24
9
22
3
8
7
5
9
4
7
p40
4
4
13
30
3
28
11
26
3
10
8
6
12
6
9
p50
5
5
17
34
3
32
13
30
4
12
9
6
16
8
11
p60
6
6
22
38
4
37
16
33
5
16
10
7
19
10
13
p70
7
7
28
43
5
42
21
38
5
20
12
9
24
13
15
p80
10
9
34
49
7
48
27
43
6
26
14
11
29
17
18
p90
14
13
42
58
12
58
34
51
8
33
16
14
36
21
23
Max
60
73
100
199
62
170
86
162
81
123
61
66
124
93
127
A-49
-------
I
Table A-41. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Miami CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
n
4
4
4
4
4
4
4
4
4
4
4
Mean
10
10
10
10
11
10
10
9
9
9
9
SD
3
4
4
4
4
4
4
4
3
3
3
cov
31
43
43
42
42
37
42
39
29
36
38
Min
7
6
7
6
6
7
6
6
7
6
6
p10
7
6
7
6
6
7
6
6
7
6
6
p20
7
6
7
6
6
7
6
6
7
6
6
p30
9
8
9
9
10
9
9
7
8
8
7
p40
9
8
9
9
10
9
9
7
8
8
7
p50
10
9
9
9
10
9
9
8
9
8
8
p60
10
9
9
9
10
10
10
9
9
8
8
p70
10
9
9
9
10
10
10
9
9
8
8
p80
15
16
17
15
17
16
16
14
13
13
14
p90
15
16
17
15
17
16
16
14
13
13
14
Max
15
16
17
15
17
16
16
14
13
13
14
Figure A-41. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Miami CMSA.
Table A-42. Distribution of hourly NO2 ambient concentrations (ppb) by year, Miami
CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
n
32713
33086
32754
30849
32721
31833
33063
33755
31031
33625
32342
Mean
10
10
10
10
11
10
10
9
9
9
9
SD
10
10
10
10
11
10
10
9
9
10
10
COV
95
103
97
98
99
99
98
96
97
117
109
Min
0
0
0
0
0
0
0
0
0
0
0
p10
1
1
1
1
1
1
1
1
1
1
0
p20
2
2
2
2
2
2
2
2
2
2
1
p30
3
3
3
3
3
4
3
3
3
2
2
p40
5
4
5
5
5
5
5
4
4
4
4
p50
7
6
7
7
7
7
7
6
6
5
6
p60
10
9
10
10
10
10
10
9
8
7
8
p70
13
12
13
12
14
13
13
12
11
10
11
p80
18
17
18
16
18
17
17
16
15
14
15
p90
25
25
25
23
26
24
24
22
21
21
22
Max
75
96
94
69
128
203
86
80
85
417
94
Figure A-42. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Miami CMSA.
A-50
-------
Figure A-43. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Miami CMSA, 1995-2006.
Table A-43. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Miami CMSA, 1995-2006.
Monitor ID
1201100031
1201100311
1201180021
1208600271
1208640022
n
3
8
11
11
11
Mean
9
9
9
6
15
SD
1
1
1
0
1
cov
7
12
11
7
9
Min
8
7
7
6
13
p10
8
7
8
6
13
p20
8
8
8
6
14
p30
8
9
8
6
14
p40
9
9
9
6
15
p50
9
9
9
6
15
p60
9
9
9
6
16
p70
9
9
9
7
16
p80
9
10
10
7
16
p90
9
10
10
7
17
Max
9
10
10
7
17
Table A-44. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Miami
CMSA, 1995-2006.
Monitor ID
1201100031
1201100311
1201180021
1208600271
1208640022
n
24440
63306
92241
87068
90717
Mean
9
9
9
6
15
SD
7
7
11
8
10
COV
81
78
128
132
67
Min
0
0
0
0
0
p10
2
2
0
1
5
p20
3
3
1
1
7
p30
4
5
1
2
9
p40
5
6
2
2
11
p50
7
7
3
3
13
p60
8
9
5
4
15
p70
10
11
11
5
18
p80
13
14
18
9
22
p90
18
18
26
17
28
Max
65
64
128
75
417
Figure A-44. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Miami CMSA, 1995-2006.
A-51
-------
Figure A-45. Distribution of annual average NO2 ambient
concentrations (ppb) by year, New York CMSA.
Table A-45. Distribution of annual average NO2 ambient concentrations (ppb) by
year, New York CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
16
15
16
14
16
16
14
17
15
14
16
5
Mean
28
28
27
27
27
26
25
25
23
21
23
25
SD
8
8
8
9
9
8
8
8
6
7
7
6
cov
28
29
30
34
31
32
32
31
28
31
31
23
Min
12
12
12
11
11
11
11
11
12
10
11
18
p10
16
17
17
15
17
16
17
16
14
13
13
18
p20
24
22
23
18
19
18
17
17
16
14
16
21
p30
25
26
24
22
24
19
21
20
18
17
18
23
p40
26
27
26
27
26
25
24
22
21
20
22
24
p50
29
27
27
28
27
26
26
25
25
21
22
25
p60
30
29
29
30
29
29
27
28
26
24
25
26
p70
31
32
31
33
33
30
27
28
27
24
27
26
p80
33
34
35
36
33
32
31
29
29
28
27
30
p90
39
41
40
40
41
38
38
38
30
30
32
34
Max
42
42
41
42
42
41
40
40
32
30
36
34
Figure A-46. Distribution of hourly NO2 ambient concentrations
(ppb) by year, New York CMSA.
Table A-46. Distribution of hourly NO2 ambient concentrations (ppb) by year, New
York CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
133504
122074
131144
116748
132646
134037
114478
141480
122724
115578
133856
42223
Mean
28
28
27
27
27
26
25
24
23
21
23
25
SD
16
16
15
16
16
15
15
15
14
13
14
13
COV
56
57
56
58
57
58
61
60
61
64
63
51
Min
0
0
0
0
0
0
0
0
0
0
1
0
p10
9
8
9
8
8
8
7
7
6
5
6
10
p20
14
13
13
13
13
12
10
11
10
8
9
13
p30
18
18
17
17
17
16
15
14
13
12
13
17
p40
22
22
22
22
22
20
19
18
16
15
16
20
p50
26
26
26
26
26
24
23
23
20
19
20
24
p60
31
31
30
31
30
28
28
27
25
23
24
28
p70
35
35
35
35
35
33
33
32
29
27
29
32
p80
40
40
40
40
40
38
38
37
35
32
35
37
p90
48
48
47
48
48
46
45
44
42
40
42
43
Max
162
162
181
240
148
118
142
129
138
156
119
92
A-52
-------
Figure A-47. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, New York CMSA set a, 1995-
2006.
Table A-47. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, New York CMSA set A, 1995-2006.
Monitor ID
0900101131
0900190031
0900900271
0900911231
340030001 1
3400300051
3401300111
3401300161
3401310031
3401700061
3402100051
3402300111
340273001 1
3403900042
3403900081
n
3
8
2
9
3
4
5
1
11
11
11
11
11
11
3
Mean
23
17
21
26
28
21
32
29
28
25
16
18
11
38
28
SD
1
2
1
1
1
1
1
2
2
1
1
1
4
2
cov
4
11
5
4
2
5
3
7
6
4
6
6
12
6
Min
23
14
20
24
28
20
31
29
24
22
15
16
10
30
27
p10
23
14
20
24
28
20
31
29
26
23
15
17
11
32
27
p20
23
15
20
25
28
20
31
29
27
23
15
18
11
32
27
p30
23
16
20
25
28
20
31
29
28
25
16
18
11
39
27
p40
24
18
20
25
28
20
32
29
28
26
16
18
11
40
27
p50
24
18
21
25
28
20
32
29
29
26
16
18
11
40
27
p60
24
18
22
26
28
20
32
29
29
26
16
19
11
41
27
p70
24
18
22
26
29
20
33
29
29
26
17
19
12
41
30
p80
24
19
22
27
29
22
33
29
29
26
17
19
12
41
30
p90
24
21
22
27
29
22
33
29
29
27
17
19
12
42
30
Max
24
21
22
27
29
22
33
29
31
27
17
20
12
42
30
Figure A-48. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, New York CMSA set a, 1995-2006.
Table A-48. Distribution of hourly NO2 ambient concentration (ppb) by monitor, New
York CMSA set A, 1995-2006.
Monitor ID
0900101131
0900190031
0900900271
0900911231
3400300011
3400300051
3401300111
3401300161
3401310031
3401700061
3402100051
3402300111
3402730011
3403900042
3403900081
n
25148
67123
16002
76418
25620
34090
41642
8368
93578
93886
94591
94366
92642
92472
23611
Mean
23
17
21
26
28
21
32
29
28
25
16
18
11
38
28
SD
13
13
14
13
14
14
16
15
14
14
11
12
9
15
13
COV
55
75
65
50
50
66
50
52
51
56
67
65
82
41
47
Min
0
0
0
0
3
3
3
3
3
2
2
3
0
3
3
p10
9
4
6
11
11
5
12
11
11
9
4
5
3
19
11
p20
12
6
8
14
15
8
17
15
15
12
7
8
3
25
16
p30
15
8
11
17
19
11
21
18
19
16
8
10
5
29
20
p40
18
10
14
20
23
14
26
22
23
19
11
13
7
33
24
p50
22
14
18
24
26
18
31
26
27
23
13
16
8
37
27
p60
25
18
22
27
31
22
35
31
31
27
16
19
10
41
30
p70
29
23
27
31
35
27
40
36
35
32
20
23
13
45
34
p80
34
29
33
36
40
33
45
41
40
37
25
28
17
50
38
p90
40
36
40
43
47
40
53
49
47
44
32
35
24
58
44
Max
109
103
101
240
119
124
148
103
150
147
79
99
95
225
122
A-53
-------
T
3600500801 3600500831 3600501101 3604700111 3605900052 3606100101 3606100561 360810097] 3608100981 3608101241 3610300012
Monitor
Figure A-49. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, New York CMSA set b, 1995-
2006.
Hourly COT
Figure A-50. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, New York CMSA set b, 1995-2006.
Table A-49. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, New York CMSA set B, 1995-2006.
Monitor ID
3600500801
3600500831
3600501101
3604700111
3605900052
3606100101
3606100561
3608100971
3608100981
3608101241
3610300092
n
5
12
6
1
11
4
10
3
7
5
6
Mean
35
28
30
33
23
36
39
26
29
25
15
SD
1
2
2
2
1
2
0
1
2
2
cov
4
9
6
10
1
6
1
4
7
14
Min
33
24
26
33
18
35
34
26
27
23
13
p10
33
25
26
33
20
35
35
26
27
23
13
p20
34
27
29
33
21
35
37
26
28
24
13
p30
35
27
29
33
22
35
38
26
28
25
13
p40
35
28
30
33
22
35
38
26
28
25
14
p50
35
29
30
33
24
36
39
26
29
25
16
p60
36
30
30
33
24
36
40
26
30
26
17
p70
36
30
30
33
24
36
40
26
30
27
17
p80
36
31
30
33
25
36
41
26
30
27
17
p90
36
31
32
33
25
36
42
26
30
28
17
Max
36
32
32
33
26
36
42
26
30
28
17
Table A-50. Distribution
York CMSA set B, 1995-
of hourly NO2 ambient concentration (ppb) by monitor, New
2006.
3600500801
3600500831
3600501101
3604700111
3605900052
3606100101
3606100561
3608100971
3608100981
3608101241
3610300092
41120
95448
46299
8300
89801
30694
81341
24104
56186
39406
48236
35
28
29
33
23
36
39
26
29
25
15
14
13
13
14
13
11
13
14
13
13
10
40
47
45
41
56
31
33
54
46
50
67
0
0
0
3
0
0
0
0
0
0
0
19
13
14
17
8
23
24
10
13
11
5
23
17
18
21
11
27
28
13
17
14
7
26
20
21
25
14
29
32
17
20
17
8
30
23
24
28
18
32
35
20
24
20
10
33
26
28
32
21
35
38
24
27
23
12
37
30
31
35
25
37
41
28
31
27
15
40
34
35
39
29
40
44
33
35
31
19
45
39
40
43
34
44
48
38
40
36
24
54
46
47
51
40
50
55
45
47
43
31
181
136
119
155
162
118
162
95
114
144
86
A-54
-------
I
I
T
Table A-51. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Philadelphia CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
8
8
8
8
8
6
7
8
6
7
7
4
Mean
23
25
24
24
23
21
23
21
20
20
19
16
SD
6
6
6
7
6
4
5
5
4
4
4
1
cov
27
24
25
30
28
20
24
26
19
22
19
9
Min
15
19
18
16
16
17
16
15
16
14
16
14
p10
15
19
18
16
16
17
16
15
16
14
16
14
p20
17
21
19
18
17
18
18
16
17
16
17
14
p30
20
21
20
19
18
18
19
18
17
18
17
15
p40
20
21
20
19
18
19
19
19
18
18
17
15
p50
22
22
21
21
20
20
21
20
19
19
18
15
p60
24
24
22
22
22
20
26
20
19
23
20
16
p70
28
29
28
29
27
26
26
24
24
23
20
16
p80
31
33
32
33
30
26
28
28
24
25
22
18
p90
32
34
32
34
32
28
30
29
25
26
26
18
Max
32
34
32
34
32
28
30
29
25
26
26
18
Figure A-51. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Philadelphia CMSA.
Figure A-52. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Philadelphia CMSA.
Table A-52. Distribution of hourly NO2 ambient concentrations (ppb) by year,
Philadelphia CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
65415
67989
68291
66847
64813
51145
59227
66779
49256
58509
56459
32357
Mean
24
25
24
24
22
21
23
21
20
20
19
16
SD
14
14
14
14
13
13
13
12
12
12
12
11
COV
60
55
57
58
59
60
59
59
62
59
62
69
Min
0
0
0
0
0
0
0
0
0
0
0
0
p10
8
8
8
7
6
6
6
6
5
6
6
4
p20
10
11
11
11
10
10
10
10
8
9
9
6
p30
14
17
15
15
14
13
14
13
11
12
11
8
p40
19
20
19
19
17
16
17
16
15
15
14
10
p50
20
24
22
23
21
19
21
20
18
18
17
13
p60
26
30
26
27
25
23
25
23
22
22
21
16
p70
30
30
30
31
29
27
29
27
26
26
25
20
p80
35
40
35
36
33
32
34
32
30
30
29
25
p90
40
42
42
42
40
39
40
38
36
36
36
31
Max
140
100
247
97
109
97
96
268
105
101
120
95
A-55
-------
I
Figure A-53. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Philadelphia CMSA, 1995-2006.
Figure A-54. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Philadelphia CMSA, 1995-2006.
Table A-53. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Philadelphia CMSA, 1995-2006.
Monitor ID
1000310031
1000310071
1000320041
3400700032
4201700121
4204500021
4209100131
4210100043
4210100292
4210100471
n
5
1
4
10
12
12
11
11
10
9
Mean
18
15
18
21
18
19
17
26
29
31
SD
1
1
1
2
2
2
3
3
3
cov
6
4
7
11
8
13
10
11
10
Min
16
15
18
19
15
16
14
22
25
26
p10
16
15
18
20
16
17
14
23
25
26
p20
17
15
18
20
16
17
15
24
26
26
p30
17
15
18
20
16
18
16
24
28
29
p40
18
15
18
21
17
18
16
26
28
30
p50
18
15
18
22
18
19
16
26
29
32
p60
18
15
19
22
18
19
17
27
31
32
p70
18
15
19
22
18
19
18
28
32
32
p80
19
15
19
23
20
20
19
28
33
34
p90
19
15
19
24
20
20
19
29
33
34
Max
19
15
19
24
21
21
21
29
33
34
Table A-54. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Philadelphia CMSA, 1995-2006.
Monitor ID
1000310031
1000310071
1000320041
3400700032
4201700121
4204500021
4209100131
4210100043
4210100292
4210100471
n
40363
6611
31615
84603
102584
100344
93572
90975
81218
75202
Mean
18
15
18
22
18
19
17
26
29
31
SD
12
9
12
13
12
12
12
13
13
12
COV
69
62
63
59
67
64
69
49
43
40
Min
0
1
0
3
0
0
0
0
0
0
p10
4
6
5
7
5
5
4
10
15
16
p20
7
7
8
10
7
8
6
14
19
20
p30
10
9
11
13
9
10
9
18
21
23
p40
12
10
13
16
12
13
11
20
25
26
p50
16
12
16
19
15
16
15
24
29
30
p60
19
15
20
23
19
20
18
28
30
31
p70
23
17
23
27
23
24
22
31
35
36
p80
28
21
28
32
28
29
27
37
40
40
p90
34
28
34
39
34
36
33
43
46
47
Max
247
69
115
114
106
268
99
190
120
140
A-56
-------
I
Figure A-55. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Washington DC CMSA.
I lourly Cones
50-
Figure A-56. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Washington DC CMSA.
Table A-55. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Washington DC CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
12
11
11
11
12
12
11
10
11
12
12
10
Mean
21
22
20
22
20
18
19
19
20
18
17
15
SD
5
4
5
5
5
5
5
6
6
5
5
4
cov
25
20
27
23
25
27
28
31
28
27
28
30
Min
11
11
10
12
11
9
9
9
10
10
9
7
p10
11
20
11
15
12
10
11
10
12
10
10
7
p20
19
20
17
18
14
13
14
13
16
15
14
10
p30
19
21
19
20
18
17
19
16
18
15
15
14
p40
22
22
21
22
20
18
20
20
18
17
17
15
p50
23
22
22
23
21
20
22
23
23
19
18
15
p60
23
24
22
24
23
21
23
23
23
21
21
16
p70
25
24
24
25
24
23
23
24
23
21
21
17
p80
25
25
25
26
24
23
23
25
25
22
21
18
p90
26
26
26
26
25
23
24
25
26
23
22
19
Max
26
27
26
27
25
23
24
25
26
24
24
20
Table A-56. Distribution of hourly NO2
Washington DC CMSA.
ambient concentrations (ppb) by year,
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
98349
91551
87646
89335
100112
101494
91594
83969
93111
99370
96396
83691
Mean
21
22
20
22
20
18
19
19
20
18
17
15
SD
13
12
12
12
12
12
12
12
12
11
12
11
COV
59
57
62
57
61
64
62
64
61
63
68
73
Min
0
0
0
0
0
0
0
0
0
0
0
0
p10
7
7
6
8
6
5
6
6
6
5
5
4
p20
10
11
9
11
9
8
9
9
9
8
7
6
p30
13
14
12
14
12
11
11
11
12
10
10
7
p40
16
17
15
16
15
13
14
14
14
13
12
9
p50
19
20
18
20
18
16
17
17
17
16
15
12
p60
23
24
21
23
21
19
20
20
21
19
18
14
p70
27
28
25
27
25
23
24
24
25
23
22
18
p80
31
32
30
32
30
28
29
30
30
28
27
23
p90
38
39
37
38
37
35
36
37
37
34
34
30
Max
145
107
155
285
114
141
89
108
102
115
115
129
A-57
-------
Figure A-57. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Washington DC CMSA set A,
1995-2006.
Table A-57. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Washington DC CMSA set A, 1995-2006.
Monitor ID
1100100172
1100100251
1100100411
1100100431
2400530012
2451000401
2451000501
5101300201
5105900051
5105900181
5105910043
5105910051
5105950011
5110710051
5115300091
n
1
12
12
12
8
11
1
12
11
3
6
4
10
8
12
Mean
25
22
23
20
18
25
21
23
10
19
22
17
20
14
11
SD
2
3
2
2
2
2
1
1
2
1
3
1
2
cov
11
12
12
11
7
10
12
3
7
9
15
6
18
Min
25
17
16
17
15
22
21
18
7
19
20
15
14
13
7
p10
25
19
21
18
15
23
21
21
9
19
20
15
16
13
9
p20
25
20
21
18
15
23
21
21
9
19
21
15
17
13
9
p30
25
21
23
18
17
23
21
22
10
19
21
17
19
14
10
p40
25
22
23
19
18
24
21
22
10
19
22
17
20
14
10
p50
25
23
24
19
18
25
21
23
10
19
22
17
21
14
11
p60
25
23
24
21
18
26
21
23
10
19
23
17
22
14
11
p70
25
23
25
22
19
26
21
24
11
20
23
17
22
14
11
p80
25
24
25
23
20
26
21
25
11
20
23
18
22
15
12
p90
25
24
25
23
20
26
21
25
11
20
23
18
23
16
12
Max
25
25
26
24
20
27
21
26
12
20
23
18
24
16
15
Figure A-58. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Washington DC CMSA set A, 1995-2006.
Table A-58. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Washington DC CMSA set A, 1995-2006.
Monitor ID
1100100172
1100100251
1100100411
1100100431
2400530012
2451000401
2451000501
5101300201
5105900051
5105900181
5105910043
5105910051
5105950011
5110710051
5115300091
n
8584
102444
103173
102217
63983
89589
7872
97517
89964
22689
50294
34022
79051
65327
101671
Mean
25
22
23
20
18
25
21
23
10
19
22
17
20
14
11
SD
11
12
12
13
12
11
12
13
7
11
11
11
12
9
7
COV
45
55
53
64
65
44
60
56
73
60
52
63
61
65
68
Min
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p10
12
9
9
6
5
12
6
8
3
6
10
6
6
5
3
p20
15
11
12
9
7
15
9
11
4
9
12
8
9
7
5
p30
18
14
15
12
10
18
12
14
5
11
14
9
12
8
6
p40
20
16
18
15
12
21
16
17
6
13
17
12
14
10
7
p50
23
19
21
18
15
23
19
20
8
16
20
14
18
11
9
p60
27
23
24
22
19
26
23
24
10
20
23
17
21
14
11
p70
30
27
28
26
23
29
27
28
12
24
27
21
25
17
13
p80
33
32
33
31
28
33
32
34
15
29
31
26
30
21
16
p90
39
39
39
38
34
39
38
41
20
36
38
32
36
28
21
Max
113
285
141
258
114
108
75
110
101
89
91
129
155
64
84
A-58
-------
Table A-59. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Washington DC CMSA set B, 1995-2006.
Monitor ID
5151000093
n
12
Mean
24
SD
2
cov
8
Min
20
p10
23
p20
23
p30
23
p40
24
p50
24
p60
25
p70
26
p80
26
p90
26
Max
27
Figure A-59. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Washington DC CMSA set B,
1995-2006.
Table A-60. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Washington DC CMSA set B, 1995-2006.
Monitor ID
5151000093
n
98221
Mean
24
SD
12
COV
48
Min
0
p10
11
p20
14
p30
17
p40
20
p50
23
p60
26
p70
29
p80
34
p90
40
Max
115
Figure A-60. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Washington DC CMSA set B, 1995-2006.
A-59
-------
T
I
Table A-61. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Atlanta MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
3
5
4
3
4
5
5
5
4
5
5
5
Mean
13
14
15
13
14
14
14
12
11
11
11
11
SD
6
9
7
10
9
7
8
6
6
6
6
6
cov
46
61
47
80
61
53
56
51
56
51
51
57
Min
7
6
8
6
7
5
4
4
5
4
4
3
p10
7
6
8
6
7
5
4
4
5
4
4
3
p20
7
6
8
6
7
6
6
6
5
5
5
5
p30
7
6
15
6
7
8
8
7
6
6
6
6
p40
15
11
15
8
7
12
12
11
6
10
10
9
p50
15
16
15
8
13
17
17
15
11
15
14
13
p60
15
17
15
8
20
17
17
15
16
15
14
14
p70
19
18
15
24
20
18
17
16
16
15
14
15
p80
19
22
25
24
24
21
20
17
16
16
16
17
p90
19
27
25
24
24
23
23
19
16
17
17
18
Max
19
27
25
24
24
23
23
19
16
17
17
18
Figure A-61. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Atlanta MSA.
Hourly Cones
50-
Table A-62. Distribution of hourly NO2 ambient concentrations (ppb) by year, Atlanta
MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
25213
40576
31069
24142
31121
40584
42761
42076
32215
42124
42279
41052
Mean
13
15
15
12
14
14
14
12
11
11
11
11
SD
12
13
13
13
14
14
14
12
11
11
11
11
COV
89
89
86
105
99
97
98
95
101
98
96
98
Min
1
1
1
0
0
1
1
1
0
1
1
1
p10
3
3
3
1
2
1
1
1
1
1
1
2
p20
3
3
5
3
4
3
3
3
2
3
3
3
p30
5
5
7
4
5
5
5
5
3
4
4
4
p40
7
8
9
6
7
7
7
6
5
6
6
5
p50
10
11
12
8
9
10
9
9
7
8
8
7
p60
13
14
15
11
12
13
13
11
9
10
10
9
p70
16
18
18
14
17
17
17
15
13
14
13
13
p80
22
24
23
20
23
23
23
20
17
19
18
18
p90
30
34
33
30
35
33
33
29
26
28
27
27
Max
93
122
181
124
242
110
172
136
91
127
97
73
Figure A-62. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Atlanta MSA.
A-60
-------
Figure A-63. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Atlanta MSA, 1995-2006.
Table A-63. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Atlanta MSA, 1995-2006.
Monitor ID
1308900021
1308930011
1312100481
1322300031
1324700011
n
10
9
12
10
12
Mean
16
15
21
5
7
SD
2
2
4
1
1
cov
11
10
17
20
11
Min
14
13
16
3
6
p10
14
13
17
4
6
p20
15
14
17
4
6
p30
15
15
18
4
6
p40
15
15
19
4
6
p50
15
16
21
5
7
p60
16
16
23
5
7
p70
17
17
24
5
8
p80
18
17
24
6
8
p90
19
18
25
6
8
Max
20
18
27
7
8
Table A-64. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Atlanta MSA, 1995-2006.
Monitor ID
1308900021
1308930011
1312100481
1322300031
1324700011
n
83891
72029
98975
80168
100149
Mean
16
15
21
5
7
SD
12
11
15
5
6
COV
77
73
73
108
81
Min
0
1
0
0
0
p10
3
4
5
1
2
p20
5
6
8
1
3
p30
8
8
11
2
3
p40
10
10
14
3
4
p50
13
12
17
3
5
p60
16
15
21
4
6
p70
20
19
26
5
8
p80
25
24
33
7
10
p90
33
32
43
11
14
Max
139
95
181
70
242
Figure A-64. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Atlanta MSA, 1995-2006.
A-61
-------
I
Table A-65. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Colorado Springs MSA.
Year
1995
1996
1997
1998
1999
2000
n
7
3
4
4
4
4
Mean
16
16
16
16
15
19
SD
7
9
6
6
6
11
cov
42
53
36
37
37
58
Min
7
7
7
7
7
9
p10
7
7
7
7
7
9
p20
8
7
7
7
7
9
p30
12
7
17
17
16
16
p40
12
18
17
17
16
16
p50
18
18
18
17
17
16
p60
21
18
19
18
18
17
p70
21
24
19
18
18
17
p80
22
24
20
20
19
35
p90
23
24
20
20
19
35
Max
23
24
20
20
19
35
Figure A-65. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Colorado Springs MSA.
Table A-66. Distribution of hourly NO2 ambient concentrations (ppb) by year,
Colorado Springs MSA.
Year
1995
1996
1997
1998
1999
2000
n
58569
25387
33469
34509
34472
33956
Mean
16
16
16
16
15
19
SD
14
16
13
12
12
20
COV
91
101
80
76
82
106
Min
0
0
0
0
0
0
p10
2
2
3
3
3
3
p20
4
4
5
5
4
6
p30
6
6
6
7
6
8
p40
8
8
9
9
9
11
p50
11
11
12
12
12
15
p60
16
16
16
16
16
20
p70
22
21
21
22
21
24
p80
29
28
27
27
26
28
p90
36
35
35
34
32
34
Max
148
246
118
85
230
308
Figure A-66. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Colorado Springs MSA.
A-62
-------
OS041600I1 OS04I6004I 0804160051 0804160061 0804160091 0804160111
Monitor
Figure A-67. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Colorado Springs MSA, 1995-
2006.
Table A-67. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Colorado Springs MSA, 1995-2006.
Monitor ID
0804160011
0804160041
0804160051
0804160061
0804160091
0804160111
0804160131
0804160181
n
6
6
1
1
1
6
1
4
Mean
8
17
18
7
12
21
22
22
SD
1
2
3
8
cov
10
10
12
37
Min
7
16
18
7
12
17
22
18
p10
7
16
18
7
12
17
22
18
p20
7
16
18
7
12
19
22
18
p30
7
16
18
7
12
19
22
18
p40
7
17
18
7
12
20
22
18
p50
7
17
18
7
12
20
22
19
p60
7
17
18
7
12
20
22
19
p70
8
18
18
7
12
23
22
19
p80
8
18
18
7
12
23
22
35
p90
9
21
18
7
12
24
22
35
Max
9
21
18
7
12
24
22
35
Hourly COT
Figure A-68. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Colorado Springs MSA, 1995-2006.
Table A-68. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Colorado Springs MSA, 1995-2006.
Monitor ID
0804160011
0804160041
0804160051
0804160061
0804160091
0804160111
0804160131
0804160181
n
51373
51288
8345
7993
8282
50707
8637
33737
Mean
8
17
18
7
12
21
22
23
SD
7
11
13
7
10
16
14
21
COV
94
66
74
99
89
77
62
94
Min
0
0
1
0
0
0
0
0
p10
1
4
3
1
2
5
5
5
p20
2
6
5
2
3
7
8
7
p30
3
9
7
3
4
10
11
10
p40
4
12
10
4
6
14
15
14
p50
5
15
15
5
7
18
20
18
p60
7
20
21
6
10
23
26
23
p70
9
24
27
8
14
27
31
28
p80
12
28
32
11
20
31
36
33
p90
18
34
36
16
29
37
41
41
Max
59
115
143
49
56
246
87
308
A-63
-------
Table A-69. Distribution of annual average NO2 ambient concentrations (ppb) by
year, El Paso MSA.
1995 199(i 1997 1998 1999 2000 2001 2002 200j 2004 2005 2006
Year
Figure A-69. Distribution of annual average NO2 ambient
concentrations (ppb) by year, El Paso MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
1
1
3
2
3
4
5
5
5
5
5
5
Mean
23
35
26
25
22
18
16
17
16
14
14
14
SD
7
8
6
5
4
4
3
4
3
4
cov
0
0
27
33
25
26
26
23
21
25
21
26
Min
23
35
21
19
17
14
10
11
11
9
10
8
p10
23
35
21
19
17
14
10
11
11
9
10
8
p20
23
35
21
19
17
14
12
13
13
11
11
11
p30
23
35
21
19
17
16
14
16
15
13
13
13
p40
23
35
23
19
23
16
16
16
16
13
14
14
p50
23
35
23
25
23
16
17
16
16
13
15
15
p60
23
35
23
31
23
16
17
17
17
15
15
16
p70
23
35
34
31
28
16
18
18
18
17
16
16
p80
23
35
34
31
28
24
20
20
19
18
17
17
p90
23
35
34
31
28
24
22
21
20
18
17
18
Max
23
35
34
31
28
24
22
21
20
18
17
18
Table A-70. Distribution of hourly NO2 ambient concentrations (ppb) by year, El Paso
MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
6960
6627
22888
15523
23447
30772
38020
41466
39968
41952
41496
37203
Mean
23
35
26
25
22
17
16
17
16
14
14
14
SD
13
15
15
15
13
13
12
13
13
12
12
12
COV
58
43
58
61
60
72
77
77
80
83
86
84
Min
3
2
0
0
0
0
0
0
0
0
0
0
p10
9
20
10
7
6
3
3
4
3
2
2
2
p20
12
23
13
12
10
5
5
5
5
4
4
4
p30
14
27
16
15
14
8
7
7
7
6
5
6
p40
17
29
20
19
17
12
10
10
9
8
7
8
p50
21
32
23
23
21
16
13
13
12
11
10
10
p60
25
36
28
27
25
20
16
17
16
14
14
14
p70
29
40
32
32
28
24
21
22
21
19
19
19
p80
34
46
38
37
33
28
27
28
27
25
24
25
p90
41
54
45
45
40
34
34
35
35
32
31
32
Max
113
219
174
166
108
125
102
153
106
97
87
99
Figure A-70. Distribution of hourly NO2 ambient concentrations
(ppb) by year, El Paso MSA.
A-64
-------
Figure A-71. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, El Paso MSA, 1995-2006.
Figure A-72. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, El Paso MSA, 1995-2006.
Table A-71. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, El Paso MSA, 1995-2006.
Monitor ID
4814100271
4814100281
4814100371
4814100441
4814100551
4814100571
4814100581
n
4
1
11
8
7
7
6
Mean
32
23
18
19
17
14
10
SD
3
2
4
1
1
1
cov
10
12
22
5
6
11
Min
28
23
15
13
16
13
8
p10
28
23
16
13
16
13
8
p20
28
23
17
13
16
13
9
p30
31
23
17
18
16
14
9
p40
31
23
17
20
16
14
10
p50
32
23
18
21
16
14
10
p60
34
23
18
21
16
15
10
p70
34
23
18
22
16
15
11
p80
35
23
19
23
17
15
11
p90
35
23
21
24
18
16
11
Max
35
23
23
24
18
16
11
Table A-72. Distribution of hourly NO2 ambient concentration (ppb) by monitor, El
Paso MSA, 1995-2006.
Monitor ID
4814100271
4814100281
4814100371
4814100441
4814100551
4814100571
4814100581
n
29730
8045
87748
62362
53960
57229
47248
Mean
32
23
18
19
17
14
10
SD
14
14
13
13
13
11
11
COV
45
60
71
67
78
79
109
Min
1
5
0
0
0
0
0
p10
16
10
5
5
3
3
1
p20
20
12
7
8
5
4
2
p30
24
13
9
11
7
6
3
p40
27
15
12
14
10
8
4
p50
30
18
14
17
13
10
5
p60
33
22
18
21
18
14
7
p70
37
27
23
25
23
19
11
p80
42
34
29
30
28
25
18
p90
49
42
36
36
35
31
27
Max
219
117
153
125
87
85
84
A-65
-------
Table A-73. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Jacksonville MSA.
Year
1995
1996
1997
1998
1999
2000
2002
2003
2004
2005
n
1
1
1
1
1
1
1
1
1
1
Mean
16
15
14
15
16
15
15
14
14
13
SD
COV
0
0
0
0
0
0
0
0
0
0
Min
16
15
14
15
16
15
15
14
14
13
p10
16
15
14
15
16
15
15
14
14
13
p20
16
15
14
15
16
15
15
14
14
13
p30
16
15
14
15
16
15
15
14
14
13
p40
16
15
14
15
16
15
15
14
14
13
p50
16
15
14
15
16
15
15
14
14
13
p60
16
15
14
15
16
15
15
14
14
13
p70
16
15
14
15
16
15
15
14
14
13
p80
16
15
14
15
16
15
15
14
14
13
p90
16
15
14
15
16
15
15
14
14
13
Max
16
15
14
15
16
15
15
14
14
13
Figure A-73. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Jacksonville MSA.
Table A-74. Distribution of hourly NO2 ambient concentrations (ppb) by year,
Jacksonville MSA.
Year
1995
1996
1997
1998
1999
2000
2002
2003
2004
2005
n
7755
8148
8326
8211
7795
7661
7944
7041
7451
7890
Mean
16
15
14
15
16
15
15
14
14
13
SD
10
10
9
10
10
10
10
10
11
9
COV
60
64
65
65
61
67
66
71
83
67
Min
0
0
0
0
0
0
0
0
0
0
p10
6
5
5
5
5
5
5
4
4
4
p20
8
7
6
7
7
7
7
6
6
6
p30
9
9
8
9
9
9
9
8
7
8
p40
11
11
10
11
12
11
11
10
9
9
p50
14
13
12
13
14
13
13
12
11
11
p60
16
15
15
15
16
15
15
14
13
13
p70
19
18
17
18
20
18
17
17
16
16
p80
23
21
21
22
24
23
21
21
20
20
p90
29
28
27
28
30
30
27
28
26
26
Max
76
80
92
66
63
72
294
76
201
64
Figure A-74. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Jacksonville MSA.
A-66
-------
Table A-75. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Jacksonville MSA, 1995-2006.
Monitor ID
1203100322
n
10
Mean
15
SD
1
cov
6
Min
13
p10
14
p20
14
p30
14
p40
15
p50
15
p60
15
p70
15
p80
16
p90
16
Max
16
Figure A-75. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Jacksonville MSA, 1995-2006.
Table A-76. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Jacksonville MSA, 1995-2006.
Monitor ID
1203100322
n
78222
Mean
15
SD
10
COV
67
Min
0
p10
5
p20
7
p30
9
p40
10
p50
12
p60
15
p70
18
p80
22
p90
28
Max
294
Figure A-76. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Jacksonville MSA, 1995-2006.
A-67
-------
I 1
Figure A-77. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Las Vegas MSA.
Table A-77. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Las Vegas MSA.
Year
1995
1996
1998
1999
2000
2001
2002
2003
2004
2005
n
1
1
3
5
6
6
9
7
7
6
Mean
27
27
12
14
12
11
11
12
11
10
SD
12
10
9
9
8
8
8
8
cov
0
0
95
71
81
84
68
66
73
76
Min
27
27
4
4
3
2
3
2
1
2
p10
27
27
4
4
3
2
3
2
1
2
p20
27
27
4
6
4
5
3
6
4
5
p30
27
27
4
8
4
5
7
8
5
5
p40
27
27
7
8
8
6
7
8
5
6
p50
27
27
7
8
8
6
9
9
9
8
p60
27
27
7
16
8
7
10
19
19
9
p70
27
27
25
24
22
22
19
19
19
19
p80
27
27
25
25
22
22
22
21
19
19
p90
27
27
25
27
25
23
22
21
20
20
Max
27
27
25
27
25
23
22
21
20
20
Table A-78. Distribution of hourly NO2 ambient concentrations (ppb) by year, Las
Vegas MSA.
Year
1995
1996
1998
1999
2000
2001
2002
2003
2004
2005
n
7951
8723
25234
43110
46403
49734
74814
58398
57484
48911
Mean
27
27
12
14
12
11
11
12
11
10
SD
20
22
14
16
14
14
13
14
13
12
COV
74
81
118
110
119
128
117
119
120
123
Min
0
0
0
0
0
0
0
0
0
0
p10
0
0
0
0
0
0
0
0
0
0
p20
11
9
0
0
0
0
0
0
0
0
p30
15
12
0
5
0
0
0
0
0
0
p40
20
17
5
6
5
0
5
5
0
0
p50
25
24
8
8
7
6
7
7
6
6
p60
31
31
10
12
10
8
10
10
9
9
p70
37
38
14
18
15
13
14
15
14
12
p80
42
44
23
28
23
21
21
24
23
18
p90
50
54
35
39
34
33
32
35
33
30
Max
410
149
103
110
100
104
87
103
73
75
Figure A-78. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Las Vegas MSA.
A-68
-------
Figure A-79. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Las Vegas MSA, 1995-2006.
Figure A-80. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Las Vegas MSA, 1995-2006.
Table A-79. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Las Vegas MSA, 1995-2006.
Monitor ID
3200300221
3200300231
3200300731
3200300781
3200305391
3200305571
3200305631
320030601 1
3200310191
3200320021
n
7
4
7
1
8
2
3
5
7
7
Mean
5
7
8
9
23
27
19
6
3
21
SD
1
2
1
3
0
0
2
1
2
cov
26
28
9
12
1
1
34
38
7
Min
4
5
7
9
19
27
19
3
1
19
p10
4
5
7
9
19
27
19
3
1
19
p20
4
5
7
9
20
27
19
4
2
20
p30
4
6
8
9
21
27
19
6
2
21
p40
4
6
8
9
22
27
19
6
2
21
p50
5
7
8
9
22
27
19
7
2
22
p60
5
9
8
9
23
27
19
7
3
22
p70
5
9
8
9
25
27
19
8
3
22
p80
6
10
9
9
25
27
19
8
3
22
p90
7
10
9
9
27
27
19
8
4
24
Max
7
10
9
9
27
27
19
8
4
24
Table A-80. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Las
Vegas MSA, 1995-2006.
Monitor ID
3200300221
3200300231
3200300731
3200300781
3200305391
3200305571
3200305631
3200306011
3200310191
3200320021
n
58087
34550
56906
8672
64921
16674
25061
42417
57230
56244
Mean
5
7
8
9
23
27
19
6
3
21
SD
7
8
10
10
16
21
15
8
5
16
COV
152
105
124
115
70
78
78
124
186
73
Min
0
0
0
0
0
0
0
0
0
0
p10
0
0
0
0
5
0
0
0
0
0
p20
0
0
0
0
7
10
5
0
0
6
p30
0
0
0
0
10
14
7
0
0
9
p40
0
5
0
5
14
19
11
0
0
13
p50
0
6
5
7
21
24
17
5
0
20
p60
5
8
8
8
28
31
23
7
0
27
p70
7
10
11
10
33
37
28
8
0
32
p80
10
13
15
14
38
43
33
12
6
36
p90
15
18
22
22
44
52
39
18
9
42
Max
91
52
104
87
103
410
87
51
71
110
A-69
-------
Table A-81. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Phoenix MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
3
3
2
4
5
5
5
3
2
5
6
6
Mean
29
28
30
29
33
30
27
29
32
25
23
22
SD
3
3
3
5
5
4
6
6
4
4
7
7
cov
12
12
10
15
14
13
23
19
11
18
29
30
Min
26
25
28
24
28
26
22
24
29
19
12
11
p10
26
25
28
24
28
26
22
24
29
19
12
11
p20
26
25
28
24
30
27
22
24
29
21
20
19
p30
26
25
28
28
31
29
22
24
29
23
20
19
p40
29
29
28
28
31
29
24
29
29
23
24
21
p50
29
29
30
29
31
29
26
29
32
24
24
22
p60
29
29
32
30
32
30
27
29
34
24
24
24
p70
33
32
32
30
34
30
29
35
34
25
26
25
p80
33
32
32
35
37
33
33
35
34
28
26
25
p90
33
32
32
35
40
36
37
35
34
31
32
31
Max
33
32
32
35
40
36
37
35
34
31
32
31
Figure A-81. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Phoenix MSA.
Table A-82. Distribution of hourly NO2 ambient concentrations (ppb) by year, Phoenix
MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
23196
23598
14629
32078
40996
41686
40463
25028
14195
42176
50583
48791
Mean
29
28
30
29
33
30
27
29
32
25
23
22
SD
17
17
16
17
22
21
16
17
17
15
15
16
COV
59
59
55
58
66
71
59
59
55
62
66
73
Min
0
0
0
0
0
0
1
0
0
0
0
0
p10
8
8
8
8
9
8
7
7
8
6
5
4
p20
12
12
13
12
13
12
11
12
14
9
8
7
p30
17
17
18
17
18
17
15
17
20
13
12
10
p40
23
22
25
23
24
22
21
23
27
18
16
13
p50
28
27
30
28
30
27
26
28
32
23
20
18
p60
33
32
35
33
36
32
31
34
37
28
25
24
p70
37
37
39
38
42
38
36
39
42
33
31
30
p80
44
43
44
44
49
45
41
45
48
39
36
37
p90
53
51
52
52
60
54
49
53
55
45
44
46
Max
128
115
114
116
198
267
118
108
101
104
131
111
Figure A-82. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Phoenix MSA.
A-70
-------
Figure A-83. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Phoenix MSA, 1995-2006.
Figure A-84. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Phoenix MSA, 1995-2006.
Table A-83. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Phoenix MSA, 1995-2006.
Monitor ID
0401300191
0401330026
0401330031
0401330101
0401340051
0401340111
0401399971
n
10
12
10
9
1
2
5
Mean
27
29
24
35
22
12
24
SD
3
3
4
3
1
3
cov
10
10
17
9
6
12
Min
24
25
19
31
22
11
21
p10
24
25
19
31
22
11
21
p20
24
26
20
31
22
11
22
p30
25
29
21
32
22
11
23
p40
27
29
23
34
22
11
23
p50
28
29
24
35
22
12
24
p60
28
30
24
35
22
12
25
p70
29
32
25
36
22
12
26
p80
29
32
28
37
22
12
27
p90
30
33
30
40
22
12
28
Max
31
34
31
40
22
12
28
Table A-84. Distribution of hourly NO2 ambient concentration (ppb) by monitor,
Phoenix MSA, 1995-2006.
Monitor ID
0401300191
0401330026
0401330031
0401330101
0401340051
0401340111
0401399971
n
81411
97376
80162
73070
7420
16459
41521
Mean
27
29
24
35
22
12
24
SD
17
17
19
18
13
8
15
COV
63
59
78
53
58
69
60
Min
0
0
0
0
2
0
0
p10
6
8
6
9
7
2
7
p20
9
12
9
16
9
4
10
p30
14
17
12
23
13
6
14
p40
20
23
16
30
17
8
19
p50
26
28
20
35
21
10
23
p60
32
33
25
40
25
13
27
p70
37
38
30
45
29
16
32
p80
42
44
35
50
33
18
37
p90
50
53
45
58
39
22
45
Max
148
151
267
164
99
53
131
A-71
-------
Figure A-85. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Provo MSA.
Table A-85. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Provo MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
1
1
1
1
1
1
1
1
1
1
1
1
Mean
23
24
23
24
24
24
24
25
22
22
21
29
SD
COV
0
0
0
0
0
0
0
0
0
0
0
0
Min
23
24
23
24
24
24
24
25
22
22
21
29
p10
23
24
23
24
24
24
24
25
22
22
21
29
p20
23
24
23
24
24
24
24
25
22
22
21
29
p30
23
24
23
24
24
24
24
25
22
22
21
29
p40
23
24
23
24
24
24
24
25
22
22
21
29
p50
23
24
23
24
24
24
24
25
22
22
21
29
p60
23
24
23
24
24
24
24
25
22
22
21
29
p70
23
24
23
24
24
24
24
25
22
22
21
29
p80
23
24
23
24
24
24
24
25
22
22
21
29
p90
23
24
23
24
24
24
24
25
22
22
21
29
Max
23
24
23
24
24
24
24
25
22
22
21
29
Table A-86. Distribution of hourly NO2 ambient concentrations (ppb) by year, Provo
MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
8002
8430
7034
8210
8563
8406
8501
8200
7730
8302
8502
6993
Mean
23
24
23
24
24
24
24
25
22
22
21
29
SD
13
15
13
13
13
13
14
14
13
15
13
34
COV
55
61
57
56
55
56
57
57
59
66
62
118
Min
0
0
0
0
0
0
0
0
0
0
0
0
p10
7
7
7
7
7
7
6
6
6
5
5
5
p20
10
10
10
10
11
10
10
10
8
8
8
7
p30
13
14
14
14
14
14
14
15
12
12
11
10
p40
17
18
18
18
19
18
19
20
16
16
15
13
p50
22
23
22
23
23
22
23
25
21
20
19
17
p60
26
28
26
28
28
27
28
30
26
25
23
22
p70
30
32
31
32
33
32
33
34
30
30
28
30
p80
34
37
35
37
37
37
38
38
34
35
33
38
p90
40
43
41
42
42
42
43
43
39
42
39
61
Max
67
97
81
78
77
74
72
80
72
90
64
164
Figure A-86. Temporal distribution of hourly NO2 ambient
concentrations (ppb) by year, Provo MSA.
A-72
-------
Figure A-87. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Provo MSA, 1995-2006.
Figure A-88. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Provo MSA, 1995-2006.
Table A-87. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, Provo MSA, 1995-2006.
Monitor ID
4904900021
n
12
Mean
24
SD
2
cov
9
Min
21
p10
22
p20
22
p30
23
p40
23
p50
24
p60
24
p70
24
p80
24
p90
25
Max
29
Table A-88. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Provo
MSA, 1995-2006.
Monitor ID
4904900021
n
96873
Mean
24
SD
16
COV
68
Min
0
p10
6
p20
9
p30
13
p40
17
p50
22
p60
27
p70
31
p80
36
p90
42
Max
164
A-73
-------
Figure A-89. Distribution of annual average NO2 ambient
concentrations (ppb) by year, St. Louis MSA.
Table A-89. Distribution of annual average NO2 ambient concentrations (ppb) by
year, St. Louis MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
10
10
10
8
9
9
8
9
9
9
6
2
Mean
18
17
17
19
19
18
17
16
15
14
13
12
SD
6
6
6
5
5
5
5
4
4
4
3
5
cov
35
33
32
25
24
29
28
26
26
31
24
40
Min
5
6
6
11
12
9
10
10
9
8
9
8
p10
8
8
8
11
12
9
10
10
9
8
9
8
p20
12
12
12
13
14
12
12
11
10
10
10
8
p30
15
16
16
18
18
16
17
14
14
12
10
8
p40
19
19
19
19
18
17
17
15
14
13
12
8
p50
19
19
19
19
20
18
18
16
16
13
13
12
p60
20
20
19
19
21
19
19
17
16
16
15
15
p70
22
20
19
20
21
21
20
19
18
17
15
15
p80
22
21
21
22
24
21
20
21
19
18
15
15
p90
24
23
23
26
27
26
25
23
20
22
17
15
Max
26
25
25
26
27
26
25
23
20
22
17
15
Figure A-90. Temporal distribution of hourly NO2 ambient
concentrations (ppb) by year, St. Louis MSA.
Table A-90. Distribution of hourly NO2 ambient concentrations (ppb) by year, St.
Louis MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
85072
86085
86314
68308
77611
77327
67871
76693
77543
75493
49948
16688
Mean
18
17
17
19
19
18
17
16
15
14
13
12
SD
12
11
11
11
12
11
11
11
10
10
9
8
COV
68
65
67
58
61
64
64
65
67
69
70
70
Min
0
0
0
0
0
0
0
0
0
0
0
p10
4
4
4
6
6
5
5
5
4
4
4
3
p20
7
7
7
9
9
8
7
7
6
6
5
5
p30
10
10
10
12
12
10
10
9
8
8
7
6
p40
13
13
12
14
14
13
13
12
11
10
9
8
p50
16
16
15
17
17
16
15
14
13
12
11
10
p60
19
19
18
20
20
19
19
17
16
15
13
12
p70
23
22
22
23
24
22
22
21
19
18
16
15
p80
28
26
26
28
29
27
27
25
23
22
20
18
p90
34
32
33
33
36
34
33
31
29
28
26
23
Max
103
84
274
97
99
85
95
124
123
130
70
53
A-74
-------
Figure A-91. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, St. Louis MSA, 1995-2006.
Table A-91. Distribution of annual average NO2 ambient concentration (ppb) by
monitor, St. Louis MSA, 1995-2006.
Monitor ID
1716300102
2918300101
2918310021
2918900012
2918900041
2918900062
2918930012
2918950011
2918970022
2918970031
2951000722
2951000801
2951000861
n
12
3
12
3
6
11
11
10
6
4
10
5
6
Mean
18
6
10
19
15
12
20
17
20
15
25
19
19
SD
2
0
1
0
2
1
2
2
1
2
2
1
2
cov
12
7
13
2
15
12
11
13
6
14
9
5
11
Min
15
5
8
19
12
10
17
13
19
12
20
19
15
p10
15
5
8
19
12
10
17
14
19
12
21
19
15
p20
16
5
9
19
13
10
18
15
20
12
23
19
18
p30
16
5
9
19
13
11
19
16
20
16
24
19
18
p40
17
6
9
19
14
12
20
17
20
16
25
19
19
p50
18
6
10
19
14
12
21
18
20
16
25
19
19
p60
18
6
11
19
14
12
22
19
20
16
25
19
20
p70
19
6
11
19
16
13
22
19
21
16
26
20
21
p80
19
6
11
19
16
13
22
19
21
17
26
20
21
p90
20
6
11
19
18
13
22
19
22
17
27
21
21
Max
21
6
12
19
18
14
24
20
22
17
27
21
21
Figure A-92. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, St. Louis MSA, 1995-2006.
Table A-92. Distribution of hourly NO2 ambient concentration (ppb) by monitor, St.
Louis MSA, 1995-2006.
Monitor ID
1716300102
2918300101
2918310021
2918900012
2918900041
2918900062
2918930012
2918950011
2918970022
2918970031
2951000722
2951000801
2951000861
n
101236
25873
99623
25801
51987
93770
95589
86912
51777
32235
85643
42884
51623
Mean
18
6
10
19
15
12
20
17
20
15
25
19
19
SD
9
6
8
11
10
9
11
11
11
10
11
11
12
COV
52
98
81
58
68
79
52
62
54
66
46
59
62
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
p10
8
1
2
7
4
3
8
6
8
4
11
7
6
p20
10
2
3
9
6
4
11
8
11
7
15
10
9
p30
12
2
4
12
8
5
13
10
13
9
18
12
11
p40
14
3
6
14
10
7
16
12
16
11
20
15
14
p50
16
4
8
17
12
9
19
15
18
13
23
17
16
p60
19
5
10
20
15
12
22
18
21
16
26
20
19
p70
21
7
12
23
18
15
25
21
25
19
29
23
23
p80
25
9
16
28
22
19
29
26
29
24
33
28
28
p90
31
13
21
34
29
25
35
32
36
30
40
34
36
Max
123
51
73
89
80
79
101
124
103
64
130
274
87
A-75
-------
I
I
Figure A-93. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Other MSA/CMSA.
Table A-93. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Other MSA/CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
186
186
187
185
192
199
201
209
202
211
207
147
Mean
15
14
14
14
15
14
13
12
12
11
11
10
SD
6
6
6
6
6
6
6
6
5
5
5
4
cov
44
43
43
43
42
41
43
45
42
44
43
41
Min
1
1
2
1
1
1
1
1
1
1
1
1
p10
5
5
5
5
6
5
5
5
5
5
5
4
p20
8
9
9
10
9
8
7
7
7
7
7
6
p30
11
11
11
11
11
11
10
9
9
9
9
9
p40
13
13
12
13
14
12
12
11
11
10
10
9
p50
15
15
14
14
15
14
13
13
12
11
11
11
p60
17
16
16
16
16
16
15
14
14
13
12
12
p70
18
18
18
18
18
17
17
16
15
14
14
13
p80
21
20
19
20
20
18
18
17
17
16
16
14
p90
22
22
22
22
23
21
20
20
18
17
17
16
Max
32
30
29
31
29
26
27
27
26
25
24
18
Figure A-94. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Other MSA/CMSA.
Table A-94. Distribution of hourly NO2 ambient concentrations (ppb) by year, Other
MSA/CMSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
186
1520743
1520290
1503051
1560074
1630060
1648640
1713558
1661992
1738133
1706730
1168444
Mean
15
14
14
14
15
14
13
13
12
11
11
10
SD
6
12
11
11
12
11
11
11
10
10
10
9
COV
44
81
82
80
83
81
84
85
84
87
87
87
Min
1
0
0
0
0
0
0
0
0
0
0
0
p10
5
2
2
2
3
2
2
2
2
2
2
2
p20
8
5
4
5
5
4
4
4
4
3
3
3
p30
11
7
6
7
7
6
6
5
5
5
5
5
p40
13
9
9
9
9
8
8
7
7
7
6
6
p50
15
12
11
11
11
11
10
9
9
8
8
8
p60
17
15
14
15
14
13
13
12
12
11
11
10
p70
18
18
18
18
18
17
16
15
15
14
14
13
p80
21
23
23
23
24
22
21
20
19
18
18
17
p90
22
31
30
31
32
29
29
28
26
25
25
23
Max
32
336
313
300
172
289
193
158
148
160
153
240
A-76
-------
Figure A-95. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Other Not MSA.
Figure A-96. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Other Not MSA.
Table A-95. Distribution of annual average NO2 ambient concentrations (ppb) by
year, Other Not MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
28
29
35
33
36
39
41
42
44
47
43
26
Mean
8
7
7
7
8
8
7
7
7
6
7
6
SD
5
5
5
5
5
4
4
4
4
4
4
5
cov
59
71
67
62
67
57
60
65
61
64
63
71
Min
1
0
0
1
0
2
1
1
1
2
1
1
p10
2
0
1
1
1
2
2
2
2
2
2
1
p20
4
2
3
3
3
3
3
2
3
2
2
2
p30
5
4
4
4
4
5
4
3
3
3
3
2
p40
7
5
5
5
5
6
5
4
4
4
5
3
p50
7
5
7
7
7
8
6
6
6
5
6
5
p60
8
7
9
7
8
8
8
8
8
7
8
8
p70
10
10
10
10
9
10
9
8
9
8
9
10
p80
13
13
12
12
12
11
10
10
11
11
11
11
p90
15
14
14
14
16
14
13
13
13
13
12
12
Max
19
14
20
19
20
19
17
16
15
16
17
16
Table A-96. Distribution of hourly NO2 ambient concentrations (ppb) by year, Other
Not MSA.
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
n
225810
234628
278906
264015
290382
316568
328407
340873
351652
375716
353229
207114
Mean
8
7
7
8
8
8
7
7
7
6
7
6
SD
9
8
8
8
9
8
7
7
7
7
8
7
COV
104
118
113
105
113
104
109
112
110
115
114
119
Min
0
0
0
0
0
0
0
0
0
0
0
0
p10
0
0
0
1
0
1
1
1
1
1
1
0
p20
2
1
1
2
2
2
1
1
2
1
1
1
p30
3
2
2
3
2
3
2
2
2
2
2
2
p40
4
3
3
4
3
4
3
3
3
3
3
2
p50
6
4
5
5
5
5
4
4
4
4
4
3
p60
7
6
6
7
6
7
6
5
5
5
6
5
p70
10
8
9
9
9
9
8
8
7
7
8
7
p80
13
11
12
12
12
12
11
11
10
10
11
10
p90
19
17
18
18
18
18
16
17
16
16
17
16
Max
217
164
207
181
286
192
139
267
201
285
262
101
A-77
-------
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.
A-6.1 Summary
This section describes the regression analyses of 1995 to 2006 NC>2 hourly concentration
data. Regression was used to estimate the annual number of exceedances of 150 ppb from the
annual mean, in 20 locations (mostly large urban areas). Exposures to concentrations above
certain thresholds may be associated with adverse health effects. These models were applied in
an as-is scenario to estimate the annual exceedances at sites with annual means equal to the
1995-2006 current average for their location. These models were also applied in a current-
standard scenario to predict the annual exceedances at sites with annual means equal to the
current NO2 standard of 53 ppb. The current-standard scenario is an extrapolation to higher
annual means than currently observed; the maximum annual mean across all complete site-years
was 51 ppb, in Los Angeles.
We found these results unsatisfactory, both because the regression models did not show a
strong relationship between the annual means and the exceedances, and because the predicted
numbers of exceedances for the current-standard scenario were in many cases extremely high
and quite uncertain. For this reason we decided not to apply the regression modeling to the other
concentration levels of interest (200, 250, and 300 ppb) but instead decided to develop empirical
exceedance estimates, as described elsewhere.
A-6.2 Data Used
All of the 1995 to 2006 NC>2 hourly concentration data from AQS were compiled and annual
summary statistics for each site-year combination were computed. Of particular interest is the
long-term air quality measured by the annual mean and the short-term air quality measured by
the annual numbers of hourly exceedances of selected levels 150, 200, 250 and 300 ppb.
Exposures to concentrations above these thresholds may be associated with adverse health
effects. To make the results temporally representative, we restricted the analyses to the 20
percent of site-years that were 75 % complete, as defined by having data for 75 % of the hours in
a year and having data for at least 75 % of the hours in a day (i.e., 18 hours or more) on at least
75 % of the days in a year. We also spatially grouped the data into 18 urban areas with high
annual means and high exceedances; these locations were all CMSAs or MSAs either with at
least one site-year annual mean above 25.7 ppb (the 90th percentile) or with at least one
exceedance of 200 ppb, as follows.
• Boston
• Cleveland
• Denver
• Detroit
• Los Angeles
• New York
• Philadelphia
• Washington DC
• Atlanta
A-78
-------
• Colorado Springs
• El Paso
• Las Vegas
• Phoenix
• St. Louis
• Chicago
• Miami
• Jacksonville
• Provo.
The remaining site-years were analyzed as two additional location groups: "Other
MSA/CMSA" site-years in an MSA or CMSA, and "Other Not MSA" site-years not in an MSA.
Thus we have a total of 20 "locations."
A-6.3 Regression Models
The regression modeling of the 1995-2006 NO2 data continues the analyses by McCurdy
(1994)4 of the 1988-1992 data. A regression model is used to estimate the mean number of
exceedances from the annual mean. McCurdy (1994) assumed normally distributed exceedances
and an exponential link function to estimate exceedances of 150, 200, 250, and 300 ppb based on
the 1988-1992 data. In this section we present the results of the regression analyses for
exceedances of 150 ppb using eight alternative models based on the 1995-2006 data.
Throughout this discussion, "exceedances" will refer to annual numbers of hourly exceedances
of 150 ppb, unless otherwise stated.
Of the eight models, the two selected regression models were the Poisson exponential model
and the normal linear model, stratified by location. The Poisson exponential model is of the
form:
• Number of exceedances has a Poisson distribution.
• Mean exceedances = exp(a + b x annual mean).
• The intercept a, and slope b, depend on the location.
The normal linear model is of the form:
• Number of exceedances has a normal distribution with standard deviation s.
• Mean exceedances = a + b x annual mean.
• The intercept a, slope b, and s all depend on the location.
The first issue to be resolved was to decide whether to apply the regression analyses to the
means and exceedances for each season separately or to each year. We examined the exceedance
data for Colorado Springs, which had the highest maximum number of annual exceedances of
200 ppb, 69, which occurred at site 804160181 in 2000. Of these 69 exceedances, 34 occurred in
the winter on January 18-20, 2000, and 35 occurred in the summer on June 12-14, 2000. This
limited analysis suggests that there is no clear pattern of seasonality in the exceedances. We
decided to apply the regression modeling to the annual means and annual exceedances.
4 McCurdy TR (1994). Analysis of high 1 hour NO2 values and associated annual averages using 1988-
1992 data. Report to the Office of Air Quality Planning and Standards, Durham NC.
A-79
-------
Table 1 describes the eight regression models fitted. As described shortly, we fitted two
distributions (normal and Poisson), two link functions (identity and exponential), and two
stratifications (all data and stratified by location). The McCurdy (1994) analysis used a normal
distribution, an exponential link, and stratified by location into Los Angeles and Not Los
Angeles.
We fitted generalized linear models where the number of exceedances has a given
distribution (we fitted normal and Poisson distributions) and where the mean number of
exceedances is a given function g of the annual mean. The function g(x) is called the link
function. We can also define the link by defining the inverse link, i.e., the solution for x of the
equation g(x) = y.
We fitted two link functions, an identity link g(x) = x and a logarithmic link g(x) = log(x),
where "log" denote the natural logarithm. The corresponding inverse links are the identity link,
which we also call the "linear" function, and the exponential function. Thus, the linear inverse
link models are of the form:
Mean exceedances = a + b x annual mean.
The exponential inverse link models are of the form:
Mean exceedances = exp(a + b x annual mean).
Table A-97. Goodness-of-fit statistics for eight generalized linear models.
Distribution
Normal
Normal
Normal
Normal
Poisson
Poisson
Poisson
Poisson
Inverse Link
Linear
Linear
Exponential
Exponential
Linear
Linear
Exponential
Exponential
Strata (a
separate
model is
fitted in
each
stratum)
All
Location
All
Location
All
Location
All
Location
R squared
for all data
0.033
0.244
0.066
0.401
0.025
Not
Shown*
0.064
0.406
MinR
squared
among
locations
0.006
0.005
Not Shown*
0.004
MaxR
squared
among
locations
0.616
0.981
Not Shown*
0.976
Log-
Likelihood
-11527
-6065
-11438
-8734
-4737
Not Shown*
-3660
-2694
Number of
strata in
final
model
1
13**
1
11***
1
Not
Shown*
1
13**
Notes:
* Model converged for only Cleveland, Atlanta, and "Other Not MSA" locations. Results are not shown since the model failed to converge for
the "Other MSA" location, so the overall goodness-of-fit is not comparable to the other seven models.
** "Other MSA" includes Chicago, Detroit, Philadelphia, Jacksonville, Las Vegas, Provo, St. Louis.
*** "Other MSA" includes Chicago, Cleveland, Detroit, Philadelphia, Jacksonville, Las Vegas, Phoenix, Provo, St. Louis.
For each link function we fitted models using the normal distribution and the Poisson
distribution. The normal model is at best an approximation since the numbers of exceedances
must be positive or zero integers, but the normal distribution is continuous and includes negative
values. T he Poisson model takes the form:
A-80
-------
Prob(y exceedances) = (My/y !)e"M, y = 0, 1, 2, ...,
where M is the mean exceedances.
We fitted these four models (two links, two distributions) either to all the data or stratified by
location. Thus the model fitted to all the data assumes that a and b have the same value for all
site-years, and the model fitted by location assumes that a and b have the same value for all site-
years at the same location but these values may vary between locations. For the normal models,
the variance of the number of exceedances is assumed to be the same for all site-years in each
stratum. For the Poisson models, the variance equals the mean number of exceedances.
The models stratified by location were fitted in two steps. First, each model was separately
fitted to each of the 20 locations. For several models and locations, there were problem cases
where the algorithm failed to converge to a solution, predicted a negative slope for the annual
mean, or had only zero or one site-year with at least one exceedance. In the second case, if the
slope is negative, then the model implies that exceedances decrease when the annual mean
increases, which is unexpected and could lead to inconsistent results for projecting exceedances
to the current-standard scenario. In the third case, there would be zero degrees of freedom and
the model would be over-fitted for that location. To deal with these problem cases, we re-
allocated all the problem locations into the "Other MSA" combined location and refitted the
models. The results in Table 1 stratified by location are for the refitted models. The re-allocated
locations are listed in the footnotes.
Table A-97 gives R squared and log-likelihood goodness-of-fit summary statistics. The R
squared statistic is the squared Pearson correlation coefficient between the observed number of
exceedances and the predicted mean number of exceedances. Negative predicted means are
replaced by zero for this calculation. Values close to 1 indicate a good fit and values close to
zero indicate a poor fit. For the models stratified by location, it is evident that the R squared
value has a wide range across the locations, varying from a very poor fit at some locations to a
very good fit at other locations.
For these models the log-likelihood is a better overall goodness-of-fit statistic. The log-
likelihood is defined as the logarithm of the fitted joint density function to all 4,177 site-years.
The better-fitting models are those with the highest values of the log-likelihood. (The log-
likelihood can only be used to compare different models; its value for a single statistical model is
not meaningful). Of the various normal models, the best-fitting is stratified by location and uses
a linear inverse link. Of the various Poisson models, the best-fitting is stratified by location and
uses an exponential inverse link. The Poisson models fit better than the normal models, which is
to be expected since the actual data are positive or zero discrete count data and the numbers of
exceedances are frequently zero, implying a very small mean.
We selected the Poisson exponential model stratified by location and the normal linear model
stratified by location. The estimated parameter values for these models are displayed in Tables
A-98 and A-99, respectively.
The fitted models for the CMSA locations are displayed in Figures A-97 to A-99. Figure A-
97 and the first three attached plots show the number of exceedances plotted against the annual
mean. These plots clearly show how weak the relationship between the exceedances and the
annual mean is. Figure A-98 and the next three attached plots are for the Poisson exponential
A-81
-------
model, plotting predicted versus observed exceedances. Figure A-99 and the final three attached
plots are for the normal linear model, plotting predicted versus observed exceedances (negative
predictions were replaced by zero). Comparing the normal and Poisson model predictions, the
normal model tends to under-predict the higher numbers of observed exceedances.
Tables A-100 and A-101 indicate the predictions for a mean of 53 ppb and for the mean
annual mean for each the Poisson exponential model and the normal linear model, respectively.
The predictions for a mean of 53 ppb estimate the number of exceedances for a hypothetical site-
year with the highest annual mean concentration under the current-standard scenario, i.e., when
the highest annual mean site-year for a given location just meets the annual standard. The
predictions for a mean equal to the mean annual mean estimate the number of exceedances for
the typical "as-is" scenario, i.e., for a hypothetical site-year with an annual mean that is the
average annual mean for that location. 95 percent confidence and prediction intervals for the
number of exceedances at given mean levels were also estimated using each model. In addition,
exceedances were also estimated at alternative annual mean concentrations. Tables A-103 and
A-104 give calculated predictions at annual mean values of 20, 30, 40, 50, 53, and 60 ppb and at
the minimum, mean, and maximum annual mean value for each location using the Poisson
exponential model and the normal linear model, respectively.
The 95% confidence interval gives the uncertainty of the expected value, i.e., of the average
number of exceedances over hypothetically infinitely many site-years with the same annual
mean. The 95% prediction interval gives the uncertainty of the value for a single site-year,
taking into account both the uncertainty of the estimated parameters and the variability of the
number of exceedances in a given site-year about the overall mean. All prediction intervals were
truncated to be greater than or equal to zero and less than or equal to 1,000. The maximum
possible number of exceedances in a year is the maximum number of hours in a leap year, 8,784.
The maximum observed exceedances in a year was 69.
For annual means within the range of the data, the predicted numbers of exceedances are
generally within the range of the observed numbers of exceedances. The normal model
predictions tend to be lower than the Poisson model predictions. At annual mean levels above
the range of the data, the Poisson model with the exponential inverse link sometimes gives
extremely high estimates, well beyond the truncation limit of 1,000. This is mainly due to the
exponential link; each increase of the annual mean by 1 ppb increases the predicted exceedances
by a multiplicative factor of exp(b), where b > 0. The upper bounds of the normal linear model
prediction intervals are at most a more reasonable 202, but these predictions are less reliable
because the Poisson model with an exponential inverse link fits the data much better. For the
normal linear model, each increase of the annual mean by 1 ppb increases the predicted
exceedances by b ppb.
Not shown here are the results for the normal model with an exponential inverse link, which
was the model formulation selected by McCurdy (1994). That model gives roughly similar
predictions to the Poisson model with the exponential inverse link.
A-82
-------
Table A-98. Parameters for Poisson exponential model stratified by location.
Location
Type
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
Location Name
Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA
Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA
Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA
Cleveland-Akron, OH CMSA
Cleveland-Akron, OH CMSA
Cleveland-Akron, OH CMSA
Denver-Boulder-Greeley, CO CMSA
Denver-Boulder-Greeley, CO CMSA
Denver-Boulder-Greeley, CO CMSA
Los Angeles-Riverside-Orange County, CA CMSA
Los Angeles-Riverside-Orange County, CA CMSA
Los Angeles-Riverside-Orange County, CA CMSA
Miami-Fort Lauderdale, FL CMSA
Miami-Fort Lauderdale, FL CMSA
Miami-Fort Lauderdale, FL CMSA
New York-Northern New Jersey-Long Island, NY-NJ-
CT-PA CMS
New York-Northern New Jersey-Long Island, NY-NJ-
CT-PA CMS
New York-Northern New Jersey-Long Island, NY-NJ-
CT-PA CMS
Washington-Baltimore, DC-MD-VA-WV CMSA
Washington-Baltimore, DC-MD-VA-WV CMSA
Washington-Baltimore, DC-MD-VA-WV CMSA
Atlanta, GA
Atlanta, GA
Atlanta, GA
Colorado Springs, CO
Colorado Springs, CO
Colorado Springs, CO
El Paso,TX
El Paso,TX
Parameter*
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Estimate
-6.887
0.144
1.000
-14.209
0.548
1.000
-4.399
0.137
1.000
-5.628
0.181
1.000
-5.780
0.342
1.000
-6.800
0.147
1.000
-6.559
0.145
1.000
-5.081
0.140
1.000
-4.846
0.284
1.000
-10.436
0.350
Standard
Error
2.832
0.116
0.000
4.374
0.164
0.000
1.186
0.038
0.000
0.253
0.006
0.000
1.641
0.114
0.000
1.269
0.037
0.000
3.054
0.135
0.000
1.917
0.099
0.000
0.401
0.012
0.000
2.455
0.074
Lower
Confidence
Bound
-14.693
-0.061
1.000
-25.210
0.283
1.000
-7.182
0.070
1.000
-6.134
0.169
1.000
-9.774
0.138
1.000
-9.560
0.079
1.000
-14.610
-0.073
1.000
-9.975
-0.040
1.000
-5.675
0.261
1.000
-16.783
0.233
Upper
Confidence
Bound
-2.757
0.430
1.000
-7.312
0.952
1.000
-2.435
0.222
1.000
-5.142
0.194
1.000
-3.068
0.606
1.000
-4.537
0.224
1.000
-2.054
0.482
1.000
-2.139
0.363
1.000
-4.097
0.309
1.000
-6.664
0.538
P-
value
**
0.02
0.22
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.03
0.28
0.01
0.16
0.00
0.00
0.00
0.00
A-83
-------
Location
Type
MSA
MSA
MSA
MSA
MSA/CMSA
MSA/CMSA
MSA/CMSA
Not MSA
Not MSA
Not MSA
Location Name
El Paso,TX
Phoenix-Mesa,AZ
Phoenix-Mesa,AZ
Phoenix-Mesa,AZ
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Parameter*
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Estimate
1.000
-1.568
0.106
1.000
-5.137
0.152
1.000
-4.672
0.227
1.000
Standard
Error
0.000
0.400
0.013
0.000
0.222
0.010
0.000
0.467
0.036
0.000
Lower
Confidence
Bound
1.000
-2.363
0.081
1.000
-5.580
0.132
1.000
-5.654
0.158
1.000
Upper
Confidence
Bound
1.000
-0.798
0.131
1.000
-4.711
0.172
1.000
-3.818
0.300
1.000
P-
value
**
0.00
0.00
0.00
0.00
0.00
0.00
Notes:
* using the report notation, a = "Intercept", and b = "mean." "Scale" equals 1 , by definition, for this model.
** probability that the Chi-square test for that parameter = 0.
Table A-99. Parameters for normal linear model stratified by location.
Location
Type
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
Location Name
Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA
Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA
Boston-Worcester-Lawrence, MA-NH-ME-CT CMSA
Cleveland-Akron, OH CMSA
Cleveland-Akron, OH CMSA
Cleveland-Akron, OH CMSA
Denver-Boulder-Greeley, CO CMSA
Denver-Boulder-Greeley, CO CMSA
Denver-Boulder-Greeley, CO CMSA
Los Angeles-Riverside-Orange County, CA CMSA
Los Angeles-Riverside-Orange County, CA CMSA
Los Angeles-Riverside-Orange County, CA CMSA
Miami-Fort Lauderdale, FL CMSA
Miami-Fort Lauderdale, FL CMSA
Parameter*
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Estimate
-0.023
0.003
0.135
-3.259
0.176
1.755
-0.439
0.044
1.097
-3.301
0.194
4.723
-0.496
0.070
Standard
Error
0.034
0.002
0.009
2.127
0.099
0.265
0.383
0.018
0.129
0.620
0.023
0.174
0.384
0.037
Lower
Confidence
Bound
-0.090
-0.001
0.119
-7.617
-0.027
1.341
-1.211
0.008
0.885
-4.519
0.148
4.402
-1.265
-0.005
Upper
Confidence
Bound
0.043
0.006
0.156
1.098
0.378
2.436
0.332
0.080
1.408
-2.083
0.240
5.085
0.273
0.144
P-value
**
0.49
0.17
0.13
0.08
0.25
0.01
0.00
0.00
0.20
0.06
A-84
-------
Location
Type
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA
MSA/CMSA
MSA/CMSA
MSA/CMSA
Not MSA
Not MSA
Not MSA
Location Name
Miami-Fort Lauderdale, FL CMSA
New York-Northern New Jersey-Long Island, NY-NJ-
CT-PA CMS
New York-Northern New Jersey-Long Island, NY-NJ-
CT-PA CMS
New York-Northern New Jersey-Long Island, NY-NJ-
CT-PA CMS
Washington-Baltimore, DC-MD-VA-WV CMSA
Washington-Baltimore, DC-MD-VA-WV CMSA
Washington-Baltimore, DC-MD-VA-WV CMSA
Atlanta, GA
Atlanta, GA
Atlanta, GA
Colorado Springs, CO
Colorado Springs, CO
Colorado Springs, CO
El Paso,TX
El Paso,TX
El Paso,TX
Phoenix-Mesa,AZ
Phoenix-Mesa,AZ
Phoenix-Mesa,AZ
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Parameter*
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Estimate
0.828
-0.230
0.013
0.407
-0.032
0.003
0.208
-0.041
0.008
0.226
-36.358
2.689
22.519
-2.017
0.131
0.920
-7.102
0.423
22.513
-0.100
0.013
1.098
-0.064
0.021
0.549
Standard
Error
0.088
0.104
0.004
0.022
0.069
0.003
0.013
0.069
0.005
0.022
11.812
0.674
3.123
0.440
0.024
0.098
15.545
0.557
2.274
0.051
0.003
0.015
0.049
0.006
0.018
Lower
Confidence
Bound
0.681
-0.435
0.005
0.368
-0.167
-0.004
0.186
-0.178
-0.002
0.189
-60.391
1.318
17.551
-2.898
0.083
0.757
-38.177
-0.689
18.697
-0.201
0.006
1.069
-0.160
0.009
0.514
Upper
Confidence
Bound
1.036
-0.024
0.020
0.454
0.104
0.010
0.236
0.096
0.017
0.277
-12.326
4.061
30.362
-1.135
0.178
1.151
23.974
1.536
27.828
0.000
0.019
1.128
0.031
0.032
0.587
P-value
**
0.03
0.00
0.64
0.35
0.55
0.11
0.00
0.00
0.00
0.00
0.65
0.45
0.05
0.00
0.19
0.00
Notes:
Using the report notation, a = "Intercept", b = "mean", and standard deviation = "Scale."
** probability that the Chi-square test for that parameter = 0.
A-85
-------
50
. •* .
» «
kH:ulnm ""ba^iiun "(.-'InctiHO "(..'IfvcJiititl *™L>cnvei DUmiL w Lus AnyeJyij •"Miaitu Nc\v Yoik I'Uiludclphiu ™ WiUi
Figure A-97. Exceedances of 150 ppb versus annual mean concentrations (ppb) for CMSA locations.
Predicted Exceedances
40
30
10
10
40
20 30
Observed Exceedances
location "Boston ~Cle\eland —Denver "Los Angeles —Miami —New York —Washington
50
Figure A-98. Predicted and observed exceedances for CMSA locations using Poisson exponential
model.
A-86
-------
Predicted Exceedances
ji:*'s • •' *
ii. •
i • •
t •
n:." '
location "Boston "Cleveland "Denver
20 .10 40
Observed Exceedances
"Los Angeles "Miami "New York "Washington
50
Figure A-99. Predicted and observed exceedances for CMSA locations using normal linear model
A-87
-------
Table A-100. As-is and current-standard scenario predictions for Poisson exponential model, with
separate coefficients for each location.
Location
Boston
Boston
Cleveland
Cleveland
Denver
Denver
Los Angeles
Los Angeles
Miami
Miami
New York
New York
Washington
Washington
Atlanta
Atlanta
Colorado
Springs
Colorado
Springs
El Paso
El Paso
Phoenix
Phoenix
Other
MSA/CMSA
Other
MSA/CMSA
Other Not
MSA
Other Not
MSA
Annual
Mean
(ppb)
53.0
16.8
53.0
21.2
53.0
18.7
53.0
24.3
53.0
9.7
53.0
25.5
53.0
19.4
53.0
12.9
53.0
16.3
53.0
17.7
53.0
27.3
53.0
13.9
53.0
7.0
Observed
Mean
Exceed-
ances
0.019
0.019
0.455
0.455
0.389
0.389
1.403
1.403
0.182
0.182
0.092
0.092
0.030
0.030
0.057
0.057
7.346
7.346
0.295
0.295
4.469
4.469
0.079
0.079
0.081
0.081
Observed
Max
Exceed-
ances
1
1
9
9
6
6
44
44
5
5
3
3
2
2
1
1
143
143
7
7
147
147
39
39
7
7
Predicted
Exceed-
ances
2.081
0.011
1000.000
0.073
17.140
0.158
53.244
0.293
1000.000
0.086
2.737
0.048
3.038
0.023
10.242
0.038
1000.000
0.792
1000.000
0.015
56.901
3.760
18.369
0.048
1000.000
0.046
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.002
0.001
578.253
0.011
2.958
0.057
44.092
0.238
35.520
0.026
0.646
0.022
0.001
0.007
0.012
0.008
1000.000
0.528
177.602
0.001
31.702
3.221
9.388
0.040
85.717
0.028
Upper
Bound
1000.000
0.091
1000.000
0.474
99.308
0.438
64.297
0.360
1000.000
0.281
1 1 .604
0.104
1000.000
0.082
1000.000
0.181
1000.000
1.189
1000.000
0.142
102.130
4.389
35.940
0.058
1000.000
0.075
95% Prediction
Interval for
Number of
Exceedances
Lower
Bound
0
0
364
0
2
0
37
0
29
0
0
0
0
0
0
0
1000
0
156
0
26
0
7
0
75
0
Upper
Bound
1000
0
1000
1
98
1
73
2
1000
1
13
1
1000
0
1000
1
1000
3
1000
1
106
8
41
1
1000
1
A-88
-------
Table A-101. As-is and current-standard scenario predictions for Normal linear model, with separate
coefficients for each location.
Location Name
Boston
Boston
Cleveland
Cleveland
Denver
Denver
Los Angeles
Los Angeles
Miami
Miami
New York
New York
Washington
Washington
Atlanta
Atlanta
Colorado
Springs
Colorado
Springs
El Paso
El Paso
Phoenix
Phoenix
Other
MSA/CMSA
Other
MSA/CMSA
Other Not MSA
Other Not MSA
Annual
Mean
(ppb)
53.0
16.8
53.0
21.2
53.0
18.7
53.0
24.3
53.0
9.7
53.0
25.5
53.0
19.4
53.0
12.9
53.0
16.3
53.0
17.7
53.0
27.3
53.0
13.9
53.0
7.0
Observed
Mean
Exceed-
ances
0.019
0.019
0.455
0.455
0.389
0.389
1.403
1.403
0.182
0.182
0.092
0.092
0.030
0.030
0.057
0.057
7.346
7.346
0.295
0.295
4.469
4.469
0.079
0.079
0.081
0.081
Observed
Max
Exceed-
ances
1
1
9
9
6
6
44
44
5
5
3
3
2
2
1
1
143
143
7
7
147
147
39
39
7
7
Predicted
Exceed-
ances
0.111
0.019
6.046
0.455
1.906
0.389
6.965
1.403
3.199
0.182
0.439
0.092
0.136
0.030
0.360
0.057
106.169
7.346
4.902
0.295
15.339
4.469
0.584
0.079
1.036
0.081
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.000
0.000
0.000
0.000
0.645
0.031
5.561
0.921
0.024
0.000
0.220
0.031
0.000
0.000
0.000
0.000
56.853
0.000
3.249
0.024
0.000
0.000
0.324
0.037
0.505
0.030
Upper
Bound
0.245
0.045
12.267
1.188
3.168
0.747
8.369
1.884
6.375
0.426
0.658
0.152
0.364
0.065
0.739
0.117
155.486
16.002
6.555
0.567
44.043
10.773
0.844
0.120
1.566
0.132
95% Prediction
Interval for
Number of
Exceedances
Lower
Bound
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
36.477
0.000
2.384
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Upper
Bound
0.412
0.289
13.612
4.198
4.490
2.648
16.360
10.703
6.871
1.871
1.272
0.897
0.608
0.443
0.957
0.514
175.862
54.709
7.421
2.172
69.369
50.219
2.752
2.232
2.238
1.161
A-89
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
We can compare these predictions with the predictions for Los Angeles from McCurdy
(1994) based on 1988-1992 data. Table A-102 gives the McCurdy (1994) exceedance estimates
for exceedances of 150 ppb together with our estimates for the 1995-2006 data based on the
Poisson exponential model (see Table A-103) and the normal linear model (see Table A-104). It
is easily seen that the McCurdy (1994) estimates agree reasonably well with our Poisson
exponential model predictions, with predicted exceedances being a little lower for annual means
up to 53 ppb, but a little higher at 60 ppb. The McCurdy (1994) model predicts 75 exceedances
at 53 ppb, compared to our Poisson exponential model prediction of 53 exceedances. However,
the McCurdy (1994) estimates are all much higher than our normal linear model predictions. For
example, the McCurdy (1994) model predicts 75 exceedances at 53 ppb, compared to our normal
linear model prediction of 7 exceedances. These findings are primarily due to the fact that
McCurdy also used an exponential link function.
Table A-102. Comparison of predicted exceedances of 150 ppb using McCurdy (1994) for 1988-1992
data and the Poisson exponential and normal linear models for 1995-2006 data.
Annual Mean
(ppb)
20
30
40
50
53
60
Predicted Exceedances of 150 ppb
McCurdy (1994)
Normal
exponential
1988-1 992 data
4
9
33
57
75
142
Current Analysis
Poisson
exponential
1995-2006 data
0
1
5
31
53
189
Current Analysis
Normal linear
1995-2006 data
1
3
4
6
7
8
A-6.4 Conclusion
These analyses found a poor relationship between the annual means and the exceedances of
150 ppb, as well as frequently unrealistically high predictions of exceedances of 150 ppb for the
current-standard scenario. The uncertainty at higher exceedance threshold concentration levels
(200 to 300 ppb) would be expected to be even higher because the numbers of site-years with
non-zero exceedances are even lower (which implies a much weaker numerical relationship
between the annual mean and the annual exceedances). For example, for Los Angeles, the
maximum number of exceedances of 150 ppb was 44, but the maximum number of exceedances
of 200 ppb was only 5. Therefore we chose not to continue the regression analyses to higher
exceedance threshold concentration levels.
A-90
-------
1
2
A-6.5
Detailed Regression Model Predictions
Table A-103. Predictions for Poisson exponential model, with separate coefficients for each location.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Annual
Mean
(ppb)
20.0
30.0
40.0
50.0
53.0
60.0
5.4
16.8
31.0
20.0
30.0
40.0
50.0
53.0
60.0
14.2
21.2
28.1
20.0
30.0
40.0
50.0
53.0
60.0
6.1
18.7
36.8
20.0
30.0
40.0
50.0
53.0
60.0
3.6
24.3
50.6
20.0
30.0
40.0
50.0
53.0
60.0
5.5
Observed
Mean
Exceed-
ances
0.019
0.019
0.019
0.019
0.019
0.019
0.019
0.019
0.019
0.455
0.455
0.455
0.455
0.455
0.455
0.455
0.455
0.455
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.389
1.403
1.403
1.403
1.403
1.403
1.403
1.403
1.403
1.403
0.182
0.182
0.182
0.182
0.182
0.182
0.182
Observed
Max
Exceed-
ances
1
1
1
1
1
1
1
1
1
9
9
9
9
9
9
9
9
9
6
6
6
6
6
6
6
6
6
44
44
44
44
44
44
44
44
44
5
5
5
5
5
5
5
Predicted
Exceed-
ances
0.018
0.076
0.321
1.352
2.081
5.692
0.002
0.011
0.089
0.039
9.244
1000.000
1000.000
1000.000
1000.000
0.002
0.073
3.193
0.189
0.740
2.902
11.376
17.140
44.600
0.028
0.158
1.871
0.135
0.825
5.050
30.917
53.244
189.281
0.007
0.293
34.208
2.882
88.023
1000.000
1000.000
1000.000
1000.000
0.020
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.004
0.010
0.006
0.003
0.002
0.001
0.000
0.001
0.010
0.004
2.693
29.509
291.652
578.253
1000.000
0.000
0.011
1.490
0.074
0.438
1.201
2.426
2.958
4.659
0.004
0.057
0.925
0.104
0.713
4.632
26.439
44.092
144.681
0.004
0.238
29.084
0.636
2.282
7.591
24.900
35.520
81.274
0.003
Upper
Bound
0.090
0.576
17.564
661.873
1000.000
1000.000
0.175
0.091
0.801
0.358
31.732
1000.000
1000.000
1000.000
1000.000
0.092
0.474
6.845
0.482
1.251
7.014
53.350
99.308
426.973
0.186
0.438
3.786
0.174
0.954
5.505
36.154
64.297
247.629
0.011
0.360
40.236
13.069
1000.000
1000.000
1000.000
1000.000
1000.000
0.154
95% Prediction
Interval for
Number of
Exceedances
Lower
Bound
0
0
0
0
0
0
0
0
0
0
2
23
184
364
1000
0
0
0
0
0
0
1
2
4
0
0
0
0
0
1
20
37
138
0
0
22
0
2
7
33
29
40
0
Upper
Bound
1
1
14
680
1000
1000
0
0
1
1
32
1000
1000
1000
1000
0
1
9
2
3
9
53
98
454
1
1
6
1
3
10
44
73
260
0
2
48
13
1000
1000
1000
1000
1000
1
A-91
-------
Location
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
El Paso
Annual
Mean
(ppb)
9.7
16.8
20.0
30.0
40.0
50.0
53.0
60.0
9.7
25.5
42.2
20.0
30.0
40.0
50.0
53.0
60.0
6.9
19.4
27.2
20.0
30.0
40.0
50.0
53.0
60.0
3.4
12.9
26.6
20.0
30.0
40.0
50.0
53.0
60.0
6.8
16.3
34.8
20.0
Observed
Mean
Exceed-
ances
0.182
0.182
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.057
0.057
0.057
0.057
0.057
0.057
0.057
0.057
0.057
7.346
7.346
7.346
7.346
7.346
7.346
7.346
7.346
7.346
0.295
Observed
Max
Exceed-
ances
5
5
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
143
143
143
143
143
143
143
143
143
7
Predicted
Exceed-
ances
0.086
0.970
0.021
0.092
0.403
1.760
2.737
7.677
0.005
0.048
0.557
0.026
0.109
0.463
1.968
3.038
8.368
0.004
0.023
0.072
0.102
0.412
1.665
6.735
10.242
27.243
0.010
0.038
0.257
2.295
39.206
669.766
1000.000
1000.000
1000.000
0.054
0.792
153.247
0.032
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.026
0.380
0.007
0.052
0.211
0.507
0.646
1.121
0.001
0.022
0.260
0.008
0.011
0.004
0.001
0.001
0.000
0.000
0.007
0.014
0.032
0.034
0.023
0.014
0.012
0.008
0.000
0.008
0.037
1.662
33.759
526.509
1000.000
1000.000
1000.000
0.029
0.528
130.906
0.005
Upper
Bound
0.281
2.475
0.065
0.163
0.773
6.107
1 1 .604
52.548
0.028
0.104
1.193
0.081
1.044
55.438
1000.000
1000.000
1000.000
0.256
0.082
0.366
0.327
4.953
122.647
1000.000
1000.000
1000.000
0.230
0.181
1.770
3.168
45.531
852.001
1000.000
1000.000
1000.000
0.102
1.189
179.401
0.230
95% Prediction
Interval for
Number of
Exceedances
Lower
Bound
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
26
523
1000
1000
1000
0
0
121
0
Upper
Bound
1
4
0
1
2
7
13
53
0
1
3
1
2
57
1000
1000
1000
1
0
1
1
5
103
1000
1000
1000
0
1
3
6
53
870
1000
1000
1000
1
3
189
1
A-92
-------
Location
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Annual
Mean
(ppb)
30.0
40.0
50.0
53.0
60.0
8.2
17.7
35.1
20.0
30.0
40.0
50.0
53.0
60.0
11.1
27.3
40.5
20.0
30.0
40.0
50.0
53.0
60.0
0.5
13.9
34.0
20.0
30.0
40.0
50.0
53.0
60.0
0.3
7.0
19.7
Observed
Mean
Exceed-
ances
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.295
4.469
4.469
4.469
4.469
4.469
4.469
4.469
4.469
4.469
0.079
0.079
0.079
0.079
0.079
0.079
0.079
0.079
0.079
0.081
0.081
0.081
0.081
0.081
0.081
0.081
0.081
0.081
Observed
Max
Exceed-
ances
7
7
7
7
7
7
7
7
147
147
147
147
147
147
147
147
147
39
39
39
39
39
39
39
39
39
7
7
7
7
7
7
7
7
7
Predicted
Exceed-
ances
1.075
35.703
1000.000
1000.000
1000.000
0.001
0.015
6.447
1.731
4.988
14.375
41 .422
56.901
119.362
0.673
3.760
15.110
0.122
0.559
2.552
1 1 .648
18.369
53.171
0.006
0.048
1.025
0.878
8.514
82.532
799.989
1000.000
1000.000
0.010
0.046
0.823
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.536
11.290
95.081
177.602
757.520
0.000
0.001
3.454
1.287
4.367
10.922
24.843
31.702
55.901
0.404
3.221
11.361
0.107
0.442
1.681
6.317
9.388
23.650
0.004
0.040
0.756
0.459
2.297
11.133
53.545
85.717
256.785
0.004
0.028
0.438
Upper
Bound
2.156
112.906
1000.000
1000.000
1000.000
0.020
0.142
12.036
2.329
5.698
18.919
69.066
102.130
254.864
1.119
4.389
20.098
0.140
0.707
3.874
21.480
35.940
119.541
0.010
0.058
1.391
1.681
31.556
611.822
1000.000
1000.000
1000.000
0.025
0.075
1.547
95% Prediction
Interval for
Number of
Exceedances
Lower
Bound
0
11
94
156
634
0
0
1
0
1
7
21
26
56
0
0
7
0
0
0
4
7
20
0
0
0
0
1
10
57
75
226
0
0
0
Upper
Bound
4
119
1000
1000
1000
0
1
14
5
10
24
71
106
254
3
8
25
1
2
6
25
41
116
0
1
4
3
32
573
1000
1000
1000
0
1
3
A-93
-------
1
2
Table A-104. Predictions for Normal linear model, with separate coefficients for each location.
Location
Name
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
Annual
Mean
20.0
30.0
40.0
50.0
53.0
60.0
5.4
16.8
31.0
20.0
30.0
40.0
50.0
53.0
60.0
14.2
21.2
28.1
20.0
30.0
40.0
50.0
53.0
60.0
6.1
18.7
36.8
20.0
30.0
40.0
50.0
53.0
60.0
3.6
24.3
50.6
20.0
30.0
40.0
50.0
53.0
60.0
5.5
9.7
16.8
20.0
Observed
Mean
Exceed-
ances
0.019
0.019
0.019
0.019
0.019
0.019
0.019
0.019
0.019
0.455
0.455
0.455
0.455
0.455
0.455
0.455
0.455
0.455
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.389
1.403
1.403
1.403
1.403
1.403
1.403
1.403
1.403
1.403
0.182
0.182
0.182
0.182
0.182
0.182
0.182
0.182
0.182
0.092
Observed
Max
Exceed-
ances
1
1
1
1
1
1
1
1
1
9
9
9
9
9
9
9
9
9
6
6
6
6
6
6
6
6
6
44
44
44
44
44
44
44
44
44
5
5
5
5
5
5
5
5
5
3
Predicted
Exceed-
ances
0.027
0.052
0.078
0.103
0.111
0.128
0.000
0.019
0.055
0.252
2.008
3.763
5.519
6.046
7.275
0.000
0.455
1.667
0.446
0.888
1.331
1.773
1.906
2.216
0.000
0.389
1.189
0.573
2.510
4.447
6.384
6.965
8.321
0.000
1.403
6.492
0.899
1.596
2.293
2.990
3.199
3.687
0.000
0.182
0.677
0.023
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.141
0.035
0.000
0.000
0.000
0.000
0.000
0.140
0.085
0.353
0.499
0.613
0.645
0.716
0.000
0.031
0.458
0.053
1.962
3.579
5.109
5.561
6.612
0.000
0.921
5.193
0.108
0.092
0.065
0.034
0.024
0.001
0.000
0.000
0.103
0.000
Upper
Bound
0.056
0.107
0.166
0.226
0.245
0.287
0.039
0.045
0.113
1.019
3.874
7.492
11.163
12.267
14.846
0.769
1.188
3.194
0.807
1.424
2.163
2.934
3.168
3.716
0.402
0.747
1.920
1.093
3.058
5.315
7.660
8.369
10.031
0.000
1.884
7.792
1.689
3.099
4.521
5.947
6.375
7.373
0.281
0.426
1.250
0.096
95% Prediction
Interval for Number
of Exceedances
Lower
Bound
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Upper
Bound
0.297
0.327
0.361
0.399
0.412
0.441
0.263
0.289
0.330
4.003
6.173
9.163
12.553
13.612
16.125
3.243
4.198
5.673
2.706
3.185
3.720
4.306
4.490
4.933
2.136
2.648
3.543
9.876
11.814
13.776
15.760
16.360
17.766
6.747
10.703
15.871
2.757
3.873
5.131
6.463
6.871
7.834
1.607
1.871
2.449
0.829
A-94
-------
Location
Name
New York
New York
New York
New York
New York
New York
New York
New York
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
El Paso
El Paso
El Paso
El Paso
Annual
Mean
30.0
40.0
50.0
53.0
60.0
9.7
25.5
42.2
20.0
30.0
40.0
50.0
53.0
60.0
6.9
19.4
27.2
20.0
30.0
40.0
50.0
53.0
60.0
3.4
12.9
26.6
20.0
30.0
40.0
50.0
53.0
60.0
6.8
16.3
34.8
20.0
30.0
40.0
50.0
Observed
Mean
Exceed-
ances
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.057
0.057
0.057
0.057
0.057
0.057
0.057
0.057
0.057
7.346
7.346
7.346
7.346
7.346
7.346
7.346
7.346
7.346
0.295
0.295
0.295
0.295
Observed
Max
Exceed-
ances
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
143
143
143
143
143
143
143
143
143
7
7
7
7
Predicted
Exceed-
ances
0.149
0.275
0.401
0.439
0.527
0.000
0.092
0.302
0.032
0.063
0.095
0.127
0.136
0.158
0.000
0.030
0.054
0.110
0.186
0.262
0.337
0.360
0.413
0.000
0.057
0.161
17.426
44.318
71.210
98.102
106.169
124.994
0.000
7.346
57.235
0.594
1.900
3.205
4.511
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.079
0.148
0.204
0.220
0.256
0.000
0.031
0.161
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.020
0.015
0.001
0.000
0.000
0.000
0.000
0.000
0.019
7.454
24.197
38.662
52.682
56.853
66.550
0.000
0.000
31.241
0.303
1.270
2.140
2.994
Upper
Bound
0.218
0.401
0.598
0.658
0.798
0.028
0.152
0.444
0.067
0.143
0.237
0.335
0.364
0.432
0.081
0.065
0.117
0.201
0.357
0.522
0.689
0.739
0.857
0.092
0.117
0.303
27.398
64.439
103.758
143.522
155.486
183.438
0.000
16.002
83.228
0.886
2.529
4.270
6.027
95% Prediction
Interval for Number
of Exceedances
Lower
Bound
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
13.462
31.411
36.477
47.873
0.000
0.000
3.296
0.000
0.000
1.049
2.085
Upper
Bound
0.955
1.088
1.228
1.272
1.375
0.707
0.897
1.118
0.445
0.483
0.531
0.589
0.608
0.654
0.412
0.443
0.471
0.573
0.672
0.787
0.916
0.957
1.055
0.452
0.514
0.637
65.075
95.397
128.958
164.793
175.862
202.115
31.109
54.709
111.173
2.474
3.866
5.361
6.936
A-95
-------
Location
Name
El Paso
El Paso
El Paso
El Paso
El Paso
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Annual
Mean
53.0
60.0
8.2
17.7
35.1
20.0
30.0
40.0
50.0
53.0
60.0
11.1
27.3
40.5
20.0
30.0
40.0
50.0
53.0
60.0
0.5
13.9
34.0
20.0
30.0
40.0
50.0
53.0
60.0
0.3
7.0
19.7
Observed
Mean
Exceed-
ances
0.295
0.295
0.295
0.295
0.295
4.469
4.469
4.469
4.469
4.469
4.469
4.469
4.469
4.469
0.079
0.079
0.079
0.079
0.079
0.079
0.079
0.079
0.079
0.081
0.081
0.081
0.081
0.081
0.081
0.081
0.081
0.081
Observed
Max
Exceed-
ances
7
7
7
7
7
147
147
147
147
147
147
147
147
147
39
39
39
39
39
39
39
39
39
7
7
7
7
7
7
7
7
7
Predicted
Exceed-
ances
4.902
5.816
0.000
0.295
2.567
1.367
5.601
9.835
14.069
15.339
18.303
0.000
4.469
10.035
0.158
0.287
0.416
0.545
0.584
0.674
0.000
0.079
0.339
0.351
0.558
0.766
0.973
1.036
1.181
0.000
0.081
0.345
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
3.249
3.844
0.000
0.024
1.719
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.100
0.173
0.239
0.304
0.324
0.368
0.000
0.037
0.200
0.193
0.290
0.384
0.477
0.505
0.571
0.000
0.030
0.190
Upper
Bound
6.555
7.789
0.000
0.567
3.416
1 1 .546
12.546
25.027
39.591
44.043
54.495
16.406
10.773
25.696
0.216
0.401
0.593
0.786
0.844
0.980
0.003
0.120
0.477
0.508
0.827
1.148
1.469
1.566
1.791
0.035
0.132
0.499
95% Prediction
Interval for Number
of Exceedances
Lower
Bound
2.384
3.065
0.000
0.000
0.516
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
Upper
Bound
7.421
8.568
0.981
2.172
4.619
47.846
51.449
57.734
66.390
69.369
76.880
46.824
50.219
58.093
2.311
2.442
2.576
2.711
2.752
2.848
2.061
2.232
2.495
1.440
1.669
1.910
2.161
2.238
2.421
1.024
1.161
1.434
A-96
-------
i A-7 Air Quality Simulations
2 A-7.1 Introduction
3 Every location across the U.S. meets the current NC>2 annual standard (US EPA, 2007e).
4 Even considering air quality data as far back as 1995, no location/monitoring site exceeded the
5 current standard. Therefore, simulation of air quality data was required to evaluate just meeting
6 the current standard or standards that are more stringent.
7
8 In developing a simulation approach to adjust air quality to meet a particular standard level,
9 policy-relevant background (PRB) levels in the U.S. were first considered. Policy-relevant
10 background is defined as the distribution of NO2 concentrations that would be observed in the
11 U. S. in the absence of anthropogenic (man-made) emissions of NO2 precursors in the U. S.,
12 Canada, and Mexico. Estimates of PRB have been reported in the draft ISA (Section 1.5.5) and
13 the Annex (AX2.9), and for most of the continental U.S. the PRB is estimated to be less than 300
14 parts per trillion (ppt). In the Northeastern U.S. where present-day NC>2 concentrations are
15 highest, this amounts to a contribution of about 1% percent of the total observed ambient NC>2
16 concentration (AX2.9). This low contribution of PRB to NC>2 concentrations provides support
17 for a proportional method to adjust air quality, i.e., an equal adjustment of air quality values
18 across the entire air quality distribution to just meet a target value.
19
20 Next, the variability in NC>2 concentrations was evaluated to determine whether a
21 proportional approach would be reasonable if applied broadly across all years of data. Since the
22 adjustment factor to meet the current standard would likely increase with increasing year, it was
23 of interest to determine the trend in both the hourly concentrations and variability by year.
24 Figure A-100 presents a summary of the annual average and hourly mean concentrations, as well
25 as the coefficient of variation (COV, standard deviation as a percent of the mean) for each
26 respective mean. Sample size for the annual average concentrations was about 350 per year,
27 while hourly concentrations numbered about 3 million per year.
28
29 As expected, there was no observed difference in the mean concentrations when comparing
30 each concentration metric within a year. The mean of the annual averages of all monitors is
31 nearly identical to the mean of the hourly concentrations. However, statistically significant
32 decreases in concentration are evident from year-to-year (p<0.0001), with concentrations
33 decreasing by about 30% across the monitoring period. Contrary to this, there is no apparent
34 trend in the COV for the annual average concentrations across the 12 years of data, generally
35 centered about 53%. The COV of the hourly concentrations is larger than the annual COV as
36 expected, however it increases with increasing year. The hourly COV ranges from a low of 84%
37 in 1998 to a high of 92% in 2006, amounting to a relative percent difference of only 10% across
38 the entire monitoring period. A non-parametric Mann-Whitney U-test indicates that there is a
39 significant difference in the COVs when comparing each year-group (p=0.004). This may result
40 in a small upward bias in the number of estimated exceedances of short-term (1-hour) potential
41 health benchmark levels if using a proportional roll-up on the more recent monitoring data
42 relative to that estimated by rolling up the historic data to just meet the current standard. While
43 the trend of increasing COV is apparent across the entire monitoring period, based on the limited
44 difference in COV from year-to-year for both the annual and hourly concentration data within
A-97
-------
1 each year-group (each is <4%), it is concluded that a proportional method could be broadly
2 applied to each data set.
20
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
19-
18
17
O
'
16
0) ._
O 15
O
O
o 14
13-
12
11
10
-O'
-•—Annual Mean
-•—Hourly Mean
-D--COV-Annual
-o--COV-Hourly
-.o-
B- 0 ,
-Q-..
'••o-
100
50
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Year
O
-- 95
-- 90
-85
80
-- 75
+ 70 .S
1
65 O
-- 60
-- 55
Figure A-100. Trends in hourly and annual average NO2 ambient monitoring concentrations and their
associated coefficients of variation (COV) for all monitors, years 1995-2006.
A-7.2 Approach
For the air quality characterization, data were first separated into two groups, an historic set
of monitoring data (1995-2000) and one containing the most recent air quality (2001-2006).
This grouping would further reduce any potential influential monitoring data affecting the
variability in hourly concentrations that may exist in one year to the next within a location. The
following air quality scenarios were considered for these sets of data:
• "as is" representing the historical and recent ambient monitoring hourly concentration
data as reported by US EPA's Air Quality System (AQS).
• "simulated" concentrations to just meet the current NC>2 NAAQS (53 ppb annual
average).
Based on the form of the current standard and observed trends in ambient monitoring, such as
the retention of similar hourly and annual COVs over time while annual average concentrations
significantly decrease over the same time period, NC>2 concentrations were proportionally
modified at each location using the maximum annual average concentration that occurred in each
A-98
-------
1 year. To just meet the current standard adjustment factors F for each location (/') and year (/)
2 were derived by the following
O
4 ^=53/Cmax,y equation (1)
5
6 where,
7
8 FJJ = Adjustment factor (unitless)
9 Cmax,ij = Maximum annual average NC>2 concentration at a monitor in a location / (ppb)
10
11 Values for each air quality adjustment factor used for each location to simulate just meeting
12 the current standard are given in Tables A-105 and A-106. It should be noted that a different
13 monitor could have been used for each year to estimate F, the selection dependent only on
14 whether the monitor contained the highest annual concentration for that year in the particular
15 location. For each location and calendar year, all the hourly concentrations were multiplied by
16 the same constant value F to make the highest annual mean equal to 53 ppb for that location and
17 year. For example, for Boston in 1995, the maximum annual mean was 30.5 ppb, giving an
18 adjustment factor ofF= 53/30.5 = 1.74 using equation 1. All hourly concentrations in Boston in
19 1995 were multiplied by 1.74. Then, using the adjusted hourly concentrations, the distributions
20 of the annual means and annual number of exceedances are computed in the same manner as the
21 as-is scenario.5
22
23 Following review of the NC>2 ISA and summarization of relevant epidemiological and
24 clinical health studies, alternative NC>2 standards of differing averaging time, form, and level
25 were also considered. Much of the discussion regarding the selection of each of these
26 components of the standard is provided in Chapter 5 of the 2nd draft NO2 REA, with only the
27 broad conclusions provided here. For averaging time, the epidemiological evidence does not
28 provide clear guidance in choosing between 1-hour and 24-hour averaging times, and given that
29 the experimental literature provides support for the occurrence of effects following exposures of
30 shorter duration than 24-hours (e.g., 1-hour), staff evaluated standards with 1-hour averaging
31 times. For the form, we have focused on standards with statistical, concentration-based forms.
32 Staff selected the 98th and 99th percentiles averaged over 3 years to balance the desire to provide
33 a stable regulatory target with the desire to limit the occurrence of peak concentrations.
34 Concentration levels ranging from 50 ppb to 200 ppb in increments of 50 ppb were selected by
35 staff based largely on the observed concentrations from both epidemiologic and controlled
36 human exposure studies. Based on these criteria for the investigated alternative standards, the
37 following scenarios were considered using the most recent years of data (i.e., 2001-2006) and
38 divided into two periods of analysis (years 2001-2003 and 2004-2006):
39
40 • "as is" representing the recent ambient monitoring hourly concentration data as reported
41 by US EPA's Air Quality System (AQS).
5 Because of the large database, we did not implement this procedure exactly as stated. For the annual means we
computed and applied the adjustment factors directly to each annual mean. For the hourly concentrations we used
the frequency distributions of the rounded hourly values, so that, in effect, we applied the adjustment factors to the
hourly values after rounding them to the nearest integer. This has a negligible impact on the calculated number of
exceedances.
A-99
-------
1 • "simulated" concentrations to just meet the current NC>2 NAAQS (53 ppb annual average
2 as described above) and alternative 1-hour standards.
O
4 Based on the averaging time and form of the alternative standards, ambient NC>2
5 concentrations were proportionally modified at each monitor using the maximum monitor
6 percentile (98th or 99th) averaged across each three year group. To just meet each of the four
7 alternative levels, the eight adjustment factors F for each location (/') and year-group (/) were
8 derived by the following
9
10 Fti = SIC%^ equation (2)
11
12 where,
13
14 Fy = Adjustment factor (unitless)
15 S = Alternative standard level (50, 100, 150, 200 ppb 1-hour concentration)
16 C%ue,ij = Maximum 98th or 99th percentile 1-hour NC>2 concentration at a monitor in a
17 location /' (ppb)
18
19 Values for each air quality adjustment factor used for each location and year-group to
20 simulate just meeting the alternatives standards are given in Tables A-107 and A-108. It should
21 be noted that a different monitor could have been used for each year group to estimate F, the
22 selection dependent only on whether the monitor contained the highest 98th or 99th 1-hour
23 concentration averaged across the three year period in the particular location. For each location
24 and year-group, all monitor hourly concentrations were multiplied by the same constant value F,
25 whereas the monitor with the maximum averaged 98th or 99th percentile containing a three year
26 average concentration at those same percentiles equivalent to the level of the alternative
27 standard. For example, for Atlanta in years 2001-2003, the maximum 3-year average 98th
28 percentile was 57 ppb, giving an adjustment factor F = 200/57 = 3.509 for the 1 -hour alternative
29 standard level of 200 ppb using equation (2). All hourly concentrations in Atlanta for each year
30 in 2001-2003 were multiplied by 3.509. Then, using the adjusted hourly concentrations, the
31 distributions of the annual number of exceedances are computed in the same manner as the as-is
32 scenario.
A-100
-------
1 Table A-105. Maximum annual average NO2 concentrations and air quality adjustment factors (F) to just
2 meet the current standard, historic monitoring data.
Location
Boston
Chicago
Cleveland
Denver
Detroit
Los Angeles
Miami
New York
Philadelphia
Washington DC
Atlanta
Colorado Springs
El Paso
Jacksonville
Las Vegas
Phoenix
Provo
St. Louis
Other CMSA
Not MSA
Metric
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
1995
30.5
1.74
32.2
1.64
27.3
1.94
34.8
1.52
21.6
2.45
46.2
1.15
14.7
3.60
41.7
1.27
31.8
1.67
26.2
2.02
18.8
2.81
23.2
2.28
23.3
2.27
15.8
3.36
27.1
1.96
32.6
1.63
22.6
2.35
26.2
2.02
31.9
1.66
19.1
2.78
1996
31.0
1.71
32.0
1.66
25.9
2.04
33.1
1.60
21.5
2.47
42.3
1.25
16.0
3.30
42.2
1.26
33.9
1.56
26.9
1.97
26.6
1.99
23.6
2.24
35.1
1.51
14.9
3.55
26.7
1.99
31.6
1.68
24.3
2.18
24.8
2.14
30.3
1.75
14.5
3.66
1997
30.4
1.74
33.6
1.58
28.1
1.89
33.9
1.56
25.9
2.05
43.2
1.23
16.6
3.19
41.1
1.29
32.4
1.63
25.9
2.05
25.2
2.10
19.8
2.68
33.6
1.58
14.4
3.69
32.0
1.66
23.3
2.27
24.8
2.14
29.4
1.80
19.7
2.69
1998
30.7
1.73
32.2
1.64
27.3
1.94
35.3
1.50
22.9
2.31
43.4
1.22
15.2
3.49
41.9
1.26
34.0
1.56
27.2
1.95
24.1
2.20
20.5
2.59
30.7
1.72
15.0
3.52
25.3
2.09
35.0
1.52
23.9
2.22
25.8
2.05
31.0
1.71
18.8
2.82
1999
29.7
1.79
31.5
1.68
24.5
2.16
19.4
2.73
18.0
2.94
50.6
1.05
16.8
3.15
41.5
1.28
31.7
1.67
25.4
2.09
23.8
2.22
19.3
2.75
27.7
1.91
15.9
3.34
26.6
1.99
40.5
1.31
24.1
2.20
27.2
1.95
29.3
1.81
19.7
2.69
2000
29.0
1.83
32.0
1.66
23.1
2.30
14.9
3.55
23.9
2.22
43.9
1.21
15.7
3.37
40.6
1.31
27.9
1.90
23.5
2.26
22.9
2.31
34.8
1.52
24.3
2.18
15.4
3.45
25.1
2.12
36.3
1.46
23.6
2.25
26.3
2.02
26.5
2.00
18.7
2.83
A-101
-------
1 Table A-106. Maximum annual average NO2 concentrations and air quality adjustment factors (F) to just
2 meet the current standard, recent monitoring data.
Location
Boston
Chicago
Cleveland
Denver
Detroit
Los Angeles
Miami
New York
Philadelphia
Washington DC
Atlanta
Colorado Springs
El Paso
Jacksonville
Las Vegas
Phoenix
Provo
St. Louis
Other CMSA
Not MSA
Metric
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
Max Annual Mean
F
2001
29.7
1.79
31.9
1.66
23.6
2.25
36.8
1.44
23.2
2.29
41.2
1.29
15.8
3.35
40.3
1.32
29.9
1.77
24.3
2.18
23.3
2.27
21.7
2.45
22.5
2.35
37.1
1.43
24.1
2.20
24.7
2.15
26.5
2.00
16.5
3.21
2002
25.3
2.10
32.4
1.63
22.3
2.38
35.4
1.50
21.4
2.47
40.2
1.32
14.3
3.71
39.7
1.33
29.5
1.80
24.8
2.14
19.4
2.73
21.4
2.48
14.6
3.62
22.3
2.38
34.7
1.53
24.8
2.14
22.9
2.32
27.4
1.93
16.4
3.23
2003
22.5
2.36
30.9
1.72
21.7
2.45
21.4
2.47
22.0
2.41
35.3
1.50
12.9
4.12
32.0
1.65
24.7
2.15
26.0
2.04
16.4
3.23
19.9
2.66
14.3
3.70
21.4
2.48
34.3
1.54
21.8
2.43
20.3
2.60
26.4
2.01
15.5
3.42
2004
25.0
2.12
29.3
1.81
22.2
2.38
27.2
1.95
18.9
2.80
33.7
1.57
13.0
4.08
30.5
1.74
25.6
2.07
24.0
2.20
17.0
3.12
18.0
2.94
13.7
3.88
19.7
2.69
31.4
1.69
22.3
2.37
22.3
2.37
25.3
2.09
15.8
3.36
2005
23.4
2.26
29.6
1.79
21.5
2.46
27.6
1.92
19.6
2.71
30.9
1.72
13.5
3.92
36.5
1.45
26.3
2.02
24.1
2.20
17.4
3.05
17.3
3.06
13.3
3.97
19.9
2.67
31.5
1.68
20.5
2.58
16.8
3.15
24.0
2.21
17.1
3.11
2006
22.5
2.35
30.6
1.73
18.2
2.91
29.1
1.82
15.9
3.34
29.7
1.78
34.2
1.55
17.8
2.98
19.6
2.70
17.9
2.96
18.0
2.94
30.6
1.73
28.9
1.83
15.0
3.52
18.5
2.87
15.6
3.39
A-102
-------
1
2
3
Table A-107. Air quality adjustment factors (F) to just meet the alternative 1-hour standards, using recent
monitoring data.
Year Group
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
Location
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
1-hour
Standard
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
98th Percentile
Maximum
Monitor
2502500401
2502500401
2502500401
2502500401
1703100631
1703100631
1703100631
1703100631
3903500601
3903500601
3903500601
3903500601
0803100021
0803100021
0803100021
0803100021
2616300161
2616300161
2616300161
2616300161
0603700301
0603700301
0603700301
0603700301
1208640022
1208640022
1208640022
1208640022
3403900042
3403900042
3403900042
3403900042
4210100471
4210100471
4210100471
4210100471
1100100251
1100100251
1100100251
1100100251
1312100481
1312100481
1312100481
Adjustment
Factor1
0.955
1.911
2.866
3.822
0.769
1.538
2.308
3.077
0.974
1.948
2.922
3.896
0.741
1.481
2.222
2.963
0.962
1.923
2.885
3.846
0.581
1.163
1.744
2.326
1.271
2.542
3.814
5.085
0.721
1.442
2.163
2.885
0.877
1.754
2.632
3.509
0.926
1.852
2.778
3.704
0.877
1.754
2.632
99th Percentile
Maximum
Monitor
2502500401
2502500401
2502500401
2502500401
1703100631
1703100631
1703100631
1703100631
3903500601
3903500601
3903500601
3903500601
0803100021
0803100021
0803100021
0803100021
2616300161
2616300161
2616300161
2616300161
0603700301
0603700301
0603700301
0603700301
1208640022
1208640022
1208640022
1208640022
3403900042
3403900042
3403900042
3403900042
4210100471
4210100471
4210100471
4210100471
1100100431
1100100431
1100100431
1100100431
1312100481
1312100481
1312100481
Adjustment
Factor1
0.867
1.734
2.601
3.468
0.708
1.415
2.123
2.830
0.877
1.754
2.632
3.509
0.662
1.325
1.987
2.649
0.838
1.676
2.514
3.352
0.505
1.010
1.515
2.020
1.154
2.308
3.462
4.615
0.661
1.322
1.982
2.643
0.820
1.639
2.459
3.279
0.847
1.695
2.542
3.390
0.785
1.571
2.356
A-103
-------
Year Group
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
Location
Atlanta
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Cleveland
1-hour
Standard
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
98th Percentile
Maximum
Monitor
1312100481
4814100441
4814100441
4814100441
4814100441
1203100322
1203100322
1203100322
1203100322
3200305391
3200305391
3200305391
3200305391
0401330101
0401330101
0401330101
0401330101
4904900021
4904900021
4904900021
4904900021
2951000861
2951000861
2951000861
2951000861
4905700021
4905700021
4905700021
4905700021
0602500061
0602500061
0602500061
0602500061
2502500021
2502500021
2502500021
2502500021
1703100631
1703100631
1703100631
1703100631
3903500601
Adjustment
Factor1
3.509
0.932
1.863
2.795
3.727
1.250
2.500
3.750
5.000
0.926
1.852
2.778
3.704
0.728
1.456
2.184
2.913
0.993
1.987
2.980
3.974
1.000
2.000
3.000
4.000
0.649
1.299
1.948
2.597
1.000
2.000
3.000
4.000
1.064
2.128
3.191
4.255
0.785
1.571
2.356
3.141
1.034
99th Percentile
Maximum
Monitor
1312100481
4814100441
4814100441
4814100441
4814100441
1203100322
1203100322
1203100322
1203100322
3200305391
3200305391
3200305391
3200305391
0401330101
0401330101
0401330101
0401330101
4904900021
4904900021
4904900021
4904900021
2951000861
2951000861
2951000861
2951000861
4905700021
4905700021
4905700021
4905700021
0602500061
0602500061
0602500061
0602500061
2502500401
2502500401
2502500401
2502500401
1703100631
1703100631
1703100631
1703100631
3903500601
Adjustment
Factor1
3.141
0.843
1.685
2.528
3.371
1.124
2.247
3.371
4.494
0.852
1.705
2.557
3.409
0.682
1.364
2.045
2.727
0.920
1.840
2.761
3.681
0.898
1.796
2.695
3.593
0.552
1.105
1.657
2.210
0.852
1.705
2.557
3.409
0.971
1.942
2.913
3.883
0.714
1.429
2.143
2.857
0.949
A-104
-------
Year Group
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
Location
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
1-hour
Standard
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
98th Percentile
Maximum
Monitor
3903500601
3903500601
3903500601
0803100021
0803100021
0803100021
0803100021
2616300161
2616300161
2616300161
2616300161
0603711031
0603711031
0603711031
0603711031
1208640022
1208640022
1208640022
1208640022
3403900042
3403900042
3403900042
3403900042
4210100471
4210100471
4210100471
4210100471
1100100431
1100100431
1100100431
1100100431
1312100481
1312100481
1312100481
1312100481
4814100551
4814100551
4814100551
4814100551
1203100322
1203100322
1203100322
1203100322
3200305391
3200305391
Adjustment
Factor1
2.069
3.103
4.138
0.904
1.807
2.711
3.614
1.145
2.290
3.435
4.580
0.785
1.571
2.356
3.141
1.205
2.410
3.614
4.819
0.800
1.600
2.400
3.200
0.971
1.942
2.913
3.883
0.993
1.987
2.980
3.974
0.943
1.887
2.830
3.774
1.027
2.055
3.082
4.110
1.282
2.564
3.846
5.128
1.020
2.041
99th Percentile
Maximum
Monitor
3903500601
3903500601
3903500601
0800130011
0800130011
0800130011
0800130011
2616300161
2616300161
2616300161
2616300161
0603711031
0603711031
0603711031
0603711031
1208640022
1208640022
1208640022
1208640022
3403900042
3403900042
3403900042
3403900042
4210100043
4210100043
4210100043
4210100043
1100100431
1100100431
1100100431
1100100431
1312100481
1312100481
1312100481
1312100481
4814100551
4814100551
4814100551
4814100551
1203100322
1203100322
1203100322
1203100322
3200305391
3200305391
Adjustment
Factor1
1.899
2.848
3.797
0.829
1.657
2.486
3.315
1.042
2.083
3.125
4.167
0.711
1.422
2.133
2.844
1.053
2.105
3.158
4.211
0.730
1.460
2.190
2.920
0.901
1.802
2.703
3.604
0.920
1.840
2.761
3.681
0.847
1.695
2.542
3.390
0.943
1.887
2.830
3.774
1.099
2.198
3.297
4.396
0.962
1.923
A-105
-------
Year Group
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
2004-2006
Location
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other
MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
1-hour
Standard
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
98th Percentile
Maximum
Monitor
3200305391
3200305391
0401330101
0401330101
0401330101
0401330101
4904900021
4904900021
4904900021
4904900021
2951000722
2951000722
2951000722
2951000722
4903530061
4903530061
4903530061
4903530061
4900500041
4900500041
4900500041
4900500041
Adjustment
Factor1
3.061
4.082
0.781
1.563
2.344
3.125
0.610
1.220
1.829
2.439
1.020
2.041
3.061
4.082
0.847
1.695
2.542
3.390
0.980
1.961
2.941
3.922
99th Percentile
Maximum
Monitor
3200305391
3200305391
0401330101
0401330101
0401330101
0401330101
4904900021
4904900021
4904900021
4904900021
2951000722
2951000722
2951000722
2951000722
0607320071
0607320071
0607320071
0607320071
0602500051
0602500051
0602500051
0602500051
Adjustment
Factor1
2.885
3.846
0.725
1.449
2.174
2.899
0.573
1.145
1.718
2.290
0.962
1.923
2.885
3.846
0.758
1.515
2.273
3.030
0.909
1.818
2.727
3.636
Notes:
1 The selected percentile (98th or 99th) in 1-hour concentration at each monitor was averaged across the
3-years of data (either 2001 -2003 or 2004-2006), with the highest concentration monitor retained for use
in calculating the adjustment to just meet the alternative standard.
A-106
-------
2
3 A-8 Method for Estimating On-Road Concentrations
4 A-8.1 Introduction
5 As an additional step in the air quality characterization, the potential impact of motor
6 vehicles on the surrogate exposure metrics was evaluated. Several studies have shown that
7 concentrations of NC>2 are at elevated levels when compared to ambient concentrations measured
8 at a distance from the roadway (e.g., Rodes and Holland, 1981; Gilbert et al., 2003; Cape et al.,
9 2004; Pleijel et al., 2004; Singer et al., 2004). On average, concentrations on or near a roadway
10 are from 1.5 to 2 times greater than ambient concentrations (US EPA, 2007f), but on occasion, as
11 high as 7 times greater (Bell and Ashenden, 1997; Bignal et al., 2007). A strong relationship
12 between measured on-road NC>2 concentrations and those with increasing distance from the road
13 has been reported under a variety of conditions (e.g., variable traffic counts, different seasons,
14 wind direction) and can be described (e.g., Cape et al., 2004) with an exponential decay equation
15 of the form
16
17 Cx =Ch + Cv£Tfa equation (3)
18 where,
19
20 Cx = NC>2 concentration at a given distance (x) from a roadway (ppb)
21 Cb = NC>2 concentration (ppb) at a distance from a roadway, not directly influenced
22 by road or non-road source emissions
23 Cv = NC>2 concentration contribution from vehicles on a roadway (ppb)
24 k = Rate constant describing NC>2 combined formation/decay with perpendicular
25 distance from roadway (meters'1)
26 x = Distance from roadway (meters)
27
28 As a function of reported concentration measures and the derived relationship, much of the
29 decline in NC>2 concentrations with distance from the road has been shown to occur within the
30 first few meters (approximately 90% within 10 meter distance), returning to near ambient levels
31 between 200 to 500 meters (Rodes and Holland, 1981; Bell and Ashenden, 1997; Gilbert et al.,
32 2003; Pleijel et al., 2004). At a distance of 0 meters, referred to here as on-road, the equation
33 reduces to the sum of the non-source influenced NC>2 concentration and the concentration
34 contribution expected from vehicle emissions on the roadway using
35
36 Cr=Ca(l+m) equation (4)
37 where,
38
39 Cr = 1-hour on-road NC>2 concentration (ppb)
40 Ca = 1-hour ambient monitoring NO2 concentration (ppb) either as is or modified to
41 just meet the current standard
42 m = Modification factor derived from estimates of Cv/Cb (from eq (1))
43
A-107
-------
1
2
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
and assuming that Ca = Cb. 6
A-8.2 Derivation of On-Road Factors
A literature review was conducted to identify published studies containing NC>2
concentrations both on-roads and with various distances from roadways. Principal criteria for
inclusion in this analysis were that either tabular, graphical, or equations were provided in the
paper that related distances from roadways and associated NC>2 concentrations. Eleven papers
were identified using these criteria, spanning several countries, various time periods, roadway
locations, seasons, and wind direction (Table A-108). The final data set contained 501 data
points, encompassing multiple NC>2 measurements from a total of 56 individual roads.
Table A-108. Reviewed studies containing NO2 concentrations at a distance from roadways.
First Author
Bell
Bignal
Cape
Gilbert
Maruo
Monn
Nitta
Pleijel
Rodes
Roorda-Knape
Singer
Year
1987
2004
2002
2001
2001
1995
1982
1994
1978
1995
2001
Country/State
Wales
England
Scotland
Quebec
Japan
Switzerland
Japan
Sweden
California
Holland
California
Season
Summer, winter
Summer, fall
Annual
Summer
Summer
Summer, Winter
Not reported
Summer
Summer
Summer
Spring through fall
Type
Rural
Urban
Urban
Urban
Urban
Urban
Urban
Rural
Urban
Urban
Urban
Wind Direction
Up, down
Combined
Combined
Down, up, combined
Combined
Combined
Combined
Combined
Down
Combined
Up, Down
Although there were, on occasion, several roads within a particular study, data for factors
thought to influence on-road concentrations were very limited or were not distinct for all studies.
The relationship noted in eq (1) was 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;
model Cr=Cb + Cv*exp (-k*x);
by author road season wind;
output out=outdata parms=Cb Cv k;
run ;
The procedure was run for all individual roads identified within each study location. 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 (n=5)
Note that Ca differs from Cb since Ca may include the influence of on-road as well as non-road sources. However,
it is expected mat for most monitors the influence of on-road emissions is minimal so mat Ca = C6.
A-108
-------
1 • k<0 (n=l)
2 • k>\ (n=2)
3 • Both k=0 and Cv =0 (n=l)
4 • Extremely large Cv (>8,000 ppb; n=2)
5 • Cb<0 (n=l)
6
7 Data were evaluated for trends using available influential factors and considering the number
8 of samples available for potential groupings. In general, the measurements reported in the
9 summer and resultant parameter estimates were observed as distinct from the measures and
10 parameter estimates from other seasons. The data were then grouped accordingly into two
11 seasonal groups, summer and not summer, containing 23 and 21 samples, respectively. These
12 two groups were also censored for any unusual parameter estimates. Resulting criteria for
13 censoring the grouped data included the following:
14
15 • Extreme value of & compared with others in group (n=l)
16 • Extreme values of estimated m due to combined low estimated Cb relative to high
17 estimated Cv (n=2)
18
19 Two approaches were considered for estimating m from the Cv and Cb pairs in each season.
20 The first approach was to regress Cb on Cv (either with or without an intercept) and use the fitted
21 slope to estimate m. Ignoring meteorological effects, Equation 1 implies that Cv results solely
22 from on-road emission sources and that Cb results solely from non-road emission sources. Since
23 these two source types are likely to have quite different diurnal profiles, we expect the hourly Cv
24 and Cb values to be approximately independent.7 Regressing Cb against Cv would imply that
25 there is some correlation between the values, which would be inconsistent with the conceptual
26 model underlying Equation 1. Further, if Cb were regressed against Cv using an intercept, the
27 physical meaning of the intercept would be unclear.
28
29 An empirical method was selected for the approach to estimate m based on the two seasonal
30 sets of ratios of Cv/Cb. The resulting distribution for each group is presented in Figure A-101.
31 Neither group could be assigned to a particular distribution (e.g., normal, lognormal, exponential,
32 gamma). Means from the two seasons were tested for significant difference using a Student's t
33 (p=0.026), while the season distributions were compared using a Kolmogorov-Smirnov test (p=
34 0.196). It was decided to retain the groups as separate to allow for some apportioning of
35 variability resulting from an apparent seasonal influence, even though the statistical test results
36 were mixed.
37
38
7 Although the fact that Cv and Cb are subject to the same meteorology introduces some correlation, because
meteorology tends to vary on a longer time scale than hourly, it is likely to have less influence than the emissions on
the correlation between hourly concentrations.
A-109
-------
1 UU /O "
90%
80%
70%
:=
g 60%
O
Q.
2 site-seasons.
A-110
-------
1
2 A particular summer on-road factor has a 1/22 chance of selection, while a specific not
3 summer value has a 1/19 probability of selection, based on respective sample sizes. This random
4 assignment was repeated for all site-years of data. Hourly NC>2 concentrations were estimated
5 for each site-year of data in a location using equation (4) and the randomly assigned on-road
6 factors. Finally, the process was simulated 100 times for each site-year of hourly data. For
7 example, the Boston CMS A location had 210 random selections from the on-road distributions
8 applied independently to the total site-years of data (105). Following 100 simulations, a total of
9 10,500 site-years of data were generated using this procedure (along with 21,000 randomly
10 assigned on-road values selected from the appropriate empirical distribution).
11
12 Simulated on-road NC>2 concentrations were used to generate concentration distributions for
13 the annual average concentrations and distributions for the number of exceedances of short-term
14 potential health effect benchmark levels. Means and median values are reported to represent the
15 central tendency of each parameter estimate. Since there were multiple simulations performed at
16 each location using all available site-years of data, results for the upper percentiles were
17 expanded to the 95th, 98th and 99th percentiles of the distribution, rather than estimate a 95%
18 interval as was done above for the non-road scenarios. It is more appropriate to apply the
19 parameter estimates outside the central tendencies to particular sites, areas within locations, or
20 for certain conditions. Minimum values for the annual mean and annual number of exceedances
21 were also estimated. One approach would have been to use the minimum values across the 100
22 simulations. However, that approach may not give the lowest possible value, because it is
23 unlikely that in 100 simulations for a site-year there is a simulation where both seasonal
24 adjustment factors are chosen to be the lowest values of 1 + m. To obtain the lowest value, two
25 simulations were conducted for each site-year. The Summer seasonal adjustment factor was set
26 to the lowest possible value (1.49) and the Not-Summer seasonal adjustment factor was the
27 lowest possible value (1.22). The annual means and exceedances for those two separate
28 simulations were used to compute the minimum values for each distribution.
29
30 As part of the air quality characterization, these data were used to estimate the number of
31 short-term concentrations above selected levels that might occur on roadways using the
32 estimated hourly Cr values, associated with air quality as is. For evaluating just meeting the
33 current annual and alternative standards, the approach described in Section A-7 to adjust the
34 ambient concentrations was applied before estimating on-road NC>2 concentrations.
35 A-8.4 Interpretation of Estimated On-Road Concentrations
36 The simulated on-road concentrations are estimates of what might occur on or near
37 roadways. The algorithm is not designed to estimate concentrations on a particular roadway, all
38 roads, or to estimate on-road exposures in a location. The algorithm assumes that the monitor is
39 measuring the concentrations that would be observed at a distance of a particular road; monitor
40 data within close proximity of a major road (>100m) have been screened out, likely controlling
41 any potential influence from major roads. It then follows that the monitors within a location are
42 linked proportionally to the distribution of roads (and types) in a location. This is likely not the
43 case, particularly in locations with few monitoring sites, therefore available monitors will likely
44 be either over- or under-representative of some roadway types.
45
A-111
-------
1 The simulation is designed to estimate the potential concentrations associated with potential
2 on-road exposures, developing central tendencies and bounds to be interpreted qualitatively with
3 the expected emissions that would occur on-roads within a location. That is, the higher-traveled
4 roadways would be better represented by on-road concentration estimates at the upper tails of the
5 distribution, while other roads with less traffic density would be better represented at the lower
6 tails of the distribution. Additional consideration should be given to where few monitor sites
7 were available in a location, or even where monitor sites are more densely distributed within a
8 particular area of a location, before interpreting estimated concentrations.
A-112
-------
i A-9 Supplemental Results Tables
2 A-9.1 Results Tables of Historic NO2 Ambient Monitoring Data (1995-2000) Adjusted to Just
3 Meeting the Current Standard
4 Table 109. Number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 historic NO2 air quality
5 adjusted to Just meeting the current annual average standard (0.053 ppm) using monitors sited >100 m of a major road.
Location
Boston
Chicago
Cleveland
Denver
Detroit
Los Angeles
Miami
New York
Philadelphia
Washington
Atlanta
Colorado
Springs
El Paso
Jacksonville
Las Vegas
Phoenix
Provo
St, Louis
Other
MSA/CMSA
Other Not
MSA
Exceedances of 150 ppb 1
mean
1
1
2
141
75
9
72
1
2
9
42
50
16
122
3
8
16
4
2
20
min
0
0
0
1
2
0
4
0
0
0
0
0
1
82
0
0
2
0
0
0
med
0
1
1
12
65
2
91
0
0
3
2
3
9
137
1
5
4
1
0
0
p95
7
5
7
648
162
56
133
4
10
34
197
283
69
147
11
26
71
16
13
116
P98
7
7
7
648
162
83
133
7
18
38
233
318
69
147
11
26
71
16
28
241
p99
7
7
7
648
162
96
133
7
18
38
233
318
69
147
11
26
71
16
40
336
Exceedances of 200 ppb 1
mean
0
0
0
24
13
1
10
0
0
1
4
32
2
12
0
0
1
1
0
4
min
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
med
0
0
0
2
13
0
10
0
0
0
0
0
1
15
0
0
0
0
0
0
p95
1
1
0
141
25
4
27
0
0
3
19
180
14
20
1
4
5
15
1
18
p98
1
1
0
141
25
6
27
2
12
4
21
241
14
20
1
4
5
15
3
53
p99
1
1
0
141
25
8
27
2
12
4
21
241
14
20
1
4
5
15
6
87
Exceedances of 250 ppb 1
mean
0
0
0
5
4
0
1
0
0
0
0
16
0
2
0
0
0
1
0
1
min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
med
0
0
0
1
2
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
p95
1
0
0
28
15
1
6
0
0
2
2
123
2
7
0
0
0
14
0
4
p98
1
0
0
28
15
2
6
0
9
3
3
135
2
7
0
0
0
14
1
15
p99
1
0
0
28
15
2
6
0
9
3
3
135
2
7
0
0
0
14
1
42
Exceedances of 300 ppb 1
mean
0
0
0
2
2
0
0
0
0
0
0
8
0
0
0
0
0
1
0
1
min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
med
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
9
10
0
2
0
0
1
1
72
0
1
0
0
0
13
0
1
p98
0
0
0
9
10
1
2
0
5
2
1
83
0
1
0
0
0
13
0
8
p99
0
0
0
9
10
2
2
0
5
2
1
83
0
1
0
0
0
13
1
21
Notes:
1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular location divided by the number of site-
years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95th, 98th, and 99th percentiles of the distribution for
he number of exceedances in any one year within the monitoring period.
A-113
-------
1
2
3
Table 110. Number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 historic NO2 air quality
adjusted to Just meeting the current annual average standard (0.053 ppm) using monitors sited <100 m of a major road.
Location
Boston
Chicago
Cleveland
Denver
Los
Angeles
Miami
New York
Philadelphia
Washington
DC
Colorado
Springs
El Paso
Las Vegas
Phoenix
St, Louis
Exceedances of 150 ppb 1
mean
2
4
35
12
8
70
1
5
12
7
23
47
77
2
min
0
0
9
0
0
2
0
0
0
7
5
0
0
0
med
0
2
16
0
0
56
0
3
9
7
24
25
9
1
p95
11
16
110
77
42
161
6
26
47
7
36
226
339
11
P98
22
16
110
77
56
161
10
26
61
7
36
226
339
13
p99
22
16
110
77
79
161
10
26
61
7
36
226
339
13
Exceedances of 200 ppb 1
mean
0
0
5
1
1
9
0
0
1
2
6
6
32
0
min
0
0
0
0
0
0
0
0
0
2
0
0
0
0
med
0
0
1
0
0
7
0
0
0
2
7
1
1
0
p95
1
0
24
10
6
34
1
3
9
2
13
28
198
1
p98
2
0
24
10
8
34
3
3
17
2
13
28
198
1
p99
2
0
24
10
9
34
3
3
17
2
13
28
198
1
Exceedances of 250 ppb 1
mean
0
0
2
0
0
2
0
0
0
1
2
3
12
0
min
0
0
0
0
0
0
0
0
0
1
0
0
0
0
med
0
0
0
0
0
0
0
0
0
1
1
0
0
0
p95
0
0
10
5
0
15
0
1
0
1
6
13
92
0
p98
1
0
10
5
1
15
3
1
3
1
6
13
92
0
p99
1
0
10
5
2
15
3
1
3
1
6
13
92
0
Exceedances of 300 ppb 1
mean
0
0
1
0
0
1
0
0
0
1
0
1
4
0
min
0
0
0
0
0
0
0
0
0
1
0
0
0
0
med
0
0
0
0
0
0
0
0
0
1
0
0
0
0
p95
0
0
3
2
0
8
0
1
0
1
2
11
31
0
p98
1
0
3
2
0
8
1
1
2
1
2
11
31
0
p99
1
0
3
2
0
8
1
1
2
1
2
11
31
0
Notes:
1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular location divided by the number of site-
years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95 , 98th, and 99th percentiles of the distribution for
he number of exceedances in any one year within the monitoring period.
A-114
-------
1
2
3
Table A-lll. Number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year on-roads, 1995-2000 historic NO2 air
quality adjusted to Just meeting the current annual average standard (0.053 ppm).
Location
Boston
Chicago
Cleveland
Denver
Detroit
Los Angeles
Miami
New York
Philadelphia
Washington
Atlanta
Colorado
Springs
El Paso
Jacksonville
Las Vegas
Phoenix
Provo
St, Louis
Other
MSA/CMSA
Other Not
MSA
Exceedances of 150 ppb 1
mean
231
386
526
980
982
323
802
199
362
562
597
866
488
1381
348
811
1434
486
199
247
min
0
0
42
15
5
0
33
0
0
0
0
0
19
365
0
15
84
0
0
0
med
108
242
407
585
860
154
788
64
174
358
215
565
317
1328
47
605
1363
368
65
45
p95
930
1288
1305
2765
2413
1219
1637
950
1352
1843
2122
2666
1443
2485
1618
2493
3215
1402
858
1234
P98
1282
1609
1568
3021
2771
1555
1885
1251
1967
2409
2566
3106
2106
2677
2108
2818
3526
1630
1262
1771
p99
1394
1802
1762
3149
2882
1935
2043
1384
2536
2563
2778
3332
2391
3110
2908
2922
3729
1843
1572
2130
Exceedances of 200 ppb 1
mean
53
111
157
497
405
97
359
50
86
176
251
308
152
610
106
229
443
144
52
95
min
0
0
1
0
2
0
2
0
0
0
0
0
0
40
0
0
1
0
0
0
med
11
32
83
111
284
24
289
5
21
64
42
80
67
549
6
88
230
51
6
7
p95
299
498
457
2097
1227
427
985
313
400
721
1094
1348
545
1426
663
954
1643
523
268
549
p98
369
615
586
2304
1439
671
1201
475
689
949
1472
1792
997
1515
894
1293
1871
693
444
928
p99
390
707
700
2451
1589
865
1353
602
865
1073
1640
1902
1126
1801
1248
1375
2058
728
592
1203
Exceedances of 250 ppb 1
mean
14
36
51
254
175
32
159
14
24
60
106
123
54
263
38
63
135
46
15
39
min
0
0
0
0
2
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
med
1
2
13
26
97
4
95
0
2
9
7
11
16
195
1
12
32
9
0
1
p95
95
195
215
1467
576
158
550
103
125
316
535
574
186
773
318
304
543
232
84
221
p98
132
289
269
1695
776
264
683
175
245
411
843
803
440
839
526
436
697
289
156
438
p99
161
364
306
1930
872
366
797
230
341
478
947
934
485
1002
596
544
817
323
231
635
Exceedances of 300 ppb 1
mean
4
13
18
126
80
11
72
4
7
23
45
61
21
114
15
17
43
16
5
17
min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
med
0
0
1
12
40
0
26
0
0
1
1
1
6
66
0
2
2
0
0
0
p95
28
86
102
866
317
54
297
35
38
133
277
299
83
407
98
78
208
92
25
91
p98
52
153
131
1182
424
105
364
64
76
217
435
373
190
443
297
132
303
133
57
198
p99
65
196
149
1286
482
172
451
81
138
247
514
421
251
470
355
181
339
163
90
318
Notes:
1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular location divided by the number of site-
years across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95 h, 98th, and 99th percentiles of the distribution for
he number of exceedances in any one year within the monitoring period.
A-115
-------
A-116
-------
1
2
3
4
5
6
A-9.2 Results Tables of Recent NO2 Ambient Monitoring Data
(2001-2006) As Is and Just Meeting the Current and Alternative
Standards
Table A-112. Estimated annual average NO2 concentrations for monitors >100 m from a major road
following adjustment to just meeting the current and alternative standards, 2001-2003 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
6
6
6
6
6
6
6
6
6
6
9
9
9
9
9
9
9
9
9
9
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
Annual Mean (ppb)
Mean
10
19
9
8
18
16
27
25
36
33
22
36
17
15
33
31
50
46
66
61
18
42
17
16
35
31
52
47
69
62
24
45
17
16
35
31
52
47
Min
5
11
5
5
10
9
15
14
21
19
17
27
13
12
26
24
39
36
52
47
17
41
17
15
34
30
51
46
68
61
21
37
16
14
32
28
48
43
Med
11
21
10
9
21
19
31
28
42
38
20
34
15
14
31
28
46
42
62
57
17
42
17
15
34
31
51
46
68
61
24
45
17
16
35
31
52
47
p99
12
26
11
10
22
20
33
30
45
40
28
47
21
20
43
39
64
59
85
79
19
43
18
16
36
32
54
49
72
65
26
53
19
17
38
34
57
51
A-117
-------
Location
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Scenario
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Site-
Years
2
2
6
6
6
6
6
6
6
6
6
6
51
51
51
51
51
51
51
51
51
51
6
6
6
6
6
6
6
6
6
6
26
26
26
26
26
26
26
26
26
26
14
14
14
Annual Mean (ppb)
Mean
70
63
21
49
20
17
40
35
59
52
79
69
22
31
13
11
26
23
39
34
52
45
9
32
11
10
22
20
33
30
44
40
20
29
14
13
29
26
43
40
58
53
20
37
17
Min
64
57
19
44
18
16
36
31
54
47
71
62
5
7
3
2
6
5
8
7
11
10
7
26
9
8
17
16
26
24
35
32
11
15
8
7
16
15
24
22
32
29
15
26
13
Med
70
63
20
50
20
17
39
34
59
51
78
68
24
32
14
12
28
24
41
36
55
48
9
34
11
10
23
20
34
31
45
41
18
27
13
12
27
24
40
37
53
49
18
35
16
p99
76
68
23
53
22
19
45
39
67
58
89
78
37
52
22
19
43
38
65
57
87
75
10
37
13
12
26
23
39
35
51
47
31
44
23
21
45
41
68
62
90
82
28
53
25
A-118
-------
Location
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Scenario
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
14
14
14
14
14
14
14
18
18
18
18
18
18
18
18
18
18
14
14
14
14
14
14
14
14
14
14
12
12
12
12
12
12
12
12
12
12
2
2
2
2
2
2
2
2
Annual Mean (ppb)
Mean
16
34
32
52
48
69
64
18
39
17
16
34
31
51
47
68
62
12
33
11
10
22
20
33
29
44
39
15
38
14
13
28
25
42
38
56
51
14
53
18
16
36
33
54
49
Min
12
25
24
38
36
51
48
9
19
8
7
16
15
24
22
32
30
4
9
4
3
7
6
11
10
14
13
10
26
10
9
20
18
29
26
39
35
14
53
18
16
36
32
54
48
Med
15
32
30
48
45
64
60
21
44
19
18
39
35
58
53
77
71
15
39
13
12
27
24
40
36
53
48
16
40
15
14
30
27
45
41
60
54
14
53
18
16
36
33
54
49
p99
23
50
46
75
70
99
93
25
53
23
21
46
42
69
63
92
84
23
53
20
18
41
37
61
55
82
73
18
48
17
15
34
31
51
46
68
61
15
53
18
16
37
33
55
49
A-119
-------
Location
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Scenario
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Site-
Years
2
2
16
16
16
16
16
16
16
16
16
16
5
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
9
9
9
9
9
9
9
9
9
9
612
612
612
Annual Mean (ppb)
Mean
72
65
10
25
10
9
19
18
29
27
39
36
27
40
20
18
39
37
59
55
79
74
24
53
23
22
47
43
70
65
94
87
17
41
17
16
35
31
52
47
69
62
13
25
8
Min
72
64
2
5
2
2
4
4
6
6
8
7
22
32
16
15
32
30
48
45
64
60
22
53
22
20
43
40
65
60
87
80
14
36
14
13
29
26
43
38
57
51
1
1
0
Med
72
65
7
18
7
6
14
13
21
19
28
25
29
41
21
20
42
39
63
59
83
78
24
53
24
22
48
44
72
67
96
89
17
38
17
15
34
30
51
45
67
61
13
25
8
p99
73
66
22
53
20
19
41
38
61
57
82
75
29
45
21
20
43
40
64
60
86
80
25
53
25
23
49
46
74
69
99
91
21
49
21
18
41
37
62
55
82
74
24
48
16
A-120
-------
Location
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
612
612
612
612
612
612
612
127
127
127
127
127
127
127
127
127
127
Annual Mean (ppb)
Mean
7
16
14
24
21
33
28
7
22
7
6
13
11
20
17
27
23
Min
0
1
1
1
1
1
1
1
3
1
1
2
2
3
3
4
4
Med
7
17
14
25
21
33
28
6
20
6
5
12
10
18
15
24
20
p99
13
31
27
47
40
63
53
16
53
16
14
33
28
49
42
66
56
A-121
-------
2 Table A-l 13. Estimated annual average NO2 concentrations for
3 following adjustment to Just meeting the current and alternative
monitors <100 m from a major road
standards, 2001-2003 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
19
19
19
19
19
19
19
19
19
19
10
10
10
10
10
10
10
10
10
10
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
44
44
44
44
Annual Mean
Mean
18
37
17
16
34
31
52
47
69
63
27
46
21
19
42
39
63
58
84
77
23
53
22
20
44
40
66
59
88
79
36
53
27
24
53
48
80
72
107
96
25
35
15
13
Min
7
13
7
6
13
12
20
18
27
24
22
36
17
15
34
31
50
46
67
62
22
53
21
19
42
38
63
57
84
76
35
53
26
23
52
47
79
70
105
94
4
5
2
2
Med
21
37
20
18
39
36
59
53
78
71
29
48
23
21
45
41
68
62
90
83
22
53
22
20
43
39
65
59
87
78
36
53
27
24
53
48
80
72
107
96
27
37
16
14
p99
30
53
28
26
57
51
85
77
113
103
32
53
25
23
50
46
75
69
100
92
24
53
23
21
46
41
69
62
92
83
37
53
27
24
55
49
82
73
109
97
41
53
24
21
A-122
-------
Location
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
El Paso
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
44
44
44
44
44
44
6
6
6
6
6
6
6
6
6
6
20
20
20
20
20
20
20
20
20
20
7
7
7
7
7
7
7
7
7
7
14
14
14
14
14
14
14
14
14
14
3
Annual Mean
Mean
30
26
44
38
59
51
10
38
13
12
26
24
39
36
52
47
30
42
21
20
43
39
64
59
85
78
24
46
21
20
43
40
64
60
86
80
21
45
20
18
39
36
59
54
79
72
21
Min
5
4
7
6
10
8
6
19
7
7
15
13
22
20
30
27
21
30
15
14
30
28
45
42
61
55
19
34
17
16
33
31
50
47
66
62
14
30
13
12
26
24
39
36
52
48
20
Med
31
27
47
41
62
54
10
40
12
11
25
22
37
34
50
45
28
40
20
19
41
37
61
56
81
75
24
45
21
20
42
39
63
59
85
79
23
48
21
19
42
39
63
58
84
77
21
p99
48
42
72
62
96
83
16
53
20
18
40
37
60
55
80
73
40
53
29
27
58
53
87
80
116
106
30
53
26
25
53
49
79
74
105
98
26
53
24
22
48
44
72
66
96
88
22
A-123
-------
Location
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Scenario
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
3
3
3
3
3
3
3
3
3
6
6
6
6
6
6
6
6
6
6
5
5
5
5
5
5
5
5
5
5
17
17
17
17
17
17
17
17
17
17
Annual Mean
Mean
53
20
18
39
35
59
53
78
71
14
33
13
12
26
24
39
36
52
47
30
45
22
21
44
41
66
62
88
82
16
37
16
14
31
28
47
42
63
56
Min
53
19
17
37
34
56
50
74
67
3
7
3
3
6
5
8
8
11
10
22
31
16
15
31
29
47
44
63
59
9
21
9
8
17
15
26
23
34
31
Med
53
20
18
40
36
60
54
80
72
15
37
14
13
28
26
42
38
56
51
34
53
25
23
50
47
75
70
100
94
16
40
16
15
33
29
49
44
65
59
p99
53
20
18
40
37
61
55
81
73
23
53
21
19
42
38
63
58
83
77
37
53
27
25
54
51
81
76
108
101
25
53
25
22
49
44
74
67
99
89
A-124
-------
2 Table A-l 14. Estimated annual average NO2 concentrations for
3 following adjustment to Just meeting the current and alternative
monitors >100 m from a major road
standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
3
3
3
3
3
3
3
3
3
3
6
6
6
6
6
6
6
6
6
6
54
54
54
54
Annual Mean
Mean
9
20
9
8
18
17
28
25
37
34
19
35
15
14
31
28
46
42
61
56
20
38
18
17
36
33
54
50
72
66
17
49
19
18
38
35
58
53
77
70
18
30
14
13
Min
7
15
8
7
15
14
23
21
31
28
16
28
12
11
25
23
37
34
50
45
18
33
16
15
33
30
49
45
65
60
14
42
16
15
32
29
48
44
64
58
5
8
4
3
Med
9
20
9
8
19
17
28
25
37
34
18
32
14
13
28
26
42
39
57
51
20
39
18
17
37
34
55
51
73
67
17
50
19
17
38
35
57
52
76
69
18
31
14
13
p99
10
23
11
10
21
19
32
29
42
39
24
44
19
17
38
35
57
52
76
69
21
42
19
18
39
36
58
53
78
71
20
53
22
20
45
41
67
61
90
82
31
53
24
22
A-125
-------
Location
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
54
54
54
54
54
54
4
4
4
4
4
4
4
4
4
4
22
22
22
22
22
22
22
22
22
22
12
12
12
12
12
12
12
12
12
12
17
17
17
17
17
17
17
17
17
17
15
Annual Mean
Mean
28
26
43
39
57
51
8
31
9
8
18
16
28
24
37
32
19
30
15
14
31
28
46
42
61
56
17
39
17
16
34
31
50
47
67
62
15
36
15
14
30
28
45
42
61
56
11
Min
7
6
11
10
14
13
7
28
9
8
17
15
26
23
35
30
10
16
8
7
15
14
23
21
31
28
14
29
14
13
27
25
41
38
55
51
7
19
7
6
14
13
20
19
27
25
3
Med
28
25
42
38
56
51
8
31
9
8
19
16
28
25
37
33
20
32
16
15
32
29
48
44
64
59
16
39
16
15
32
30
48
44
64
59
16
42
16
15
32
29
48
44
63
59
14
p99
49
44
73
66
97
88
8
32
10
8
19
17
29
25
38
33
27
43
21
19
43
39
64
58
85
78
25
51
24
22
48
44
72
67
96
89
22
51
22
20
44
41
66
61
88
81
18
A-126
-------
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Scenario
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
15
15
15
15
15
15
15
15
15
12
12
12
12
12
12
12
12
12
12
2
2
2
2
2
2
2
2
2
2
11
11
11
11
11
11
11
11
11
11
9
9
9
9
9
9
9
9
Annual Mean
Mean
34
11
10
21
19
32
29
42
38
14
42
14
13
29
26
43
40
58
53
14
53
17
15
35
30
52
45
69
59
9
24
9
9
18
17
27
26
37
34
24
41
19
17
37
35
56
52
Min
10
3
3
6
6
10
9
13
12
8
24
8
8
17
15
25
23
34
31
13
53
17
15
34
29
51
44
68
59
1
4
1
1
3
3
4
4
6
5
21
36
16
15
32
30
49
45
Med
44
14
12
27
24
41
37
54
49
15
45
15
14
31
28
46
42
62
57
14
53
17
15
35
30
52
45
69
59
6
16
6
6
12
12
19
18
25
23
24
40
19
17
37
35
56
52
p99
53
17
15
34
30
51
45
67
61
18
53
19
17
37
34
56
51
74
68
14
53
18
15
35
30
53
45
70
60
20
53
20
19
40
38
60
57
80
76
26
44
20
19
41
38
61
57
A-127
-------
Location
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
9
9
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
565
565
565
565
565
565
565
565
565
565
116
116
116
116
116
116
116
116
116
116
Annual Mean
Mean
75
69
24
53
15
14
29
27
44
41
58
55
15
38
15
14
30
28
45
42
60
56
11
26
9
8
19
17
28
25
38
34
7
21
6
6
13
12
19
18
26
24
Min
65
60
21
53
13
12
25
23
38
35
50
47
12
29
13
12
25
24
38
36
50
47
1
2
1
1
1
1
2
2
3
2
1
3
1
1
2
1
2
2
3
3
Med
75
69
22
53
14
13
27
26
41
38
55
51
14
36
15
14
29
27
44
41
58
55
11
26
9
8
19
17
28
25
38
34
6
19
6
6
12
11
18
17
24
22
p99
82
76
29
53
18
17
35
33
53
50
71
66
18
49
18
17
36
34
54
51
72
68
23
52
20
18
40
35
59
53
79
71
16
53
15
14
31
29
46
43
62
57
A-128
-------
2 Table A-l 15. Estimated annual average NO2 concentrations for
3 following adjustment to Just meeting the current and alternative
monitors <100 m from a major road
standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
14
14
14
14
14
14
14
14
14
14
9
9
9
9
9
9
9
9
9
9
5
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
28
28
28
28
Annual Mean
Mean
17
38
18
17
36
33
54
50
73
66
26
46
20
18
40
37
61
55
81
74
19
48
19
18
38
35
58
53
77
71
28
53
25
23
51
46
76
70
101
93
25
42
20
18
Min
10
24
11
10
22
20
32
29
43
39
18
31
14
13
28
25
42
38
56
51
14
41
15
13
29
27
44
40
59
54
27
53
25
23
49
45
74
68
98
90
9
15
7
6
Med
17
36
18
16
35
32
53
48
71
64
28
51
22
20
44
40
67
61
89
81
18
53
19
17
38
35
56
52
75
69
28
53
25
23
50
46
75
69
100
91
27
47
21
19
p99
25
53
27
24
53
49
80
73
106
97
31
53
24
22
48
44
72
66
96
87
22
53
23
21
46
42
69
63
92
84
29
53
26
24
53
48
79
72
105
96
34
53
26
24
A-129
-------
Location
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
El Paso
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
28
28
28
28
28
28
4
4
4
4
4
4
4
4
4
4
13
13
13
13
13
13
13
13
13
13
6
6
6
6
6
6
6
6
6
6
17
17
17
17
17
17
17
17
17
17
3
Annual Mean
Mean
40
36
59
54
79
72
10
38
12
10
23
20
35
30
46
40
28
43
22
20
44
41
67
61
89
81
22
48
21
20
43
40
64
59
85
79
18
43
18
17
37
34
55
51
73
68
15
Min
13
12
20
18
27
24
6
24
7
6
14
12
21
18
28
24
18
28
15
13
29
27
44
40
59
54
18
36
17
16
34
32
51
48
69
64
13
30
13
12
27
25
40
37
53
49
13
Med
43
39
64
58
86
78
9
38
11
10
23
20
34
30
46
40
28
42
23
21
45
41
68
62
90
82
22
50
22
20
43
40
65
60
86
80
18
40
18
16
35
32
53
49
70
65
13
p99
53
48
79
72
106
96
14
53
16
14
33
28
49
43
65
57
36
53
29
27
58
53
88
80
117
107
26
53
26
24
51
47
77
71
102
95
24
53
24
22
48
44
72
67
96
89
18
A-130
-------
Location
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Scenario
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
8
8
8
8
8
8
8
8
8
8
13
13
13
13
13
13
13
13
13
13
Annual Mean
Mean
44
15
14
30
28
45
42
61
56
19
52
20
19
40
37
60
56
79
75
22
37
17
16
34
32
51
48
68
63
13
37
13
13
27
25
40
38
54
51
Min
39
13
12
27
25
40
37
54
49
19
51
19
18
39
37
58
55
78
73
11
19
9
8
17
16
26
24
35
32
8
19
8
8
17
16
25
23
33
31
Med
40
14
12
27
25
41
37
54
50
19
52
20
19
40
37
60
56
79
75
20
33
15
14
31
28
46
43
61
57
13
38
13
12
26
24
39
37
52
49
p99
53
18
17
37
34
55
51
74
68
20
53
20
19
41
38
61
57
81
76
32
53
25
23
49
46
74
69
99
91
22
53
23
21
46
43
68
64
91
86
A-131
-------
1 Table A-116. Estimated number of exceedances of 1-
2 following adjustment to Just meeting the current and
hour concentration levels (100,150, and 200 ppb) for monitors >100 m from a major road
alternative standards, 2001-2003 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
0
8
0
0
4
2
163
72
546
426
1
71
0
0
37
15
525
339
1568
1187
0
233
0
0
72
33
674
466
1707
1276
2
525
Min
0
0
0
0
0
0
31
12
155
117
0
1
0
0
1
0
132
62
680
440
0
166
0
0
49
29
602
396
1576
1163
1
41
Med
0
2
0
0
2
1
170
68
624
494
0
36
0
0
17
8
373
203
1343
989
0
208
0
0
75
32
673
467
1622
1224
2
525
p99
0
31
0
0
18
10
307
165
874
701
5
314
1
0
160
71
1176
893
2868
2345
1
326
1
0
92
38
747
534
1922
1440
2
1008
Exceedances of 150
Mean
0
0
0
0
0
0
4
2
56
21
0
2
0
0
1
1
37
15
301
182
0
11
0
0
2
1
72
33
398
239
0
62
Min
0
0
0
0
0
0
0
0
8
1
0
0
0
0
0
0
1
0
50
23
0
7
0
0
1
0
49
29
340
166
0
1
Med
0
0
0
0
0
0
2
1
53
15
0
0
0
0
0
0
17
8
180
119
0
9
0
0
2
1
75
32
410
269
0
62
ppb
p99
0
0
0
0
2
0
18
10
138
68
0
8
0
0
5
3
160
71
819
563
0
18
0
0
3
2
92
38
443
281
0
123
Exceedances of 200 ppb
Mean
0
0
0
0
0
0
1
0
4
2
0
0
0
0
0
0
2
1
37
15
0
1
0
0
0
0
6
2
72
33
0
3
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
6
1
49
29
0
0
Med
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
17
8
0
1
0
0
0
0
6
2
75
32
0
3
p99
0
0
0
0
0
0
5
2
18
10
0
1
0
0
1
0
8
5
160
71
0
3
0
0
1
0
6
4
92
38
0
5
A-132
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Scenario
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
1
0
58
13
932
465
2318
1798
9
438
8
6
146
63
1058
587
2461
1753
7
63
0
0
21
7
241
118
914
500
0
438
1
0
85
50
Min
0
0
54
12
675
336
1808
1315
0
341
0
0
88
21
770
438
2073
1395
0
0
0
0
0
0
0
0
3
0
0
215
0
0
5
3
Med
1
0
58
13
932
465
2318
1798
3
431
3
1
140
54
1088
586
2450
1786
2
38
0
0
9
2
172
85
893
461
0
341
0
0
43
26
p99
1
0
61
14
1189
593
2827
2281
34
520
30
28
217
117
1295
748
2860
2048
34
259
5
4
112
37
1019
563
2712
1717
0
835
2
0
243
149
Exceedances of 150
Mean
0
0
2
1
58
13
465
230
3
45
3
3
18
12
146
63
664
328
0
4
0
0
1
0
21
7
129
54
0
76
0
0
6
3
Min
0
0
1
0
54
12
336
187
0
10
0
0
1
1
88
21
497
258
0
0
0
0
0
0
0
0
0
0
0
13
0
0
0
0
Med
0
0
2
1
58
13
465
230
1
30
1
1
7
4
140
54
672
311
0
0
0
0
0
0
9
2
96
34
0
35
0
0
4
2
ppb
p99
0
0
2
1
61
14
593
273
16
101
15
13
47
39
217
117
839
408
10
23
0
0
13
10
112
37
603
282
0
216
0
0
18
10
Exceedances of 200 ppb
Mean
0
0
1
0
3
2
58
13
2
15
2
2
8
6
28
17
146
63
0
0
0
0
0
0
2
1
21
7
0
9
0
0
1
0
Min
0
0
0
0
2
1
54
12
0
1
0
0
0
0
4
1
88
21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
1
0
3
2
58
13
0
6
0
0
3
1
14
6
140
54
0
0
0
0
0
0
0
0
9
2
0
3
0
0
0
0
p99
0
0
1
0
4
2
61
14
12
45
12
9
30
28
72
45
217
117
4
8
0
0
5
4
19
13
112
37
0
33
0
0
2
0
A-133
-------
Location
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Scenario
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
454
352
1044
827
1
23
0
0
19
9
331
201
1299
963
0
95
0
0
58
33
777
519
2041
1711
0
228
0
0
93
47
896
580
1974
1558
Min
154
100
602
412
0
0
0
0
0
0
27
11
244
166
0
6
0
0
4
1
266
157
1128
893
0
0
0
0
0
0
24
5
208
102
Med
392
291
994
753
0
9
0
0
9
6
244
117
1165
857
0
67
0
0
33
19
641
400
1856
1516
0
188
0
0
71
43
970
588
2439
1848
p99
893
723
1575
1381
4
148
0
0
89
47
872
619
2482
1953
1
291
1
1
244
163
1779
1299
3741
3285
1
673
0
0
274
143
1902
1361
3394
2835
Exceedances of 150
Mean
85
50
315
214
0
1
0
0
0
0
19
9
177
93
0
2
0
0
1
1
58
33
399
263
0
10
0
0
3
1
93
47
514
316
Min
5
3
86
46
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
4
1
114
61
0
0
0
0
0
0
0
0
5
1
Med
43
26
256
149
0
0
0
0
0
0
9
6
97
47
0
2
0
0
1
1
33
19
295
178
0
7
0
0
1
0
71
43
515
300
ppb
p99
243
149
665
514
0
6
0
0
4
2
89
47
563
334
1
10
1
1
3
3
244
163
1081
788
0
44
0
0
10
4
274
143
1230
806
Exceedances of 200 ppb
Mean
13
6
85
50
0
0
0
0
0
0
1
1
19
9
0
0
0
0
0
0
3
2
58
33
0
0
0
0
0
0
8
3
93
47
Min
0
0
5
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
0
0
Med
6
4
43
26
0
0
0
0
0
0
0
0
9
6
0
0
0
0
0
0
1
1
33
19
0
0
0
0
0
0
7
1
71
43
p99
47
21
243
149
0
1
0
0
0
0
8
4
89
47
1
1
1
1
1
1
17
7
244
163
0
1
0
0
0
0
30
11
274
143
A-134
-------
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
1
434
0
0
61
29
429
266
924
727
0
385
0
0
50
24
622
366
1553
1185
1
732
1
1
160
82
821
585
1770
1279
0
260
0
0
Min
0
3
0
0
0
0
4
0
43
25
0
106
0
0
13
6
279
127
876
625
0
723
0
0
124
55
819
554
1656
1245
0
0
0
0
Med
0
386
0
0
17
3
382
178
1015
763
0
378
0
0
40
19
647
383
1692
1262
1
732
1
1
160
82
821
585
1770
1279
0
33
0
0
p99
6
1315
3
2
335
178
1517
1095
2644
2226
1
847
1
1
94
49
1020
627
2035
1651
2
741
2
2
195
108
823
615
1883
1312
1
1022
0
0
Exceedances of 150
Mean
0
62
0
0
3
1
61
29
266
162
0
25
0
0
2
1
50
24
322
180
1
134
1
1
10
8
160
82
585
370
0
10
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
13
6
106
56
0
90
0
0
4
3
124
55
554
335
0
0
0
0
Med
0
21
0
0
0
0
17
3
178
81
0
21
0
0
1
0
40
19
332
174
1
134
1
1
10
8
160
82
585
370
0
1
0
0
ppb
p99
1
291
1
0
23
8
335
178
1095
749
1
65
0
0
10
6
94
49
559
329
2
177
2
2
15
12
195
108
615
404
0
71
0
0
Exceedances of 200 ppb
Mean
0
8
0
0
0
0
8
4
61
29
0
3
0
0
0
0
7
3
50
24
1
18
1
1
1
1
20
10
160
82
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
13
6
0
7
0
0
0
0
10
4
124
55
0
0
0
0
Med
0
0
0
0
0
0
0
0
17
3
0
2
0
0
0
0
4
1
40
19
1
18
1
1
1
1
20
10
160
82
0
0
0
0
p99
0
48
0
0
3
2
56
26
335
178
0
12
0
0
1
1
16
11
94
49
2
29
2
2
2
2
29
15
195
108
0
3
0
0
A-135
-------
Location
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
37
15
533
330
1152
936
0
91
0
0
66
22
1064
823
2582
2254
0
512
0
0
175
87
2187
1647
3660
3315
0
223
0
0
82
34
798
470
Min
0
0
1
0
12
4
0
8
0
0
8
4
312
194
1344
1068
0
491
0
0
66
31
1709
1176
3154
2806
0
45
0
0
6
1
337
161
Med
2
1
104
49
389
259
0
74
0
0
91
30
1281
1022
2672
2377
0
498
0
0
206
109
2386
1877
3852
3503
0
131
0
0
32
8
643
364
p99
172
86
1867
1158
3533
3036
0
187
0
0
115
39
1538
1260
3252
2917
0
548
0
0
253
121
2466
1887
3975
3637
1
540
1
1
214
107
1375
915
Exceedances of 150
Mean
0
0
37
15
288
191
0
0
0
0
0
0
66
22
617
455
0
5
0
0
1
0
175
87
1476
1000
0
11
0
0
2
1
82
34
Min
0
0
0
0
0
0
0
0
0
0
0
0
8
4
121
72
0
3
0
0
0
0
66
31
1017
601
0
0
0
0
0
0
6
1
Med
0
0
2
1
37
20
0
0
0
0
0
0
91
30
778
588
0
4
0
0
0
0
206
109
1702
1197
0
2
0
0
0
0
32
8
ppb
p99
3
2
172
86
1022
688
0
0
0
0
0
0
115
39
1007
784
0
9
0
0
2
0
253
121
1709
1202
0
51
0
0
9
3
214
107
Exceedances of 200 ppb
Mean
0
0
1
1
37
15
0
0
0
0
0
0
1
0
66
22
0
0
0
0
0
0
5
1
175
87
0
0
0
0
0
0
5
2
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
4
0
0
0
0
0
0
3
0
66
31
0
0
0
0
0
0
0
0
Med
0
0
0
0
2
1
0
0
0
0
0
0
0
0
91
30
0
0
0
0
0
0
4
0
206
109
0
0
0
0
0
0
2
0
p99
0
0
7
4
172
86
0
0
0
0
0
0
4
1
115
39
0
0
0
0
0
0
9
3
253
121
0
1
0
0
1
1
22
9
A-136
-------
Location
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
1941
1469
0
48
0
0
2
1
42
13
240
95
1
121
1
0
9
4
77
32
284
140
Min
1203
794
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
1747
1288
0
13
0
0
0
0
11
1
119
32
0
24
0
0
0
0
11
2
81
28
p99
2737
2195
5
411
0
0
24
9
363
150
1550
664
7
925
7
6
180
78
684
423
1621
927
Exceedances of 150
Mean
470
266
0
2
0
0
0
0
2
1
19
6
0
14
0
0
1
1
9
4
43
18
Min
161
63
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
364
197
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
4
1
ppb
p99
915
588
0
29
0
0
3
1
24
9
219
83
1
224
1
1
25
11
180
78
498
293
Exceedances of 200 ppb
Mean
82
34
0
0
0
0
0
0
0
0
2
1
0
3
0
0
1
0
2
1
9
4
Min
6
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
32
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
214
107
0
4
0
0
0
0
4
2
24
9
1
57
1
0
7
6
42
19
180
78
A-137
-------
1 Table A-117. Estimated number of exceedances of 1-hour concentration levels (200 and 250 ppb) for
2 monitors >100 m from a major road following adjustment to just meeting the current and alternative
3 standards, 2001-2003 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250
Mean
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
1
0
4
2
0
0
0
0
0
0
1
0
11
3
0
1
0
0
0
0
1
1
5
3
1
8
1
1
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
2
0
0
0
0
0
0
0
0
5
1
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
12
4
0
1
0
0
0
0
1
1
5
3
0
2
0
0
ppb
p99
0
0
0
0
0
0
0
0
9
2
0
0
0
0
0
0
3
1
19
7
0
0
0
0
0
0
2
1
13
4
0
2
0
0
0
0
1
1
5
4
8
34
7
5
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
2
1
0
1
0
0
0
0
1
0
2
1
1
6
1
1
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
1
0
0
0
0
1
0
2
1
0
1
0
0
p99
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
1
0
5
3
0
0
0
0
0
0
1
0
3
2
0
1
0
0
0
0
1
0
2
1
5
28
5
3
A-138
-------
Location
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250
Mean
5
3
12
9
39
21
0
0
0
0
0
0
0
0
4
1
0
2
0
0
0
0
3
1
19
9
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
1
0
6
3
0
Min
0
0
1
0
5
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
1
1
4
3
28
9
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
0
9
4
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
3
1
0
ppb
p99
25
16
40
34
91
56
0
4
0
0
2
0
10
6
24
16
0
6
0
0
0
0
9
4
63
34
0
0
0
0
0
0
2
1
14
6
1
1
0
0
1
1
1
1
43
15
0
Exceedances of 300 ppb
Mean
3
3
8
6
18
12
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
6
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
Min
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
1
1
3
1
7
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
p99
15
13
30
28
47
39
0
1
0
0
0
0
5
4
13
10
0
0
0
0
0
0
2
0
18
10
0
0
0
0
0
0
0
0
4
2
0
1
0
0
1
1
1
1
3
3
0
A-139
-------
Location
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Scenario
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250
Mean
0
0
0
0
0
1
0
14
7
0
1
0
0
0
0
1
1
12
6
0
0
0
0
0
0
1
0
10
5
1
7
1
1
1
1
8
2
35
17
0
0
0
0
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
3
0
0
0
0
3
1
21
7
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
12
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
8
3
1
7
1
1
1
1
8
2
35
17
0
0
0
0
0
0
0
0
ppb
p99
0
0
0
0
0
3
1
47
25
0
6
0
0
1
1
8
3
80
40
0
3
0
0
1
1
5
1
19
15
1
10
2
2
2
2
12
2
48
27
0
0
0
0
0
0
2
1
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
2
1
0
1
1
1
1
1
1
1
10
8
0
0
0
0
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
3
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
1
1
1
1
1
10
8
0
0
0
0
0
0
0
0
p99
0
0
0
0
0
0
0
10
4
0
2
0
0
1
0
3
2
23
8
0
1
0
0
0
0
1
1
10
6
0
2
1
1
2
2
2
2
15
12
0
0
0
0
0
0
0
0
A-140
-------
Location
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 250
Mean
3
1
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
17
5
0
0
0
0
0
0
1
0
11
3
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
17
4
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ppb
p99
17
6
0
0
0
0
0
0
0
0
7
4
0
0
0
0
0
0
0
0
29
9
0
1
0
0
0
0
3
1
43
15
0
2
0
0
0
0
1
0
6
3
0
20
0
0
6
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
3
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
1
1
9
3
0
0
0
0
0
0
0
0
3
1
0
9
0
0
1
A-141
-------
Location
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
100
150
150
200
200
Percentile
99
98
99
98
99
Exceedances of 250
Mean
0
1
1
3
1
Min
0
0
0
0
0
Med
0
0
0
0
0
ppb
p99
2
14
8
57
25
Exceedances of 300 ppb
Mean
0
1
0
1
1
Min
0
0
0
0
0
Med
0
0
0
0
0
p99
1
7
6
25
11
A-142
-------
1 Table A-118. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) for monitors <100 m from a major road
2 following adjustment to Just meeting the current and alternative standards, 2001-2003 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
0
119
0
0
67
33
812
490
1863
1544
4
194
1
1
120
62
1075
732
2721
2174
0
491
0
0
165
84
1241
908
2865
2241
19
Min
0
0
0
0
0
0
44
8
252
188
0
34
0
0
20
8
482
304
1527
1131
0
448
0
0
127
55
1154
849
2683
2078
8
Med
0
65
0
0
44
17
689
435
1933
1424
0
188
0
0
112
60
1062
736
2904
2267
0
491
0
0
144
69
1176
856
2726
2126
19
p99
1
540
1
0
221
120
2524
1615
4698
4145
36
357
7
5
267
152
1915
1346
4067
3458
0
534
0
0
224
128
1394
1019
3187
2518
30
Exceedances of 150 ppb
Mean
0
4
0
0
2
1
67
33
431
245
0
8
0
0
4
3
120
62
660
440
0
34
0
0
8
2
165
84
768
495
1
Min
0
0
0
0
0
0
0
0
6
1
0
0
0
0
0
0
20
8
255
132
0
27
0
0
5
1
127
55
679
429
1
Med
0
0
0
0
0
0
44
17
397
239
0
4
0
0
1
0
112
60
667
436
0
29
0
0
6
2
144
69
724
448
1
p99
0
34
0
0
8
4
221
120
1439
821
0
39
0
0
37
28
267
152
1236
866
0
45
0
0
12
4
224
128
901
609
1
Exceedances of 200 ppb
Mean
0
0
0
0
0
0
6
2
67
33
0
2
0
0
1
1
11
5
120
62
0
2
0
0
0
0
20
8
165
84
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
20
8
0
1
0
0
0
0
9
5
127
55
0
Med
0
0
0
0
0
0
3
0
44
17
0
0
0
0
0
0
5
2
112
60
0
2
0
0
0
0
13
7
144
69
0
p99
0
5
0
0
1
0
22
10
221
120
0
15
0
0
7
5
45
38
267
152
0
4
0
0
0
0
37
13
224
128
0
A-143
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
Scenario
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
152
5
1
171
79
1836
1015
4161
3265
13
113
0
0
40
14
403
206
1403
801
0
546
0
0
103
56
566
451
1214
988
3
67
0
0
Min
118
1
1
104
49
1647
843
4075
3150
0
0
0
0
0
0
0
0
0
0
0
210
0
0
34
17
277
216
567
476
0
2
0
0
Med
152
5
1
171
79
1836
1015
4161
3265
5
87
0
0
25
6
369
178
1523
858
0
564
0
0
81
44
456
375
976
804
0
41
0
0
p99
186
8
1
237
108
2024
1187
4247
3379
65
399
3
1
160
69
1288
702
3545
2238
0
827
2
0
252
139
1139
928
2279
1859
21
174
2
0
Exceedances of 150 ppb
Mean
16
0
0
17
11
171
79
1015
528
0
6
0
0
1
0
40
14
225
100
0
86
0
0
4
1
103
56
401
280
0
2
0
0
Min
8
0
0
8
5
104
49
843
377
0
0
0
0
0
0
0
0
0
0
0
23
0
0
0
0
34
17
183
124
0
0
0
0
Med
16
0
0
17
11
171
79
1015
528
0
1
0
0
0
0
25
6
199
83
0
81
0
0
1
0
81
44
334
227
0
0
0
0
p99
23
0
0
26
17
237
108
1187
678
6
40
1
0
8
6
160
69
752
358
0
165
0
0
17
5
252
139
827
614
0
7
0
0
Exceedances of 200 ppb
Mean
4
0
0
5
1
26
18
171
79
0
0
0
0
0
0
4
1
40
14
0
10
0
0
0
0
11
4
103
56
0
0
0
0
Min
1
0
0
1
1
12
8
104
49
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
34
17
0
0
0
0
Med
4
0
0
5
1
26
18
171
79
0
0
0
0
0
0
0
0
25
6
0
7
0
0
0
0
6
2
81
44
0
0
0
0
p99
7
0
0
8
1
39
27
237
108
1
6
0
0
3
1
21
8
160
69
0
28
0
0
2
0
39
18
252
139
0
0
0
0
A-144
-------
Location
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 100 ppb
Mean
74
36
999
655
2837
2276
0
146
0
0
92
50
1278
892
2873
2469
0
232
0
0
92
44
1061
663
2476
1915
2
768
0
0
158
79
1112
Min
4
2
217
110
1159
818
0
14
0
0
14
5
500
321
1555
1297
0
10
0
0
0
0
152
70
847
487
0
535
0
0
117
50
943
Med
50
22
845
538
2494
1994
0
136
0
0
67
33
1241
855
2746
2355
0
301
0
0
87
51
1140
709
2734
2095
3
860
0
0
131
72
1078
p99
277
140
2654
1901
5476
4778
1
273
0
0
230
132
2065
1536
4264
3713
1
400
1
0
197
112
1922
1286
3650
3037
3
909
0
0
226
115
1315
Exceedances of 150 ppb
Mean
2
1
74
36
589
334
0
4
0
0
2
1
92
50
679
461
0
7
0
0
1
0
92
44
589
341
0
79
0
0
13
5
158
Min
0
0
4
2
100
58
0
0
0
0
0
0
14
5
216
116
0
0
0
0
0
0
0
0
54
14
0
39
0
0
5
1
117
Med
0
0
50
22
484
258
0
4
0
0
2
0
67
33
635
420
0
6
0
0
0
0
87
51
636
359
0
93
0
0
16
6
131
p99
18
7
277
140
1750
1103
0
7
0
0
3
2
230
132
1222
878
0
18
0
0
6
2
197
112
1156
700
0
105
0
0
17
8
226
Exceedances of 200 ppb
Mean
0
0
5
3
74
36
0
0
0
0
0
0
5
3
92
50
0
0
0
0
0
0
5
2
92
44
0
15
0
0
0
0
25
Min
0
0
0
0
4
2
0
0
0
0
0
0
0
0
14
5
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
12
Med
0
0
1
0
50
22
0
0
0
0
0
0
5
4
67
33
0
0
0
0
0
0
4
1
87
51
0
17
0
0
0
0
31
p99
2
0
34
21
277
140
0
2
0
0
0
0
9
6
230
132
0
1
0
0
1
0
16
7
197
112
0
24
0
0
0
0
33
A-145
-------
Location
El Paso
El Paso
El Paso
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Scenario
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
757
2330
1891
0
543
0
0
89
43
1038
698
1825
1584
2
133
0
0
105
46
1681
1318
3238
2934
0
141
0
0
46
18
570
309
1687
1219
Min
595
2153
1721
0
0
0
0
0
0
1
0
39
17
0
3
0
0
1
0
504
337
1460
1247
0
0
0
0
0
0
50
10
375
207
Med
743
2188
1794
0
514
0
0
81
37
1069
729
1904
1660
1
157
0
0
135
55
2064
1640
3766
3470
0
87
0
0
25
8
395
194
1452
968
p99
934
2649
2158
2
1134
0
0
196
97
2033
1386
3647
3162
6
268
0
0
201
90
2634
2117
4662
4284
1
547
1
1
202
86
1760
1127
3880
3043
Exceedances of 150 ppb
Mean
79
686
442
0
22
0
0
2
1
89
43
615
410
0
2
0
0
1
0
105
46
996
713
0
6
0
0
2
1
46
18
309
167
Min
50
535
325
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
219
145
0
0
0
0
0
0
0
0
10
0
Med
72
664
407
0
13
0
0
0
0
81
37
632
413
0
1
0
0
0
0
135
55
1238
860
0
1
0
0
0
0
25
8
194
97
p99
115
860
594
0
73
0
0
12
3
196
97
1244
827
0
7
0
0
3
2
201
90
1653
1222
0
30
0
0
11
7
202
86
1127
668
Exceedances of 200 ppb
Mean
14
158
79
0
2
0
0
0
0
5
3
89
43
0
0
0
0
0
0
5
2
105
46
0
1
0
0
0
0
3
2
46
18
Min
6
117
50
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
Med
18
131
72
0
0
0
0
0
0
3
1
81
37
0
0
0
0
0
0
5
1
135
55
0
0
0
0
0
0
0
0
25
8
p99
19
226
115
0
12
0
0
0
0
17
13
196
97
0
0
0
0
0
0
12
7
201
90
0
5
0
0
1
1
17
11
202
86
A-146
-------
1
2
3
4
Table A-119. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for
monitors <100 m from a major road following adjustment to just meeting the current and alternative
standards, 2001-2003 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Los Angeles
Los Angeles
Los Angeles
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 250
Mean
0
0
0
0
0
0
1
0
11
4
0
0
0
0
0
0
3
2
19
9
0
0
0
0
0
0
2
0
32
13
0
0
0
0
0
0
12
8
36
21
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
16
7
0
0
0
0
0
0
5
4
20
9
0
0
0
Med
0
0
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
13
3
0
0
0
0
0
0
2
0
26
12
0
0
0
0
0
0
12
8
36
21
0
0
0
ppb
p99
0
0
0
0
0
0
3
2
39
16
0
0
0
0
0
0
27
21
62
42
0
0
0
0
0
0
4
0
53
21
0
0
0
0
0
0
19
11
52
33
1
1
0
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
1
1
4
3
0
0
0
0
0
0
0
0
8
2
0
0
0
0
0
0
5
1
17
11
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
1
0
0
0
0
0
0
1
1
8
5
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
6
2
0
0
0
0
0
0
5
1
17
11
0
0
0
p99
0
0
0
0
0
0
1
0
8
4
0
0
0
0
0
0
7
5
37
28
0
0
0
0
0
0
0
0
12
4
0
0
0
0
0
0
8
1
26
17
0
1
0
A-147
-------
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
El Paso
El Paso
El Paso
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
0
0
0
0
0
7
2
0
1
0
0
0
0
1
1
20
8
0
0
0
0
0
0
1
0
11
5
0
0
0
0
0
0
1
0
9
5
0
0
0
0
0
0
0
0
11
4
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
16
4
0
0
0
0
0
0
0
0
5
1
0
0
0
0
0
0
0
0
7
5
0
0
0
0
0
0
0
0
11
3
0
3
0
0
1
1
6
4
34
13
0
2
0
0
0
0
4
2
56
31
0
0
0
0
0
0
7
5
60
31
0
0
0
0
0
0
2
0
20
9
0
0
0
0
0
0
2
1
27
13
0
6
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
3
1
8
6
0
2
0
0
0
0
2
0
17
5
0
0
0
0
0
0
2
0
18
7
0
0
0
0
0
0
0
0
3
2
0
0
0
0
0
0
1
0
6
2
0
0
0
A-148
-------
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
0
0
0
5
3
33
19
0
0
0
0
0
0
1
0
10
4
0
0
0
0
0
0
0
0
8
5
0
0
0
0
0
0
1
0
6
3
0
0
0
1
0
20
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
3
39
23
0
0
0
0
0
0
0
0
5
3
0
0
0
0
0
0
0
0
9
5
0
0
0
0
0
0
0
0
1
0
0
0
0
8
5
40
26
0
2
0
0
0
0
3
2
35
15
0
0
0
0
0
0
1
0
20
12
0
1
0
0
0
0
7
3
30
16
0
0
0
0
0
13
5
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
5
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
17
8
0
0
0
0
0
0
0
0
12
3
0
0
0
0
0
0
0
0
3
2
0
1
0
0
0
0
1
1
11
7
A-149
-------
2 Table A-120. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) for monitors >100 m from a major road
3 following adjustment to Just meeting the current and alternative standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
0
13
0
0
5
0
169
101
512
400
0
66
0
0
25
10
399
247
1311
965
1
212
0
0
144
72
1403
952
2527
2142
Min
0
0
0
0
0
0
55
30
255
183
0
15
0
0
4
0
90
50
584
398
0
76
0
0
66
23
1148
780
1957
1683
Med
0
15
0
0
2
0
156
85
498
374
0
28
0
0
7
4
290
149
1133
802
0
229
0
0
169
90
1527
1021
2675
2287
p99
0
31
0
0
18
3
291
200
708
574
0
238
0
0
105
46
874
601
2227
1742
4
330
1
0
196
103
1533
1055
2948
2457
Exceedances of 150 ppb
Mean
0
0
0
0
0
0
5
0
83
42
0
1
0
0
0
0
25
10
218
110
0
12
0
0
7
4
144
72
851
505
Min
0
0
0
0
0
0
0
0
24
10
0
0
0
0
0
0
4
0
41
24
0
2
0
0
1
0
66
23
689
374
Med
0
0
0
0
0
0
2
0
68
38
0
0
0
0
0
0
7
4
123
49
0
9
0
0
3
2
169
90
906
549
p99
0
0
0
0
0
0
18
3
174
96
0
4
0
0
0
0
105
46
551
330
0
24
0
0
16
9
196
103
959
591
Exceedances of 200 ppb
Mean
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
25
10
0
1
0
0
0
0
19
10
144
72
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
6
2
66
23
Med
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
7
4
0
0
0
0
0
0
19
7
169
90
p99
0
0
0
0
0
0
0
0
18
3
0
0
0
0
0
0
3
0
105
46
0
3
0
0
1
0
31
20
196
103
A-150
-------
Location
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 100 ppb
Mean
0
662
0
0
158
80
1088
866
2338
1966
1
55
0
0
36
16
430
271
1339
918
0
493
0
0
57
27
367
229
793
578
1
34
0
Min
0
463
0
0
41
9
686
504
1829
1480
0
0
0
0
0
0
4
1
36
19
0
167
0
0
3
0
114
52
382
243
0
0
0
Med
0
661
0
0
148
75
1071
860
2271
1894
0
38
0
0
20
7
388
240
1222
841
0
475
0
0
51
20
364
229
111
560
0
24
0
p99
0
841
0
0
321
184
1597
1341
3253
2774
5
280
0
0
176
79
1577
1045
3826
2907
0
854
1
0
124
68
628
405
1237
948
3
154
1
Exceedances of 150 ppb
Mean
0
36
0
0
3
1
158
80
774
530
0
1
0
0
1
0
36
16
241
134
0
102
0
0
3
1
57
27
229
136
0
1
0
Min
0
25
0
0
0
0
41
9
415
250
0
0
0
0
0
0
0
0
0
0
0
11
0
0
0
0
3
0
52
22
0
0
0
Med
0
31
0
0
2
1
148
75
111
549
0
0
0
0
0
0
20
7
203
104
0
83
0
0
2
0
51
20
229
126
0
0
0
p99
0
54
0
0
8
4
321
184
1226
868
0
9
0
0
7
3
176
79
957
560
0
231
0
0
9
2
124
68
405
269
0
3
0
Exceedances of 200 ppb
Mean
0
1
0
0
0
0
12
4
158
80
0
0
0
0
0
0
3
1
36
16
0
19
0
0
0
0
10
2
57
27
0
0
0
Min
0
0
0
0
0
0
0
0
41
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
Med
0
0
0
0
0
0
12
2
148
75
0
0
0
0
0
0
1
0
20
7
0
8
0
0
0
0
5
2
51
20
0
0
0
p99
0
4
0
0
0
0
31
9
321
184
0
0
0
0
0
0
20
8
176
79
0
62
0
0
1
0
29
5
124
68
0
1
0
A-151
-------
Location
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Scenario
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
0
36
16
521
323
1440
1089
0
222
0
0
58
31
734
572
1916
1595
0
214
0
0
75
39
714
503
1534
1287
0
509
0
0
46
21
Min
0
0
0
10
0
211
111
0
9
0
0
3
1
368
253
1300
1007
0
0
0
0
0
0
24
2
180
116
0
1
0
0
0
0
Med
0
33
12
582
348
1583
1201
0
105
0
0
50
21
660
519
1739
1440
0
219
0
0
64
18
649
416
1632
1313
0
615
0
0
12
3
p99
1
92
46
963
630
2448
1854
0
617
0
0
163
108
1420
1103
3484
2985
3
683
3
1
256
165
1599
1237
3038
2636
5
1187
4
2
202
109
Exceedances of 150 ppb
Mean
0
1
0
36
16
285
149
0
8
0
0
1
0
58
31
435
275
0
8
0
0
2
1
75
39
446
301
0
70
0
0
1
1
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
3
1
176
91
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
33
12
309
163
0
3
0
0
0
0
50
21
413
276
0
3
0
0
0
0
64
18
378
246
0
28
0
0
0
0
p99
0
5
3
92
46
566
347
0
29
0
0
7
2
163
108
850
565
0
39
0
0
13
8
256
165
1120
825
0
284
0
0
11
7
Exceedances of 200 ppb
Mean
0
0
0
2
1
36
16
0
0
0
0
0
0
4
2
58
31
0
1
0
0
0
0
5
3
75
39
0
8
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
1
0
33
12
0
0
0
0
0
0
1
0
50
21
0
0
0
0
0
0
1
1
64
18
0
0
0
0
0
0
p99
0
1
1
11
6
92
46
0
2
0
0
0
0
23
12
163
108
0
6
0
0
3
1
25
14
256
165
0
56
0
0
4
2
A-152
-------
Location
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Scenario
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 100 ppb
Mean
390
252
883
687
0
649
0
0
81
47
727
508
1605
1356
8
816
13
10
151
66
816
437
1593
1139
0
296
0
0
42
23
542
437
999
Min
0
0
9
3
0
177
0
0
11
7
258
143
695
556
0
751
0
0
119
32
751
418
1526
1065
0
0
0
0
0
0
4
1
13
Med
413
218
1184
862
0
655
0
0
66
37
706
462
1691
1415
8
816
13
10
151
66
816
437
1593
1139
0
15
0
0
1
0
45
31
172
p99
959
704
1831
1487
0
1088
1
0
190
123
1317
987
2441
2125
15
880
25
19
183
100
880
455
1660
1213
0
1209
0
0
182
88
2121
1767
3377
Exceedances of 150 ppb
Mean
46
21
252
135
0
64
0
0
4
1
81
47
449
295
3
161
5
4
29
21
151
66
492
254
0
9
0
0
0
0
42
23
338
Min
0
0
0
0
0
7
0
0
0
0
11
7
122
66
0
139
0
0
4
0
119
32
478
246
0
0
0
0
0
0
0
0
0
Med
12
3
218
77
0
54
0
0
3
1
66
37
397
239
3
161
5
4
29
21
151
66
492
254
0
0
0
0
0
0
1
0
23
p99
202
109
704
454
0
190
0
0
13
5
190
123
894
649
6
183
10
7
54
41
183
100
505
261
0
43
0
0
1
0
182
88
1372
Exceedances of 200 ppb
Mean
4
2
46
21
0
8
0
0
0
0
10
5
81
47
1
43
2
1
13
10
42
27
151
66
0
0
0
0
0
0
1
1
42
Min
0
0
0
0
0
0
0
0
0
0
0
0
11
7
0
14
0
0
0
0
11
3
119
32
0
0
0
0
0
0
0
0
0
Med
0
0
12
3
0
7
0
0
0
0
10
4
66
37
1
43
2
1
13
10
42
27
151
66
0
0
0
0
0
0
0
0
1
p99
34
12
202
109
0
27
0
0
1
0
28
17
190
123
1
72
4
2
25
19
72
51
183
100
0
1
0
0
0
0
5
3
182
A-153
-------
Location
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Scenario
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
936
0
102
0
0
39
15
1113
804
2726
2223
202
790
0
0
219
215
325
292
939
681
0
289
0
0
44
25
584
460
1356
1227
0
93
Min
11
0
41
0
0
9
0
793
534
1971
1614
0
727
0
0
0
0
18
6
625
345
0
31
0
0
1
0
351
259
920
841
0
0
Med
142
0
62
0
0
33
15
1032
681
2820
2304
0
778
0
0
2
1
229
163
986
686
0
182
0
0
33
20
533
412
1278
1148
0
25
p99
3249
1
253
0
0
74
32
1453
1137
3109
2642
606
864
1
0
655
645
727
706
1206
1013
0
762
0
0
110
60
918
757
1947
1770
3
748
Exceedances of 150 ppb
Mean
257
0
1
0
0
0
0
39
15
630
351
13
259
0
0
130
96
219
215
279
261
0
17
0
0
0
0
44
25
358
269
0
5
Min
0
0
0
0
0
0
0
9
0
399
219
0
6
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
182
119
0
0
Med
11
0
0
0
0
0
0
33
15
524
264
0
115
0
0
0
0
2
1
137
92
0
1
0
0
0
0
33
20
317
230
0
0
p99
1058
0
4
0
0
2
0
74
32
949
601
39
655
0
0
390
289
655
645
698
691
0
66
0
0
0
0
110
60
616
498
0
66
Exceedances of 200 ppb
Mean
23
0
0
0
0
0
0
2
1
39
15
0
176
0
0
0
0
175
153
219
215
0
1
0
0
0
0
1
0
44
25
0
0
Min
0
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
Med
0
0
0
0
0
0
0
1
0
33
15
0
1
0
0
0
0
0
0
2
1
0
0
0
0
0
0
1
0
33
20
0
0
p99
88
0
0
0
0
0
0
7
3
74
32
0
526
0
0
1
0
526
460
655
645
0
2
0
0
0
0
2
1
110
60
0
5
A-154
-------
Location
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
0
0
8
3
136
77
485
340
0
134
0
0
10
6
93
63
258
201
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
56
23
330
211
0
18
0
0
0
0
8
4
55
34
p99
2
1
123
52
1175
752
2552
2064
4
1195
4
2
140
85
879
697
1616
1364
Exceedances of 150 ppb
Mean
0
0
0
0
8
3
66
31
0
19
0
0
1
1
10
6
49
33
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
17
6
0
0
0
0
0
0
0
0
2
1
p99
0
0
5
3
123
52
655
347
2
306
2
2
18
11
140
85
596
437
Exceedances of 200 ppb
Mean
0
0
0
0
1
0
8
3
0
3
0
0
0
0
2
1
10
6
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
0
0
2
1
17
5
123
52
1
56
1
1
4
2
34
22
140
85
A-155
-------
1 Table A-121. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for
2 monitors >100 m from a major road following adjustment to just meeting the current and alternative
3 standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250
Mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
3
1
28
15
0
0
0
0
0
0
1
0
26
10
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
4
0
0
0
0
0
0
0
0
1
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
2
0
31
13
0
0
0
0
0
0
1
0
24
9
0
0
0
0
ppb
p99
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
11
1
0
0
0
0
0
0
7
4
48
28
0
0
0
0
0
0
3
1
67
24
0
0
0
0
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
4
0
0
0
0
0
0
0
0
3
1
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
2
0
0
0
0
0
0
0
0
2
1
0
0
0
0
p99
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
16
9
0
0
0
0
0
0
0
0
8
4
0
0
0
0
A-156
-------
Location
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250
Mean
0
0
0
0
6
2
0
4
0
0
0
0
1
0
13
5
0
0
0
0
0
0
0
0
4
2
0
0
0
0
0
0
0
0
8
4
0
0
0
0
0
0
1
1
11
5
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
2
0
0
2
0
0
0
0
1
0
8
2
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
2
1
0
ppb
p99
0
0
3
1
36
15
0
12
0
0
0
0
2
1
38
14
0
1
0
0
0
0
3
1
17
10
0
0
0
0
0
0
2
1
41
21
0
2
0
0
0
0
7
5
51
25
0
Exceedances of 300 ppb
Mean
0
0
0
0
1
0
0
1
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
1
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
0
0
0
0
7
3
0
2
0
0
0
0
1
0
9
2
0
0
0
0
0
0
1
1
5
3
0
0
0
0
0
0
0
0
7
2
0
1
0
0
0
0
3
1
13
8
0
A-157
-------
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Scenario
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250
Mean
1
0
0
0
0
1
0
7
3
0
1
0
0
0
0
1
0
16
8
0
24
1
0
8
6
23
14
54
32
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
22
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
17
9
0
24
1
0
8
6
23
14
54
32
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ppb
p99
10
0
0
0
0
7
5
49
29
0
3
0
0
0
0
3
2
38
24
0
46
2
0
16
11
46
28
85
58
0
0
0
0
0
0
0
0
11
6
0
0
0
0
0
0
0
0
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
4
1
0
13
0
0
5
4
13
10
29
21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
1
0
13
0
0
5
4
13
10
29
21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
5
0
0
0
0
4
2
11
7
0
0
0
0
0
0
1
0
13
5
0
25
0
0
10
7
25
19
54
41
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
A-158
-------
Location
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250
Mean
3
1
0
68
0
0
0
0
68
25
196
175
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
0
2
1
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ppb
p99
8
6
0
205
0
0
0
0
205
75
589
526
0
0
0
0
0
0
0
0
5
2
0
2
0
0
0
0
3
2
25
9
1
14
1
0
2
2
11
7
42
33
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
0
0
0
0
130
96
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
2
0
0
1
0
0
0
0
1
0
390
289
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
1
5
3
0
8
0
0
2
2
4
2
18
11
A-159
-------
1 Table A-122. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) for monitors <100 m from a major road
2 following adjustment to Just meeting the current and alternative standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
0
101
0
0
78
34
795
529
2028
1646
1
218
0
0
88
40
1013
669
2594
2056
0
509
0
0
137
78
1016
684
2184
1819
2
Min
0
6
0
0
2
0
125
64
699
498
0
10
0
0
0
0
256
132
1003
691
0
388
0
0
34
18
534
322
1310
1065
2
Med
0
54
0
0
37
11
628
388
1726
1387
0
242
0
0
69
36
1061
663
2924
2257
0
536
0
0
168
87
923
662
2012
1653
2
p99
1
324
1
1
242
113
2015
1404
4268
3617
5
402
2
0
203
106
1719
1203
3738
3118
1
680
1
1
212
140
1411
1011
3035
2501
3
Exceedances of 150 ppb
Mean
0
3
0
0
2
1
78
34
457
294
0
6
0
0
2
1
88
40
602
337
0
33
0
0
6
2
137
78
632
472
0
Min
0
0
0
0
0
0
2
0
49
25
0
0
0
0
0
0
0
0
104
33
0
21
0
0
0
0
34
18
288
189
0
Med
0
1
0
0
0
0
37
11
330
199
0
3
0
0
0
0
69
36
589
329
0
32
0
0
5
1
168
87
622
474
0
p99
0
16
0
0
9
5
242
113
1263
837
0
20
0
0
11
4
203
106
1105
680
1
48
1
1
14
4
212
140
901
688
1
Exceedances of 200 ppb
Mean
0
0
0
0
0
0
6
3
78
34
0
0
0
0
0
0
5
2
88
40
0
1
0
0
0
0
17
7
137
78
0
Min
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
34
18
0
Med
0
0
0
0
0
0
1
1
37
11
0
0
0
0
0
0
2
0
69
36
0
1
0
0
0
0
16
5
168
87
0
p99
0
2
0
0
1
1
29
15
242
113
0
2
0
0
2
0
25
11
203
106
0
2
0
0
1
1
39
18
212
140
0
A-160
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
Scenario
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
243
2
1
154
63
1918
1304
3752
3086
3
163
0
0
105
47
1031
691
2571
1911
3
691
5
3
117
53
557
377
1031
783
2
106
0
0
Min
187
1
0
130
55
1711
1138
3591
2882
0
0
0
0
0
0
19
8
244
105
0
377
0
0
53
18
306
210
600
471
0
6
0
0
Med
254
1
0
163
66
2000
1382
3766
3153
1
165
0
0
99
38
1178
766
2601
2066
0
709
0
0
114
54
549
368
1019
759
2
94
0
0
p99
288
3
2
169
68
2043
1391
3899
3224
15
419
5
2
301
162
1956
1364
4079
3258
12
970
18
13
189
87
825
561
1487
1141
6
256
2
2
Exceedances of 150 ppb
Mean
5
0
0
4
4
154
63
1177
725
0
5
0
0
4
1
105
47
626
368
1
195
1
1
6
5
117
53
377
239
0
3
0
0
Min
4
0
0
4
3
130
55
1025
625
0
0
0
0
0
0
0
0
6
1
0
91
0
0
0
0
53
18
210
134
0
0
0
0
Med
5
0
0
4
4
163
66
1245
761
0
2
0
0
2
0
99
38
686
389
0
201
0
0
1
0
114
54
368
237
0
0
0
0
p99
7
1
0
5
4
169
68
1260
788
2
22
0
0
22
10
301
162
1242
818
5
286
5
5
22
20
189
87
561
350
1
10
0
0
Exceedances of 200 ppb
Mean
2
0
0
2
1
11
5
154
63
0
0
0
0
0
0
10
5
105
47
1
36
1
1
5
3
15
6
117
53
0
1
0
0
Min
1
0
0
1
0
11
4
130
55
0
0
0
0
0
0
0
0
0
0
0
15
0
0
0
0
3
0
53
18
0
0
0
0
Med
1
0
0
1
0
11
6
163
66
0
0
0
0
0
0
5
2
99
38
0
30
0
0
0
0
12
1
114
54
0
0
0
0
p99
3
0
0
3
2
12
6
169
68
0
5
0
0
5
2
49
28
301
162
3
69
4
3
18
13
34
21
189
87
0
5
0
0
A-161
-------
Location
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 100 ppb
Mean
112
55
1328
879
3112
2490
1
291
1
1
123
69
1201
952
2868
2430
0
247
0
0
79
39
895
629
1974
1653
0
617
0
0
85
48
697
Min
8
6
280
127
1197
826
0
96
0
0
67
35
686
535
1704
1429
0
23
0
0
7
0
389
231
925
761
0
461
0
0
51
30
461
Med
110
50
1207
814
2885
2291
1
205
0
0
112
55
1267
1006
2793
2382
0
250
0
0
62
27
773
567
1698
1425
0
517
0
0
66
34
666
p99
245
130
2661
1790
5534
4698
2
768
2
2
220
128
1774
1452
4130
3520
1
510
1
1
202
103
1700
1227
3326
2839
0
873
0
0
137
79
963
Exceedances of 150 ppb
Mean
4
2
112
55
787
441
0
18
0
0
6
3
123
69
747
514
0
5
0
0
1
0
79
39
550
363
0
64
0
0
8
3
85
Min
0
0
8
6
106
47
0
5
0
0
3
2
67
35
406
273
0
0
0
0
0
0
7
0
186
100
0
37
0
0
4
2
51
Med
5
0
110
50
735
422
0
6
0
0
5
3
112
55
798
556
0
3
0
0
0
0
62
27
492
308
0
51
0
0
9
3
66
p99
10
6
245
130
1597
914
0
78
0
0
15
7
220
128
1159
814
0
13
0
0
8
1
202
103
1093
725
0
103
0
0
10
4
137
Exceedances of 200 ppb
Mean
0
0
10
5
112
55
0
5
0
0
1
1
11
8
123
69
0
0
0
0
0
0
3
2
79
39
0
12
0
0
0
0
16
Min
0
0
0
0
8
6
0
0
0
0
0
0
5
5
67
35
0
0
0
0
0
0
0
0
7
0
0
6
0
0
0
0
7
Med
0
0
11
5
110
50
0
1
0
0
0
0
9
7
112
55
0
0
0
0
0
0
1
1
62
27
0
11
0
0
0
0
12
p99
2
2
26
10
245
130
0
25
0
0
2
2
24
18
220
128
0
2
0
0
1
1
14
9
202
103
0
20
0
0
0
0
28
A-162
-------
Location
El Paso
El Paso
El Paso
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Scenario
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
478
1636
1354
0
1113
0
0
181
105
1883
1558
3169
3025
2
158
0
0
74
36
1047
799
2236
1883
0
241
0
0
20
11
374
276
1060
934
Min
306
1263
1010
0
998
0
0
123
58
1755
1443
3007
2868
0
0
0
0
0
0
7
3
110
50
0
1
0
0
0
0
33
15
227
174
Med
433
1607
1340
0
1113
0
0
181
105
1883
1558
3169
3025
0
26
0
0
11
4
548
380
1597
1234
0
114
0
0
11
3
318
212
900
809
p99
695
2039
1712
0
1228
0
0
238
152
2010
1672
3330
3181
6
562
1
0
242
110
2326
1853
4452
3845
1
748
1
1
173
98
1389
1112
2989
2735
Exceedances of 150 ppb
Mean
48
425
288
0
38
0
0
1
0
181
105
1258
988
0
3
0
0
2
1
74
36
649
410
0
9
0
0
0
0
20
11
198
139
Min
30
267
158
0
24
0
0
0
0
123
58
1144
878
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
2
Med
34
384
264
0
38
0
0
1
0
181
105
1258
988
0
0
0
0
0
0
11
4
280
143
0
2
0
0
0
0
11
3
147
82
p99
79
624
443
0
52
0
0
1
0
238
152
1371
1097
0
13
0
0
7
4
242
110
1588
1080
0
53
0
0
2
2
173
98
871
693
Exceedances of 200 ppb
Mean
9
85
48
0
0
0
0
0
0
5
3
181
105
0
0
0
0
0
0
4
3
74
36
0
0
0
0
0
0
0
0
20
11
Min
5
51
30
0
0
0
0
0
0
1
1
123
58
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
10
66
34
0
0
0
0
0
0
5
3
181
105
0
0
0
0
0
0
0
0
11
4
0
0
0
0
0
0
0
0
11
3
p99
13
137
79
0
0
0
0
0
0
8
4
238
152
0
1
0
0
1
0
14
12
242
110
0
1
0
0
1
1
4
2
173
98
A-163
-------
1 Table A-123. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for
2 monitors <100 m from a major road following adjustment to just meeting the current and alternative
3 standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250 ppb
Mean
0
0
0
0
0
0
1
0
11
5
0
0
0
0
0
0
0
0
12
3
0
0
0
0
0
0
2
0
26
15
0
0
0
0
0
0
4
2
16
8
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
1
0
0
0
0
0
0
3
2
15
5
0
0
0
0
Med
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
5
1
0
0
0
0
0
0
1
0
23
14
0
0
0
0
0
0
4
2
16
10
0
0
0
0
p99
0
1
0
0
0
0
5
3
42
21
0
0
0
0
0
0
2
2
46
17
0
1
0
0
1
1
4
1
60
34
0
1
0
0
1
1
4
3
16
10
0
0
0
0
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
6
2
0
0
0
0
0
0
2
1
4
4
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
4
3
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
1
0
0
0
0
0
0
1
0
4
4
0
0
0
0
p99
0
0
0
0
0
0
1
1
9
5
0
0
0
0
0
0
2
0
11
4
0
1
0
0
1
1
1
1
14
4
0
1
0
0
1
0
3
2
5
4
0
0
0
0
A-164
-------
Location
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
El Paso
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250 ppb
Mean
0
0
1
1
16
8
1
6
1
1
2
2
5
5
30
8
0
0
0
0
0
0
1
1
17
8
0
1
0
0
0
0
3
2
18
10
0
0
0
0
0
0
0
0
8
3
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
2
0
8
5
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
10
2
0
1
0
0
0
0
0
0
30
3
0
0
0
0
0
0
0
0
14
9
0
0
0
0
0
0
3
2
18
8
0
0
0
0
0
0
0
0
4
1
0
p99
0
0
10
6
78
41
3
23
3
3
8
6
20
18
52
25
0
1
0
0
0
0
6
5
44
21
0
7
0
0
0
0
7
3
30
22
0
1
0
0
1
0
1
1
24
14
0
Exceedances of 300 ppb
Mean
0
0
0
0
4
1
1
5
1
1
1
1
5
3
6
5
0
0
0
0
0
0
0
0
4
2
0
0
0
0
0
0
1
1
6
3
0
0
0
0
0
0
0
0
1
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
2
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
5
3
0
0
0
0
0
0
0
0
0
0
0
p99
0
0
5
2
22
10
3
19
3
3
5
5
18
13
22
20
0
0
0
0
0
0
2
2
10
6
0
0
0
0
0
0
2
2
15
7
0
1
0
0
0
0
1
1
8
1
0
A-165
-------
Location
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Scenario
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250 ppb
Mean
2
0
0
0
0
2
1
21
12
0
0
0
0
0
0
0
0
13
6
0
0
0
0
0
0
1
0
8
4
0
0
0
0
0
0
0
0
1
0
Min
1
0
0
0
0
1
0
11
6
0
0
0
0
0
0
0
0
5
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
2
0
0
0
0
2
1
18
11
0
0
0
0
0
0
0
0
13
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
3
0
0
0
0
3
2
35
20
0
0
0
0
0
0
0
0
20
10
0
0
0
0
0
0
3
2
27
13
0
1
0
0
1
1
1
1
8
4
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
0
8
3
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
Min
0
0
0
0
0
0
0
4
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
9
3
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
0
0
0
0
0
0
0
10
4
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
7
4
0
1
0
0
0
0
1
1
2
2
A-166
-------
1 Table A-124. Estimated annual average NO2 concentrations on-roads following adjustment to just meeting
2 the current and alternative standards, 2001-2003 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Site-
Years
600
600
600
600
600
600
600
600
600
600
900
900
900
900
900
900
900
900
900
900
300
300
300
300
300
300
300
300
300
300
200
200
200
200
200
200
200
200
200
200
600
600
600
600
600
600
600
Annual Mean (ppb)
Mean
17
34
17
15
33
30
50
45
66
60
39
65
30
28
60
56
91
83
121
111
32
76
31
28
63
57
94
85
126
113
42
80
31
28
63
56
94
84
125
112
37
89
36
31
72
63
108
Min
7
14
6
6
13
12
19
18
26
23
21
35
16
15
33
30
49
45
66
60
22
53
22
19
43
39
65
58
86
78
27
48
20
18
40
36
60
54
80
72
24
56
23
20
46
40
68
Median
18
36
17
15
34
31
51
46
68
62
37
62
29
26
58
53
86
79
115
106
32
75
31
28
62
56
93
84
124
112
40
81
30
27
60
54
90
80
120
107
36
87
35
30
69
60
104
p99
30
61
29
26
57
52
86
78
114
104
68
114
52
48
104
96
156
143
208
191
45
106
44
40
88
79
132
119
176
158
64
129
48
43
96
85
143
128
191
171
57
131
55
48
110
95
164
A-167
-------
Location
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Scenario
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
600
600
600
5100
5100
5100
5100
5100
5100
5100
5100
5100
5100
600
600
600
600
600
600
600
600
600
600
2600
2600
2600
2600
2600
2600
2600
2600
2600
2600
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
1800
1800
1800
1800
1800
1800
Annual Mean (ppb)
Mean
94
144
125
41
56
24
21
48
41
71
62
95
83
16
59
20
18
40
36
60
55
80
73
36
52
26
24
52
48
78
72
105
96
36
67
31
29
63
59
94
88
125
117
33
71
31
28
62
56
Min
60
91
79
6
8
4
3
7
6
11
9
14
12
9
33
11
10
22
20
33
30
45
40
14
18
10
9
20
18
30
28
40
37
18
33
16
15
32
30
48
45
65
60
11
24
10
9
20
19
Median
90
138
121
40
55
23
20
47
41
70
61
94
81
15
58
20
18
39
35
59
53
78
71
34
49
25
23
49
45
74
68
98
90
33
63
29
27
58
54
87
82
117
109
34
73
32
29
63
58
p99
143
219
191
82
113
48
42
96
83
144
125
191
166
25
92
31
28
62
57
94
85
125
113
73
103
53
48
105
96
158
144
210
192
66
126
58
54
116
109
174
163
232
217
63
133
58
53
117
107
A-168
-------
Location
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Scenario
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Site-
Years
1800
1800
1800
1800
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
200
200
200
200
200
200
200
200
200
200
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
500
500
500
500
500
Annual Mean (ppb)
Mean
92
85
123
113
22
60
20
17
39
35
59
52
78
70
27
69
25
23
51
46
76
69
102
92
26
96
33
30
66
59
98
89
131
118
19
46
18
16
35
32
53
49
70
65
49
72
36
33
71
Min
31
28
41
37
5
12
4
4
9
8
13
12
18
16
13
32
12
11
24
22
37
33
49
44
18
68
23
21
46
41
69
62
91
82
3
7
3
2
5
5
8
7
10
9
28
40
21
19
41
Median
95
87
127
116
24
62
21
19
42
37
63
56
83
75
27
68
25
23
50
45
75
68
100
90
26
94
32
29
65
58
97
87
129
116
14
33
13
12
25
23
38
35
51
47
47
69
35
32
69
p99
175
160
233
214
53
130
46
42
93
83
139
125
186
166
44
116
41
37
82
74
123
112
165
149
37
135
47
42
93
84
140
126
186
168
51
124
47
43
94
87
141
130
188
173
77
114
56
52
112
A-169
-------
Location
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
500
500
500
500
500
300
300
300
300
300
300
300
300
300
300
900
900
900
900
900
900
900
900
900
900
61200
61200
61200
61200
61200
61200
61200
61200
61200
61200
12700
12700
12700
12700
12700
12700
12700
12700
12700
12700
Annual Mean (ppb)
Mean
66
107
100
142
133
43
96
42
39
85
78
127
117
169
157
31
74
31
28
63
56
94
84
125
113
23
45
15
13
30
25
44
38
59
50
12
40
12
10
24
21
36
31
48
41
Min
38
62
58
82
77
28
67
28
26
55
51
83
77
110
102
18
45
18
16
36
33
54
49
73
65
1
1
0
0
1
1
1
1
2
1
1
4
1
1
3
2
4
3
5
4
Median
65
104
97
138
129
41
93
41
38
82
76
123
114
164
152
30
71
30
27
61
55
91
82
122
110
22
44
15
12
29
25
44
37
58
50
11
35
11
9
21
18
32
27
42
36
p99
105
167
157
223
209
64
144
64
59
127
118
191
177
255
236
50
118
50
44
99
89
149
133
198
178
50
99
33
28
65
55
98
83
130
111
33
109
33
28
67
57
100
85
134
114
A-170
-------
2 Table A-125. Estimated annual average NO2 concentrations on-roads following adjustment to just meeting
3 the current and alternative standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Site-Years
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
300
300
300
300
300
300
300
300
300
300
600
600
600
600
600
600
600
600
600
600
5400
5400
5400
5400
5400
Annual Mean (ppb)
Mean
16
35
17
15
33
30
50
46
67
61
35
63
28
25
55
50
83
75
110
100
36
69
33
30
66
60
99
91
132
121
31
90
35
32
71
64
106
96
141
129
33
56
26
23
52
Min
9
19
10
9
19
18
29
27
39
36
20
35
16
14
32
29
48
43
63
58
23
42
21
19
42
38
63
57
83
77
18
54
20
19
41
37
61
56
81
74
6
10
5
4
9
Median
15
34
16
15
32
29
48
44
64
59
33
59
26
24
52
48
78
71
105
95
36
68
32
30
65
60
97
89
130
119
30
88
34
31
69
62
103
94
137
125
32
54
25
23
50
p99
24
57
26
24
52
48
78
71
104
95
60
107
47
43
94
85
141
128
187
171
53
103
48
44
95
87
143
131
191
175
47
141
54
49
108
98
161
147
215
196
65
109
51
46
102
A-171
-------
Location
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
Atlanta
Scenario
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-Years
5400
5400
5400
5400
5400
400
400
400
400
400
400
400
400
400
400
2200
2200
2200
2200
2200
2200
2200
2200
2200
2200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
1500
1500
1500
1500
Annual Mean (ppb)
Mean
47
78
70
104
94
14
55
17
14
33
29
50
43
66
58
35
55
28
25
55
50
83
76
111
101
31
70
30
28
61
56
91
84
121
113
28
64
27
25
55
51
82
76
110
101
20
62
19
17
Min
8
14
12
18
16
9
35
11
10
22
19
33
29
44
38
12
20
10
9
19
18
29
27
39
35
18
37
17
16
35
32
52
48
69
64
9
23
9
8
17
16
26
24
34
32
4
13
4
4
Median
46
76
68
101
91
13
53
16
14
32
28
48
42
65
56
35
55
28
25
55
50
83
76
111
101
30
68
29
27
58
54
87
81
116
108
28
66
28
26
56
51
83
77
111
103
22
68
21
19
p99
93
153
139
204
185
20
80
24
21
47
41
71
62
94
82
61
99
49
45
98
89
147
134
195
178
59
123
57
53
114
106
171
159
228
212
52
121
52
48
104
96
156
144
208
192
42
128
40
36
A-172
-------
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Site-Years
1500
1500
1500
1500
1500
1500
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
200
200
200
200
200
200
200
200
200
200
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
900
900
900
900
900
900
900
900
900
900
300
300
300
Annual Mean (ppb)
Mean
38
34
57
52
76
69
25
75
26
24
52
47
78
71
103
95
24
96
31
27
63
54
94
80
125
107
16
43
16
15
33
31
49
46
66
62
43
73
34
31
67
62
101
93
134
125
43
94
26
Min
8
7
12
11
17
15
10
30
11
10
21
19
32
29
42
39
17
67
22
19
43
37
65
56
86
74
2
5
2
2
4
3
5
5
7
7
26
45
20
19
41
38
61
57
82
76
26
67
16
Median
42
38
63
57
84
76
25
75
26
24
51
47
77
71
103
94
23
93
30
26
60
52
90
77
120
103
11
30
11
11
23
21
34
32
45
43
42
71
33
30
65
61
98
91
131
122
41
93
25
p99
79
71
119
107
159
143
43
127
44
40
88
81
132
121
176
161
37
145
47
41
95
81
142
122
189
162
46
123
47
44
94
89
141
133
188
177
65
109
51
47
101
94
152
141
202
188
71
131
43
A-173
-------
Location
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-Years
300
300
300
300
300
300
300
400
400
400
400
400
400
400
400
400
400
56500
56500
56500
56500
56500
56500
56500
56500
56500
56500
11600
11600
11600
11600
11600
11600
11600
11600
11600
11600
Annual Mean (ppb)
Mean
24
52
49
78
73
104
98
27
68
27
25
54
51
81
76
108
102
20
47
17
15
34
30
51
46
68
61
12
39
12
11
23
21
35
32
46
43
Min
15
32
30
47
45
63
59
16
38
16
15
32
30
48
46
65
61
1
3
1
1
2
2
3
2
3
3
1
3
1
1
2
2
3
3
4
4
Median
23
50
47
74
70
99
93
26
66
26
25
52
49
78
74
105
98
20
45
17
15
33
30
50
44
66
59
10
34
10
10
21
19
31
29
41
38
p99
41
87
81
130
122
174
163
42
119
42
40
85
80
127
120
170
160
45
105
38
34
76
68
114
102
151
135
33
109
32
30
65
60
97
90
129
120
A-174
-------
1 Table A-126. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) on-roads following adjustment to just meeting
2 the current and alternative standards, 2001-2003 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
12
455
8
4
411
287
1172
942
1865
1605
252
1478
71
45
1197
929
2918
2527
4311
3908
103
2065
92
54
1306
983
2996
2527
4402
3901
403
2384
Min
0
1
0
0
1
0
118
63
383
289
0
61
0
0
44
24
540
384
1566
1174
0
715
0
0
254
150
1299
997
2519
2051
12
394
Med
1
339
0
0
302
159
1135
874
1904
1618
113
1206
14
7
951
706
2670
2264
4039
3635
51
2090
44
18
1224
909
2959
2507
4441
3890
242
2606
P99
138
1544
90
47
1511
1244
2756
2392
3619
3354
1460
4547
641
431
4002
3374
6157
5782
7376
7082
429
3714
393
257
2727
2349
4830
4206
6097
5698
1728
3658
Exceedances of 150 ppb
Mean
0
79
0
0
66
34
411
287
922
713
33
395
7
4
283
196
1197
929
2362
1993
14
677
12
7
327
216
1306
983
2440
2017
51
999
Min
0
0
0
0
0
0
1
0
53
24
0
1
0
0
0
0
44
24
299
179
0
87
0
0
33
15
254
150
997
661
0
11
Med
0
16
0
0
12
4
302
159
860
632
3
223
0
0
138
80
951
706
2103
1728
3
592
1
1
256
145
1224
909
2426
1967
6
915
p99
10
595
9
5
541
301
1511
1244
2325
1996
383
2053
118
74
1564
1232
4002
3374
5533
5130
89
1865
85
59
1003
740
2727
2349
4024
3685
404
2783
Exceedances of 200 ppb
Mean
0
12
0
0
8
4
123
73
411
287
5
110
1
0
71
45
454
323
1197
929
2
222
2
1
92
54
522
351
1306
983
6
382
Min
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
4
0
44
24
0
14
0
0
0
0
54
34
254
150
0
0
Med
0
1
0
0
0
0
41
15
302
159
0
30
0
0
14
7
281
172
951
706
0
148
0
0
44
18
438
272
1224
909
1
283
p99
2
195
0
0
90
47
764
551
1511
1244
97
843
22
10
641
431
2124
1689
4002
3374
23
746
19
8
393
257
1542
1068
2727
2349
54
1880
A-175
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Scenario
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
92
49
1589
1155
3064
2716
3801
3536
185
2779
157
86
1793
1249
3642
2990
4863
4305
414
1170
31
13
701
433
2081
1529
3258
2698
21
1680
80
47
820
635
Min
0
0
265
139
1459
1047
2653
2164
1
977
1
1
419
224
1706
1111
2972
2372
0
0
0
0
0
0
1
0
25
6
0
487
0
0
56
21
Med
19
6
1395
954
3060
2709
3763
3485
118
2670
100
50
1670
1071
3584
2899
4785
4227
211
913
3
0
450
226
1909
1270
3187
2560
4
1685
30
14
771
580
P99
608
404
3446
3025
4339
4131
4959
4706
752
5049
629
412
3929
3210
5876
5278
6794
6444
2395
4433
374
185
3357
2518
5842
5099
6956
6487
272
2832
647
464
2054
1833
Exceedances of 150 ppb
Mean
6
2
383
228
1589
1155
2692
2278
34
1079
29
16
516
296
1793
1249
3112
2457
67
295
2
1
142
69
701
433
1607
1118
1
761
8
4
251
176
Min
0
0
11
2
265
139
1024
631
0
160
0
0
37
6
419
224
1111
713
0
0
0
0
0
0
0
0
0
0
0
55
0
0
1
0
Med
1
1
237
105
1395
954
2665
2191
11
907
7
4
377
197
1670
1071
3024
2328
13
128
0
0
43
14
450
226
1366
852
0
723
0
0
164
102
p99
54
17
1621
1070
3446
3025
4131
3867
194
3026
162
94
1748
1133
3929
3210
5278
4669
687
1901
36
15
1145
690
3357
2518
5200
4370
18
1904
118
56
1215
1013
Exceedances of 200 ppb
Mean
0
0
92
49
623
386
1589
1155
10
391
8
5
157
86
786
477
1793
1249
12
78
0
0
31
13
238
127
701
433
0
334
1
0
80
47
Min
0
0
0
0
32
12
265
139
0
23
0
0
1
1
88
28
419
224
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
Med
0
0
19
6
429
238
1395
954
2
272
2
1
100
50
649
345
1670
1071
0
16
0
0
3
0
97
38
450
226
0
263
0
0
30
14
p99
3
1
608
404
2341
1621
3446
3025
51
1417
45
37
629
412
2351
1677
3929
3210
165
725
6
2
374
185
1641
1041
3357
2518
2
1319
15
5
647
464
A-176
-------
Location
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Scenario
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
1743
1483
2504
2225
205
900
37
23
906
665
2430
2063
3598
3241
161
1788
82
56
1509
1219
3340
3036
4566
4255
156
1941
107
67
1445
1154
3041
2700
4247
3903
Min
533
360
1198
1022
0
0
0
0
0
0
140
102
526
351
0
68
0
0
52
21
879
670
1958
1672
0
12
0
0
1
0
106
52
458
329
Med
1772
1480
2433
2198
83
639
6
2
661
438
2248
1859
3471
3106
54
1567
18
7
1288
965
3077
2806
4248
3932
41
1963
20
8
1305
941
3381
2947
4764
4376
P99
2868
2570
4193
3697
1569
3696
412
307
3630
3144
5699
5314
6902
6578
1109
5152
706
577
4554
4031
6750
6336
7526
7332
1170
5165
828
540
4550
4043
6473
6158
7631
7330
Exceedances of 150 ppb
Mean
820
635
1457
1220
24
178
4
2
171
109
906
665
1944
1601
17
472
7
4
343
250
1509
1219
2790
2472
17
656
10
6
401
277
1445
1154
2574
2211
Min
56
21
319
200
0
0
0
0
0
0
0
0
88
33
0
0
0
0
0
0
52
21
474
314
0
0
0
0
0
0
1
0
34
16
Med
771
580
1448
1193
2
61
0
0
65
33
661
438
1726
1372
1
278
0
0
171
112
1288
965
2595
2270
0
402
0
0
183
102
1305
941
2774
2306
p99
2054
1833
2570
2401
310
1461
74
45
1310
924
3630
3144
5190
4731
278
2540
153
106
2045
1521
4554
4031
6182
5787
190
2992
135
86
2317
1789
4550
4043
5987
5699
Exceedances of 200 ppb
Mean
372
269
820
635
4
40
0
0
37
23
301
195
906
665
2
119
1
0
82
56
569
423
1509
1219
2
208
1
0
107
67
622
453
1445
1154
Min
5
1
56
21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
52
21
0
0
0
0
0
0
0
0
1
0
Med
289
188
771
580
0
4
0
0
6
2
144
79
661
438
0
35
0
0
18
7
345
232
1288
965
0
60
0
0
20
8
362
223
1305
941
p99
1499
1246
2054
1833
89
494
9
3
412
307
1995
1445
3630
3144
64
856
33
15
706
577
2773
2256
4554
4031
32
1442
15
5
828
540
3001
2521
4550
4043
A-177
-------
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
98
1572
53
29
704
516
1550
1304
2296
1997
85
2053
57
31
1097
819
2353
2044
3215
2935
34
2790
121
68
1374
1070
2916
2479
4086
3671
88
1347
61
41
Min
0
28
0
0
0
0
25
8
103
67
0
414
0
0
62
33
650
472
1234
1070
0
1387
3
2
451
253
1544
1166
2480
2103
0
3
0
0
Med
11
1629
3
0
470
279
1609
1249
2590
2170
34
2050
21
11
988
697
2417
2035
3281
3002
16
2741
74
34
1312
1046
2930
2420
4037
3606
6
627
2
1
P99
1014
4537
624
370
3040
2566
4684
4271
5867
5514
592
3852
403
232
2693
2355
4040
3711
5249
4859
189
4492
491
301
2842
2336
4586
4122
5972
5490
974
4346
687
547
Exceedances of 150 ppb
Mean
12
714
5
2
191
121
704
516
1275
1037
7
820
4
2
256
159
1097
819
1993
1684
3
1295
11
5
422
277
1374
1070
2412
1988
10
583
5
3
Min
0
1
0
0
0
0
0
0
6
1
0
26
0
0
2
1
62
33
414
246
0
348
0
0
25
8
451
253
1125
829
0
0
0
0
Med
0
437
0
0
44
16
470
279
1206
891
1
712
0
0
154
80
988
697
1975
1623
2
1238
5
2
370
222
1312
1046
2353
1945
0
144
0
0
p99
168
2873
91
51
1556
1183
3040
2566
4194
3749
51
2383
34
19
1302
954
2693
2355
3658
3390
15
2672
61
31
1185
848
2842
2336
4122
3480
205
3276
132
67
Exceedances of 200 ppb
Mean
2
304
1
0
53
29
296
199
704
516
1
277
0
0
57
31
420
269
1097
819
1
588
2
1
121
68
633
439
1374
1070
1
211
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
0
9
5
62
33
0
56
0
0
3
2
83
31
451
253
0
0
0
0
Med
0
95
0
0
3
0
98
45
470
279
0
177
0
0
21
11
313
168
988
697
1
542
2
2
74
34
589
394
1312
1046
0
32
0
0
p99
37
1856
17
11
624
370
1952
1577
3040
2566
9
1443
7
4
403
232
1671
1328
2693
2355
2
1540
13
7
491
301
1568
1195
2842
2336
15
1935
11
4
A-178
-------
Location
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
839
686
1605
1457
2143
1986
527
1932
100
67
1876
1578
3841
3549
4880
4683
241
3555
227
149
2950
2523
4716
4456
5567
5365
91
2057
91
50
1441
1069
3129
2672
Min
0
0
5
4
37
22
2
68
0
0
77
42
1127
805
2525
2196
1
1734
1
0
664
392
2997
2596
4162
3837
0
409
0
0
93
31
926
647
Med
272
197
880
728
1546
1346
321
1830
11
7
1820
1468
3960
3699
4936
4747
88
3628
83
35
2998
2454
4712
4506
5597
5406
26
1957
26
9
1321
944
3093
2603
P99
3736
3498
4632
4470
5259
5079
2418
4469
769
533
4400
4191
5589
5445
6351
6195
1602
5424
1512
1063
5067
4836
6269
6068
6940
6818
663
4366
663
388
3589
3107
5295
4998
Exceedances of 150 ppb
Mean
232
159
839
686
1393
1229
57
503
4
2
462
340
1876
1578
3300
2994
21
1452
19
11
913
658
2950
2523
4282
3986
8
683
8
3
366
230
1441
1069
Min
0
0
0
0
4
1
0
2
0
0
2
1
77
42
623
417
0
142
0
0
19
12
664
392
2251
1902
0
8
0
0
0
0
93
31
Med
37
20
272
197
667
534
4
335
0
0
278
151
1820
1468
3468
3176
0
1300
0
0
715
470
2998
2454
4357
4057
0
545
0
0
227
117
1321
944
p99
2062
1557
3736
3498
4418
4218
455
2311
43
27
2165
1789
4400
4191
5329
5198
178
3827
178
97
3311
2830
5067
4836
5995
5744
113
2452
113
59
1766
1331
3589
3107
Exceedances of 200 ppb
Mean
61
41
362
265
839
686
5
118
0
0
100
67
751
583
1876
1578
1
435
1
1
227
149
1429
1095
2950
2523
1
208
1
0
91
50
577
384
Min
0
0
0
0
0
0
0
0
0
0
0
0
6
2
77
42
0
4
0
0
1
0
69
40
664
392
0
0
0
0
0
0
3
0
Med
2
1
70
47
272
197
0
16
0
0
11
7
546
382
1820
1468
0
199
0
0
83
35
1201
822
2998
2454
0
110
0
0
26
9
434
256
p99
687
547
2663
2209
3736
3498
53
951
3
0
769
533
2927
2501
4400
4191
19
2217
19
7
1512
1063
3920
3542
5067
4836
14
1225
14
7
663
388
2210
Mil
A-179
-------
Location
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
4483
4005
54
804
4
1
188
90
760
473
1451
1042
9
748
9
4
202
110
610
421
1078
806
Min
2135
1708
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
4458
3973
5
519
0
0
52
14
478
233
1203
762
0
317
0
0
33
12
226
120
583
360
P99
6647
6245
637
3700
89
29
1555
926
3576
2750
4890
4194
154
3899
154
77
1700
1148
3452
2674
4980
4175
Exceedances of 150 ppb
Mean
2627
2144
5
203
0
0
24
9
188
90
540
316
1
269
1
1
38
17
202
110
470
301
Min
624
342
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
2560
2059
0
59
0
0
1
0
52
14
286
123
0
55
0
0
2
0
33
12
144
67
p99
4998
4417
102
1646
4
1
358
172
1555
926
2967
2153
27
2015
27
14
564
294
1700
1148
2886
2169
Exceedances of 200 ppb
Mean
1441
1069
1
52
0
0
4
1
47
19
188
90
0
97
0
0
9
4
64
31
202
110
Min
93
31
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
1321
944
0
5
0
0
0
0
4
0
52
14
0
9
0
0
0
0
4
1
33
12
p99
3589
3107
12
614
0
0
89
29
575
286
1555
926
7
1089
7
6
154
77
796
492
1700
1148
A-180
-------
1 Table A-127. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) on-roads
2 following adjustment to Just meeting the current and alternative standards, 2001-2003 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 250 ppb
Mean
0
2
0
0
1
1
34
17
167
103
1
34
0
0
21
13
175
118
583
424
0
79
0
0
30
18
213
133
650
448
1
142
0
0
23
10
236
134
791
520
4
150
4
3
61
34
340
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12
3
0
0
0
0
0
0
15
2
85
47
0
0
0
0
0
0
2
1
69
20
0
1
0
0
1
0
11
Med
0
0
0
0
0
0
4
2
65
30
0
3
0
0
1
0
68
36
374
252
0
35
0
0
7
4
145
70
569
392
0
60
0
0
3
1
109
41
585
340
1
97
1
1
31
11
230
p99
0
30
0
0
21
12
301
189
927
684
29
407
1
0
291
190
1111
845
2541
2031
6
324
5
3
176
110
740
515
1715
1278
9
992
1
1
217
104
1104
792
2626
2075
34
604
30
28
312
194
1265
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
8
4
66
34
0
12
0
0
7
4
71
45
283
196
0
32
0
0
12
7
92
54
327
216
0
55
0
0
6
2
92
49
383
228
3
70
3
2
29
16
157
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
33
15
0
0
0
0
0
0
0
0
11
2
0
1
0
0
0
0
1
Med
0
0
0
0
0
0
0
0
12
4
0
0
0
0
0
0
14
7
138
80
0
8
0
0
1
1
44
18
256
145
0
11
0
0
1
1
19
6
237
105
1
38
1
0
7
4
100
p99
0
8
0
0
9
5
90
47
541
301
7
208
0
0
118
74
641
431
1564
1232
3
172
3
0
85
59
393
257
1003
740
1
431
1
1
54
17
608
404
1621
1070
28
385
26
20
162
94
629
A-181
-------
Location
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Scenario
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 250 ppb
Mean
193
970
610
2
22
0
0
7
3
84
40
308
172
0
152
0
0
24
13
168
115
445
332
1
12
0
0
11
6
98
63
391
268
0
35
0
0
23
15
210
148
718
558
0
69
0
0
32
Min
1
142
52
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
1
0
0
0
0
0
Med
126
812
456
0
1
0
0
0
0
19
4
142
57
0
83
0
0
4
2
99
54
359
254
0
0
0
0
0
0
27
13
213
123
0
3
0
0
1
1
82
46
478
338
0
8
0
0
1
p99
764
2742
1940
48
258
1
0
117
53
799
466
1967
1296
0
860
3
1
291
186
953
787
1605
1405
21
223
0
0
181
123
841
577
2354
1838
10
409
1
1
350
250
1368
1035
3078
2666
2
570
1
0
297
Exceedances of 300 ppb
Mean
86
516
296
1
7
0
0
2
1
31
13
142
69
0
65
0
0
8
4
80
47
251
176
0
4
0
0
4
2
37
23
171
109
0
11
0
0
7
4
82
56
343
250
0
25
0
0
10
Min
1
37
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
50
377
197
0
0
0
0
0
0
3
0
43
14
0
23
0
0
0
0
30
14
164
102
0
0
0
0
0
0
6
2
65
33
0
1
0
0
0
0
18
7
171
112
0
0
0
0
0
p99
412
1748
1133
14
98
0
0
36
15
374
185
1145
690
0
509
0
0
118
56
647
464
1215
1013
3
92
0
0
74
45
412
307
1310
924
1
167
1
1
153
106
706
577
2045
1521
0
250
0
0
135
A-182
-------
Location
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Scenario
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 250 ppb
Mean
19
254
169
774
564
0
130
0
0
16
8
123
75
365
250
0
93
0
0
15
7
154
91
540
366
1
254
1
1
34
17
277
175
775
550
0
76
0
0
19
12
144
98
455
338
0
24
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
12
8
0
5
0
0
0
0
8
4
116
56
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
92
47
517
315
0
17
0
0
0
0
18
7
142
74
0
39
0
0
4
1
75
37
430
263
1
188
1
1
16
9
222
119
739
503
0
4
0
0
0
0
17
8
98
64
0
0
0
p99
208
1676
1243
3359
2768
12
1198
3
2
225
129
1186
841
2146
1780
2
791
1
1
107
52
954
634
1929
1550
2
789
2
2
189
112
848
605
1891
1475
2
857
0
0
328
233
1451
1033
2955
2548
14
218
0
Exceedances of 300 ppb
Mean
6
107
67
401
277
0
56
0
0
5
2
53
29
191
121
0
32
0
0
4
2
57
31
256
159
1
108
1
1
11
5
121
68
422
277
0
31
0
0
5
3
61
41
232
159
0
4
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
3
0
0
0
0
3
2
25
8
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
20
8
183
102
0
4
0
0
0
0
3
0
44
16
0
11
0
0
0
0
21
11
154
80
1
60
1
1
5
2
74
34
370
222
0
0
0
0
0
0
2
1
37
20
0
0
0
p99
86
828
540
2317
1789
3
633
1
0
91
51
624
370
1556
1183
1
257
0
0
34
19
403
232
1302
954
2
490
2
2
61
31
491
301
1185
848
0
455
0
0
132
67
687
547
2062
1557
0
59
0
A-183
-------
Location
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250 ppb
Mean
0
19
10
279
198
956
749
0
127
0
0
60
38
571
402
1748
1383
0
66
0
0
25
13
232
136
740
500
0
15
0
0
1
0
13
5
67
28
0
36
0
0
3
1
23
10
86
43
Min
0
0
0
0
0
11
6
0
0
0
0
0
0
5
2
133
69
0
0
0
0
0
0
0
0
4
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
118
74
747
546
0
23
0
0
4
2
368
210
1564
1123
0
14
0
0
3
0
121
52
603
360
0
0
0
0
0
0
0
0
8
1
0
1
0
0
0
0
0
0
7
2
p99
0
156
95
1571
1273
3353
2927
2
831
2
0
401
273
2634
2195
4177
3849
2
504
2
2
243
156
1331
894
2544
2058
3
237
0
0
18
5
218
101
753
396
3
530
3
1
59
30
380
176
997
597
Exceedances of 300 ppb
Mean
0
4
2
100
67
462
340
0
43
0
0
19
11
227
149
913
658
0
22
0
0
8
3
91
50
366
230
0
5
0
0
0
0
4
1
24
9
0
14
0
0
1
1
9
4
38
17
Min
0
0
0
0
0
2
1
0
0
0
0
0
0
1
0
19
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
11
7
278
151
0
2
0
0
0
0
83
35
715
470
0
2
0
0
0
0
26
9
227
117
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
0
p99
0
43
27
769
533
2165
1789
0
303
0
0
178
97
1512
1063
3311
2830
1
202
1
1
113
59
663
388
1766
1331
0
95
0
0
4
1
89
29
358
172
1
250
1
1
27
14
154
77
564
294
A-184
-------
1 Table A-128. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) on-roads following adjustment to just meeting
2 the current and alternative standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
5
462
8
4
372
267
1045
860
1735
1493
151
1357
42
24
934
688
2546
2085
3841
3417
294
2163
181
117
1971
1650
3235
3014
3842
3684
81
2835
Min
0
13
0
0
13
1
189
106
518
371
0
56
0
0
25
16
450
207
1342
1025
2
506
0
0
489
296
1827
1560
2544
2352
0
1050
Med
0
375
0
0
294
196
987
790
1702
1441
49
1146
4
1
751
506
2312
1835
3674
3192
161
2130
78
40
1944
1635
3247
2996
3955
3785
23
2744
p99
75
1359
111
53
1162
958
2192
1946
3216
2836
984
4138
381
268
3211
2718
5611
5069
6844
6489
1097
3718
805
572
3540
3172
4452
4292
4942
4758
462
4917
Exceedances of 150 ppb
Mean
0
91
0
0
64
36
372
267
819
644
15
335
2
1
190
120
934
688
1996
1603
34
761
16
9
626
448
1971
1650
2922
2667
6
1263
Min
0
0
0
0
0
0
13
1
81
56
0
0
0
0
0
0
25
16
176
81
0
19
0
0
18
7
489
296
1487
1215
0
131
Med
0
40
0
0
20
8
294
196
745
564
0
185
0
0
74
34
751
506
1762
1369
5
660
2
0
475
303
1944
1635
2898
2619
0
1187
p99
1
526
2
1
451
320
1162
958
1886
1571
211
1754
64
35
1196
842
3211
2718
4973
4496
220
2125
126
71
1835
1466
3540
3172
4205
4014
72
3068
Exceedances of 200 ppb
Mean
0
16
0
0
8
4
114
71
372
267
1
85
0
0
42
24
328
209
934
688
4
236
1
0
181
117
945
705
1971
1650
0
489
Min
0
0
0
0
0
0
0
0
13
1
0
0
0
0
0
0
0
0
25
16
0
0
0
0
0
0
56
22
489
296
0
17
Med
0
1
0
0
0
0
62
27
294
196
0
17
0
0
4
1
185
84
751
506
0
117
0
0
78
40
834
569
1944
1635
0
353
p99
0
174
0
0
111
53
630
459
1162
958
44
673
1
0
381
268
1656
1263
3211
2718
35
988
18
4
805
572
2339
1960
3540
3172
11
1744
A-185
-------
Location
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
Scenario
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
170
105
1834
1482
3440
3074
4552
4203
177
1184
50
28
984
727
2390
2011
3366
3042
17
1487
47
22
586
417
1284
1026
1863
1577
168
1050
51
30
1072
806
Min
0
0
305
179
1603
1185
2776
2311
0
3
0
0
1
0
32
16
94
74
0
351
0
0
24
8
295
144
643
463
0
0
0
0
0
0
Med
80
35
1776
1395
3403
2990
4499
4153
64
995
7
3
774
510
2185
1826
3232
2896
2
1544
11
3
512
334
1330
1023
1955
1648
69
872
9
4
870
607
p99
860
593
3723
3413
5488
5118
6565
6255
1275
4030
553
373
3726
3141
5978
5513
6976
6662
158
2466
349
202
1563
1321
2306
2070
2677
2495
1029
3687
387
283
3498
2942
Exceedances of 150 ppb
Mean
16
9
581
406
1834
1482
2966
2584
18
296
3
1
220
137
984
727
1944
1582
1
745
4
1
170
96
586
417
1066
823
17
226
4
2
231
147
Min
0
0
10
3
305
179
1162
111
0
0
0
0
0
0
1
0
13
5
0
59
0
0
0
0
24
8
175
78
0
0
0
0
0
0
Med
1
0
454
279
1776
1395
2909
2517
1
145
0
0
90
44
774
510
1775
1398
0
683
0
0
85
34
512
334
1089
797
1
103
0
0
112
54
p99
141
95
1945
1546
3723
3413
5062
4568
289
1706
62
28
1466
1106
3726
3141
5414
4876
14
1788
54
27
787
547
1563
1321
2070
1821
187
1593
65
35
1319
930
Exceedances of 200 ppb
Mean
2
1
170
105
871
638
1834
1482
2
74
0
0
50
28
370
240
984
727
0
358
0
0
47
22
259
158
586
417
2
50
0
0
51
30
Min
0
0
0
0
44
13
305
179
0
0
0
0
0
0
0
0
1
0
0
3
0
0
0
0
1
0
24
8
0
0
0
0
0
0
Med
0
0
80
35
750
516
1776
1395
0
15
0
0
7
3
199
102
774
510
0
246
0
0
11
3
162
77
512
334
0
8
0
0
9
4
p99
30
18
860
593
2531
2000
3723
3413
42
792
6
3
553
373
2052
1569
3726
3141
1
1209
4
2
349
202
1019
787
1563
1321
36
457
7
4
387
283
A-186
-------
Location
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Scenario
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
2596
2237
3774
3401
87
1914
72
46
1381
1126
2992
2697
4127
3865
80
1697
75
50
1202
983
2575
2307
3639
3363
59
1665
43
23
673
504
1487
1262
2200
1937
Min
136
59
509
354
0
173
0
0
98
47
972
746
2047
1901
0
25
0
0
0
0
121
78
444
312
0
9
0
0
0
0
3
1
33
12
Med
2635
2187
3945
3532
25
1881
18
7
1275
972
2884
2628
3980
3733
13
1613
12
5
1039
786
2738
2377
3880
3624
3
1972
1
0
550
323
1714
1382
2730
2322
p99
5616
5196
6796
6472
687
4739
580
407
4410
3998
6362
6108
7122
7038
709
4543
655
474
3904
3560
5756
5427
7144
6808
609
4187
486
294
2442
2031
3909
3514
4963
4610
Exceedances of 150 ppb
Mean
1072
806
2129
1762
5
623
4
2
325
231
1381
1126
2504
2197
5
587
5
3
316
226
1202
983
2150
1897
5
803
3
2
174
110
673
504
1221
1013
Min
0
0
39
17
0
3
0
0
0
0
98
47
658
414
0
0
0
0
0
0
0
0
41
24
0
0
0
0
0
0
0
0
1
0
Med
870
607
2079
1645
0
458
0
0
200
136
1275
972
2431
2110
0
341
0
0
142
84
1039
786
2212
1917
0
706
0
0
35
12
550
323
1322
1025
p99
3498
2942
5061
4544
77
2456
64
38
1985
1474
4410
3998
5849
5555
71
2664
64
35
1907
1586
3904
3560
5298
4835
87
2722
63
35
1169
895
2442
2031
3493
3050
Exceedances of 200 ppb
Mean
390
263
1072
806
0
188
0
0
72
46
537
396
1381
1126
1
179
0
0
75
50
506
382
1202
983
1
363
0
0
43
23
279
187
673
504
Min
0
0
0
0
0
0
0
0
0
0
4
1
98
47
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
232
137
870
607
0
74
0
0
18
7
402
265
1275
972
0
57
0
0
12
5
291
195
1039
786
0
169
0
0
1
0
112
44
550
323
p99
1881
1424
3498
2942
6
1195
4
2
580
407
2666
2227
4410
3998
9
1250
9
6
655
474
2568
2209
3904
3560
14
1739
9
4
486
294
1518
1220
2442
2031
A-187
-------
Location
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
67
2324
78
48
1200
969
2426
2165
3214
2989
45
2755
131
68
1280
867
2673
2104
3839
3231
55
1206
61
46
767
672
1416
1319
1932
1805
353
2309
83
48
Min
0
544
0
0
67
36
597
425
1018
942
0
1395
0
0
375
162
1395
978
2340
1870
0
5
0
0
0
0
10
6
40
34
0
457
0
0
Med
22
2292
26
16
1078
837
2427
2139
3240
3023
37
2666
79
48
1213
770
2605
2067
3798
3180
0
475
0
0
195
151
676
576
1297
1124
136
2251
10
5
p99
547
4255
605
393
2944
2696
4412
4088
5374
5060
182
4892
542
267
2873
2328
4495
4116
5787
5279
726
4215
761
681
3573
3409
4502
4349
5122
4937
1794
4472
656
387
Exceedances of 150 ppb
Mean
5
1114
6
3
317
216
1200
969
2069
1795
11
1329
25
15
394
227
1280
867
2245
1685
7
561
9
5
227
178
767
672
1225
1130
25
640
3
1
Min
0
47
0
0
2
0
67
36
384
290
0
408
0
0
25
5
375
162
1012
640
0
0
0
0
0
0
0
0
5
4
0
3
0
0
Med
0
980
0
0
184
108
1078
837
2035
1714
7
1290
21
11
306
135
1213
770
2149
1588
0
98
0
0
17
11
195
151
496
418
1
402
0
0
p99
56
2855
61
40
1531
1231
2944
2696
3955
3681
53
3072
106
66
1310
899
2873
2328
4166
3451
229
3201
260
181
2007
1689
3573
3409
4215
4083
184
2655
32
14
Exceedances of 200 ppb
Mean
0
452
1
0
78
48
509
366
1200
969
5
627
10
7
131
68
582
353
1280
867
0
217
1
0
61
46
354
288
767
672
2
146
0
0
Min
0
4
0
0
0
0
5
2
67
36
0
84
0
0
0
0
71
20
375
162
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
308
0
0
26
16
360
227
1078
837
2
545
5
3
79
48
478
261
1213
770
0
16
0
0
0
0
40
27
195
151
0
23
0
0
p99
9
1871
12
5
605
393
2005
1669
2944
2696
24
1943
48
35
542
267
1736
1275
2873
2328
14
1873
19
10
761
681
2568
2246
3573
3409
18
1088
1
0
A-188
-------
Location
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Scenario
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
1909
1542
3807
3498
4812
4573
394
2971
195
178
678
573
1995
1742
3195
2946
50
1785
55
39
1055
893
2434
2210
3650
3366
32
886
11
6
359
240
1101
843
Min
200
87
1607
1334
2636
2410
0
1011
0
0
0
0
348
175
1802
1445
0
293
0
0
86
48
848
681
1497
1354
0
0
0
0
0
0
0
0
Med
1791
1408
3912
3573
4882
4646
255
2923
0
0
671
503
1982
1767
3171
2902
10
1630
13
7
909
787
2271
2034
3499
3212
1
615
0
0
159
80
847
585
p99
4162
3846
5509
5227
6298
6049
1237
4766
694
686
1866
1606
3648
3465
4646
4483
364
4265
397
292
2742
2506
4567
4449
6031
5662
430
3726
194
103
2328
1840
4238
3718
Exceedances of 150 ppb
Mean
436
298
1909
1542
3305
2964
214
1360
71
52
266
246
678
573
1526
1293
4
647
4
2
249
190
1055
893
1991
1784
2
265
1
0
63
34
359
240
Min
0
0
200
87
1116
858
0
175
0
0
0
0
0
0
90
37
0
1
0
0
0
0
86
48
557
403
0
0
0
0
0
0
0
0
Med
203
96
1791
1408
3337
2989
3
1173
0
0
79
30
671
503
1497
1214
0
485
0
0
161
109
909
787
1797
1607
0
88
0
0
7
2
159
80
p99
2151
1648
4162
3846
5211
4904
706
3241
624
579
808
770
1866
1606
3172
2986
79
2455
88
60
1125
949
2742
2506
4111
3827
46
1870
14
5
738
459
2328
1840
Exceedances of 200 ppb
Mean
83
48
740
524
1909
1542
138
598
16
9
195
178
325
293
678
573
0
218
0
0
55
39
414
327
1055
893
0
78
0
0
11
6
114
64
Min
0
0
4
0
200
87
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
86
48
0
0
0
0
0
0
0
0
Med
10
5
517
284
1791
1408
0
641
0
0
0
0
175
131
671
503
0
106
0
0
13
7
316
243
909
787
0
8
0
0
0
0
21
7
p99
656
387
2827
2310
4162
3846
662
1990
309
195
694
686
991
900
1866
1606
8
1309
12
4
397
292
1476
1308
2742
2506
5
867
1
1
194
103
1128
739
A-189
-------
Location
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
98
99
98
99
98
99
98
99
98
99
Exceedances of 100 ppb
Mean
1859
1531
10
737
9
6
197
157
590
493
1008
894
Min
0
0
0
0
0
0
0
0
0
0
0
0
Med
1664
1299
0
299
0
0
24
14
198
141
525
414
p99
5423
4967
189
3833
184
122
1688
1449
3357
2982
4621
4343
Exceedances of 150 ppb
Mean
839
619
1
274
1
1
41
29
197
157
439
362
Min
0
0
0
0
0
0
0
0
0
0
0
0
Med
580
374
0
47
0
0
1
0
24
14
117
79
p99
3697
3160
29
2075
27
20
571
477
1688
1449
2814
2474
Exceedances of 200 ppb
Mean
359
240
0
105
0
0
9
6
70
50
197
157
Min
0
0
0
0
0
0
0
0
0
0
0
0
Med
159
80
0
6
0
0
0
0
3
2
24
14
p99
2328
1840
8
1106
7
4
184
122
887
716
1688
1449
A-190
-------
1 Table A-129. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) on-roads
2 following adjustment to Just meeting the current and alternative standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Scenario
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
Percentile
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 250 ppb
Mean
0
3
0
0
1
0
34
18
155
103
0
23
0
0
10
5
115
70
419
283
0
78
0
0
55
33
416
281
1159
875
0
183
0
0
50
28
382
256
1052
788
0
20
0
0
12
6
135
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
7
2
107
43
0
0
0
0
0
0
3
0
76
33
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
5
1
100
52
0
1
0
0
0
0
30
12
267
138
0
22
0
0
9
4
268
149
1102
753
0
86
0
0
10
3
270
150
937
666
0
1
0
0
0
0
43
p99
0
37
0
0
18
3
314
204
709
577
3
270
0
0
156
96
842
552
1951
1516
4
437
1
0
312
219
1418
1078
2624
2196
0
905
2
0
305
210
1512
1159
2763
2339
7
293
0
0
198
113
1106
Exceedances of 300 ppb
Mean
0
0
0
0
0
0
8
4
64
36
0
7
0
0
2
1
42
24
190
120
0
25
0
0
16
9
181
117
626
448
0
65
0
0
16
9
170
105
581
406
0
5
0
0
3
1
50
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18
7
0
0
0
0
0
0
0
0
10
3
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
20
8
0
0
0
0
0
0
4
1
74
34
0
2
0
0
2
0
78
40
475
303
0
17
0
0
1
0
80
35
454
279
0
0
0
0
0
0
7
p99
0
3
0
0
2
1
111
53
451
320
0
132
0
0
64
35
381
268
1196
842
0
200
0
0
126
71
805
572
1835
1466
0
409
0
0
141
95
860
593
1945
1546
1
86
0
0
62
28
553
A-191
-------
Location
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Scenario
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 250 ppb
Mean
80
465
312
0
168
0
0
13
5
109
57
319
202
0
13
0
0
13
7
135
86
505
345
0
55
0
0
17
10
197
138
696
521
0
53
0
0
20
11
196
137
632
490
0
155
0
0
11
Min
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
11
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
17
279
156
0
80
0
0
2
0
43
14
213
111
0
0
0
0
1
0
49
23
317
199
0
9
0
0
1
0
104
56
551
389
0
6
0
0
1
0
61
34
403
281
0
28
0
0
0
p99
783
2350
1858
0
792
1
1
137
76
595
387
1131
864
7
198
1
1
154
99
861
606
2227
1706
0
534
0
0
160
105
1306
1018
3109
2666
2
564
2
1
199
127
1442
1059
2810
2421
2
1082
1
0
163
Exceedances of 300 ppb
Mean
28
220
137
0
78
0
0
4
1
47
22
170
96
0
4
0
0
4
2
51
30
231
147
0
17
0
0
4
2
72
46
325
231
0
17
0
0
5
3
75
50
316
226
0
65
0
0
3
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
3
90
44
0
24
0
0
0
0
11
3
85
34
0
0
0
0
0
0
9
4
112
54
0
1
0
0
0
0
18
7
200
136
0
1
0
0
0
0
12
5
142
84
0
4
0
0
0
p99
373
1466
1106
0
536
0
0
54
27
349
202
787
547
1
72
0
0
65
35
387
283
1319
930
0
233
0
0
64
38
580
407
1985
1474
0
207
0
0
64
35
655
474
1907
1586
0
661
0
0
63
A-192
-------
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Scenario
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Exceedances of 250 ppb
Mean
6
110
64
351
236
0
174
0
0
21
12
203
132
642
472
3
293
6
4
50
27
274
148
708
441
0
78
0
0
22
16
144
109
436
360
0
30
0
0
14
7
253
167
947
695
66
330
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
9
4
0
12
0
0
0
0
7
1
99
32
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
16
4
0
0
0
Med
0
12
4
169
75
0
79
0
0
5
1
97
53
505
338
0
202
2
1
41
22
169
89
618
352
0
1
0
0
0
0
6
3
61
42
0
1
0
0
0
0
67
29
731
453
0
220
0
p99
96
895
661
1661
1363
1
1065
1
1
184
122
1179
890
2226
1874
15
1150
26
19
199
115
1044
603
1943
1438
1
862
2
0
520
431
1399
1133
2813
2568
1
271
0
0
121
71
1395
1092
3135
2794
612
847
3
Exceedances of 300 ppb
Mean
2
43
23
174
110
0
66
0
0
6
3
78
48
317
216
1
142
4
2
25
15
131
68
394
227
0
33
0
0
9
5
61
46
227
178
0
6
0
0
3
1
83
48
436
298
26
225
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
1
0
0
0
0
0
0
25
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
1
0
35
12
0
21
0
0
0
0
26
16
184
108
0
85
1
0
21
11
79
48
306
135
0
0
0
0
0
0
0
0
17
11
0
0
0
0
0
0
10
5
203
96
0
68
0
p99
35
486
294
1169
895
0
519
0
0
61
40
605
393
1531
1231
8
600
19
13
106
66
542
267
1310
899
0
630
0
0
260
181
761
681
2007
1689
0
60
0
0
32
14
656
387
2151
1648
435
694
0
A-193
-------
Location
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other MSA/CMSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
As is
Current std
50
50
100
100
150
150
200
200
Percentile
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Exceedances of 250 ppb
Mean
0
130
109
236
220
375
330
0
71
0
0
13
9
152
111
529
428
0
23
0
0
2
1
35
18
148
89
0
42
0
0
2
2
26
17
89
65
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
16
6
222
175
0
14
0
0
0
0
78
44
445
333
0
0
0
0
0
0
2
0
34
13
0
1
0
0
0
0
0
0
4
2
p99
0
658
646
748
715
1167
1050
0
701
0
0
155
132
801
654
1765
1593
1
381
0
0
53
24
460
277
1330
954
2
616
2
2
57
38
440
293
1051
833
Exceedances of 300 ppb
Mean
0
71
52
195
178
266
246
0
26
0
0
4
2
55
39
249
190
0
7
0
0
1
0
11
6
63
34
0
17
0
0
1
1
9
6
41
29
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
79
30
0
0
0
0
0
0
13
7
161
109
0
0
0
0
0
0
0
0
7
2
0
0
0
0
0
0
0
0
1
0
p99
0
624
579
694
686
808
770
0
338
0
0
88
60
397
292
1125
949
0
144
0
0
14
5
194
103
738
459
2
316
2
2
27
20
184
122
571
477
A-194
-------
2 A-10 References
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4 adjacent to rural roads. Water Air SoilPollut. 95:87-98.
5 Beckerman B, Jerrett M, Brook JR, Verma DK, Arain MA, Finkelstein MM. (2008).
6 Correlation of nitrogen dioxide with other traffic pollutants near a major expressway. Atmos
1 Environ. 42:275-290.
8 Signal KL, Ashmore MR, Headley AD, Stewart K, Weigert K. (2007). Ecological impacts of
9 air pollution from road transport on local vegetation. Applied Geochemistry. 22:1265-1271.
10 Cape JN, Tang YS, van Dijk N, Love L, Sutton MA, Palmer SCF. (2004). Concentrations of
11 ammonia and nitrogen dioxide at roadside verges, and their contribution to nitrogen
12 deposition. Environ Pollut. 132:469-478.
13 Chan AT and Chung MW. (2003). Indoor-outdoor air quality relationships in vehicle: effect of
14 driving environment and ventilation modes. Atmos Environ. 37:3795-3808.
15 FHWA. (2005). Highway Statistics 2005, Urbanized Areas - 2005, Miles and Daily Vehicle-
16 Miles of Travel (Table HM-71). Available at:
17 http://www.fhwa.dot.gov/policy/ohim/hs05/htm/hm71 .htm.
18 Gilbert NL, Woodhouse S, Stieb DM, Brook JR. (2003). Ambient nitrogen dioxide and distance
19 from a major highway. Sci TotalEnviron. 312:43-46.
20 Heeb NV, Saxer CJ, Forss A-M, Bruhlmann S. (2008). Trends of NO-, NO2-, and NH3-
21 emissions from gasoline-fueled Euro-3- to Euro-4-passenger cars. Atmos Environ.
22 42(10):2543-2554.
23 Maruo YY, Ogawa S, Ichino T, Murao N, Uchiyama M. (2003). Measurement of local
24 variations in atmospheric nitrogen dioxide levels in Sapporo, Japan using a new method with
25 high spatial and high temporal resolution. Atmos Environ. 37:1065-1074.
26 McCurdy TR. (1994). Analysis of high 1 hour NO2 values and associated annual averages
27 using 1988-1992 data. Report to the Office of Air Quality Planning and Standards, Durham
28 NC.
29 Monn Ch, Carabias V, Junker M, Waeber R, Karrer M, Wanner FIU. (1997). Small-scale spatial
30 variability of particulate matter <10 |j,m (PMio) and nitrogen dioxide. Atmos Environ.
31 31(15)2243-2247.
32 Nitta H, Sato T, Nakai S, Maeda K, Aoki S, Ono M. (1993). Respiratory health associated with
33 exposure to automobile exhaust. I. Results of cross-section studies in 1979, 1982, 1983.
34 Arch Environ Health. 48(l):53-58.
35 Pleijel H, Karlsson GP, Gerdin EB. (2004). On the logarithmic relationship between NO2
36 concentration and the distance from a highroad. Sci Total Environ. 332:261-264.
37 Rodes C, Sheldon L, Whitaker D, Clayton A, Fitzgerald K, Flanagan J, DiGenova F, Hering S,
38 Frazier C. (1998). Measuring Concentrations of Selected Air Pollutants Inside California
39 Vehicles. California Environmental Protection Agency, Air Resources Board. Final Report,
40 December 1998.
41 Rodes CE and Holland DM. (1981). Variations of NO, NO2 and O3 concentrations downwind
42 of a Los Angeles freeway. Atmos Environ. 15:243-250.
43 Roorda-Knape MC, Janssen NAH, De Hartog JJ, Van Vliet PHN, Harssema H, Brunekreef B.
44 (1998). Air Pollution from traffic in city districts near major roadways. Atmos Environ.
45 32(11)1921-1930.
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1 Shorter JH, Herndon S, Zahniser MS, Nelson DD, Wormhoudt J, Demerjian KL, Kolb CE.
2 (2005). Real-Time measurements of nitrogen oxide emissions from in-use New York City
3 transit buses using a chase vehicle. Environ Sci Technol. 39:7991-8000.
4 Singer BC, Hodgson AT, Hotchi T, Kim JJ (2004). Passive measurement of nitrogen oxides to
5 assess traffic-related pollutant exposure for the East Bay Children's Respiratory Health
6 Study. AtmosEnviron. 38:393-403.
7 US EPA. (2007a). US EPA Air Quality System (AQS). Download Detailed AQS Data.
8 Available at: http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm.
9 US EPA. (2007b). Field Guide to Air Quality Data (vl.0.0). February 28, 2007. Available at:
10 http://www.epa.gov/ttn/airs/aqsdatamart/documentation/index.htm.
11 US EPA. (2007c). Nitrogen Dioxide Health Assessment Plan: Scope and Methods for Exposure
12 and Risk Assessment. September 2007. Office of Air Quality Planning and Standards.
13 Available at: http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html.
14 US EPA. (2007d). ALLNEICAP Annual 11302007 file posted at:
15 http://www.epa.gov/ttn/chief/net/2002inventory.htmlffinventorydata.
16 US EPA. (2007e). Air Trends. Nitrogen Dioxide, http://www.epa.gov/airtrends/nitrogen.html.
17 Westerdahl D, Fruin S, Sax T, Fine PM, Sioutas C. (2005). Mobile platform measurements of
18 ultrafine particles and associated pollutant concentrations on freeways and residential streets
19 in Los Angeles. Atmos Environ. 39:3597-3610.
20 US EPA. (2007f). Integrated Science Assessment for Oxides of Nitrogen-Health Criteria
21 (First External Review Draft) and Annexes (August 2007). Research Triangle Park, NC:
22 National Center for Environmental Assessment. Available at:
23 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=l 81712.
24
25
26
27
A-196
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Appendix B. Supplement to the NO2 Exposure Assessment
-------
1 Table of Contents
2 Appendix B. Supplement to theNO2 Exposure Assessment i
3 B-l Overview 8
4 B-2 Human Exposure Modeling using APEX 9
5 B-2.1 History 9
6 B-2.2 APEXModel Overview 10
7 B-2.2.1 Study Area Characterization 11
8 B-2.2.2 Simulated Individuals 13
9 B-2.2.3 Activity Pattern Sequences 16
10 B-2.2.4 Calculating Microenvironmental Concentrations 20
11 B-2.2.5 Exposure Calculations 25
12 B-2.2.6 Exposure Model Output 25
13 B-3 Philadelphia Exposure Assessment Case-Study 27
14 B-3.1 Study Area Selection and Description 27
15 B-3.2 Exposure Period of Analysis 28
16 B-3.3 Populations Analyzed 28
17 B-3.4 Simulated Individuals 28
18 B-3.4.1 Asthma Prevalence Rates 28
19 B-3.5 Air Quality Data Generated by AERMOD 29
20 B-3.5.1 Meteorological Inputs 30
21 B-3.5.2 Surface Characteristics and Land Use Analysis 32
22 B-3.5.3 Meteorological Data Analysis 36
23 B-3.5.4 On-Road Emissions Preparation 37
24 B-3.5.5 Stationary Sources Emissions Preparation 44
25 B-3.5.6 Fugitive and Airport Emissions Preparation 49
26 B-3.5.7 Receptor Locations 53
27 B-3.5.8 Other AERMOD Specifications 54
28 B-3.5.9 Air Quality Concentration Adjustment 55
29 B-3.5.10 Meteorological Data Used By APEX 56
30 B-3.5.11 Mi croenvironment Descriptions 56
31 B-3.5.12 Adjustment for Just Meeting the Current Standard 62
32 B-3.6 Philadelphia Exposure Modeling Results 64
33 B-3.6.1 Overview 64
34 B-3.6.2 Evaluation of Modeled NO2 Air Quality Concentrations (as is) 64
35 B-3.6.3 Comparison of estimated on-roadNO2 concentrations 67
36 B-3.6.4 Annual Average Exposure Concentrations (as is) 70
37 B-3.6.5 One-Hour Exposures (as is) 71
38 B-3.6.6 One-Hour Exposures Associated with Just Meeting the Current Standard 81
39 B-3.6.7 Additional Exposure Results 83
40 B-4 Atlanta Exposure Assessment Case-Study 96
41 B-5 References 97
42 Attachment 1: Technical Memorandum on Longitudinal Diary Construction
43 Approach 102
44 Attachment 2: Detailed Evaluation Cluster-Markov Algorithm 109
45
-------
1 List of Tables
2 Table B-l. Examples of profile variables in APEX 13
3 Table B-2. Summary of activity pattern studies used in CHAD 18
4 Table B-3. Mass balance model parameters 20
5 Table B-4. Factors model parameters 21
6 Table B-5. List of microenvironments and calculation methods used 22
7 Table B-6. Mapping of CHAD activity locations to APEX microenvironments 23
8 Table B-7. Example of APEX output files 26
9 Table B-8. Asthma prevalence rates by age and gender used for Philadelphia 29
10 Table B-9. Number of AERMET raw hourly surface meteorology observations, percent
11 acceptance rate, 2001-2003 30
12 Table B-10. Number of calms reported by AERMET by year for Philadelphia 31
13 Table B-l 1. Number and AERMET acceptance rate of upper-air observations 2001-2003 31
14 Table B-12. Seasonal definitions and specifications for Philadelphia 33
15 Table B-13. Monthly precipitation compared to NCDC 30-year climatic normal for
16 Philadelphia, 2001-2003 37
17 Table B-14. Hourly scaling factors (in percents) applied to Philadelphia County AADT
18 volumes 39
19 Table B-15. Seasonal scaling factors applied to Philadelphia County AADT volumes 40
20 Table B-l6. Signals per mile, by link type, applied to Philadelphia County AADT volumes.... 40
21 Table B-17. Statistical summary of AADT volumes (one direction) for Philadelphia County
22 AERMOD simulations 40
23 Table B-l8. Average calculated speed by link type 42
24 Table B-19. On-road area source sizes 43
25 Table B-20. Combined stacks parameters for stationary NOx emission sources in Philadelphia
26 County 46
27 Table B-21. Matched stacks between the CAMD and NEI database 47
28 Table B-22. Emission parameters for the three Philadelphia County fugitive NOx area emission
29 sources 49
30 Table B-23. Philadelphia International airport (PHL)NOX emissions 52
31 Table B-24. Philadelphia County NOx monitors 53
32 Table B-25. Comparison of ambient monitoring and AERMOD predicted NO2 concentrations in
33 Philadelphia 55
34 Table B-26. Air conditioning prevalence estimates with 95% confidence intervals 56
35 Table B-27. Geometric means (GM) and standard deviations (GSD) for air exchange rates by
36 city, A/C type, and temperature range 57
37 Table B-28. Probability of gas stove cooking by hour of the day 59
38 Table B-29. Adjustment factors and potential health effect benchmark levels used by APEX to
39 simulate just meeting the current standard 62
40 Table B-30. Summary statistics of on-road hourly NO2 concentrations (ppb) and the numbers of
41 potential health effect benchmark levels using AERMOD and the on-road ambient
42 monitor simulation approaches in Philadelphia 69
43 Table B-31. Estimated number of asthmatics in Philadelphia County exposed at or above
44 potential health effect benchmark levels (1 to 6 times per year), using modeled air
45 quality (as is) and with just meeting the current standard (std), and with and without
46 indoor sources 84
in
-------
1 Table B-32. Estimated percent of asthmatics in Philadelphia County exposed at or above
2 potential health effect benchmark levels (1 to 6 times per year), using modeled air
3 quality (as is) and with just meeting the current standard (std), and with and without
4 indoor sources 85
5 Table B-33. Estimated number of asthmatic children in Philadelphia County exposed at or above
6 potential health effect benchmark levels (1 to 6 times per year), using modeled air
7 quality (as is) and with just meeting the current standard (std), and with and without
8 indoor sources 90
9 Table B-34. Estimated percent of asthmatic children in Philadelphia County exposed at or above
10 potential health effect benchmark levels (1 to 6 times per year), using modeled air
11 quality (as is) and with just meeting the current standard (std), and with and without
12 indoor sources 91
IV
-------
1 List of Figures
2
3 Figure B-l. Example of a profile function file for A/C prevalence 16
4 Figure B-2. Land-use and sectors around the Philadelphia-area surface meteorological station
5 (KPHL). Sector borders are 80, 184, 262, and 312 degrees from geographic North.
6 Philadelphia city center is labeled 35
7 Figure B-3. Estimated ZQ values for the Philadelphia case-study analysis using visual and
8 AERSURFACE land-use estimations 36
9 Figure B-4. Example of Light- and heavy-duty vehicle NOx emissions grams/mile (g/mi) for
10 arterial and freeway functional classes, 2001 42
11 Figure B-5. Differences in facility-wide annual NOx emission totals between NEI and CAMD
12 data bases for Philadelphia County 2002 49
13 Figure B-6. Locations of the four ancillary area sources. Also shown are centroid receptor
14 locations 51
15 Figure B-7. Centroid locations within fixed distances to major point and mobile sources in
16 Philadelphia county 53
17 Figure B-8. Frequency distribution of distance between each Census receptor and its nearest
18 road-centered receptor in Philadelphia County 54
19 Figure B-9. Example input file from APEX for Indoors-residence microenvironment 58
20 Figure B-10. Example input file from APEX for all Indoors microenvironments (non-residence).
21 60
22 Figure B-11. Example input file from APEX for outdoor near road microenvironment 61
23 Figure B-12 . Distribution of AERMOD estimated annual average NC>2 concentrations at each of
24 the 16,857 receptors in Philadelphia County for years 2001-2003 65
25 Figure B-13. Measured and modeled diurnal pattern of NO2 concentrations at three ambient
26 monitor sites 66
27 Figure B-14. Comparison of on-road factors developed from AERMOD concentration estimates
28 and those derived from published NO2 measurement studies 68
29 Figure B-15. Estimated annual average total NO2 exposure concentrations for all simulated
30 persons in Philadelphia County, using modeled 2001-2003 air quality (as is), with
31 modeled indoor sources 70
32 Figure B-16. Comparison of AERMOD predicted and ambient monitoring annual average NO2
33 concentrations (as is) and APEX exposure concentrations (with and without modeled
34 indoor sources) in Philadelphia County for year 2002 71
35 Figure B-17. Estimated maximum NO2 exposure concentration for all simulated persons in
36 Philadelphia County, using modeled 2001-2003 air quality (as is), with and without
37 modeled indoor sources. Values above the 99th percentile are not shown 73
38 Figure B-18. Estimated number of all simulated asthmatics in Philadelphia County with at least
39 one NO2 exposure at or above the potential health effect benchmark levels, using
40 modeled 2001 -2003 air quality (as is), with modeled indoor sources 73
41 Figure B-19. Estimated number of simulated asthmatic children in Philadelphia County with at
42 least one NO2 exposure at or above the potential health effect benchmark levels, using
43 modeled 2001 -2003 air quality (as is), with modeled indoor sources 74
44 Figure B-20. Comparison of the estimated number of all simulated asthmatics in Philadelphia
45 County with at least one NO2 exposure at or above potential health effect benchmark
-------
1 levels, using modeled 2002 air quality (as is) , with and without modeled indoor
2 sources 74
3 Figure B-21. Fraction of time all simulated persons in Philadelphia County spend in the twelve
4 microenvironments associated with the potential NC>2 health effect benchmark levels,
5 a) > 200 ppb, b) > 250 ppb, and c) > 300 ppb, year 2002 simulation with indoor
6 sources 77
7 Figure B-22. Fraction of time all simulated persons in Philadelphia County spend in the twelve
8 microenvironments associated with the potential NC>2 health effect benchmark levels,
9 a) > 200 ppb, b) > 250 ppb, and c) > 300 ppb, year 2002 simulation without indoor
10 sources 78
11 Figure B-23. Estimated percent of all asthmatics in Philadelphia County with repeated NC>2
12 exposures above potential health effect benchmark levels, using 2003 modeled air
13 quality (as is), with modeled indoor sources 80
14 Figure B-24. Estimated percent of all asthmatics in Philadelphia County with repeated NC>2
15 exposures above potential health effect benchmark levels, using modeled 2002 air
16 quality (as is), with and without indoor sources 80
17 Figure B-25. Estimated percent of all asthmatics in Philadelphia with at least one exposure at or
18 above the potential health effect benchmark level, using modeled 2001-2003 air
19 quality just meeting the current standard, with modeled indoor sources 82
20 Figure B-26. Estimated number of all asthmatics in Philadelphia with at least one exposure at or
21 above the potential health effect benchmark level, using modeled 2002 air quality just
22 meeting the current standard, with and without modeled indoor sources 82
23 Figure B-27. Estimated percent of asthmatics in Philadelphia County with repeated exposures
24 above health effect benchmark levels, using modeled 2002 air quality just meeting the
25 current standard, with and without modeled indoor sources 83
26 Figure B-28. Estimated percent of all asthmatics in Philadelphia County with at least one NC>2
27 exposure at or above potential health effect benchmark level, using 2001-2003
28 modeled air quality (as is), with modeled indoor sources 86
29 Figure B-29. Estimated percent of all asthmatics in Philadelphia County with at least one NC>2
30 exposure at or above potential health effect benchmark level, using 2001-2003
31 modeled air quality (as is), with no indoor sources 86
32 Figure B-30. Estimated percent of all asthmatics in Philadelphia County with at least one NC>2
33 exposure at or above potential health effect benchmark level, using 2001-2003
34 modeled air quality just meeting the current standard (std), with modeled indoor
35 sources 87
36 Figure B-31. Estimated percent of all asthmatics in Philadelphia County with at least one NC>2
37 exposure at or above potential health effect benchmark level, using 2001-2003
38 modeled air quality just meeting the current standard (std), with no indoor sources.. 87
39 Figure B-32. Estimated percent of all asthmatics in Philadelphia County with repeated NC>2
40 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with
41 modeled indoor sources 88
42 Figure B-33. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
43 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is),
44 without indoor sources 88
VI
-------
1 Figure B-34. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
2 exposures at or above 200 ppb 1-hour, using 2001-2003 modeled air quality just
3 meeting the current standard (std), with modeled indoor sources 89
4 Figure B-35. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
5 exposures at or above 200 ppb 1-hour, using 2001-2003 modeled air quality just
6 meeting the current standard (std), with no indoor sources 89
7 Figure B-36. Estimated percent of asthmatic children in Philadelphia County with at least one
8 NO2 exposure at or above potential health effect benchmark level, using 2001-2003
9 modeled air quality (as is), with modeled indoor sources 92
10 Figure B-37. Estimated percent of asthmatic children in Philadelphia County with at least one
11 NO2 exposure at or above potential health effect benchmark level, using 2001-2003
12 modeled air quality (as is), with no indoor sources 92
13 Figure B-38. Estimated percent of asthmatic children in Philadelphia County with at least one
14 NO2 exposure at or above potential health effect benchmark level, using 2001-2003
15 modeled air quality just meeting the current standard (std), with modeled indoor
16 sources 93
17 Figure B-39. Estimated percent of asthmatic children in Philadelphia County with at least one
18 NO2 exposure at or above potential health effect benchmark level, using 2001-2003
19 modeled air quality just meeting the current standard (std), with no indoor sources.. 93
20 Figure B-40. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
21 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with
22 modeled indoor sources 94
23 Figure B-41. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
24 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with
25 no indoor sources 94
26 Figure B-42. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
27 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality meeting the
28 current standard (std), with modeled indoor sources 95
29 Figure B-43. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
30 exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality meeting the
31 current standard (std), with no indoor sources 95
32 Figure B-44. Land-use and sectors around the Atlanta-area surface meteorological station
33 (KATL). Sector borders are 43, 104, and 255 degrees from geographic North. Atlanta
34 city center is labeled 96
35
VII
-------
i B-1 Overview
2 This appendix contains supplemental descriptions of the methods and data used in the NC>2
3 exposure assessment, as well as detailed results from the exposure analyses performed. First, a
4 broad description of the exposure modeling approach is described, applicable to the two
5 exposure modeling case-studies conducted to date: Philadelphia and Atlanta. This is followed
6 with details regarding the required inputs for the model and the assumptions made for both of the
7 case-study assessments. The primary output for each exposure assessment was the numbers of
8 exceedances of short-term (1-hour) potential health effect benchmark levels experienced by the
9 asthmatic population residing within each location.
10 The first simulation location included Philadelphia County and was summarized in the 1st
11 draft Risk and Exposure Assessment (REA). The results from this assessment are presented here
12 as they existed in that document and the draft Technical Support Document draft (TSD) and no
13 adjustments were made to modeling approach used to generate the exposure results. However,
14 additional comparative analyses are presented here to clarify certain issues raised in the review
15 of this case-study by CASAC in May, 2008. These include additional comparisons of the
16 AERMOD modeled air quality with the available ambient monitor data (section 3.6.2) as well as
17 a comparison of the two on-road concentration estimation approaches used (section 3.6.3)
18 A second case-study was conducted in portions of the Atlanta Metropolitan Statistical Area
19 (MSA) that includes four counties. Some of the recommendations by CASAC on the modeling
20 approach, evaluation, and assumptions made have been incorporated in this case-study. Details
21 on the exposure modeling approach for the Atlanta exposure case-study are provided here.
22
B-8
-------
i B-2 Human Exposure Modeling using APEX
2 The Air Pollutants Exposure model (APEX) is a personal computer (PC)-based program
3 designed to estimate human exposure to criteria and air toxic pollutants at the local, urban, and
4 consolidated metropolitan levels. APEX, also known as TREVI.Expo, is the human inhalation
5 exposure module of EPA's Total Risk Integrated Methodology (TRIM) model framework (US
6 EPA, 1999), a modeling system with multimedia capabilities for assessing human health and
7 ecological risks from hazardous and criteria air pollutants. It is being developed to support
8 evaluations with a scientifically sound, flexible, and user-friendly methodology. Additional
9 information on the TRIM modeling system, as well as downloads of the APEX Model, user's
10 guide, and other supporting documentation, can be found on EPA's Technology Transfer
11 Network (TTN) at http://www.epa.gov/ttn/fera.
12 B-2.1 History
13 APEX was derived from the National Ambient Air Quality Standards (NAAQS) Exposure
14 Model (NEM) series of models, developed to estimate exposure to the criteria pollutants (e.g.,
15 carbon monoxide (CO), ozone Os). In 1979, EPA began by assembling a database of human
16 activity patterns that could be used to estimate exposures to indoor and outdoor pollutants
17 (Roddin et al., 1979). These data were then combined with measured outdoor concentrations in
18 NEM to estimate exposures to CO (Biller et al., 1981; Johnson and Paul, 1983). In 1988,
19 OAQPS began to incorporate probabilistic elements into the NEM methodology and use activity
20 pattern data based on various human activity diary studies to create an early version of
21 probabilistic NEM for Os (i.e., pNEM/Os). In 1991, a probabilistic version of NEM was
22 extended to CO (pNEM/CO) that included a one-compartment mass-balance model to estimate
23 CO concentrations in indoor microenvironments. The application of this model to Denver,
24 Colorado has been documented in Johnson et al. (1992). Additional enhancements to pNEM/O3
25 in the early- to mid-1990's allowed for probabilistic exposure assessments in nine urban areas for
26 the general population, outdoor children, and outdoor workers (Johnson et al., 1996a; 1996b;
27 1996c). Between 1999 and 2001, updated versions of pNEM/CO (versions 2.0 and 2.1) were
28 developed that relied on activity diary data from EPA's Consolidated Human Activities Database
29 (CHAD) and enhanced algorithms for simulating gas stove usage, estimating alveolar ventilation
30 rate (a measure of human respiration), and modeling home-to-work commuting patterns.
31
32 The first version of APEX was essentially identical to pNEM/CO (version 2.0) except that it
33 was capable of running on a PC instead of a mainframe. The next version, APEX2, was
34 substantially different, particularly in the use of a personal profile approach (i.e., simulation of
35 individuals) rather than a cohort simulation (i.e., groups of similar persons). APEX3 introduced
36 a number of new features including automatic site selection from national databases, a series of
37 new output tables providing summary exposure and dose statistics, and a thoroughly reorganized
38 method of describing microenvironments and their parameters. Most of the spatial and temporal
39 constraints of pNEM and APEX1 were removed or relaxed by version 3.
40
41 The version of APEX used in this exposure assessment is APEX4, described in the APEX
42 User's Guide and the APEX Technical Support Document (US EPA, 2006a; 2006b) and referred
43 to here as the APEX User's Guide and TSD.
B-9
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i B-2.2 APEX Model Overview
2 APEX estimates human exposure to criteria and toxic air ... . ,. ,.
„,,_,,,, F ,., , , ... A microenvironment is a three-
j pollutants at the local, urban, or consolidated metropolitan .. . . ,. . .
, , , , , ,. . . , , , dimensional space in which human
4 area levels using a stochastic, microenvironmental approach. , , ... ^ . , .
c TU j 1 j 1 1 . j . r- 1 r-u .u .- 1 contact with an environmental
5 The model randomly selects data for a sample of hypothetical „ , ,, . . , ,. ,
r • ,. •, , f ^,1 i .• j ^ u / • 1 * pollutant takes pace and which can
6 individuals from an actual population database and simulates [ , , , ,, u . . _,
-7 uu +u +- i • A- -^ P + +u u+- A be treated as a well-characterized,
7 each hypothetical individual s movements through time and , ,. , . . ,.
o ;F iU . ,- , x, . iU • , relatively homogeneous locaton
8 space (e.g., at home, in vehicles) to estimate their exposure to ... ' ,, ,. , ,
„ it \ ' Anrv • r . .- j .u with respect to pollutant
9 a pollutant. APEX simulates commuting, and thus exposures concentrations L a SDecified time
,,-, .1 . . i i it ,- c • i- • 1 1 1 ouiii/ciiuduuiio lui a oUcL/Micu in Me
10 that occur at home and work locations, tor individuals who . ,
11 work in different areas than they live.
12
13 APEX can be conceptualized as a simulated field study that would involve selecting an actual
14 sample of specific individuals who live in (or work and live in) a geographic area and then
15 continuously monitoring their activities and subsequent inhalation exposure to a specific air
16 pollutant during a specific period of time.
17
18 The main differences between APEX and an actual field study are that in APEX:
19 • The sample of individuals is a virtual sample, not actual persons. However, the
20 population of individuals appropriately balanced according to various demographic
21 variables and census data using their relative frequencies, in order to obtain a
22 representative sample (to the extent possible) of the actual people in the study area
23 • The activity patterns of the sampled individuals (e.g., the specification of indoor and
24 other microenvironments visited and the time spent in each) are assumed by the model to
25 be comparable to individuals with similar demographic characteristics, according to
26 activity data such as diaries compiled in EPA's Consolidated Human Activity Database
27 (or CHAD; US EPA, 2002; McCurdy et al., 2000)
28 • The pollutant exposure concentrations are estimated by the model using a set of user-
29 input ambient outdoor concentrations (either modeled or measured) and information on
30 the behavior of the pollutant in various microenvironments;
31 • Variation in ambient air quality levels can be simulated by either adjusting air quality
32 concentrations to just meet alternative ambient standards, or by reducing source
33 emissions and obtaining resulting air quality modeling outputs that reflect these potential
34 emission reductions, and
35 • The model accounts for the most significant factors contributing to inhalation exposure -
36 the temporal and spatial distribution of people and pollutant concentrations throughout
37 the study area and among microenvironments - while also allowing the flexibility to
38 adjust some of these factors for alternative scenarios and sensitivity analyses.
39
40 APEX is designed to simulate human population exposure to criteria and air toxic pollutants
41 at local, urban, and regional scales. The user specifies the geographic area to be modeled and the
42 number of individuals to be simulated to represent this population. APEX then generates a
43 personal profile for each simulated person that specifies various parameter values required by the
44 model. The model next uses diary-derived time/activity data matched to each personal profile to
45 generate an exposure event sequence (also referred to as activity pattern or diary) for the
46 modeled individual that spans a specified time period, such as one year. Each event in the
B-10
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1 sequence specifies a start time, exposure duration, geographic location, microenvironment, and
2 activity performed. Probabilistic algorithms are used to estimate the pollutant concentration
3 associated with each exposure event. The estimated pollutant concentrations account for the
4 effects of ambient (outdoor) pollutant concentration, penetration factors, air exchange rates,
5 decay/deposition rates, and proximity to emission sources, depending on the microenvironment,
6 available data, and estimation method selected by the user. Because the modeled individuals
7 represent a random sample of the population of interest, the distribution of modeled individual
8 exposures can be extrapolated to the larger population. The model simulation can be broadly
9 described in five steps that follow:
10
11 1. Characterize the study area. APEX selects census tracts within a study area - and thus
12 identifies the potentially exposed population - based on user-defined criteria and
13 availability of air quality and meteorological data for the area.
14 2. Generate simulated individuals. APEX stochastically generates a sample of
15 hypothetical individuals based on the census data for the study area and human profile
16 distribution data (such as age-specific employment probabilities).
17 3. Construct a sequence of activity events. APEX constructs an exposure event sequence
18 spanning the period of the simulation for each of the simulated individuals and based on
19 the activity pattern data.
20 4. Calculate hourly concentrations in microenvironments. APEX users define
21 microenvironments that people in the study area would visit by assigning location codes
22 in the activity pattern to the user-specified microenvironments. The model then
23 calculates hourly concentrations of a pollutant in each of these microenvironments for the
24 period of simulation, based on the user-provided microenvironment descriptions and
25 hourly air quality data. Microenvironmental concentrations are calculated for each of the
26 simulated individuals.
27 5. Estimate exposures.
28
29 APEX estimates a concentration for each exposure event based on the microenvironment
30 occupied during the event. These values can be averaged by clock hour to produce a sequence of
31 hourly average exposures spanning the specified exposure period. These hourly values may be
32 further aggregated to produce daily, monthly, and annual average exposure values.
33 B-2.2.1 Study Area Characterization
34 The APEX study area has traditionally been on the scale of a city or slightly larger
35 metropolitan area, although it is now possible to model larger areas such as combined statistical
36 areas (CSAs). In the exposure analyses performed as part of this NAAQS review, the study area
37 is defined by either a single or a few counties. The demographic data used by the model to
38 create personal profiles is provided at the census block level. For each block the model requires
39 demographic information representing the distribution of age, gender, race, and work status
40 within the study population. Each block has a location specified by latitude and longitude for
41 some representative point (e.g., geographic center). The current release of APEX includes input
42 files that already contain this demographic and location data for all census tracts, block groups,
43 and blocks in the 50 United States, based on the 2000 Census. In this assessment, exposures
44 were evaluated at the block level.
B-11
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1
2 B-2.2.1.1 Air Quality Data
3 Air quality data can be input to the model as measured data from an ambient monitor or that
4 generated by air quality modeling. This exposure analysis used modeled air quality data, whereas
5 the principal emission sources included both mobile and stationary sources as well as fugitive
6 emissions. Air quality data used for input to APEX were generated using AERMOD, a steady-
7 state, Gaussian plume model (EPA, 2004). The following steps were performed using
8 AERMOD.
9
10 1 Collect and analyze general input parameters. Meteorological data, processing
11 methodologies used to derive input meteorological fields (e.g., temperature, wind
12 speed, precipitation), and information on surface characteristics and land use are
13 needed to help determine pollutant dispersion characteristics, atmospheric
14 stability and mixing heights.
15 2. Estimate emissions. The emission sources modeled included, major stationary
16 emission sources, on-road emissions that occur on major roadways, and fugitive
17 emissions.
18 3. Define receptor locations. Three sets of receptors were identified for the
19 dispersion modeling, including ambient monitoring locations, census block
20 centroids, and links along major roadways.
21 4. Estimate concentrations at receptors. Hourly concentrations were estimated for
22 each year of the simulation (years 2001 through 2003) by combining
23 concentration contributions from each of the emission sources and accounting for
24 sources not modeled.
25
26 In APEX, the ambient air quality data are assigned to geographic areas called districts. The
27 districts are used to assign pollutant concentrations to the blocks/tracts and microenvironments
28 being modeled. The ambient air quality data are provided by the user as hourly time series for
29 each district. As with blocks/tracts, each district has a representative location (latitude and
30 longitude). APEX calculates the distance from each block/tract to each district center, and
31 assigns the block/tract to the nearest district, provided the block/tract representative location
32 point (e.g., geographic center) is in the district. Each block/tract can be assigned to only one
33 district. In this assessment the district was synonymous with the receptor modeled in the
34 dispersion modeling.
35
36 B-2.2.1.2 Meteorological Data
37 Ambient temperatures are input to APEX for different sites (locations). As with districts,
38 APEX calculates the distance from each block to each temperature site and assigns each block to
39 the nearest site. Hourly temperature data are from the National Climatic Data Center Surface
40 Airways Hourly TD-3280 dataset (NCDC Surface Weather Observations). Daily average and 1-
41 hour maxima are computed from these hourly data.
42
43 There are two files that are used to provide meteorological data to APEX. One file, the
44 meteorological station location file, contains the locations of meteorological data recordings
45 expressed in latitude and longitude coordinates. This file also contains start and end dates for the
46 data recording periods. The temperature data file contains the data from the locations in the
B-12
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temperature zone location file. This file contains hourly temperature readings for the period
being modeled for the meteorological stations in and around the study area.
B-2.2.2
Simulated Individuals
4 APEX stochastically generates a user-specified number of simulated persons to represent the
5 population in the study area. Each simulated person is represented by a personal profile, a
6 summary of personal attributes that define the individual. APEX generates the simulated person
7 or profile by probabilistically selecting values for a set of profile variables (Table B-l). The
8 profile variables could include:
9 • Demographic variables, generated based on the census data;
10 • Physical variables, generated based on sets of distribution data;
11 • Other daily varying variables, generated based on literature-derived distribution data that
12 change daily during the simulation period.
13 APEX first selects demographic and physical attributes for each specified individual, and
14 then follows the individual over time and calculates his or her time series of exposure.
15 Table B-l. Examples of profile variables in APEX.
Variable
Type
Demographic
Physical
Profile Variables
Age
Gender
Home block
Work tract
Employment status
Air conditioner
Gas Stove
Description
Age (years)
Male or Female
Block in which a simulated person lives
Tract in which a simulated person works
Indicates employment outside home
Indicates presence of air conditioning at home
Indicates presence of gas stove at home
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
B-2.2.2.1 Population Demographics
APEX takes population characteristics into account to develop accurate representations of
study area demographics. Specifically, population counts by area and employment probability
estimates are used to develop representative profiles of hypothetical individuals for the
simulation.
APEX is flexible in the resolution of population data provided. As long as the data are
available, any resolution can be used (e.g., county, census tract, census block). For this
application of the model, census block level data were used. Block-level population counts come
from the 2000 Census of Population and Housing Summary File 1 (SF-1). This file contains the
100-percent data, which is the information compiled from the questions asked of all people and
about every housing unit.
As part of the population demographics inputs, it is important to integrate working patterns
into the assessment. In the 2000 U.S. Census, estimates of employment were developed by
B-13
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1 census information (US Census Bureau, 2007). The employment statistics are broken down by
2 gender and age group, so that each gender/age group combination is given an employment
3 probability fraction (ranging from 0 to 1) within each census tract. The age groupings used are:
4 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.
5 Children under 16 years of age were assumed to be not employed.
6
7 Since this analysis was conducted at the census block level, block level employment
8 probabilities were required. It was assumed that the employment probabilities for a census tract
9 apply uniformly to the constituent census blocks.
10
11 B-2.2.2.2 Commuting
12 In addition to using estimates of employment by tract, APEX also incorporates home-to-
13 work commuting data. Commuting data were originally derived from the 2000 Census and were
14 collected as part of the Census Transportation Planning Package (CTPP) (US DOT, 2007). The
15 data used contain counts of individuals commuting from home to work locations at a number of
16 geographic scales. These data were processed to calculate fractions for each tract-to-tract flow to
17 create the national commuting data distributed with APEX. This database contains commuting
18 data for each of the 50 states and Washington, D.C.
19 Commuting within the Home Tract
20 The APEX data set does not differentiate people that work at home from those that
21 commute within their home tract.
22 Commuting Distance Cutoff
23 A preliminary data analysis of the home-work counts showed that a graph of log(flows)
24 versus log(distance) had a near-constant slope out to a distance of around 120 kilometers.
25 Beyond that distance, the relationship also had a fairly constant slope but it was flatter, meaning
26 that flows were not as sensitive to distance. A simple interpretation of this result is that up to
27 120 km, the majority of the flow was due to persons traveling back and forth daily, and the
28 numbers of such persons decrease fairly rapidly with increasing distance. Beyond 120 km, the
29 majority of the flow is made up of persons who stay at the workplace for extended times, in
30 which case the separation distance is not as crucial in determining the flow.
31 To apply the home-work data to commuting patterns in APEX, a simple rule was chosen. It
32 was assumed that all persons in home-work flows up to 120 km are daily commuters, and no
33 persons in more widely separated flows commute daily. This meant that the list of destinations
34 for each home tract was restricted to only those work tracts that are within 120 km of the home
35 tract. When the same cutoff was performed on the 1990 census data, it resulted in 4.75% of the
36 home-work pairs in the nationwide database being eliminated, representing 1.3% of the workers.
37 The assumption is that this 1.3% of workers do not commute from home to work on a daily
38 basis. It is expected that the cutoff reduced the 2000 data by similar amounts.
39 Eliminated Records
B-14
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2
3
4
5
6
7
8
9
10
1 1
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
3 1
32
33
34
35
36
37
3 8
39
A number of tract-to-tract pairs were eliminated from the database for various reasons. A
fair number of tract-to-tract pairs represented workers who either worked outside of the U.S.
(9,63 1 tract pairs with 107,595 workers) or worked in an unknown location (120,830 tract pairs
with 8,940,163 workers). An additional 515 workers in the commuting database whose data
were missing from the original files, possibly due to privacy concerns or errors, were also
deleted.
Commuting outside the study area
APEX allows for some flexibility in the treatment of persons in the modeled population who
commute to destinations outside the study area. By specifying "KeepLeavers = No" in the
simulation control parameters file, people who work inside the study area but live outside of it
are not modeled, nor are people who live in the study area but work outside of it. By specifying
"KeepLeavers = Yes," these commuters are modeled. This triggers the use of two additional
parameters, called LeaverMult and LeaverAdd. While a commuter is at work, if the workplace is
outside the study area, then the ambient concentration is assumed to be related to the average
concentration over all air districts at the same point in time, and is calculated as:
Ambient Concentration = LeaverMult xavg(t) + LeaverAdd equation (1)
where:
Ambient Concentration =
LeaverMult =
avg(t) =
LeaverAdd =
Calculated ambient air concentrations for locations outside
of the study area (ppm or ppm)
Multiplicative factor for city -wide average concentration,
applied when working outside study area
Average ambient air concentration over all air districts in
study area, for time t (ppm or ppm)
Additive term applied when working outside study area
All microenvironmental concentrations for locations outside of the study area are determined
from this ambient concentration by the same function as applies inside the study area.
Block-level commuting
For census block simulations, APEX requires block-level commuting file. A special software
preprocesser was created to generate this files for APEX on the basis of the tract-level
commuting data and finely-resolved land use data. The software calculates commuting flows
between census blocks for the employed population according equation (2).
=Fl°W *F
»*
equation (2)
where:
Flow biock = 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 P0p = fraction of home tract' s working population residing in the home block.
F land = fraction of work tract's commercial/industrial land area in the work block
B-15
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1 Thus, it is assumed that the frequency of commuting to a workplace block within a tract is
2 proportional to the amount of commercial and industrial land in the block.
O
4 B-2.2.2.3 Profile Functions
5 A Profile Functions file contains settings used to generate results for variables related to
6 simulated individuals. While certain settings for individuals are generated automatically by
7 APEX based on other input files, including demographic characteristics, others can be specified
8 using this file. For example, the file may contain settings for determining whether the profiled
9 individual's residence has an air conditioner, a gas stove, etc. As an example, the Profile
10 Functions file contains fractions indicating the prevalence of air conditioning in the cities
11 modeled in this assessment (Figure B-l). APEX uses these fractions to stochastically generate
12 air conditioning status for each individual. The derivation of particular data used in specific
13 microenvironments is provided below.
14
AC_Home
! Has air conditioning at home
TABLE
INPUT 1 PROBABILITY 2 "A/C probabilities"
0.850.15
RESULT INTEGER 2 "Yes/No"
12
#
15
16 Figure B-l. Example of a profile function file for A/C prevalence.
17 B-2.2.3 Activity Pattern Sequences
18 Exposure models use human activity pattern data to predict and estimate exposure to
19 pollutants. Different human activities, such as spending time outdoors, indoors, or driving, will
20 have varying pollutant exposure concentrations. To accurately model individuals and their
21 exposure to pollutants, it is critical to understand their daily activities.
22
23 The Consolidated Human Activity Database (CHAD) provides data for where people spend
24 time and the activities performed. CHAD was designed to provide a basis for conducting multi-
25 route, multi-media exposure assessments (McCurdy et al., 2000). The data contained within
26 CHAD come from multiple activity pattern surveys with varied structures (Table B-2), however
27 the surveys have commonality in containing daily diaries of human activities and personal
28 attributes (e.g., age and gender).
29
30 There are four CHAD-related input files used in APEX. Two of these files can be
31 downloaded directly from the CHADNet (http://www.epa.gov/chadnetl), and adjusted to fit into
32 the APEX framework. These are the human activity diaries file and the personal data file, and
33 are discussed below. A third input file contains metabolic information for different activities
34 listed in the diary file, these are not used in this exposure analysis. The fourth input file maps
35 five-digit location codes used in the diary file to APEX microenvironments; this file is discussed
36 in the section describing microenvironmental calculations (Section B-2.2.4.4).
37
38 B-2.2.3.1 Personal Information file
B-16
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1 Personal attribute data are contained in the CHAD questionnaire file that is distributed with
2 APEX. This file also has information for each day individuals have diaries. The different
3 variables in this file are:
4
5 • The study, person, and diary day identifiers
6 • Day of week
7 • Gender
8 • Employment status
9 • Age in years
10 • Maximum temperature in degrees Celsius for this diary day
11 • Mean temperature in degrees Celsius for this diary day
12 • Occupation code
13 • Time, in minutes, during this diary day for which no data are included in the database
14
15 B-2.2.3.2 Diary Events file
16 The human activity diary data are contained in the events file that is distributed with APEX.
17 This file contains the activities for the nearly 23,000 people with intervals ranging from one
18 minute to one hour. An individuals' diary varies in length from one to 15 days. This file
19 contains the following variables:
20
21 • The study, person, and diary day identifiers
22 • Start time of this activity
23 • Number of minutes for this activity
24 • Activity code (a record of what the individual was doing)
25 • Location code (a record of where the individual was)
26
27
28
B-17
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1 Table B-2. Summary of activity pattern studies used in CHAD.
Study Name
Baltimore
California
Adolescents
and Adults
(GARB)
California
Children
(GARB)
Cincinnati
(EPRI)
Denver
(EPA)
Los Angeles:
Elementary
School
Children
Los Angeles:
High School
Adolescents
National:
NHAPS-Air
National:
NHAPS-
Water
Washington,
D.C. (EPA)
Location
A single
building in
Baltimore
California
California
Cincinnati
MSA
Denver
MSA
Los
Angeles
Los
Angeles
National
National
Wash. DC
MSA
Study
time
period
01/1997-
02/1997,
07/1 998-
08/1 998
1 0/1 987-
09/1 988
04/1 989-
02/1 990
03/1 985-
04/1985,
08/1 985
11/1982-
02/1 983
10/1989
09/1 990-
10/1990
09/1 992-
10/1994
09/1 992-
10/1994
11/1982-
02/1 983
Ages
72-93
12-17
18-94
0-11
0-86
18-70
10-12
13-17
0-93
0-93
18-98
Persons
26
181
1,552
1,200
888
432
17
19
4,326
4,332
639
Person
-days
292
181
1,552
1,200
2,587
791
51
42
4,326
4,332
639
Diary type
/study
design
Diary
Recall
/Random
Recall
/Random
Diary
/Random
Diary
/Random
Diary
Diary
Recall
/Random
Recall
/Random
Diary
/Random
Reference
Williams et al. (2000)
Robinson et al.
(1989);
Wiley etal. (1991 a)
Wiley etal. (1991b)
Johnson (1989)
Johnson (1984);
Aklandetal. (1985)
Spier etal. (1992)
Spier etal. (1992)
Klepeis etal. (1996);
Tsang and Klepeis
(1996)
Klepeis etal. (1996);
Tsang and Klepeis
(1996)
Hartwell etal. (1984);
Aklandetal. (1985)
2
3
4
5
6
1
8
9
10
11
12
13
14
15
16
17
18
B-2.2.3.3 Construction of Longitudinal Activity Sequences
Typical time-activity pattern data available for inhalation exposure modeling consist of a
sequence of location/activity combinations spanning a 24-hour duration, with 1 to 3 diary-days
for any single individual. Exposure modeling requires information on activity patterns over
longer periods of time, e.g., a full year. For example, even for pollutant health effects with short
averaging times (e.g., NC>2 1-hour average concentration) it may be desirable to know the
frequency of exceedances of a concentration over a long period of time (e.g., the annual number
of exceedances of a 1-hour average NC>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 activity patterns across the simulated population. This in turn will
influence the ability of the model to accurately represent either long-term average high-end
exposures, or the number of individuals exposed multiple times to short-term high-end
concentrations.
B-18
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1 A common approach for constructing long-term activity patterns from short-term records is
2 to re-select a daily activity pattern from the pool of data for each day, with the implicit
3 assumption that there is no correlation between activities from day to day for the simulated
4 individual. This approach tends to result in long-term activity patterns that are very similar
5 across the simulated population. Thus, the resulting exposure estimates are likely to
6 underestimate the variability across the population, and therefore, underestimate the high-end
7 exposure concentrations or the frequency of exceedances.
8
9 A contrasting approach is to select a single activity pattern (or a single pattern for each
10 season and/or weekday-weekend) to represent a simulated individual's activities over the
11 duration of the exposure assessment. This approach has the implicit assumption that an
12 individual's day-to-day activities are perfectly correlated. This approach tends to result in long-
13 term activity patterns that are very different across the simulated population, and therefore may
14 over-estimate the variability across the population.
15 Cluster-Markov Algorithm
16 A new algorithm has been developed and incorporated into APEX to represent the day-to-
17 day correlation of activities for individuals. The algorithms first use cluster analysis to divide the
18 daily activity pattern records into groups that are similar, and then select a single daily record
19 from each group. This limited number of daily patterns is then used to construct a long-term
20 sequence for a simulated individual, based on empirically-derived transition probabilities. This
21 approach is intermediate between the assumption of no day-to-day correlation (i.e., re-selection
22 for each time period) and perfect correlation (i.e., selection of a single daily record to represent
23 all days).
24
25 The steps in the algorithm are as follows.
26 1. For each demographic group (age, gender, employment status), temperature range, and
27 day-of-week combination, the associated time-activity records are partitioned into 3
28 groups using cluster analysis. The clustering criterion is a vector of 5 values: the time
29 spent in each of 5 microenvironment categories (indoors - residence; indoors - other
30 building; outdoors - near road; outdoors - away from road; in vehicle).
31 2. For each simulated individual, a single time-activity record is randomly selected from
32 each cluster.
33 3. A Markov process determines the probability of a given time-activity pattern occurring
34 on a given day based on the time-activity pattern of the previous day and cluster-to-
35 cluster transition probabilities. The cluster-to-cluster transition probabilities are
36 estimated from the available multi-day time-activity records. If insufficient multi-day
37 time-activity records are available for a demographic group, season, day-of-week
38 combination, then the cluster-to-cluster transition probabilities are estimated from the
39 frequency of time-activity records in each cluster in the CHAD data base.
40
41 Details regarding the Cluster-Markov algorithm and supporting evaluations are provided in
42 Attachment 1.
B-19
-------
1 B-2.2.4 Calculating Microenvironmental Concentrations
Probabilistic algorithms are used to estimate the pollutant concentration associated with each
exposure event. The estimated pollutant concentrations account for the effects of ambient
(outdoor) pollutant concentration, penetration factor, air exchange rate, decay/deposition rate,
and proximity to microenvironments can use the transfer factors method while the others use the
mass balance emission sources, depending on the microenvironment, available data, and the
estimation method selected by the user.
APEX calculates air concentrations in the various microenvironments visited by the
simulated person by using the ambient air data for the relevant blocks, the user-specified
estimation method, and input parameters specific to each microenvironment. APEX calculates
hourly concentrations in all the microenvironments at each hour of the simulation for each of the
simulated individuals using one of two methods: by mass balance or a transfer factors method.
B-2.2.4.1 Mass Balance Model
The mass balance method simulates an enclosed microenvironment as a well-mixed volume
in which the air concentration is spatially uniform at any specific time. The concentration of an
air pollutant in such a microenvironment is estimated using the following processes:
• Inflow of air into the microenvironment
• Outflow of air from the microenvironment
• Removal of a pollutant from the microenvironment due to deposition, filtration, and
chemical degradation
• Emissions from sources of a pollutant inside the microenvironment.
Table B-3 lists the parameters required by the mass balance method to calculate
concentrations in a microenvironment. A proximity factor (fpr0ximity) is used to account for
differences in ambient concentrations between the geographic location represented by the
ambient air quality data (e.g., a regional fixed-site monitor or modeled concentration) and the
geographic location of the microenvironment (e.g., near a roadway). This factor could take a
value either greater than or less than 1. Emission source (ES) represents the emission rate for the
emission source and concentration source (CS) is the mean air concentration resulting from the
source. Rrem0va/ is defined as the removal rate of a pollutant from a microenvironment due to
deposition, filtration, and chemical reaction. The air exchange rate (^atr exchange) is expressed in
air changes per hour.
Table B-3. Mass balance model parameters.
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Variable
' proximity
CS
" removal
" air exchange
V
Definition
Proximity factor
Concentration source
Removal rate due to deposition,
filtration, and chemical reaction
Air exchange rate
Volume of microenvironment
Units
unitless
ppb
1/hr
1/hr
mj
Value Range
' proximity — "
CS>0
"removal — "
" air exchange — U
V>0
37
38
The mass balance equation for a pollutant in a microenvironment is described by:
B-20
-------
- = ACM - ACout - AC_, + AC_ equation (3)
dt
2 where:
3 dCME(t) = Change in concentration in a microenvironment at time t (ppb),
4 A Cin = Rate of change in microenvironmental concentration due to influx
5 of air (ppb/hour),
6 A COM = Rate of change in microenvironmental concentration due to outflux
7 of air (ppb/hour),
8 A Cremovai = Rate of change in microenvironmental concentration due to
9 removal processes (ppb/hour), and
10 A. C source = Rate of change in microenvironmental concentration due to an
11 emission source inside the microenvironment (ppb/hour).
12
13 Within the time period of an hour each of the rates of change, A Cin, A Cout, A Cremova/, and
14 A.CSource, is assumed to be constant. At each hour time step of the simulation period, APEX
15 estimates the hourly equilibrium, hourly ending, and hourly mean concentrations using a series
16 of equations that account for concentration changes expected to occur due to these physical
17 processes. Details regarding these equations are provided in the APEX User's Guide. APEX
18 reports hourly mean concentration as hourly concentration for a specific hour. The calculation
19 then continues to the next hour by using the end concentration for the previous hour as the initial
20 microenvironmental concentration. A description of the input parameters estimates used for
21 microenvironments using the mass balance approach is provided below.
22
23 B-2.2.4.2 Factors Model
24 The factors method is simpler than the mass balance method. It does not calculate
25 concentration in a microenvironment from the concentration in the previous hour and it has
26 fewer parameters. Table B-4 lists the parameters required by the factors method to calculate
27 concentrations in a microenvironment without emissions sources.
28 Table B-4. Factors model parameters.
Variable
' proximity
' penetration
Definition
Proximity factor
Penetration factor
Units
unitless
unitless
Value Range
' proximity — "
0 ^ f penetration - 1
29
30 The factors method uses the following equation to calculate hourly mean concentration in a
31 microenvironment from the user-provided hourly air quality data:
/-ihourlymecm /~i v f v /" '
32 ^ME ~ ^ambient X J proximity * J penetration equation (4)
33 where:
34 ^houriymean = Hourly concentration in a microenvironment (ppb)
35 C ambient = Hourly concentration in ambient environment (ppb)
36 /proximity = Proximity factor (unitless)
37 /penetration = Penetration factor (unitless)
B-21
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
The ambient NC>2 concentrations are from the air quality data input file. The proximity factor
is a unitless parameter that represents the proximity of the microenvironment to a monitoring
station. The penetration factor is a unitless parameter that represents the fraction of pollutant
entering a microenvironment from outside the microenvironment via air exchange. The
development of the specific proximity and penetration factors used in this analysis are discussed
below for each microenvironment using this approach.
B-2.2.4.3 Microenvironments Modeled
In APEX, microenvironments represent the exposure locations for simulated individuals. For
exposures to be estimated accurately, it is important to have realistic microenvironments that
match closely to the locations where actual people spend time on a daily basis. As discussed
above, the two methods available in APEX for calculating pollutant levels within
microenvironments are: 1) factors and 2) mass balance. A list of microenvironments used in this
study, the calculation method used, and the parameters used to calculate the microenvironment
concentrations can be found in Table B-5.
Table B-5. List of microenvironments and calculation methods used.
Microenvironment
No.
1
2
3
4
5
6
7
8
9
10
11
12
0
Name
Indoors - Residence
Indoors - Bars and restaurants
Indoors - Schools
Indoors - Day-care centers
Indoors -Office
Indoors -Shopping
Indoors - Other
Outdoors - Near road
Outdoors - Public garage - parking lot
Outdoors - Other
In-vehicle - Cars and Trucks
In-vehicle - Mass Transit (bus, subway, train)
Not modeled
Calculation
Method
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Mass balance
Factors
Factors
Factors
Factors
Factors
Parameter
Types used 1
AERand DE
AERandDE
AERandDE
AERand DE
AERandDE
AERandDE
AERandDE
PR
PR
None
PE and PR
PE and PR
1 AER=air exchange rate, DE=decay-deposition rate, PR= proximity factor, PE=penetration
factor
Each of the microenvironments is designed to simulate an environment in which people spend
time during the day. CHAD locations are linked to the different microenvironments in the
Microenvironment Mapping File (see below). There are many more CHAD locations than
microenvironment locations (there are 113 CHAD codes versus 12 microenvironments in this
assessment), therefore most of the microenvironments have multiple CHAD locations mapped to
them.
26
27 B-2.2.4.4
Mapping of APEX Microenvironments to CHAD Diaries
B-22
-------
1 The Microenvironment Mapping file matches the APEX Microenvironments to CHAD
2 Location codes. Table B-6 gives the mapping used for the APEX simulations.
3 Table B-6. Mapping of CHAD activity locations to APEX microenvironments.
CHAD LOG. Description
APEX micro
U Uncertain of correct code = -1
X No data = -1
30000 Residence, general = 1
30010 Your residence = 1
30020 Other residence = 1
30100 Residence, indoor = 1
30120 Your residence, indoor = 1
30121 ..., kitchen = 1
30122 •••, living room or family room = 1
30123 •••, dining room = 1
30124 ..., bathroom = 1
30125 ..., bedroom = 1
30126 ..., study or office = 1
30127 ..., basement = 1
30128 •••, utility or laundry room = 1
30129 ..., other indoor = 1
30130 Other residence, indoor = 1
30131 ..., kitchen = 1
30132 •••, living room or family room = 1
30133 •••, dining room = 1
30134 ..., bathroom = 1
30135 ..., bedroom = 1
30136 •••, study or office = 1
30137 ..., basement = 1
30138 •••, utility or laundry room = 1
30139 ..., other indoor = 1
30200 Residence, outdoor = 10
30210 Your residence, outdoor = 10
30211 ..., pool or spa = 10
30219 ..., other outdoor = 10
30220 Other residence, outdoor = 10
30221 ..., pool or spa = 10
30229 ..., other outdoor = 10
30300 Residential garage or carport = 7
30310 ..., indoor = 7
30320 ..., outdoor = 10
30330 Your garage or carport = 1
30331 ..., indoor = 1
30332 ..., outdoor = 10
30340 Other residential garage or carport = 1
30341 ..., indoor = 1
30342 ..., outdoor = 10
30400 Residence, none of the above = 1
31000 Travel, general = 11
31100 Motorized travel = 11
31110 Car = 11
31120 Truck = 11
31121 Truck (pickup or van) = 11
31122 Truck (not pickup or van) = 11
31130 Motorcycle or moped = 8
31140 Bus = 12
31150 Train or subway = 12
31160 Airplane = 0
31170 Boat = 10
31171 Boat, motorized = 10
Unknown
Unknown
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Indoors-Residence
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Indoors-Other
Indoors-Other
Outdoors-Other
Indoors-Residence
Indoors-Residence
Outdoors-Other
Indoors-Residence
Indoors-Residence
Outdoors-Other
Indoors-Residence
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
Outdoors-Near_Road
In Vehicle-Mass_Transit
In Vehicle-Mass_Transit
Zero_concentration
Outdoors-Other
Outdoors-Other
B-23
-------
31172 Boat, other = 10
31200 Non-motorized travel = 10
31210 Walk = 10
31220 Bicycle or inline skates/skateboard = 10
31230 In stroller or carried by adult = 10
31300 Waiting for travel = 10
31310 ..., bus or train stop = 8
31320 ..., indoors = 7
31900 Travel, other = 11
31910 ..., other vehicle = 11
32000 Non-residence indoor, general = 7
32100 Office building/ bank/ post office = 5
32200 Industrial/ factory/ warehouse = 5
32300 Grocery store/ convenience store = 6
32400 Shopping mall/ non-grocery store = 6
32500 Bar/ night club/ bowling alley = 2
32510 Bar or night club = 2
32520 Bowling alley = 2
32600 Repair shop = 7
32610 Auto repair shop/ gas station = 7
32620 Other repair shop = 7
32700 Indoor gym /health club = 7
32800 Childcare facility = 4
32810 ..., house = 1
32820 ..., commercial = 4
32900 Large public building = 7
32910 Auditorium/ arena/ concert hall = 7
32920 Library/ courtroom/ museum/ theater = 7
33100 Laundromat = 7
33200 Hospital/ medical care facility = 7
33300 Barber/ hair dresser/ beauty parlor = 7
33400 Indoors, moving among locations = 7
33500 School = 3
33600 Restaurant = 2
33700 Church = 7
33800 Hotel/ motel = 7
33900 Dry cleaners = 7
34100 Indoor parking garage = 7
34200 Laboratory = 7
34300 Indoor, none of the above = 7
35000 Non-residence outdoor, general = 10
35100 Sidewalk, street = 8
35110 Within 10 yards of street = 8
35200 Outdoor public parking lot /garage = 9
35210 •••, public garage = 9
35220 ..., parking lot = 9
35300 Service station/ gas station = 10
35400 Construction site = 10
35500 Amusement park = 10
35600 Playground = 10
35610 ••-, school grounds = 10
35620 ..., public or park = 10
35700 Stadium or amphitheater = 10
35800 Park/ golf course = 10
35810 Park = 10
35820 Golf course = 10
35900 Pool/ river/ lake = 10
36100 Outdoor restaurant/ picnic = 10
36200 Farm = 10
36300 Outdoor, none of the above = 10
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Near_Road
Indoors-Other
In Vehicle-Cars_and_Trucks
In Vehicle-Cars_and_Trucks
Indoors-Other
Indoors-Office
Indoors-Office
Indoors-Shopping
Indoors-Shopping
Indoors-Bars_and_Restaurants
Indoors-Bars_and_Restaurants
Indoors-Bars_and_Restaurants
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Day_Care_Centers
Indoors-Residence
Indoors-Day_Care_Centers
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Schools
Indoors-Bars_and_Restaurants
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Indoors-Other
Outdoors-Other
Outdoors-Near_Road
Outdoors-Near_Road
Outdoors-Public_Garage-Parking
Outdoors-Public_Garage-Parking
Outdoors-Public_Garage-Parking
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
Outdoors-Other
B-24
-------
7=1
1 B-2.2.5 Exposure Calculations
2 APEX calculates exposure as a time series of exposure concentrations that a simulated
3 individual experiences during the simulation period. APEX determines the exposure using
4 hourly ambient air concentrations, calculated concentrations in each microenvironment based on
5 these ambient air concentrations (and indoor sources if present), and the minutes spent in a
6 sequence of microenvironments visited according to the composite diary. The hourly exposure
7 concentration at any clock hour during the simulation period is determined using the following
8 equation:
9
N
i hourly-mean ,
ME(j) T (j)
7=1
i — ~ equation (5)
11 where:
12 d = Hourly exposure concentration at clock hour / of the simulation period
13 (ppb)
14 N = Number of events (i.e., microenvironments visited) in clock hour / of
15 the simulation period.
16 C^j"ea" = Hourly mean concentration in mi croenvironmenty (ppm)
17 t(j) = Time spent in microenvironment y' (minutes)
18 T = 60 minutes
19
20 From the hourly exposures, APEX calculates time series of 1-hour average exposure
21 concentrations that a simulated individual would experience during the simulation period.
22 APEX then statistically summarizes and tabulates the hourly (or daily, annual average)
23 exposures. In this analysis, the exposure indicator is 1-hr exposures above selected health effect
24 benchmark levels. From this, APEX can calculate two general types of exposure estimates:
25 counts of the estimated number of people exposed to a specified NC>2 concentration level and the
26 number of times per year that they are so exposed; the latter metric is in terms of person-
27 occurrences or person-days. The former highlights the number of individuals exposed at least
28 one or more times per modeling period to the health effect benchmark level of interest. APEX
29 can also report counts of individuals with multiple exposures. This person-occurrences measure
30 estimates the number of times per season that individuals are exposed to the exposure indicator
31 of interest and then accumulates these estimates for the entire population residing in an area.
32
33 APEX tabulates and displays the two measures for exposures above levels ranging from 200
34 to 300 ppb by 50 ppb increments for 1-hour average exposures. These results are tabulated for
35 the population and subpopulations of interest.
36
37 B-2.2.6 Exposure Model Output
38 All of the output files written by APEX are ASCII text files. Table B-7 lists each of the
39 output data files written for these simulations and provides descriptions of their content.
40 Additional output files that can produced by APEX are given in Table 5-1 of the APEX User's
B-25
-------
1
2
3
4
Guide, and include hourly exposure, ventilation, and energy expenditures, and even detailed
event-level information, if desired. The names and locations, as well as the output table levels
(e.g., output percentiles, cut-points), for these output files are specified by the user in the
simulation control parameters file.
Table B-7. Example of APEX output files.
Output File Type
Log
Profile Summary
Microenvironment
Summary
Sites
Output Tables
Description
The Log file contains the record of the APEX model simulation as it progresses.
If the simulation completes successfully, the log file indicates the input files and
parameter settings used for the simulation and reports on a number of different
factors. If the simulation ends prematurely, the log file contains error messages
describing the critical errors that caused the simulation to end.
The Profile Summary file provides a summary of each individual modeled in the
simulation.
The Microenvironment Summary file provides a summary of the time and
exposure by microenvironment for each individual modeled in the simulation.
The Sites file lists the tracts, districts, and zones in the study area, and identifies
the mapping between them.
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.
6
7
B-26
-------
2 B-3 Philadelphia Exposure Assessment Case-Study
3 This section documents detailed methodology and input data used in the Philadelphia
4 inhalation exposure assessment for NO2 conducted in support of the current review of the NO2
5 primary NAAQS. Two important components of the analysis include the approach for
6 estimating temporally and spatially variable NO2 concentrations and simulating contact of
7 humans with these pollutant concentrations. A combined air quality and exposure modeling
8 approach has been used here to generate estimates of 1-hour NO2 exposures within Philadelphia.
9 Details on the approaches used are provided below and include the following:
10
11 • Description of the area assessed and populations considered
12 • Summary of the air quality modeling methodology and associated input data
13 • Description of the inhalation exposure model and associated input data
14 • Evaluation of estimated NO2 exposures using modeling methodology
15
16 B-3.1 Study Area Selection and Description
17 The selection of areas to include in the exposure analysis takes into consideration the location
18 of field and epidemiology studies, the availability of ambient monitoring and other input data,
19 the desire to represent a range of geographic areas, population demographics, general
20 climatology, and results of the ambient air quality characterization.
21 Philadelphia was selected as a location of interest through a similar statistical analysis of the
22 ambient NO2 air quality data described in Appendix A for each monitoring site within a location.
23 Criteria were established for selecting sites with high annual means and/or high numbers of
24 exceedances of potential health effect benchmark concentrations. The analysis considered all
25 data combined, as well as the more recent air quality data (2001-2006) separately.
26
27 The 90th percentile served as the point of reference for the annual means, and across all
28 complete site-years for 2001-2006, this value was 23.5 ppb. Seventeen locations contained one
29 or more site-years with an annual average concentration at or above the 90th percentile. When
30 combined with the number of 1-hour NO2 concentrations at or above 200 ppb, only two locations
31 fit these criteria, Philadelphia and Los Angeles. In comparing the size of the potential modeling
32 domains and the anticipated complexity in modeling influence of roadway exposures,
33 Philadelphia was determined to be a more manageable case-study.
34
35 Philadelphia County is comprised of 17,315 blocks containing a population of 1,517,550
36 persons. For this analysis the population studied was limited those residents of Philadelphia
37 County residing in census blocks that were either within 400 meters of a major roadway or
38 within 10 km of a major emission source (see section B-3.5 for definition). This was done to
39 maintain balance between the representation of the study area/objectives and the computational
40 load regarding file size and processing time. There were 16,857 such blocks containing a
41 population of 1,475,651.
42
B-27
-------
i B-3.2 Exposure Period of Analysis
2 The exposure periods modeled were 2001 through 2003 to envelop the most recent year of
3 travel demand modeling (TDM) data available for the respective study locations (i.e., 2002) and
4 to include a 3 years of meteorological data to achieve a degree of stability in the dispersion and
5 exposure model estimates.
6 B-3.3 Populations Analyzed
7 A detailed consideration of the population residing in each modeled area was included where
8 the exposure modeling was performed. The assessment includes the general population (All
9 Persons) residing in each modeled area and considered susceptible and vulnerable populations as
10 identified in the ISA. These include population subgroups defined from either an exposure or
11 health perspective. The population subgroups identified by the ISA (US EPA, 2007a) that were
12 included and that can be modeled in the exposure assessment include:
13
14 • Children (ages 5-18)
15 • Asthmatic children (ages 5-18)
16 • All persons (all ages)
17 • All Asthmatics (all ages)
18
19 In addition to these population subgroups, individuals anticipated to be exposed more
20 frequently to NC>2 were considered, including those commuting on roadways and persons
21 residing near major roadways. To date, this document provides a summary of the subpopulations
22 of interest (all asthmatics and asthmatic children), supplemented with additional exposure and
23 risk results for the total population where appropriate.
24 B-3.4 Simulated Individuals
25 Due to the large size of the air quality input files, the modeled area was separated into three
26 sections. The number of simulated persons in each model run (3 sections per 3 years) was set to
27 50,000, yielding a total of 150,000 persons simulated for each year. The parameters controlling
28 the location and size of the simulated area were set to include the county(s) in the selected study
29 area. The settings that allow for replacement of CHAD data that are missing gender,
30 employment or age values were all set to preclude replacing missing data. The width of the age
31 window was set to 20 percent to increase the pool of diaries available for selection. The variable
32 that controls the use of additional ages outside the target age window was set to 0.1 to further
33 enhance variability in diary selection. See the APEX User's Guide for further explanation of
34 these parameters. The total population simulated for Philadelphia County was approximately
35 1.48 million persons, of which there a total simulated population of 163,000 asthmatics. The
36 model simulated approximately 281,000 children, of which there were about 48,000 asthmatics.
37 Due to random sampling, the actual number of specific subpopulations modeled varied slightly
38 by year.
39 B-3.4.1 Asthma Prevalence Rates
40 One of the important population subgroups for the exposure assessment is asthmatic children.
41 Evaluation of the exposure of this group with APEX requires the estimation of children's asthma
42 prevalence rates. The proportion of the population of children characterized as being asthmatic
B-28
-------
1
2
3
4
5
6
7
8
9
10
11
12
was estimated by statistics on asthma prevalence rates recently used in the NAAQS review for
Os (US EPA, 2007d; 2007e). Specifically, the analysis generated age and gender specific asthma
prevalence rates for children ages 0-17 using data provided in the National Health Interview
Survey (NHIS) for 2003 (CDC, 2007). These asthma rates were characterized by geographic
regions, namely Midwest, Northeast, South, and West. Adult asthma prevalence rates for
Philadelphia County were obtained from the Behavioral Risk Factor Surveillance System
(BRFSS) survey information (PA DOH, 2008). The average rates for adult males and females in
Philadelphia for 2001-2003 were 7% and 12%, respectively. These rates were assumed to apply
to all adults uniformly. Table B-8 provides a summary of the prevalence rates used in the
exposure analysis by age and gender.
Table B-8. Asthma prevalence rates by age and gender used for Philadel
Region
(Study Area)
Northeast
(Philadelphia)
Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18+
Females
Prevalence se L95 U95
0.068
0.072
0.075
0.077
0.082
0.116
0.161
0.185
0.171
0.145
0.135
0.141
0.166
0.174
0.151
0.146
0.146
0.157
0.070
0.066
0.038
0.022
0.020
0.023
0.030
0.037
0.041
0.040
0.035
0.031
0.031
0.034
0.034
0.029
0.028
0.031
0.054
0.007
0.021
0.038
0.042
0.043
0.063
0.092
0.108
0.096
0.080
0.078
0.084
0.102
0.109
0.095
0.093
0.088
0.068
0.040
0.442
0.221
0.145
0.138
0.151
0.205
0.266
0.298
0.284
0.246
0.223
0.227
0.259
0.266
0.232
0.221
0.232
0.322
0.140
)hia.
Males
Prevalence se L95 U95
0.048
0.046
0.052
0.068
0.100
0.149
0.207
0.228
0.222
0.212
0.177
0.166
0.183
0.171
0.170
0.182
0.204
0.242
0.120
0.033
0.018
0.015
0.018
0.023
0.029
0.042
0.045
0.043
0.041
0.037
0.035
0.036
0.031
0.029
0.029
0.032
0.061
0.010
0.019
0.027
0.037
0.059
0.094
0.129
0.143
0.142
0.136
0.108
0.102
0.116
0.113
0.115
0.127
0.142
0.133
0.090
0.200
0.108
0.097
0.120
0.164
0.226
0.316
0.343
0.332
0.316
0.275
0.259
0.276
0.250
0.244
0.254
0.284
0.399
0.150
Notes:
se - Standard error
L95 - Lower limit on 95th confidence interval
U95 - Upper limit on 95th confidence interval
13
14
15
16
17
18
B-3.5 Air Quality Data Generated by AERMOD
Air quality data input to the model were generated by air quality modeling using AERMOD.
Principal emission sources included both mobile and stationary sources as well as fugitive
emissions. The methodology is described below.
B-29
-------
1 B-3.5.1 Meteorological Inputs
All meteorological data used for the AERMOD dispersion model simulations were processed
with the AERMET meteorological preprocessor, version 06341. This section describes the input
data and processing methodologies used to derive input meteorological fields for each of the five
regions of interest.
B-3.5.1.1 Data Selection
Raw surface meteorological data for the 2001 to 2003 period were obtained from the
Integrated Surface Hourly (ISH) Database,l maintained by the National Climatic Data Center
(NCDC). The ISH data used for this study consists of typical hourly surface parameters
(including air and dew point temperature, atmospheric pressure, wind speed and direction,
precipitation amount, and cloud cover) from hourly Automated Surface Observing System
(ASOS) stations. No on-site observations were used.
The surface meteorological station used for this analysis is located at Philadelphia
International (KPHL) airport. The selection of surface meteorological stations minimized the
distance from the station to city center, minimized missing data, and maximized land-use
representativeness of the station site compared to the city center.
The total number of surface observations and the percentage of those observations accepted
by AERMET (i.e., those observations that were both not missing and within the expected ranges
of values), are shown by Table B-9. Note that instances of calm winds are not rejected by the
AERMET processor, but are later treated as calms in the dispersion analysis. There were 1,772
hours in Philadelphia (7%) with calm winds (see Table B-10).
Table B-9. Number of AERMET raw hourly surface meteorology observations, percent acceptance rate,
2001-2003.
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
Surface Variable
Precipitation
Station Pressure
Cloud Height
Sky Cover
Horizontal Visibility
Temperature
Dew Point
Temperature
Relative Humidity
Wind Direction
Wind Speed
Philadelphia (KPHL)
n=26,268
% Accepted a
100
99
99
95
99
99*
99
99
97
99
Notes:
a Percentages are rounded down to the nearest integer.
* The majority of unaccepted records are due to values
being out of range.
28
http://wwwl .ncdc.noaa.gov/pub/data/techrpts/tr20010 l/tr2001-01 .pdf
B-30
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1 Table B-10. Number of calms reported by AERMET by year for Philadelphia.
2
3
4
5
6
7
8
9
10
11
12
Year
2001
2002
2003
Total
Number of Calms
610
470
692
1772
Mandatory and significant levels of upper-air data were obtained from the NOAA
Radiosonde Database.2 Upper air observations show less spatial variation than do surface
observations; thus they are both representative of larger areas and measured with less spatial
frequency than are surface observations. The selection of upper-air station locations for each
city minimized both the proximity of the station to city center and the amount of missing data in
the records. The selected stations for Philadelphia was Washington Dulles Airport (KIAD). The
total number of upper-air observations per station per height interval, and the percentage of those
observations accepted by AERMET, are shown in Table B-l 1.
Table B-ll. Number and AERMET acceptance rate of upper-air observations 2001-2003.
Height
Level
Surface
0-500m
500-
1000m
1000-
1500m
1500-
2000m
Variable
Pressure
Height
Temperature
DewPoint Temperature
WindDirection
WindSpeed
Pressure
Height
Temperature
DewPoint Temperature
WindDirection
WindSpeed
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Philadelphia (KIAD)
n
2152
2152
2152
2152
2152
2152
4320
4320
4320
4320
4320
4320
3702
3702
3702
3702
3702
3702
4204
4204
4204
4204
4204
4204
3354
3354
3354
3354
3354
3354
% Accepted
100
100
100
100
100
85*
100
100
100
99
63
62
100
100
100
99*
73
73
100
100
100
97*
71
71
100
100
100
95*
50
50
http://raob.fsl.noaa.gov/
B-31
-------
Height
Level
2000-
2500m
2500-
3000m
3000-
3500m
3500-
4000m
>4000
m
Variable
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Philadelphia (KIAD)
n
3246
3246
3246
3246
3246
3246
3736
3736
3736
3736
3736
3736
3614
3614
3614
3614
3614
3614
2830
2830
2830
2830
2830
2830
7619
7619
7619
7619
7619
7619
% Accepted
100
100
100
93*
50
50
100
100
100
90*
64
64
100
100
100
90*
65
65
100
100
100
87*
50
50
88*
71 *
99*
79*
55
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-3.5.2
Surface Characteristics and Land Use Analysis
3 In addition to the standard meteorological observations of wind, temperature, and cloud
4 cover, AERMET analyzes three principal variables to help determine atmospheric stability and
5 mixing heights: the Bowen ratio3, surface albedo4 as a function of the solar angle, and surface
6 roughness.5
J For any moist surface, the Bowen Ratio is the ratio of heat energy used for sensible heating (conduction and
convection) to the heat energy used for latent heating (evaporation of water or sublimation of snow). The Bowen
ratio ranges from about 0.1 for the ocean surface to more than 2.0 for deserts. Bowen ratio values tend to decrease
with increasing surface moisture for most land-use types.
B-32
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
The January 2008 version of AERSURFACE was used to estimate land-use patterns and
calculate the Bowen ratio, surface albedo, and surface roughness as part of the AERMET
processing. AERSURFACE uses the US Geological Survey (USGS) National Land Cover Data
1992 archives (NLCD92). 6 Three to four land-use sectors were manually identified around the
surface meteorological station using this land-use data. These land-use sectors are used to
identify the Bowen ratio and surface albedo, which are assumed to represent an area around the
station of radius 10 km, and to calculate surface roughness by wind direction.
A monthly temporal resolution was used for the Bowen ratio, albedo, and surface roughness
at the meteorological site. Because the site was located at an airport, a lower surface roughness
was calculated for the 'Commercial/Industrial/Transportation' land-use type to reflect the
dominance of transportation land cover rather than commercial buildings. Philadelphia has at
least one winter month of continuous snow cover, which tends to increase albedo, decrease
Bowen ratio, and decrease surface roughness for most land-use types during the winter months
compared to a snow-free area. Seasons were assigned based on 1971-2000 NCDC 30-year
climatic normals and on input from the state climatologist (Table B-12).
Table B-12. Seasonal definitions and specifications for Philadelphia.
Location
Philadelphia
Winter
(continuous
snow)
Dec, Jan, Feb
Winter
(no snow)
Spring
Mar, Apr, May
Summer
Jun, Jul, Aug
Fall
Sep, Oct, Nov
Season definitions provided by the AERSURFACE manual as follows:
Winter (continuous snow): Winter with continuous snow on ground
Winter (no snow): Late autumn after frost and harvest, or winter with no snow
Spring: Transitional spring with partial green coverage or short annuals
Summer: Midsummer with lush vegetation
Fall: Autumn with unharvested cropland
20
21
22
23
24
25
26
27
28
29
30
Figure B-2 illustrates show the manually created land-use sectors around the application site;
a 1.9 mile (3 km) radius circle was used. Data are from the NLCD92 database. Prior to the
release of AERSURFACE, the user was required to manually pull values of Bowen ratio (Po),
albedo (a), and surface roughness (z0) per season and per land-use sector from look-up tables in
the AERMET User's Guide. Using the look-up tables, values of these three surface
characteristics vary by the four seasons and by eight basic land-use categories. Furthermore, the
AERMOD Implementation Guide was somewhat ambiguous about whether Bowen ratio values
should also vary with wind direction sector, as does the surface roughness. AERSURFACE
resolves these issues by providing a uniform methodology for calculation of surface effects on
dispersion; it also only varies surface roughness by wind direction.
The ratio of the amount of electromagnetic radiation reflected by the earth's surface to the amount incident upon it.
Value varies with surface composition. For example, snow and ice vary from 80% to 85% and bare ground from
10% to 20%.
5 The presence of buildings, trees, and other irregular land topography that is associated with its efficiency as a
momentum sink for turbulent air flow, due to the generation of drag forces and increased vertical wind shear.
6 http://seamless.usgs.gov/
B-33
-------
2 Before AERSURFACE, without an automated algorithm to determine land-use patterns, it
3 was simplest for the user to visually estimate land usage by sector. With AERSURFACE, the
4 land-use is automatically determined. The proximity of the meteorological site to an airport and
5 whether the site was located in an arid region were previously not explicitly accounted for as
6 they now are in AERSURFACE. Snow cover, too, is critical for determination of a, but was
7 largely left to user's discretion regarding its presence. With AERSURFACE, the lookup tables
8 have separate columns for winter without much snow and for winter with abundant snow. The
9 user determines if winter at a particular location contains at least one month of continuous snow
10 cover, and AERSURFACE will pull values of the surface characteristics from the appropriate
11 winter column.
12
13 We conducted a sensitivity test to evaluate the impacts of using this new tool on the present
14 analysis. Figure B-3 shows a sample comparison of surface roughness values at the Philadelphia
15 site with and without the use of AERSURFACE. In the Figure, estimated surface roughness
16 values using visual land-use estimations and look-up table values are shown in muted shades and
17 AERSURFACE values in dark shades. Monthly season definitions are the same in both cases.
18 However, in the AERSURFACE case, winter was specified as having a one-month period of
19 snow cover. Also, in the AERSURFACE case the site was specified as being at an airport.
20
21 In this case, ZQ values are much lower with AERSURFACE than with a visual estimation of
22 land-use. In the AERSURFACE tool, Philadelphia was noted as being at an airport, tending to
23 represent the lower building heights in the region and the inverse distance weighting
24 implemented in the tool. Thus, lower z0 values were obtained over most developed-area sectors
25 in this scenario. The indication that at least one month of continuous snow cover is present also
26 tends to lower wintertime ZQ values. In addition to these systematic differences, the automated
27 AERSURFACE land-use analysis for Philadelphia tended to identify less urban coverage and
28 more water coverage, lowering roughness values, but it also tended to identify more forest cover
29 and less cultivated land cover than our visual analysis, increasing some z0 values.
30
31 Po and a also varied significantly between the scenarios. However, this was largely due to
32 two practical matters: First, the independence of these variables of wind direction in the
33 AERSURFACE case and secondly the use of monthly-varying moisture conditions in one test
34 case and not another. Thus we have not presented those results
35
B-34
-------
' '
OpenWater
LowintenatyReadentiai
HighlntensityResidential
Commerdal/lndustrial/Transportation
BareRocWSandClay
Quames/StnpMines/GraveiPits
Transltonal
DeclduousFore$t
Evergreerforest
MlxedForest
Shnjbland
Grassland/Herbaceous
PastureMay
crops
uroan/RecreatioraiGrasses —39 socr'H
woodywenaniB
EmergenlHerDaceousweiianas
1
2
3
4
Figure B-2. Land-use and sectors around the Philadelphia-area surface meteorological station (KPHL).
Sector borders are 80,184,262, and 312 degrees from geographic North. Philadelphia city center is labeled.
B-35
-------
1
2
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
0.7
0.6
01
_l
in
8 0.4
c
O 0.3
0.2
50
100 150 200
Wind direction (degrees)
250
300
350
Minimum (old) » Maximum (old) » Average (old) • Minimum (new) • Maximum (new) • Average (new)
Figure B-3. Estimated z0 values for the Philadelphia case-study analysis using visual and AERSURFACE
land-use estimations.
B-3.5.3
Meteorological Data Analysis
The AERMET application location and elevation were taken as the center of the modeled
city, estimated using Google Earth version 4.2.0198.2451 (beta) and defined as 39.952 °N,
75.164 °W, 12 m. The 2001-2003 AERSURFACE processing was run three times - once
assuming the entire period was drier than normal, once assuming the entire period was wetter
than normal, and once assuming the entire period was of average precipitation accumulation.
These precipitation assumptions influence the Bowen ratio, discussed above.
To create meteorological input records that best represent the city for each of the three years,
the resulting surface output files for each site were then pieced together on a month-by-month
basis, with selection based on the relative amount of precipitation in each month. Any month
where the actual precipitation amount received was at least twice the 1971-2000 NCDC 30-year
climatic normal monthly precipitation amount was considered wetter than normal, while any
month that received less than half the normal amount of precipitation amount was considered
drier than normal; all other months were considered to have average surface moisture conditions.
Table B-13 indicates the surface moisture condition for each month evaluated in this
Philadelphia case-study.
B-36
-------
Table B-13. Monthly preci
Year
2001
2002
2003
Jan
74.8%
Jul
29.9%
Jan
69.9%
Jul
51 .0%
Jan
53.2%
Jul
46.5%
ritation compared to NCDC 30-year climatic normal for Philadelphia, 2001-2003.
Feb
103.6%
Aug
26.0%
Feb
17.7%
Aug
59.0%
Feb
165.0%
Aug
86.1%
Mar
144.2%
Sep
67.1%
Mar
96.4%
Sep
89.1%
Mar
102.7%
Sep
120.8%
Apr
43.9%
Oct
30.6%
Apr
52.7%
Oct
202.7%
Apr
62.0%
Oct
162.8%
May
102.9%
Nov
17.9%
May
89.2%
Nov
94.2%
May
108.5%
Nov
92.9%
Jun
180.1%
Dec
64.6%
Jun
93.9%
Dec
117.9%
Jun
246.2%
Dec
158.6%
Shading:
Less than or equal to half the normal monthly precipitation amount
Less than twice the normal precipitation level and greater than half the
normal amount
At least twice the normal precipitation level
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
B-3.5.4 On-Road Emissions Preparation
Information on traffic data in the Philadelphia area was obtained from the Delaware Valley
Regional Planning Council (DVRPC7) via their most recent, baseline travel demand modeling
(TDM) simulation - that is, the most recent simulation calibrated to match observed traffic data.
DVRPC provided the following files.
• Shapefiles of TDM outputs for the 2002 baseline year for all links in their network.
• Input files for the MOBELE6.2 emissions model that characterize local inputs that differ
from national defaults, including fleet registration distribution information.
• Postprocessing codes they employ for analysis of TDM outputs into emission inventory
data, to ensure as much consistency as possible between the methodology used for this
study and that of DVRPC. These include DVRPC's versions of the local SVMT.DEF,
HVMT.DEF, and FVMT.DEF MOBILE6.2 input files describing the vehicle miles
traveled (VMT) by speed, hour, and facility, respectively, by county in the Delaware
Valley area.
• A lookup table used to translate average annual daily traffic (AADT) generated by the
TDM into hourly values.
Although considerable effort was expended to maintain consistency between the DVRPC
approach to analysis of TDM data and that employed in this analysis, including several personal
communications with agency staff on data interpretation, complete consistency was not possible
due to the differing analysis objectives. The DVRPC creates countywide emission inventories.
This study created spatially and temporally resolved emission strengths for dispersion modeling.
B-3.5.4.1
Emission Sources and Locations
7 http://www.dvrpc.org/
B-37
-------
1 The TDM simulation's shapefile outputs include annual average daily traffic (AADT)
2 volumes and a description of the loaded highway network. The description of the network
3 consists of a series of nodes joining individual model links (i.e., roadway segments) to which the
4 traffic volumes are assigned, and the characteristics of those links, such as endpoint location,
5 number of lanes, link distance, and TDM-defined link daily capacity.8
6
7 To reduce the scope of the analysis, the full set of links in the DVRPC network was first
8 filtered to include only those roadway types considered major (i.e., freeway, parkway, major
9 arterial, ramp), and that had AADT values greater than 15,000 vehicles per day (one direction).
10
11 However, the locations of links in the model do not necessarily agree well with the roads
12 they are attempting to represent. While the exact locations of the links may not be mandatory for
13 DVRPC's travel demand modeling, the impacts of on-road emissions on fixed receptors is
14 crucially linked to the distance between the roadways and receptors. Hence, it was necessary to
15 modify the link locations from the TDM to the best known locations of the actual roadways. The
16 correction of link locations was done based on the locations of the nodes that define the end
17 points of links with a GIS analysis, as follows.
18
19 A procedure was developed to relocate TDM nodes to more realistic locations. The
20 nodes in the TDM represent the endpoints of links in the transportation planning network and are
21 specified in model coordinates. The model coordinate system is a Transverse Mercator
22 projection of the TranPlan Coordinate System with a false easting of 31068.5, false northing of-
23 200000.0, central meridian: -75.00000000, origin latitude of 0.0, scale factor of 99.96, and in
24 units of miles. The procedure moved the node locations to the true road locations and translated
25 to dispersion model coordinates. The Pennsylvania Department of Transportation (PA DOT)
26 road network database9 was used as the specification of the true road locations. The nodes were
27 moved to coincide with the nearest major road of the corresponding roadway type using a built-
28 in function of ArcGIS. Once the nodes had been placed in the corrected locations, a line was
29 drawn connecting each node pair to represent a link of the adjusted planning network.
30
31 To determine hourly traffic on each link, the AADT volumes were converted to hourly
32 values by applying DVRPC's seasonal and hourly scaling factors. To determine hourly traffic
33 on each link, the AADT volumes were converted to hourly values by applying DVRPC's
34 seasonal and hourly scaling factors. The heavy-duty vehicle fraction - which is assumed by
35 DVRPC to be about 6% in all locations and times - was also applied.10 Another important
The TDM capacity specifications are not the same as those defined by the Highway Capacity Manual (HCM).
Following consultation with DVRPC, the HCM definition of capacity was used in later calculations discussed
below.
9 http://www.pasda.psu.edu/
10 As shown by Figure B-4 NOX emissions from HDVs tend to be higher than their LDV counterparts by about a
factor of 10. However, the HDV fraction is less than 10% of the total VMT in most circumstances, mitigating their
influence on composite emission factors, although this mitigating effect is less pronounced at some times than
others. For example, nighttimes on freeways tend to show a smaller reduction in HDV volume than in total volume,
and thus an increased HDV fraction. This effect is not captured in most TDMs or emission postprocessors and -
both to maintain consistency with the local MPO's vehicle characterizations and emissions modeling and due to lack
of other relevant data - was also not included here. The net result of this is likely to be slightly underestimated
emissions from major freeways during late-night times.
B-38
-------
1
2
3
4
5
6
7
8
9
10
11
variable, the number of traffic signals occurring on a given link, was taken from the TDM link-
description information.
Several of these parameters are shown in the following set of tables.
• Table B-14
• TableB-15
• Table B-16
• Table B-17
hourly scaling factors
seasonal scaling factors
number of signals per roadway mile
statistical summaries of AADT volumes for links included in the study.
Table B-14. Hourly scaling factors (in percents) applied to Philadelphia County AADT volumes.
Road
Type
Freeway
Arterial
Local
Ramp
Road
Type
Freeway
Arterial
Local
Region
CBD
Fringe
Urban
Suburban
Rural
CBD
Fringe
Urban
Suburban
Rural
CBD
Fringe
Urban
Suburban
Rural
CBD
Fringe
Urban
Suburban
Rural
Region
CBD
Fringe
Urban
Suburban
Rural
CBD
Fringe
Urban
Suburban
Rural
CBD
Fringe
Urban
0:00
1.23
1.23
1.23
0.96
0.71
1.43
1.53
1.13
0.70
0.60
1.11
1.00
1.19
0.53
0.55
1.23
1.23
1.23
0.96
0.71
12:00
4.97
4.97
4.97
5.05
4.92
5.27
5.52
5.42
5.75
5.55
6.26
6.31
5.25
1:00
0.86
0.86
0.86
0.64
0.48
0.96
0.97
0.68
0.40
0.36
0.71
0.55
0.74
0.29
0.32
0.86
0.86
0.86
0.64
0.48
13:00
5.77
5.77
5.77
5.19
5.01
5.57
5.40
5.54
5.71
5.50
6.74
5.64
5.40
2:00
0.74
0.74
0.74
0.54
0.38
0.61
0.62
0.52
0.32
0.34
0.45
0.37
0.53
0.21
0.25
0.74
0.74
0.74
0.54
0.38
14:00
6.40
6.40
6.40
5.90
5.75
5.95
6.08
6.16
6.12
6.00
6.88
6.64
6.44
3:00
0.84
0.84
0.84
0.61
0.48
0.50
0.47
0.45
0.33
0.41
0.37
0.21
0.43
0.20
0.30
0.84
0.84
0.84
0.61
0.48
15:00
6.60
6.60
6.60
6.80
7.12
6.63
6.88
7.04
7.05
7.11
6.78
7.32
7.35
4:00
1.23
1.23
1.23
0.90
0.95
0.58
0.54
0.63
0.55
0.77
0.41
0.39
0.54
0.37
0.57
1.23
1.23
1.23
0.90
0.95
16:00
7.02
7.02
7.02
7.58
7.88
7.39
7.36
7.39
7.66
7.82
7.64
7.85
7.80
5:00
2.50
2.50
2.50
2.16
2.54
1.17
1.10
1.68
1.71
2.29
0.97
0.98
1.32
1.25
1.89
2.50
2.50
2.50
2.16
2.54
17:00
6.76
6.76
6.76
7.67
8.18
7.81
8.08
7.42
7.98
7.98
8.10
9.52
7.85
6:00
4.87
4.87
4.87
5.39
6.05
2.89
2.99
4.26
4.51
5.47
2.39
1.98
3.37
3.94
5.26
4.87
4.87
4.87
5.39
6.05
18:00
6.27
6.27
6.27
6.51
6.27
6.36
6.24
6.08
6.42
6.26
6.57
6.25
6.41
7:00
6.52
6.52
6.52
7.33
7.77
5.50
5.77
6.68
7.04
7.37
4.82
5.31
6.54
7.51
7.93
6.52
6.52
6.52
7.33
7.77
19:00
4.20
4.20
4.20
4.27
4.31
4.78
4.98
4.74
4.81
4.48
4.96
5.50
5.02
8:00
6.47
6.47
6.47
6.85
6.79
6.87
6.53
6.86
6.84
6.62
6.72
5.91
6.86
7.50
6.84
6.47
6.47
6.47
6.85
6.79
20:00
3.52
3.52
3.52
3.34
3.45
4.05
4.21
3.77
3.83
3.50
3.96
5.29
4.04
9:00
5.75
5.75
5.75
5.52
5.22
5.87
5.60
5.47
5.37
5.36
6.50
5.78
5.09
5.24
4.94
5.75
5.75
5.75
5.52
5.22
21:00
3.06
3.06
3.06
2.97
2.97
3.74
3.82
3.31
3.13
2.80
3.02
2.87
3.46
10:00
4.99
4.99
4.99
4.90
4.64
5.37
5.14
5.09
4.95
5.09
4.60
5.14
4.65
4.66
4.57
4.99
4.99
4.99
4.90
4.64
22:00
2.50
2.50
2.50
2.32
2.10
3.18
3.13
2.61
2.15
1.88
2.88
2.46
2.79
11:00
5.02
5.02
5.02
4.94
4.78
5.17
4.86
5.17
5.36
5.35
4.93
5.19
4.95
5.22
4.89
5.02
5.02
5.02
4.94
4.78
23:00
1.92
1.92
1.92
1.66
1.27
2.36
2.19
1.93
1.34
1.11
2.25
1.56
2.01
B-39
-------
Ramp
Suburban
Rural
CBD
Fringe
Urban
Suburban
Rural
5.78
5.20
4.97
4.97
4.97
5.05
4.92
5.57
5.11
5.77
5.77
5.77
5.19
5.01
6.01
5.89
6.40
6.40
6.40
5.90
5.75
7.11
7.41
6.60
6.60
6.60
6.80
7.12
8.20
8.53
7.02
7.02
7.02
7.58
7.88
8.98
8.93
6.76
6.76
6.76
7.67
8.18
6.83
6.75
6.27
6.27
6.27
6.51
6.27
5.02
4.82
4.20
4.20
4.20
4.27
4.31
3.83
3.64
3.52
3.52
3.52
3.34
3.45
2.90
2.70
3.06
3.06
3.06
2.97
2.97
1.82
1.73
2.50
2.50
2.50
2.32
2.10
1.05
0.99
1.92
1.92
1.92
1.66
1.27
1
2
3
4
5
6
7
Table B-15. Seasonal scaling factors applied to Philadelphia County AADT volumes.
Season
Winter
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Road
Type
Freeway
Freeway
Freeway
Freeway
Arterial
Arterial
Arterial
Arterial
Local
Local
Local
Local
Ramp
Ramp
Ramp
Ramp
Factor
0.945
1.006
1.041
1.009
0.942
1.004
1.041
1.013
0.933
1.012
1.05
1.004
0.944
1.005
1.041
1.011
Table B-16. Signals per mile, by link type, applied to Philadelphia County AADT volumes.
Functional Class
Freeway
Local
Major Arterial
Minor Arterial
Parkway
Ramp
Region Type
CBD
0
8
8
8
4
0
Fringe
0
6
6
6
2
0
Rural
0
1.5
1
1.3
0.5
0
Suburban
0
3
2
2
1
0
Urban
0
5
4
4
1.5
0
Table B-17. Statistical summary of AADT volumes (one direction) for Philadelphia County AERMOD
simulations.
Statistic
Count
Minimum
AADT
Maximum
AADT
Road Type
Arterial
Freeway
Ramp
Arterial
Freeway
Ramp
Arterial
Freeway
CBD
186
11
0
15088
15100
44986
39025
Fringe
58
10
4
15282
18259
16796
44020
56013
Suburban
210
107
3
15010
15102
15679
48401
68661
Urban
580
98
1
15003
15100
16337
44749
68661
B-40
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Average
AADT
Ramp
Arterial
Freeway
Ramp
21063
25897
40538
21196
40168
24468
24743
20736
33979
18814
16337
22368
31294
16337
B-3.5.4.2 Emission Source Strength
On-road mobile emission factors were derived from the MOBILE6.2 emissions model as
follows. The DVRPC-provided external data files describing the vehicle miles traveled (VMT)
distribution by speed, functional class, and hour, as well as the registration distribution and Post-
1994 Light Duty Gasoline Implementation for Philadelphia County were all used in the model
runs without modification. To further maintain consistency with the recent DVRPC inventory
simulations and maximize temporal resolution, the DVRPC's seasonal particulate matter (PM)
MOBILE6 input control files were also used. These files include county-specific data describing
the vehicle emissions inspection and maintenance (I/M) programs, on-board diagnostics (OBD)
start dates, VMT mix, vehicle age distributions, default diesel fractions, and representative
minimum and maximum temperatures, humidity, and fuel parameters. The simulations are
designed to calculate average running NOX emission factors.
11
These input files were modified for the current project to produce running NOx emissions in
grams per mile for a specific functional class (Freeway, Arterial, or Ramp) and speed. Iterative
MOBILE6.2 simulations were conducted to create tables of average Philadelphia County
emission factors resolved by speed (2.5 to 65 mph), functional class, season, and year (2001,
2002, or 2003) for each of the eight combined MOBILE vehicle classes (LDGV, LDGT12,
LDGT34, HDGV, LDDV, LDDT, HDDV, and MC)12. The resulting tables were then
consolidated into speed, functional class, and seasonal values for combined light- and heavy-duty
vehicles. Figure B-4 shows an example of the calculated emission factors for Autumn, 2001.
11 Basing the present emissions model input files on MPO-provided PM, rather than NOX input files should not cause
confusion. MPO-provided PM files were used because they contain quarterly rather than annual or biannual
information. In all cases the output species were modified to produce gaseous emissions. Further, many of the
specified input parameters do not affect PM emissions, but were included by the local MPO to best represent local
conditions, which were preserved in the present calculations of NOX emissions. This usage is consistent with the
overall approach of preserving local information wherever possible.
12 HDDV - Heavy-Duty Diesel Vehicle, HDGV - Heavy-Duty Gasoline Vehicle, LDDT - Light-Duty Diesel Truck,
LDDV - Light-Duty Diesel Vehicle, LDGT12 - Light-Duty Gasoline Truck with gross vehicle weight rating < 6,000
Ibs and a loaded vehicle weight of < 5,750 Ibs, LDGT 34 - Light-Duty Gasoline Truck with gross vehicle weight
rating between 6,001 - 8,500 and a loaded vehicle weight of < 5,750 Ibs, LDGV - Light-Duty Gasoline Vehicle, MC
- Motorcycles.
B-41
-------
1
2
3
4
5
6
1
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
X
O
z
01
OJ
20
18
16
14
12
10
8
6
4
2
0
v77^
-Fall Arterial LDV
Fall Freeway LDV
•Fall Arterial HDV
Fall Freeway HDV
0
10
20
50
60
70
30 40
Average Speed (mph)
Figure B-4. Example of Light- and heavy-duty vehicle NOx emissions grams/mile (g/mi) for arterial and
freeway functional classes, 2001.
To determine the emission strengths for each link for each hour of the year, the Philadelphia
County average MOBILE6.2 speed-resolved emissions factor tables were merged with the TDM
link data, which had been processed to determine time-resolved speeds. The speed calculations
were made as follows.
The spatial-mean speed of each link at each time was calculated following the methodology
of the Highway Capacity Manual.13 Generally, the spatial-mean speed calculation is a function
of the time-resolved volume-to-capacity ratio, with capacity the limiting factor. In the case of
freeway calculations, this is determined by the HDV fraction, posted speed, and the general
hilliness of the terrain, which was assumed to be uniformly flat for this region. The case of
arterials without intersections is similar, but also considers urban effects. The case of arterials
with intersections further considers the number of signals and length of each link and
signalization parameters. It was assumed that all signals are identical, operating with a 120-
second cycle and a protected left turn phase. Each link's speed is calculated independently. For
example, a series of adjacent arterial links could show very different spatial-mean speeds if one
link contains one or more intersections. That is, no up- or down-stream impacts are considered
on individual link speeds. Speeds were assumed to be equal for light- and heavy-duty vehicles.
Table B-18 shows the resulting average speed for each functional class within each TDM
region. Several values are shown as N/A, due to the focus only on major links as discussed
above.
Table B-18. Average calculated speed by link type.
Average Speed (mph)
CBD
Fringe
Suburban
Urban
Rural
13 As defined in Chapter 9 of Recommended Procedure for Long-Range Transporation Planning and Sketch
Planning. NCHRP Report 387, National Academy Press, 1997. 151 pp., ISBN No: 0-309-060-58-3.
B-42
-------
Ramp
Arterial
Freeway
N/A
34
51
35
31
62
35
44
66
35
32
62
N/A
N/A
N/A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
The resulting emission factors were then coupled with the TDM-based activity estimates to
calculate emissions from each of the 1,268 major roadway links. However, many of the links
were two sides of the same roadway segment. To speed model execution time, those links that
could be combined into a single emission source were merged together. This was done only for
the 628 links (314 pairs) where opposing links were paired in space and exhibited similar activity
levels within 20% of each other.
B-3.5.4.3 Other Emission Parameters
Each roadway link is characterized as a rectangular area source with the width given by the
number of lanes and an assumed universal lane width of 12 ft (3.66 m). The length and
orientation of each link is determined as the distance and angle between end nodes from the
adjusted TDM locations. In cases where the distance is such that the aspect ratio is greater than
100:1, the links were disaggregated into sequential links, each with a ratio less than that
threshold. There were 27 links that exceeded this ratio and were converted to 55 segmented
sources. Thus, the total number of area sources included in the dispersion simulations is 982.
Table B-19 shows the distribution of on-road area source sizes. Note that there are some road
segments whose length was zero after GIS adjustment of node location. This is assumed to be
compensated by adjacent links whose length will have been expanded by a corresponding
amount.
Table B-19. On-road area source sizes.
Minimum
Median
Average
1-a Deviation
Maximum
Segment
Width (m)
3.7
11.0
13.7
7.7
43.9
Lanes
1.0
3.0
3.8
2.1
12.0
Segment
Length (m)
0.0
220.6
300.2
259.5
1340.2
Resulting daily emission estimates were temporally allocated to hour of the day and season
using MOBILE6.2 emission factors, coupled with calculated hourly speeds from the
postprocessed TDM and allocated into SEASHR emission profiles for the AERMOD dispersion
model. That is, 96 emissions factors are attributed to each roadway link to describe the emission
strengths for 24 hours of each day of each of four seasons and written to the AERMOD input
control file.
The release height of each source was determined as the average of the light- and heavy-duty
vehicle fractions, with an assumed light- and heavy-duty emission release heights of 1.0 ft
(0.3048 m) and 13.1 ft (4.0 m), respectively.14 Because AERMOD only accepts a single release
height for each source, the 24-hour average of the composite release heights is used in the
modeling. Since surface-based mobile emissions are anticipated to be terrain following, no
14 4.0 m includes plume rise from truck exhaust stacks. See Diesel Paniculate Matter Exposure Assessment Study
for the Ports of Los Angeles and Long Beach. State of California Air Resources Board, Final Report, April 2006.
B-43
-------
1 elevated or complex terrain was included in the modeling. That is, all sources are assumed to lie
2 in a flat plane.
4 B-3.5.5 Stationary Sources Emissions Preparation
5 Data for the parameterization of major point sources in Philadelphia comes primarily from
6 two sources: the 2002 National Emissions Inventory (NEI; US EPA, 2007b) and Clean Air
7 Markets Division (CAMD) Unit Level Emissions Database (US EPA, 2007c). These two
8 databases have complimentary information.
9
10 The NEI database contains stack locations, emissions release parameters (i.e., height,
1 1 diameter, exit temperature, exit velocity), and annual emissions for 707 NOx-emitting stacks
12 (206 of which are considered fugitive release points) in Philadelphia County. The CAMD
13 database, on the other hand, has information on hourly NOX emission rates for all the units in the
14 US, where the units are the boilers or equivalent, each of which can have multiple stacks. The
15 alignment of facilities between the two databases is not exact, however. Some facilities listed in
16 the NEI, are not included in the CAMD database. Of those facilities that do match, in many cases
17 there is no clear pairing between the individual stacks assigned within the databases.
18
19 B-3.5.5.1 Data Source Alignment
20 To align the information between the two databases and extract the useful portion of each for
21 dispersion modeling, the following methodology was used.
22
23 1 . Attention was limited stacks within the NEI data base that (a) lie within Philadelphia
24 County and (b) were part of a facility with total emissions from all stacks exceeding
25 lOOtpyNOx.
26 2. Individual stacks that had identical stack physical parameters and were co-located
27 within about 10m were combined to be simulated as a single stack with their
28 emissions summed.
29 3. All fugitive releases were removed from the list, to be analyzed as a separate source
30 group.
31
32 The resulting 19 distinct, combined stacks from the NEI are shown in Table B-20.
33
34 The CAMD database was then queried for facilities that matched the facilities identified from
35 the NEI database. Facility matching was done on the facility name, Office of Regulatory
36 Information Systems (ORIS) identification code (when provided) and facility total emissions to
37 ensure a best match between the facilities. Once facilities were paired, individual units and
38 stacks in the data bases were paired, based on annual emission totals. Table B-21 shows the
39 matching scheme for the seven major facilities in Philadelphia County. 15
15 Note that Jefferson Smurfit does not exist in the CAMD database. The matching here was based on facility types
as follows. Smurfit in PA was taken as a packaging/recycling facility, and the stack assumed to be a Cogen facility,
based on information in the NEEDS database (http://www.epa.gov/interstateairquality/pdfs/NEEDS-NODA.xls).
The best matched cogen plant in Philadelphia County in both the NEEDS and CAMD database is the Gray's Ferry
Cogen Partnership (ORIS 54785), which was a reasonable match for Smurfit's total emissions. It was assumed that
the hourly emission profile also matches well.
B-44
-------
2 In Table B-21, there are sometimes multiple CAMD units that pair with a single NEI
3 combined stack. In these cases the hourly emission rates from the matching CAMD units are
4 summed for each hour. For example, in the case of stack 859 for "Sunoco, Inc - Philadelphia"
5 five CAMD hourly records are summed into a single hourly record. Then each resulting hourly
6 value is scaled by a factor of 1032.8 / 938.9 = 1.10, so that the annual total matches the NEI
7 annual total.
8
9 Similarly, there are sometimes multiple combined stacks that pair with single units. In this
10 case the CAMD values are disaggregated according to NEI-defmed stack contributions. For
11 example, "Sunoco, Inc - Philadelphia" stack 855's profile is determined by taking the hourly
12 profile from CAMD unit number 52106-150101, and scaling each value by a factor of 26.2 tpy /
13 48.2 tpy total = 0.54. Then each resulting hourly value is scaled by a factor of 48.2/162.1 = 0.3
14 so that the sum of the annual totals for the 4 stacks corresponding to unit number 52106-150101
15 matches the NEI total. For consistency, in each case the 2001 and 2003 hourly emission profiles
16 were determined using the same scaling factors, but applied to the respective CAMD emission
17 profile.
18
19 It is clear from Table B-21 that most facilities agree well in total annual NOX emissions
20 between the two databases. However, in the case of the "Sunoco Chemicals (Former Allied
21 Signal)" facility, nearly half of the NEI emissions (without fugitives) do not appear in the
22 CAMD database. The reason for this is unknown and no information was readily available on
23 the relative accuracy of the two databases.
24
25 Figure B-5 illustrates the discrepancy versus fraction of hours with positive emissions,
26 according to the CAMD data base. The figure suggests that the discrepancies are not primarily
27 the result of facilities with episodic emissions (i.e., "peak load" facilities). Although there is
28 good agreement on facility-wide emissions between the two data bases, there are larger
29 discrepancies between CAMD unit emissions and NEI stack emissions. This is to be expected
30 given the discrepancy in resolution between the two data bases.
31
B-45
-------
Table B-20. Combined stacks parameters for stationary NOx emission sources in Philadelphia County.
Stack
No
817
818
819
820
821
855
856
857
858
859
860
861
862
863
864
865
866
867
868
NEI
Site ID
NEIPA2218
NEIPA2218
NEI40720
NEI40720
NEI40720
NEI40723
NEI40723
NEI40723
NEI40723
NEI40723
NEI7330
NEI7330
NEI7330
NEI7330
NEIPA101353
NEIPA101353
NEIPA101356
NEIPA101356
NEIPA2222
Facility Name
EXELON GENERATION
CO - DELAWARE STATION
EXELON GENERATION
CO - DELAWARE STATION
JEFFERSON SMURFIT
CORPORATION (U S)
JEFFERSON SMURFIT
CORPORATION (U S)
JEFFERSON SMURFIT
CORPORATION (U S)
Sunoco Inc. - Philadelphia
Sunoco Inc. - Philadelphia
Sunoco Inc. - Philadelphia
Sunoco Inc. - Philadelphia
Sunoco Inc. - Philadelphia
SUNOCO CHEMICALS
(FORMER ALLIED SIGNAL)
SUNOCO CHEMICALS
(FORMER ALLIED SIGNAL)
SUNOCO CHEMICALS
(FORMER ALLIED SIGNAL)
SUNOCO CHEMICALS
(FORMER ALLIED SIGNAL)
TRIGEN-SCHUYLKILL
TRIGEN -SCHUYLKILL
GRAYS FERRY
COGENERATION
PARTNERS
GRAYS FERRY
COGENERATION
PARTNERS
TRIGEN -EDISON
SIC
Code
4911
4911
2631
2631
2631
2911
2911
2911
2911
2911
2869
2869
2869
2869
4961
4961
4911
4911
4961
NAICS
Code
221 1 1 2
221 1 1 2
32213
32213
32213
32411
32411
32411
32411
32411
325998
325998
325998
325998
22
22
22
22
62
ORIS
Facili
ty
Code
3160
3160
54785
54785
Stack
Emissions
(tpy)
4.82
287.8
0.148
113.8
1 1 4.46
26.2
1.3
1.4
19.3
1032.8
0.033
49.1
34.6
77.2
128.6
61.5
143.2
90.3
130.5
Stack X
(deg)
-75.1358
-75.1358
-75.2391
-75.2391
-75.2391
-75.2027
-75.2003
-75.203
-75.2027
-75.2124
-75.0715
-75.0715
-75.0715
-75.0715
-75.1873
-75.1873
-75.1873
-75.1873
-75.1569
Stack Y
(deg)
39.96769
39.96769
40.03329
40.03329
40.03329
39.92535
39.91379
39.92539
39.92535
39.90239
40.00649
40.00649
40.00649
40.00649
39.94239
39.94239
39.94239
39.94239
39.94604
Stack
Ht
(m)
49
64
16
53
53
24
24
25
25
61
5
41
42
42
69
78
78
85
78
Exit
Temp
(K)
515
386
477
427
477
450
644
511
527
489
476
422
422
422
450
450
396
443
589
Stack
Diam
(m)
4.2
3.7
0.4
2.4
2.4
2.1
1.5
1.9
1.9
5.8
0.5
1.4
1.6
1.6
4.9
7.3
5.5
3.2
3.7
Exit
Velocity
(mis)
0
17
19
10
12
9
22
10
11
11
7
22
17
22
6
2
20
21
9
Facility
Emission
with
Fugitive
(tpy)
297.8
297.8
228.4
228.4
228.4
3112.2
3112.2
3112.2
3112.2
3112.2
160.9
160.9
160.9
160.9
190.1
190.1
233.5
233.5
130.5
B-46
-------
Table B-21. Matched stacks between the CAMD and NEI database.
NEI Facility
Name
Exelon
Generation Co
- Delaware
Station
Sunoco Inc. -
Philadelphia
Sunoco
Chemicals
(Former Allied
Signal)
Trigen -
Schuylkill
NEI
Comb.
Stack
Number
817
818
855
856
857
858
859
860
861
862
863
864
865
NEI
Comb.
Stack
Emiss
(tpy)
4.8
287.8
26.2
1.3
1.4
19.3
1032.8
0.0
49.1
34.6
77.2
128.6
61.5
NEI
Unit
Emiss
(tpy)
4.8
287.8
48.2
1032.8
160.9
128.6
61.5
NEI
Facility
Emiss
(tpy,
w/out
Fugitive)
292.6
1081.0
160.9
190.1
CAMD
Facility
Name
Delaware
Philadelphia
Refinery
Sunoco
Chemicals
Frankford
Plant
Trigen
Energy -
Schuykill
CAMD
Units *
3160-9
3160-71
3160-81
52106-
150101
52106-
150137
52106-
150110
52106-
150138
52106-
150139
52106-
150140
880007-52
50607-23
50607-24
CAMD
Unit
Emiss
(tpy) *
1.542
123.8
164
162.1
194.2
162.1
194.2
194.2
194.2
84.5
163.1
2.9
CAMD
Comb.
Unit
Totals
(tpy)
1.542
287.8
162.1
938.9
84.5
163.1
15.6
CAMD
Facility
Totals
(tpy)
289.3
1101.0
84.5
178.7
Stack 5
(%,
relative
to
CAMD
value)
213%
0%
-70%
10%
90%
-21%
293%
Stack
5
(tpy)
3.3
0.0
113.9
93.9
76.4
-34.5
45.9
Facility
5(%
relative
to
CAMD
value)
1%
-2%
90%
6%
Facility
5 (tpy)
3.3
-20.3
76.4
11.4
B-47
-------
NEI Facility
Name
Grays Ferry
Cogeneration
Partners
Trigen -
Edison
Jefferson
Smurfit
Corporation
(U S) ***
NEI
Comb.
Stack
Number
866
867
868
819
820
821
NEI
Comb.
Stack
Emiss
(tpy)
143.2
90.3
130.5
0.1
113.8
114.5
NEI
Unit
Emiss
(tpy)
143.2
90.3
130.5
228.4
NEI
Facility
Emiss
(tpy,
w/out
Fugitive)
233.5
130.5
228.4
CAMD
Facility
Name
Grays Ferry
Cogen
Partnership
Trigen
Energy
Corporation-
Edison St
CAMD
Units *
50607-26
54785-2
54785-25
880006-1
880006-2
880006-3
880006-4
54785-2
54785-25
CAMD
Unit
Emiss
(tpy) *
12.7
143.2
90.3
19.8
17.3
36.1
37.8
143.2
90.3
CAMD
Comb.
Unit
Totals
(tpy)
143.2
90.3
111
233.5
CAMD
Facility
Totals
(tpy)
233.5
111.0
233.5
Stack 5
(%,
relative
to
CAMD
value)
0%
0%
18%
-2%
Stack
5
(tpy)
0.0
0.0
19.4
-5.1
Facility
5(%
relative
to
CAMD
value)
0%
18%
-2%
Facility
5 (tpy)
0.0
19.4
-5.1
Notes:
* In the format "ORIS ID - UNIT ID"
** All CAMD values are for 2002
*** Jefferson Smurfit not in CAMD; will use Grays Ferry as surrogate
B-48
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1
2
3
4
5
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
2002 Annual NOx Emission
Mass [NEI-CAMD] Difference
Facility-Wide 2002 NOx Emission Frequency
versus
Difference in 2002 NOx Emission Mass Between the CAMD and NEI Databases
(ORIS ID labeled)
cn -
40 -
•— • on
c
o
** 0 -
o
AC] -
880007
50607 *
•ml
3160 J!S>K
O4/ OO(Jefferson SiDurfit)
52106
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
NOx Emission Frequency for 2002
(% hours in 2002 where NOx was emitted)
Figure B-5. Differences in facility-wide annual NOx emission totals between NEI and CAMD data bases for
Philadelphia County 2002.
B-3.5.6 Fugitive and Airport Emissions Preparation
Fugitive emission releases in Philadelphia County, as totaled in the NEI database, were
modeled as area sources with the profile of these releases determined by the overall facility
profile of emissions. In addition, emissions associated with the Philadelphia International
Airport were estimated.
B-3.5.6.1 Fugitive Releases
Thirty five combined stacks were identified during the point source analysis (see previous
section) that were associated with facilities considered major emitters, but where the emissions
from the stacks are labeled Fugitive in the NEI. These stacks have zero stack diameter, zero
emission velocity, and exit temperature equal to average ambient conditions (295 K). Thus, we
determined it was not appropriate to include these in the point source group simulation.
These 35 stacks occur at only two facilities in the County: Exelon Generation Co - Delaware
Station (NEI Site ID: NEIPA2218) and Sunoco Inc. - Philadelphia (NEI Site ID: NEI40723).
Consequently, they were grouped by facility. The Sunoco emissions further fall into two distinct
categories based on release heights. Thus, to accommodate all these sources most efficiently, we
created three area source groups: one for Sunoco emissions at 3.0 m, one for Sunoco emissions
greater than 23.0 m, and one for Exelon. The "stacks" within the NEI and their parameters
comprising each of these sources are shown in Table B-22 along with their groupings and the
resulting combined area source parameters.
Table B-22. Emission parameters for the three Philadelphia County fugitive NOx area emission sources.
Grp.
No.
NEI
Site ID
Facility Name
NEI 2002
Emissions
(tpy)
Stack X
Stack Y
Stack
Height
(m)
Stacks
Used for
Emission
Profile 1
Scaled Emissions (tpy) 2
2001
2002
2003
B-49
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Grp.
No.
1
2
3
NEI
Site ID
NEIPA
2218
NEI40
723
NEI40
723
Facility Name
EXELON
GENERATIO
NCO-
DELAWARE
STATION
Sunoco Inc. -
Philadelphia
Sunoco Inc. -
Philadelphia
NEI 2002
Emissions
(tpy)
0.1
5.1
5.2
65.3
350.9
12.7
355.7
31.1
6.2
182.4
1.1
7.5
1.0
2.0
49.4
106.3
188.5
87.8
36.1
9.7
61.2
13.6
17.0
17.2
12.2
12.6
23.7
19.2
10.0
1,680.4
79.5
13.1
15.3
2.5
10.2
19.0
211.2
350.8
Stack X
-75.13582
-75.12528
-75.21408
-75.21300
-75.20972
-75.20945
-75.20876
-75.20845
-75.20809
-75.20707
-75.20651
-75.20301
-75.20114
-75.20090
-75.20079
-75.20047
-75.20043
-75.20024
-75.20020
-75.19995
-75.19766
-75.19751
-75.19735
-75.19723
-75.19720
-75.19713
-75.19699
-75.19644
-75.21322
-75.20833
-75.20850
-75.20844
-75.20838
-75.20828
-75.20889
Stack Y
39.96769
39.96680
39.90811
39.90878
39.90467
39.90778
39.90185
39.90708
39.91580
39.90946
39.90988
39.91362
39.91273
39.91621
39.91615
39.91366
39.91377
39.91406
39.91410
39.91596
39.91696
39.91696
39.91590
39.91597
39.91698
39.91596
39.91599
39.91493
39.90899
39.90278
39.90246
39.90239
39.90231
39.90237
39.90279
Stack
Height
(m)
5
8
6.5
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3.0
23
26
27
27
27
27
30
26.7
Stacks
Used for
Emission
Profile 1
817+818
855+856+
857+858+
859
855+856+
857+858+
859
Scaled Emissions (tpy) 2
2001
4.8
1,873.
8
391.2
2002
5.2
1,681.
4
351.0
2003
6.4
2,202
.4
459.8
1 See Table B-20 for stack definitions.
2 Scaled emissions are determined by summing the scaled, hourly values
from the CAMD database, as used in the dispersion modeling.
B-50
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1
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3
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5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
In the case of the Sunoco emissions, the vertices of the area sources were determined by a
convex hull encapsulating all the points. In the case of Excelon, only two points are provided,
which is insufficient information to form a closed polygon. Instead, the boundary of the facility
was digitized into a 20-sided polygon. Figure B-6 shows the locations of these polygons.
Emission profiles for the fugitive releases were determined from the CAMD hourly emission
database in a method similar to that for the point sources. We determined scaling factors based
on the ratio of the 2002 fugitive releases described by the NEI to the total, non-fugitive point
source releases from the same facility. All stacks within that facility were combined on an
hourly basis for each year and the fugitive to non-fugitive scaling factor applied, ensuring that
the same temporal emission profile was used for fugitives as for other releases from the facility,
since the origins of the emissions should be parallel. We created external hourly emissions files
for each of the three fugitive area sources with appropriate units (grams per second per square
meter).
Sunoco (ReleaseHght = 3m)
\ ^ Sunoco (ReleaseHght = 23+ m)
KPHL Airport Baggage Handling Area
Figure B-6. Locations of the four ancillary area sources. Also shown are centroid receptor locations.
B-3.5.6.2 Philadelphia International Airport Emissions
Another significant source of NOx emissions in Philadelphia County not captured in the
earlier simulations is from operation of the Philadelphia International Airport (PHL). PHL is the
only major commercial airport in the County and is the largest airport in the Delaware Valley.
B-51
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1
2
3
4
5
6
7
The majority of NOx emissions in the NEI16 database attributable to airports in Philadelphia
County are from non-road mobile sources, specifically ground support equipment. There is
another airport in the County: Northeast Philadelphia Airport. However, because it serves
general aviation, is generally much smaller in operations than PHL, and has little ground support
equipment activity - which is associated primarily with commercial aviation - all airport
emissions in the County were attributed to PHL. The PHL emissions were taken from the non-
road section of the 2002 NEI, and are shown by Table B-23.
Table B-23. Philadelphia International airport (PHL) NOX emissions.
State and
County
Philadelphia,
PA
PHL Total
scc
2265008005
2267008005
2270008005
2275020000
2275050000
NOx
(tpy)
4.6
5.1
196.2
0.01
2.5
208.4
SCC Level 1
Description
Mobile
Sources
Mobile
Sources
Mobile
Sources
Mobile
Sources
Mobile
Sources
SCC Level 3
Description
Off-highway
Vehicle
Gasoline, 4-
Stroke
LPG
Off-highway
Vehicle
Diesel
Aircraft
Aircraft
SCC Level 6
Description
Airport
Ground
Support
Equipment
Airport
Ground
Support
Equipment
Airport
Ground
Support
Equipment
Commercial
Aircraft
General
Aviation
SCC Level 8
Description
Airport
Ground
Support
Equipment
Airport
Ground
Support
Equipment
Airport
Ground
Support
Equipment
Total: All
Types
Total
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
As with the fugitive sources discussed above, the airport emissions are best parameterized as
area sources. The boundary of the area source was taken as the region of operation of baggage
handling equipment, including the terminal building and the region surrounding the gates. This
region was digitized into an 18-sided polygon of size 1,326,000 m2, and included in the
AERMOD input control file.
The activity profile for PHL was taken to have seasonal and hourly variation (SEASHR),
based on values from the EMS-HAP model.17 These factors are disaggregated in the EMS-HAP
model database based on source classification codes (SCCs), which were linked to those from
the NEI database. The EMS-HAP values provide hourly activity factors by season, day type, and
hour; to compress to simple SEASHR modeling, the hourly values from the three individual day
types were averaged together. The total emissions for each SCC were then disaggregated into
seasonal and hourly components and the resulting components summed to create total PHL
emissions for each hour of the four annual seasons. These parameterized emissions were then
normalized to the total cargo handling operational area, to produce emission factors in units of
grams per second per square meter and included in the AERMOD input file. Figure B-6 also
illustrates the location of the PHL area source.
16 http ://www. epa. gov/ttn/chief/net/2002inventory. html
17 EPA 2004, User's Guide for the Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version
3.0, EPA-454/B-03-006.
B-52
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1 B-3.5.7 Receptor Locations
Three sets of receptors were chosen to represent the locations of interest. First, all NOx
monitor locations, shown by Table B-24, within the Philadelphia county were included as
receptor locations. Although all receptors are assumed to be on a flat plane, they are placed at
the standard breathing height of 5.9 ft (1.8 m).
Table B-24. Philadelphia County NOx monitors.
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Site ID
421010004
421010029
421010047
Latitude
40.0089
39.9572
39.9447
Longitude
-75.0978
-75.1731
-75.1661
The second receptor locations were selected to represent the locations of census block
centroids near major NOX sources. GIS analysis was used to determine all block centroids in
Philadelphia County that lie within a 0.25 mile (400 m) of the roadway segments and also all
block centroids that lie within 6.2 miles (10 km) of any major point source. 12,982 block
centroids were selected due to their proximity to major roadways; 16,298 centroids were selected
due to their proximity to major sources. The union of these sets produced 16,857 unique block
centroid receptor locations, each of which was assigned a height of 5.9 ft (1.8 m). The locations
of centroids that met either distance criteria - and were thus included in the modeling - is shown
by Figure B-7.
819*
Figure B-7. Centroid locations within fixed distances to major point and mobile sources in Philadelphia
county.
B-53
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1
2
3
4
5
6
7
9
10
11
12
13
14
15
16
17
18
19
20
The third set of receptors was chosen to represent the on-road microenvironment. For this
set, one receptor was placed at the center of each of the 982 sources.
The distance relationship between the road segments and block centroids can be estimated by
looking at the distance between the road-centered and the block centroid receptors. Figure B-8
shows the histogram of the shortest distance between each centroid receptor and its nearest
roadway-centered receptor.
100%
a
o
OLOOLOO
or^iocsio
T-c\iM-
CM
Distance (m)
Figure B-8. Frequency distribution of distance between each Census receptor and its nearest road-centered
receptor in Philadelphia County.
The block centroids selected were those within 10 km of any major point source or 400 m
from any receptor edge, so the distances to the nearest major road segment can be significantly
greater than 400 m. The mode of the distribution is about 150 m and the median distance to the
closest roadway segment center is about 450 m. However, these values represent the distances
of the block centroids to road centers instead of road edges, so that they overestimate the actual
distances to the zone most influenced by roadway by an average of 14 m and a range of 4 m to
44 m (see Table B-19 above).
21 B-3.5.8
Other AERMOD Specifications
22 Since each of the case-study locations were MSA/CMS As, all emission sources were
23 characterized as urban. The AERMOD toxics enhancements were also employed to speed
24 calculations from area sources. NOX chemistry was applied to all sources to determine NO2
25 concentrations. For the each of the roadway, fugitive, and airport emission sources, the ozone
26 limiting method (OLM) was used, with plumes considered ungrouped. Because an initial NO2
27 fraction of NOX is anticipated to be about 10% or less (Finlayson-Pitts and Pitts, 2000; Yao et al.,
28 2005), a conservative value of 10% for all sources was selected. For all point source simulations
B-54
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1
2
3
4
5
6
7
8
9
10
11
12
13
1.
the Plume Volume Molar Ratio Method (PVMRM) was used to estimate the conversion of NOX
to NC>2, with the following settings:
Hourly series of Oj concentrations were taken from EPA's AQS database18. The
complete national hourly record of monitored O3 concentrations were filtered for the
four monitors within Philadelphia County (stations 421010004, 421010014,
421010024, and 421010136). The hourly records of these stations were then
averaged together to provide an average Philadelphia County concentrations of Os for
each hour of 2001-2003.
The equilibrium value for the NO2:NOX ratio was taken as 75%, the national average
ambient ratio.19
The initial NC>2 fraction of NOX is anticipated to be about 10% or less. A default
value of 10% was used for all stacks (Finlayson-Pitts and Pitts, 2000).
14 B-3.5.9 Air Quality Concentration Adjustment
15 The hourly concentrations estimated from each of the three source categories were combined
16 at each receptor. Then a local concentration, reflecting the concentration contribution from
17 emission sources not included in the simulation, was added to the sum of the concentration
18 contributions from each of these sources at each receptor. The local concentration was estimated
19 from the difference between the model predictions at the local NC>2 monitors and the observed
20 values. It should be noted that this local concentration may also include any model error present
21 in estimating concentration at the local monitoring sites. Table B-25 presents a summary of the
22 estimated local concentration added to the AERMOD hourly concentration data.
23
24 Table B-25. Comparison of ambient monitoring and AERMOD predicted NO2 concentrations in
25 Philadelphia.
Year and
Monitor ID
Annual Average NO2 concentration (ppb)
Monitor
AERMOD
Inititial
Difference1
AERMOD
Final2
2001
4210100043
4210100292
4210100471
mean
26
28
30
7
22
20
18
6
10
11
19
33
32
2002
4210100043
4210100292
4210100471
mean
24
28
29
7
21
19
17
7
10
11
18
32
31
2003
4210100043
4210100292
4210100471*
24
25
25
7
22
26
17
3
-1
13
28
32
18 http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm
19 Appendix W to CFR 51, page 466. http://www.epa.gov/scram001/guidance/guide/appw_03.pdf.
B-55
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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.
2 B-3.5.10 Meteorological Data Used By APEX
4
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
APEX used the same meteorological data that was used for the AERMOD modeling, the
station located at Philadelphia International (KPHL) airport.
B-3.5.11 Microenvironment Descriptions
B-3.5.11.1 Microenvironment 1: Indoor-Residence
The Indoors-Residence microenvironment uses several variables that affect NC>2 exposure:
whether or not air conditioning is present, the average outdoor temperature, the NO2 removal
rate, and an indoor concentration source. The first two of these variables affect the air exchange
rate.
Since the selection of an air exchange rate distribution is conditioned on the presence or
absence of an air-conditioner, for each modeled area the air conditioning status of the residential
microenvironments is simulated randomly using the probability that a residence has an air
conditioner. For this study, location-specific air conditioning prevalence was taken from the
American Housing Survey of 2003 (AHS, 2003a; 2003b). Previous analyses (US EPA, 2007d)
detail the specification of uncertainty estimates in the form of confidence intervals for the air
conditioner prevalence using the following:
Standard Error (P) =
3850 P (l-P)
N
Confidence Interval (P) = P + l .96 x Standard Error (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
Philadelphia
Housing
Units
1,943,492
A/C
Prevalence
(%)
90.6
se
1.3
L95
88.1
U95
93.2
Notes:
se - Standard error
L95 - Lower limit on 95th confidence interval
U95 - Upper limit on 95th confidence interval
26
B-56
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Air exchange rate data for the indoor residential microenvironment were obtained from US
EPA (2007d). Briefly, residential air exchange rate (AER) data were obtained from several
studies (Avol et al., 1998; Williams et al., 2003a, 2003b; Meng et al., 2004; Weisel et al., 2004;
Chillrud at al, 2004; Kinney et al., 2002; Sax et al., 2004; Wilson et al., 1986, 1996; Colome et
al., 1993, 1994; Murray and Burmaster, 1995). Influential characteristics (e.g., temperature, air
conditioning), where reported in the study, were also compiled for use in statistical analyses.
Descriptive statistics were generated for each location/variable type and evaluated using
statistical comparison testing (e.g., ANOVA). Based on the summary statistics and the statistical
comparisons, different AER distributions were fit for each combination of A/C type, city, and
temperature. In general, lognormal distributions provided the best fit, and are defined by a
geometric mean (GM) and standard deviation (GSD). To avoid unusually extreme simulated
AER values, bounds of 0.1 and 10 were selected for minimum and maximum AER, respectively.
For Philadelphia, a distribution was selected from a location thought to have similar
characteristics to the city to be modeled, qualitatively considering factors that might influence
AERs. These factors include the age composition of housing stock, construction methods, and
other meteorological variables not explicitly treated in the analysis, such as humidity and wind
speed patterns. The distributions used for Philadelphia are provided in Table B-27.
Table B-27. Geometric means (GM) and standard deviations (GSD) for air exchange rates by city, A/C type,
and temperature range.
Area
Modeled
Philadelphia
Study City
New York
City
A/C Type
Central or
Room A/C
No A/C
Temp
(°C)
<=10
10-25
>25
<=10
10-20
>20
N
20
42
19
48
59
32
GM
0.7108
1.1392
1 .2435
1.0165
0.7909
1 .6062
GSD
2.0184
2.6773
2.1768
2.1382
2.0417
2.1189
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
For this analysis, the same NC>2 removal rate distribution was used for all microenvironments
that use the mass balance method. This removal rate is based on data provided by Spicer et al.
(1993). A total of 6 experiments, under variable source emission characteristics including
operation of gas stove, were conducted in an unoccupied test house. A distribution could not be
described with the limited data set, therefore a uniform distribution was approximated by the
bounds of the 6 values, a minimum of 1.02 and a maximum of 1.45 h
-i
An excerpt from the APEX input file describing the indoor residential microenvironment is
provided in Figure B-9. The first section of the input file excerpt specifies the air exchange rate
distributions for the microenvironment. Average temperature and air conditioning presence,
which are city-specific, were coded into air exchange rate conditional variables, Cl and C2,
respectively. Average temperatures were separated into five categories (variable Cl, numbered
1-5): 50 ° F, 50-68 ° F, 68-77 ° F, 77-86 ° F, and 86 ° F and above. For variable C2, air
conditioning status can range from 7 to 2 (7 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 often
distributions (i.e., 5 temperature distributions by 2 air conditioning distributions). In the input
file example however, there are actually four AER distributions for homes with an air
B-57
-------
1 conditioner and three for those without; the last few distributions for each air conditioning setting
2 were the same due to the available data to populate the field. The parameter estimates for the
3 removal factor (DE) is also shown following the AER data.
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34 Figure B-9. Example input file from APEX for In doors-residence microenvironment.
35
36 Indoor source contributions
37 A number of studies, as described in the NOX ISA, have noted the importance of gas cooking
38 appliances as sources of NC>2 emissions. An indoor emission source term was included in the
39 APEX simulations to estimate exposure to indoor sources of NC>2. Three types of data were used
40 to implement this factor:
41 • The fraction of households in the Philadelphia MSA that use gas for cooking fuel
42 • The range of contributions to indoor NO2 concentrations that occur from cooking
43 with gas
44 • The diurnal pattern of cooking in households.
45
46 The fraction of households in Philadelphia County that use gas cooking fuel (i.e., 55%) was
47 taken from the US Census Bureau's American Housing Survey for the Philadelphia Metropolitan
48 Area: 2003.
49
B-58
Micro number
Parameter Type
Condition # 1
Condition #2
ResampHours
ResampDays
ResampWork
= 1 ! Indoors -
= AER
= AvgTempCat
= AC Home
= NO
= YES
= YES
Block DType Season Area
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
Micro number
Pollutant = 1
Parameter Type
ResampHours
ResampDays
ResampWork
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
= 1
= DE
= NO
= NO
= YES
Block DType Season Area
1 1
1 1
C1
1
2
3
4
5
1
2
3
4
5
C2
1
1
1
1
1
2
2
2
2
2
C3
1
1
1
1
1
1
1
1
1
1
residence -
Shape
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
Lognormal
AIR EXCHANGE RATES
Par1
0.711
1.139
1.139
1.244
1.244
1.016
0.791
1.606
1.606
1.606
Par2 Par3 Par4 LTrunc UTrunc
2
2
2
2
2
2
2
2
2
2
018
677
677
177
177
138
042
119
119
119
0
0
0
0
0
0
0
0
0
0 .
0
0
0
0
0
0
0
0
0
0
.1 10
.1 10
.1 10
.1 10
.1 10
.1 10
.1 10
.1 10
.1 10
.1 10
! DECAY RATES
C1
1
C2
1
C3
1
Shape
Uniform
Par1 Par2 ParS Par4
1.02
1.45
LTrunc
1.02
UTrunc
1.45
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Data used for estimating the contribution to indoor NC>2 concentrations that occur during
cooking with gas fuel were derived from a study sponsored by the California Air Resources
Board (CARB, 2001). For this study a test house was set up for continuous measurements of
NC>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 NC>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 indoor concentrations for any test in any
room were used as the lower and upper bounds of a uniform distribution
This resulted in a minimum average net indoor concentration of 4 ppb and a maximum net
average indoor concentration of 188 ppb.
An analysis by Johnson et al (1999) of survey data on gas stove usage collected by Koontz et
al (1992) showed an average number of meals prepared each day with a gas stove of 1.4. The
diurnal allocation of these cooking events was estimated as follows.
• Food preparation time obtained from CHAD diaries was stratified by hour of the day,
and summed for each hour, and summed for total preparation time.
• The fraction of food preparation occurring in each hour of the day was calculated as
the total number of minutes for that hour divided by the overall total preparation time.
The result was a measure of the probability of food preparation taking place during
any hour, given one food preparation event per day.
• Each hourly fraction was multiplied by 1.4, to normalize the expected value of daily
food preparation events to 1.4.
The estimated probabilities of cooking by hour of the day are presented in Table B-28. For
this analysis it was assumed that the probability that food preparation would include stove usage
was the same for each hour of the day, so that the diurnal allocation of food preparation events
would be the same as the diurnal allocation of gas stove usage. It was also assumed that each
cooking event lasts for exactly 1 hour, implying that the average total daily gas stove usage is 1.4
hours.
Table B-28. Probability of gas stove cooking by hour of the day.
Hour of Day
0
1
2
3
4
5
6
7
8
9
Probability of
Cooking
0
0
0
0
0
5
10
10
10
5
B-59
-------
1
2
3
4
5
6
1
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Hour of Day
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Probability of Cooking
5
5
10
5
5
5
15
20
15
10
5
5
0
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?,
The remaining five indoor microenvironments, which represent Bars and Restaurants,
Schools, Day Care Centers, Office, Shopping, and Other environments, are all modeled using the
same data and functions (Figure B-10). As with the Indoor-Residence microenvironment, these
microenvironments use both air exchange rates and removal rates to calculate exposures within
the microenvironment. The air exchange rate distribution (GM = 1.109, GSD = 3.015, Min =
0.07, Max = 13.8) was developed based on an indoor air quality study (Persily et al, 2005; see
US EPA, 2007d for details in derivation). The decay rate is the same as used in the Indoor-
Residence microenvironment discussed previously. The Bars and Restaurants microenvironment
included an estimated contribution from indoor sources as was described for the Indoor-
Residence, only there was an assumed 100% prevalence rate and the cooking with the gas
appliance occurred at any hour of the day.
Micro number = 2 ! Bars & restaurants - AIR EXCHANGE RATES
Parameter Type = AER
ResampHours = NO
ResampDays = YES
ResampWork = YES
Block DType Season Area C1 C2 C3 Shape Par1 Par2 Par3 Par4 LTrunc UTrunc
1 1 1 1111 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 1111 Uniform 1.02 1.45 . . 1.02 1.45
Figure B-10. Example input file from APEX for all Indoors microenvironments (non-residence).
B-60
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1 Microenvironments 8 and 9: Outdoor microenvironments
2 Two outdoor microenvironments, the Near Road and Public Garage/Parking Lot, used the
3 factors method to calculate pollutant exposure. Penetration factors are not applicable to outdoor
4 environments (effectively, PEN=1). Proximity factors were developed from the AERMOD
5 concentration predictions, i.e., the block-centroid-to-nearest-roadway concentration ratios. Based
6 on the resulting sets of ratio values, the ratio distributions were stratified by hour of the day into
7 3 groups as indicated by the "hours-block" specification in the example file in Figure B-l 1. The
8 lower and upper bounds for sampling were specified as the 5th and 95th percentile values,
9 respectively, of each distribution.
10
11 Micro number =8 ! Outdoor near road PROXIMITY FACTOR
12 Pollutant = 1
13 Parameter Type = PR
14 Hours - Block = 111111222222222222233311
15 ResampHours = YES
16 ResampDays = YES
17 ResampWork = YES
18 Block DType Season Area C1 C2 C3 Shape Par1 Par2 Par3 Par4 LTruncUTrunc ResampOut
19 111 1111 LogNormal 1.251 1.478 0. . 0.86 2.92 Y
20 211 1111 LogNormal 1.555 1.739 0. . 0.83 4.50 Y
21 311 1111 LogNormal 1.397 1.716 0. . 0.73 4.17 Y
22 |
23 Figure B-ll. Example input file from APEX for outdoor near road microenvironment.
24
25 B-3.5.11.3 Microenvironment 10: Outdoors-General.
26 The general outdoor environment concentrations are well represented by the modeled
27 concentrations. Therefore, both the penetration factor and proximity factor for this
28 microenvironment were set to 1.
29
30 B-3.5.11.4 Microenvironments 11 and 12: In Vehicle- Cars and Trucks, and Mass Transit
31 Penetration factors were developed from data provided in Chan and Chung (2003). Inside-
32 vehicle and outdoor NC>2 concentrations were measured with for three ventilation conditions, air-
33 recirculation, fresh air intake, and with windows opened. Since major roads were the focus of
34 this assessment, reported indoor/outdoor ratios for highway and urban streets were used here.
35 Mean values range from about 0.6 to just over 1.0, with higher values associated with increased
36 ventilation (i.e., window open). A uniform distribution was selected for the penetration factor
37 for Inside-Cars/Trucks (ranging from 0.6 to 1.0) due to the limited data available to describe a
38 more formal distribution and the lack of data available to reasonably assign potentially
39 influential characteristics such as use of vehicle ventilation systems for each location. Mass
40 transit systems, due to the frequent opening and closing of doors, was assigned a uniform
41 distribution ranging from 0.8 to 1.0 based on the reported mean values for fresh air intake and
42 open windows. Proximity factors were developed as described above for Microenvironments 8
43 and 9.
44
B-61
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1 B-3.5.12 Adjustment for Just Meeting the Current Standard
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
To simulate just meeting the current standard, dispersion modeled concentration were not
rolled-up as was done for the monitor concentrations used in the air quality characterization. A
proportional approach was used as done in the Air Quality Characterization, but to reduce
computer processing time, the health effect benchmark levels were proportionally reduced by the
similar factors described for each specific location and simulated year. Since it is a proportional
adjustment, the end effect of adjusting concentrations upwards versus adjusting benchmark
levels downward within the model is the same. The difference in the exposure and risk modeling
was that the modeled air quality concentrations were used to generate the adjustment factors.
Table B-29 provides the adjustment factors used and the adjusted potential health effect
benchmark concentrations to simulate just meeting the current standard. When modeling indoor
sources, the indoor concentration contributions needed to be scaled downward by the same
proportions.
Table B-29. Adjustment factors and potential health effect benchmark levels used by APEX to simulate just
meeting the current standard.
Simulated
Year
(factor)
2001
(1.59)
2002
(1.63)
2003
(1.64)
Potential Health
Effect Benchmark
Level (ppb)
Actual
150
200
250
300
150
200
250
300
150
200
250
300
Adjusted
94
126
157
189
92
122
153
184
91
122
152
183
When considering the indoor sources, an additional scaling was performed so as not to
affect their estimated concentrations while adjusting the benchmark levels downward. To clarify
how this was done, exposure concentrations an individual experiences are first defined as the
sum of the contribution from ambient concentrations and from indoor sources (if present) and
this concentration can be either above or below a selected concentration level of interest:
*- exposure •"- ^ ambient ' " ^ indoor ^ ^ threshold
equation (6)
where,
ct
exposure
ambient
= individual exposure concentration
= proportion of exposure concentration from ambient
= ambient concentration in the absence of indoor sources
B-62
-------
IB = proportion of exposure concentration from indoor
2 Cmdoor = indoor source concentration contribution
3 Cthreshoid = an exposure concentration of interest
4
5 It follows that if we are interested in adjusting the ambient concentrations upwards by
6 some proportional factor F, this can be described with the following:
7
8 Fx AxCambient +BxCmdoor >Cthreshold equation (7)
9
10 This is equivalent to
11
12 AxCambient +Bx(Cmdoor IF) >(Cthreshold IF} equation (8)
13
14 Therefore, if the potential health effect benchmark level and the indoor concentrations are
15 both proportionally scaled downward by the same adjustment factor, the contribution of both
16 sources of exposure (i.e., ambient and indoor) are maintained and the same number of estimated
17 exceedances would be obtained as if the ambient concentration were proportionally adjusted
18 upwards by factor F.
19
B-63
-------
i B-3.6 Philadelphia Exposure Modeling Results
2 B-3.6.1 Overview
3 The results of the exposure and risk characterization are presented here for Philadelphia
4 County. Several scenarios were considered for the exposure assessment, including two
5 averaging time for NO2 concentrations (annual and 1-hour), inclusion of indoor sources, and for
6 evaluating just meeting the current standard. To date, year 2002 served as the base year for all
7 scenarios, years 2001 and 2003 were only evaluated for a limited number of scenarios.
8 Exposures were simulated for four groups; children and all persons, and the asthmatic population
9 within each of these.
10
11 The exposure results summarized below focus on the population group where exposure
12 estimations are of greatest interest, namely asthmatic individuals. The complete results for each
13 of these two population subgroups are provided in section B-3.6.7. However, due to certain
14 limitations in the data summaries output from the current version of APEX, some exposure data
15 could only be output for the entire population modeled (i.e., all persons - includes asthmatics and
16 healthy persons of all ages). The summary data for the entire population (e.g., annual average
17 exposure concentrations, time spent in microenvironments at or above a potential health effect
18 benchmark level) can be representative of the asthmatic population since the asthmatic
19 population does not have its microenvironmental concentrations and activities estimated any
20 differently from those of the total population.
21 B-3.6.2 Evaluation of Modeled NO2 Air Quality Concentrations (as is)
22 Since the current NO2 standard is 0.053 ppm annual average, the predicted air quality
23 concentrations were first summarized by calculating annual average concentration. The
24 distribution for the AERMOD predicted NO2 concentrations at each of the 16,857 receptors for
25 years 2001 through 2003 are illustrated in Figure B-12. Variable concentrations were estimated
26 by the dispersion model over the three year period (2001-2003). The NO2 concentration
27 distribution was similar for years 2001 and 2002, with mean annual average concentrations of
28 about 21 ppb and a COV of just over 30%. On average, NO2 annual average concentrations
29 were lowest during simulated year 2003 (mean annual average concentration was about 16 ppb),
30 largely a result of the comparably lower local concentration added (Table B-28). While the
31 mean annual average concentrations were lower than those estimated for 2001 and 2002, a
32 greater number of annual average concentrations were estimated above 53 ppb for year 2003. In
33 addition, year 2003 also contained greater variability in annual average concentrations as
34 indicated by a COV of 53%.
B-64
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
200
"o. 180
140 -
d1
120 -
100 -
01
0)
2
I
ra
| 80
c
1!
•5
Q.
O
60 -
40 -
15 20
-5
-3 -2-101
Normal Quantile
Figure B-12 . Distribution of AERMOD estimated annual average NO2 concentrations at each of the 16,857
receptors in Philadelphia County for years 2001-2003.
Diurnal variability in NC>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 NC>2 concentration at each hour of the day for
the three monitors located in Philadelphia County. The diurnal distributions among the modeled
versus measured concentrations are similar at all of the monitors, with peak NC>2 concentrations
generally coinciding with the typical peak commute times of 6:00-9:00 AM and 5:00-8:00 PM.
The pattern is represented best at monitor 4210100043 (top graph in Figure B-13), however the
AERMOD concentrations are approximately 8 ppb lower at the earlier times of the day following
the adjustment for sources not modeled (section B-3.5.9). There is greater variability in the
modeled NC>2 concentrations at the other two monitors when compared with the measured data
(middle and bottom graphs of Figure B-13), although the patterns are still similar. The greatest
difference in NC>2 concentrations occurs during the later commute period, most notable at
monitor 4210100292. Given the concentration adjustment to correct for sources not modeled
was applied to all receptors equally across the entire modeling domain, it is not surprising that
the modeled concentrations are higher in some instances while others not. The pattern in the
concentrations is the important feature to replicate, of which AERMOD does reasonably, and
based on these three receptors, may slightly overestimate peak concentrations more times than
underestimate them.
B-65
-------
35
S 30 -
a
I 254
20 -
15 -
< 10
5 -
—•—Monitor-4210100043
--+-- AERMOD
---X-- AERMOD + Correction
'X
x' \
X-..
X---X---X---X---X'
•-K.
-+-. + ..+ ••-
60
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of Day
50 -
40 -
30 -
20 -
10 -
—•— Monitor - 4210100292
--+-- AERMOD
--X-- AERMOD + Correction
*-.+ ..+-^
'+..
+ —+-..+ ..+—*
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of Day
60
50 -
40 -
30 -
20 -
10 -
• Monitor - 4210100471
--+-- AERMOD
--X-- AERMOD + Correction
^--4...^-+
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
2 Hour of Day
3 Figure B-13. Measured and modeled diurnal pattern of NO2 concentrations at three ambient monitor sites.
B-66
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2 B-3.6.3 Comparison of estimated on-road NO2 concentrations
3 The two independent approaches used to estimate on-road NO2 concentrations, one using
4 ambient monitor data combined with an on-road simulation factor (section A-8) and the other
5 using the AERMOD dispersion model (section B-3.5), were compared to one another. There are
6 no on-road NO2 concentration measurements in Philadelphia for the modeled data to be
7 compared with, although it should be noted that the data used to estimate the simulation factors
8 and applied to the monitor data are measurement based.
9
10 First a comparison can be made between the factor used for estimating on-road
11 concentrations in the air quality analysis and similar factors calculated using AERMOD
12 estimated concentrations. As described in section A-8, an empirical distribution of on-road
13 simulation factors was derived from on-road and near-road NO2 concentration measurements
14 published in the extant literature. The derived empirical distribution was separated into two
15 components, one for application to summertime ambient concentrations, and the second for all
16 other seasons. The two empirical distributions are presented in Figure B-14, and represent the
17 factors multiplied by the ambient monitor concentration (> 100 m from a major road) and used to
18 estimate the on-road concentration in the air quality characterization. The one-hour NO2
19 concentrations estimated at every AERMOD receptor in Philadelphia were compared with the
20 concentrations estimated at their closest on-road receptor to generate a similar ratio (i.e., on-
21 road/non-road concentrations). These ratios were also stratified into two seasonal categories, one
22 containing the summer ratios (June, July, and August) and the other for all other times of the
23 year. The AERMOD on-road factor distributions in semi-empirical form are also presented in
24 Figure B-14. There are similarities in comparing each of the AERMOD with the measurement
25 study derived distributions, most importantly at the upper percentiles. Intersection of the two
26 approaches occurs at about the 70th percentile and continues through the 90th percentile. While
27 the two seasonal distributions for AERMOD are very similar to one another, they diverge at the
28 upper percentiles, with the summer ratios containing greater values at the same percentiles. This
29 is similar to what was observed in the measurement derived distribution, although the summer
30 ratio distribution consistently contained greater values at all percentiles compared with the non-
31 summer distribution.
32
33 There are differences that exist when comparing the two approaches at the mid to lower
34 percentiles, with the AERMOD ratios consistently lower than the empirically derived factors.
35 This is likely due to the differences in the population of samples used to generate each type of
36 distribution. The measurement study derived distribution used data from on-road concentration
37 measurements and from monitoring sites located at a distance from the road, sites that by design
38 of the algorithm and the factor selection criteria are likely not under the influence of non-road
39 NO2 emission sources. Thus, the measurement study derived ratios never fall below a value of
40 one, there are no on-road concentrations less than any corresponding non-road influenced
41 concentrations. This was, by design, a reasonable assumption for estimating the on-road
42 concentrations for the air quality characterization. The AERMOD receptors however, include all
43 types of emission sources such that there are possibilities for concentrations at non-road
44 receptors that are greater than on-road, a more realistic depiction of the actual relationship
45 between on-road and non-road receptors. Furthermore, the AERMOD distribution extends
B-67
-------
1 beyond the range of values offered by the measurement study derived ratios at the very upper
2 percentiles. This could indicate that the AERMOD approach is better accounting for locally high
3 NC>2 concentrations than those reported by the limited measurement studies.
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
1.0 -
0.9 -
0.8 -
I" 0.7 -
s
|0.6H
Q.
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
The apparent contradiction between the similarity of the hourly concentration distributions
and the large differences in the exceedance distributions can be explained by the fact that 200
ppb is the 99.605th percentile of the AERMOD hourly concentrations and is the 99.974th
percentile of the simulated on-road monitor concentrations. Thus on average, 0.395 % of hourly
AERMOD values exceed 200 ppb per year and 0.026 % of hourly simulated on-road monitored
values exceed 200 ppb per year. These differences could be due to the greater number of
receptors modeled by AERMOD (n=979) compared with the on-road monitor simulation (n=5).
Again, the AERMOD generated data could include locations greatly influenced by roadway
emissions that are not captured by the simplified approach conducted in the Air Quality
Characterization.
Table B-30. Summary statistics of on-road hourly NO2 concentrations (ppb) and the numbers of potential
health effect benchmark levels using AERMOD and the on-road ambient monitor simulation approaches in
Philadelphia.
Statistic
N
Mean
Stdev
Variance
pO
p5
p10
p15
p20
p25
p30
p35
p40
p45
p50
p55
p60
p65
p70
p75
p80
p85
p90
p95
p100
1-hour NO2
concentrations
AERMOD
8,576,040
36.2
32.1
1,030
12
12
12
13
14
15
17
18
20
22
25
28
31
35
40
45
52
61
75
98
707
Monitor
Simulation
4,183,900
35.4
24.9
620
0
5
9
11
14
16
19
22
25
27
30
34
38
41
45
49
54
60
68
81
681
Exceedances of
150 ppb
AERMOD
979
113
142
20,171
0
2
8
13
21
27
32
39
45
56
65
73
86
106
122
143
176
216
267
390
1,072
Monitor
Simulation
500
18
47
2,187
0
0
0
0
0
1
1
1
1
1
1
1
2
3
6
8
15
24
63
92
278
Exceedances of
200 ppb
AERMOD
979
35
61
3,751
0
0
0
1
2
3
4
6
8
10
13
15
20
24
31
39
56
72
95
148
530
Monitor
Simulation
500
2
8
61
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
4
11
59
Exceedances of
250 ppb
AERMOD
979
12
30
900
0
0
0
0
0
0
0
1
1
2
2
3
4
5
7
10
15
21
31
58
299
Monitor
Simulation
500
0.6
1.6
2.6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
11
15
B-69
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1 B-3.6.4 Annual Average Exposure Concentrations (as is)
2 The hourly NO2 concentrations output from AERMOD were input into the exposure model,
3 providing a range of estimated exposures output by APEX. Figure B-l 5 illustrates the annual
4 average exposure concentrations for the entire simulated population (both asthmatics and healthy
5 individual of all ages), for each of the years analyzed and where indoor sources were modeled.
6 While years 2001 and 2002 contained very similar population exposure concentration
7 distributions, the modeled year 2003 contained about 20% lower annual average concentrations.
8 The lower exposure concentrations for year 2003 are similar to what was observed for the
9 predicted air quality (Figure B-l2), however, all persons were estimated to contain exposures
10 below an annual average concentration of 53 ppb, even considering indoor source concentration
11 contributions. Again, while Figure B-l5 summarizes the entire population, the data are
12 representative of what would be observed for the population of asthmatics or asthmatic children.
13
14
15
16
17
18
19
20
21
22
23
24
25
100
90 -
10 -
2001 with indoor sources
2002 with indoor sources
2003 with indoor sources
15 20 25 30
Annual Average NO2 Exposure (ppb)
35
40
45
Figure B-15. Estimated annual average total NO2 exposure concentrations for all simulated persons in
Philadelphia County, using modeled 2001-2003 air quality (as is), with modeled indoor sources.
The AERMOD predicted air quality and the estimated exposures for year 2002 were
compared using their respective annual average NO2 concentrations (Figure B-l6). As a point of
reference, the annual average concentration for 2002 ambient monitors ranged from 24 ppb to 29
ppb. Many of the AERMOD predicted annual average concentrations were below that of the
lowest ambient monitoring concentration of 24 ppb, although a few of the receptors contained
concentrations above the highest measured annual average concentration. Estimated exposure
concentrations were below that of both the modeled and measured air quality. For example,
exposure concentrations were about 5 ppb less than the modeled air quality when the exposure
B-70
-------
1
2
3
4
6
7
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
estimation included indoor sources, and about 10 ppb less for when exposures were estimated
without indoor sources. In comparing the estimated exposures with and without indoor sources,
indoor sources were estimated to contribute between 1 and 5 ppb to the total annual average
exposures.
100
0
Q_
90 -
80 -
70 -
60 -
50 -
40 -
30 -
20 -
10 -
AERMOD Predicted 2002 air quality (as is)
APEX Exposure 2002 no indoor sources
APEX Exposure 2002 with indoor sources
15 20 25 30
Annual Average NO2 Cocentrations (ppb)
35
40
45
Figure B-16. Comparison of AERMOD predicted and ambient monitoring annual average NO2
concentrations (as is) and APEX exposure concentrations (with and without modeled indoor sources) in
Philadelphia County for year 2002.
B-3.6.5
One-Hour Exposures (as is)
Since there is interest in short-term exposures, a few analyses were performed using the
APEX estimated exposure concentrations. As part of the standard analysis, APEX reports the
maximum exposure concentration for each simulated individual in the simulated population.
This can provide insight into the proportion of the population experiencing any NC>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 (therefore, the maximum number of exceedances is 365 for
a single person). Estimates of repeated exposures are also recorded, that is where 1-hour
exposure concentrations were above a selected level in a day added together across multiple days
(therefore, the maximum number of multiple exceedances is also 365). Persons of interest in this
exposure analysis are those with particular susceptibility to NC>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 majority of the results presented in this section are
for the simulated asthmatic population. However, the exposure analysis was performed for the
total population to assess numbers of persons exposed to these levels and to provide additional
B-71
-------
1 information relevant to the asthmatic population (such as time spent in particular
2 microenvironments).
O
4
5 B-3.6.5.1 Maximum Estimated Exposure Concentrations
6 A greater variability was observed in maximum exposure concentrations for the 2003 year
7 simulation compared with years 2001 and 2002 (Figure B-17). While annual average exposure
8 concentrations for the total population were the lowest of the 3-year simulation, year 2003
9 contained a greater number of individual maximum exposures at and above the lowest potential
10 health effect benchmark level. When indoor sources are not modeled however, over 90% of the
11 simulated persons do not have an occurrence of a 1-hour exposure above 200 ppb in a year.
12
13 B-3.6.5.2 Number of Estimated Exposures above Selected Levels
14 When considering the total asthmatic population simulated in Philadelphia County and using
15 current air quality of 2001-2003, nearly 50,000 persons were estimated to be exposed at least one
16 time to a one-hour concentration of 200 ppb in a year (Figure B-18). These exposures include
17 both the NC>2 of ambient origin and that contributed by indoor sources. The number of
18 asthmatics exposed to greater concentrations (e.g., 250 or 300 ppb) drops dramatically and is
19 estimated to be somewhere between 1,000 - 15,000 depending on the 1-hour concentration level
20 and the year of air quality data used. Exposures simulated for year 2003 contained the greatest
21 number of asthmatics exposed in a year consistently for all potential health effect benchmark
22 levels, while year 2002 contained the lowest number of asthmatics. Similar trends across the
23 benchmark levels and the simulation years were observed for asthmatic children, albeit with
24 lower numbers of asthmatic children with exposures at or above the potential health effect
25 benchmark levels.
B-72
-------
100
60
oi
Q_
C
Q-
30
20
10
«2001 with indoor sources
o 2002 with indoor sources
A 2003 with indoor sources
x 2002 no indoor sources
1
2
3
4
50 100 150 200 250 300 350
Maximum 1-hour Exposure (ppb) in a Year
400
450
500
Figure B-17. Estimated maximum NO2 exposure concentration for all simulated persons in Philadelphia
County, using modeled 2001-2003 air quality (as is), with and without modeled indoor sources. Values above
the 99th percentile are not shown.
6.0E-I-4
O.OE+0
200
250
300
2003 AQ (as is) - with indoor souces
2002 AQ (as is) - with indoor sources
2001 AQ (as is) - with indoor sources
Simulated Year - Scenario
Potential Health Effect Benchmark Level (ppb)
5
6
7
Figure B-18. Estimated number of all simulated asthmatics in Philadelphia County with at least one NO2
exposure at or above the potential health effect benchmark levels, using modeled 2001-2003 air quality (as is),
with modeled indoor sources.
B-73
-------
1.4E+4
O.OE+0
200
250
300
Potential Health Effect Benchmark Level (ppb)
2003 AQ (as is) - with indoor souces
2002 AQ (as is) - with indoor sources
2001 AQ (as is) - with indoor sources
Simulated Year - Scenario
2 Figure B-19. Estimated number of simulated asthmatic children in Philadelphia County with at least one
3 NO2 exposure at or above the potential health effect benchmark levels, using modeled 2001-2003 air quality
4 (as is), with modeled indoor sources.
« co
~ *
£ C
^ a;
I
0 -53
pi
1 s
(1)
"re
|
2.0E+4-
1 .OE+4
O.OE+0
,-''
__,-' i
'''
^
^ •*
_,*"
{das'
.s
Jf
- — z —
200
250
300
2002 AQ (as is) - with indoor sources
2002 AQ (as is) - no indoor sources
Simulated Year - Scenario
Potential Health Effect Benchmark Level (ppb)
5
6
1
Figure B-20. Comparison of the estimated number of all simulated asthmatics in Philadelphia County with at
least one NO2 exposure at or above potential health effect benchmark levels, using modeled 2002 air quality
(as is), with and without modeled indoor sources.
B-74
-------
1 For example, nearly 12,000 were estimated to be exposed to at least a one-hour NC>2
2 concentration of 200 ppb in a year (Figure B-19). Additional exposure estimates were generated
3 using the modeled 2002 air quality (as is) and where the contribution from indoor sources was
4 not included in the exposure concentrations. APEX allows for the same persons to be simulated,
5 i.e., demographics of the population were conserved, as well as using the same individual time-
6 location-activity profiles generated for each person. Figure B-20 compares the estimated number
7 of asthmatics experiencing exposures above the potential health effect benchmarks, both with
8 indoor sources and without indoor sources included in the model runs. The number of
9 asthmatics at or above the selected concentrations is reduced by between 50-80%, depending on
10 benchmark level, when not including indoor source (i.e., gas cooking) concentration
11 contributions.
12
13 An evaluation of the time spent in the 12 microenvironments was performed to estimate
14 where simulated individuals are exposed to concentrations above the potential health effect
15 benchmark levels. Currently, the output generated by APEX is limited to compiling the
16 microenvironmental time for the total population (includes both asthmatic individuals and
17 healthy persons) and is summarized to the total time spent above the selected potential health
18 effect benchmark levels. As mentioned above, the data still provide a reasonable approximation
19 for each of the population subgroups (e.g., asthmatics or asthmatic children) since their
20 microenvironmental concentrations and activities are not estimated any differently from those of
21 the total population by APEX.
22
23 As an example, Figure B-21 (a, b, c) summarizes the percent of total time spent in each
24 microenvironment for simulation year 2002 that was associated with estimated exposure
25 concentrations at or above 200, 250, and 300 ppb (results for years 2001 and 2003 were similar).
26 Estimated exposures included the contribution from one major category of indoor sources (i.e.,
27 gas cooking). The time spent in the indoor residence and bars/restaurants were the most
28 important for concentrations >200 ppb, contributing to approximately 75% of the time persons
29 were exposed (Figure B-21 a). This is likely a result of the indoor source concentration
30 contribution to each individual's exposure concentrations. The importance of the particular
31 microenvironment however changes with differing potential health effect benchmark levels.
32 This is evident when considering the in-vehicle and outdoor near-road microenvironments,
33 progressing from about 19% of the time exposures were at the lowest potential health effect
34 benchmark level (200 ppb) to a high of 64% of the time exposures were at the highest
35 benchmark level (300 ppb, Figure B-21c).
36
37 The microenvironments where higher exposure concentrations occur were also evaluated for
38 the exposure estimates generated without indoor source contributions. Figure B-22 illustrates
39 that the time spent in the indoor microenvironments contributes little to the estimated exposures
40 above the selected benchmark levels. The contribution of these microenvironments varied only
41 slightly with increasing benchmark concentration, ranging from about 2-5%. Most of the time
42 associated with high exposures was associated with the transportation microenvironments (In-
43 Vehicle or In-Public Transport) or outdoors (Out-Near Road, Out-Parking Lot, Out-Other). The
44 importance of time spent outdoors near roadways exhibited the greatest change in contribution
45 with increased health benchmark level, increasing from around 30 to 44% of time associated
46 with concentrations of 200 and 300 ppb, respectively. While more persons are likely to spend
B-75
-------
1 time inside a vehicle than outdoors near roads, there is attenuation of the on-road concentration
2 that penetrates the in-vehicle microenvironment, leading to lowered concentrations, occurring
3 less frequently above 300 ppb than outdoors near roads.
B-76
-------
1
2
3
4
In-Public Trans
Mother
Out-Other
Out-Parking Lot
Out-Near Road
In-Other
In-Shopping -^
In-Officej
I
In-Day Care—'\
In-School—'
In-Bar & Restaurant
In-Residence
a) > 200 ppb
In-Public Trans
k Other
In-Residence
Out-Other
Out-Parking Lot
b) > 250 ppb
In-Bar & Restaurant
In-Bar & Restaurant
In-Public Trans |n.Residence I r In-School
Other I / \r In-Day Care
Out-Other
Out-Near Road
Out-Parking Lot
c) > 300 ppb
Figure B-21. Fraction of time all simulated persons in Philadelphia County spend in the twelve
microenvironments associated with the potential NO2 health effect benchmark levels, a) > 200 ppb, b) > 250
ppb, and c) > 300 ppb, year 2002 simulation with indoor sources.
B-77
-------
1
2
3
4
In-Bar& Restaurant
In-Residence-
Other
In-Public Trans—
In-Vehicle
Out-Near Road
Out-Parking Lot
Out-Other
a) > 200 ppb
In-Bar& Restaurant^ In-School
In-Residence-A
O—In-Day Care
^— In-Office
In-Shopping
-—In-Other
In-Vehicle
Out-Near Road
Out-Other
Out-Parking Lot
b) > 250 ppb
In-Bar& Restaurant/—In-School
In-Residence-L /^ ln"9a)!.9are
In-Public
In-Vehicle
Out-Near Road
Out-Other
Out-Parking Lot
c) > 300 ppb
Figure B-22. Fraction of time all simulated persons in Philadelphia County spend in the twelve
microenvironments associated with the potential NO2 health effect benchmark levels, a) > 200 ppb, b) > 250
ppb, and c) > 300 ppb, year 2002 simulation without indoor sources.
B-78
-------
B-3.6.5.3 Number of Repeated Exposures Above Selected Levels
In the analysis of persons exposed, the results show the number or percent of those
with at least one exposure at or above the selected potential health effect benchmark
level. Given that the benchmark is for a small averaging time (i.e., one-hour) it may be
possible that individuals are exposed to concentrations at or above the potential health
effect benchmark levels more than once in a given year. Since APEX simulates the
longitudinal diary profile for each individual, the number of times above a selected level
is retained for each person. Figure B-23 presents such an analysis for the year 2003, the
year containing the greatest number of exposure concentrations at or above the selected
benchmarks. Estimated exposures include both those resulting from exposures to NC>2 of
ambient origin and those resulting from indoor source NC>2 contributions. While a large
fraction of individuals experience at least one exposure to 200 ppb or greater over a 1-
hour time period in a year (about 32 percent), only around 14 percent were estimated to
contain at least 2 exposures. Multiple exposures at or above the selected benchmarks
greater than or equal to 3 or more times per year are even less frequent, with around 5
percent or less of asthmatics exposed to 1-hour concentrations greater than or equal to
200 ppb 3 or more times in a year.
Exposure estimates for year 2002 are presented to provide an additional perspective,
including a lower bound of repeated exposures for this population subgroup and for
exposure estimates generated with and without modeled indoor sources (Figure B-24).
Most asthmatics exposed to a 200 ppb concentration are exposed once per year and only
around 11 percent would experience 2 or more exposures at or above 200 ppb when
including indoor source contributions. The percent of asthmatics experiencing multiple
exposures a and abovet 250 and 300 ppb is much lower, typically less than 1 percent of
all asthmatics are exposed at the higher potential benchmark levels. Also provided in
Figure B-24 are the percent of asthmatics exposed to selected levels in the absence of
indoor sources. Again, without the indoor source contribution, there are reduced
occurrences of multiple exposures at all of the potential health effect benchmark levels
compared with when indoor sources were modeled.
B-79
-------
Estimated Number of
Repeated Exposures in a Year
Potential Health Effect Benchmark
Level (ppb)
Figure B-23. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures above potential health effect benchmark levels, using 2003 modeled air quality (as is), with
modeled indoor sources.
3 Estimated Number of
4 Repeated Exposures
300 - no
indoor
Health Effect Benchmark Level (ppb)
Figure B-24. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures above potential health effect benchmark levels, using modeled 2002 air quah'ty (as is), with
and without indoor sources.
B-80
-------
B-3.6.6 One-Hour Exposures Associated with Just Meeting the Current
Standard
To simulate just meeting the current NC>2 standard, the potential health effect
benchmark level was adjusted in the exposure model, rather than adjusting all of the
hourly concentrations for each receptor and year simulated. Similar estimates of short-
term exposures (i.e., 1-hour) were generated for the total population and population
subgroups of interest (i.e., asthmatics and asthmatic children).
B-3.6.6.1 Number of Estimated Exposures above Selected Levels
In considering exposures estimated to occur associated with air quality simulated
to just meet the current annual average NC>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 affected considering each scenario is
expressed as the percent of the subpopulation of interest. Figure B-25 illustrates the
percent of asthmatics estimated to experience at least one exposure at or above the
selected potential health effect benchmark concentrations, with just meeting the current
standard and including indoor source contributions. While it was estimated that about
30% percent of asthmatics would be exposed to 200 ppb (1-hour average) at least once in
a year for as is air quality, it was estimated that around 80 percent of asthmatics would
experience at least one concentration above the lowest potential health effect benchmark
level in a year representing just meeting the current standard. Again, estimates for
asthmatic children exhibited a similar trend, with between 75 to 80 percent exposed to a
concentration at or above the lowest potential health effect benchmark level at least once
per year for a year just meeting the current standard (data not shown). The percent of all
asthmatics experiencing the higher benchmark levels is reduced to between 31 and 45
percent for the 250 ppb, 1-hour benchmark, and between 10 and 24 percent for the 300
ppb, 1-hour benchmark level associated with air quality representing just meeting the
current annual average standard.
B-81
-------
90
200
250
300
2003 AQ (std) - with indoor soucrces
2002 AQ (std) - with indoor soucrces
2001 AQ (std) -with indoor soucrces
Simulated Year - Scenario
Potential Health Effect Benchmark Level (ppb)
Figure B-25. Estimated percent of all asthmatics in Philadelphia with at least one exposure at or
above the potential health effect benchmark level, using modeled 2001-2003 air quality just meeting
the current standard, with modeled indoor sources.
In evaluating the influence of indoor source contribution for the scenario just meeting
the current standard, the numbers of individuals exposed at selected levels are reduced
without indoor sources, ranging from about 26 percent lower for the 200 ppb level to
around 11 percent for the 300 ppb level when compared with exposure estimates that
accounted for indoor sources (Figure B-26).
c £
1.5E+5-
1.2E+5-
9.0E+4-
I |.| I 6.0E+4-
Z T3 I
tS o I 3.0E+4
O.OE+0
200
250
300
Potential Health Effect Benchmark Level (ppb)
2002 AQ (std) - with indoor sources
2002 AQ (std) - no indoor sources
Simulated Year - Scenario
Figure B-26. Estimated number of all asthmatics in Philadelphia with at least one exposure at or
above the potential health effect benchmark level, using modeled 2002 air quality just meeting the
current standard, with and without modeled indoor sources.
B-82
-------
B-3.6.6.2 Number of Repeated Exposures Above Selected Levels
For air quality simulated to just meet the current standard, repeated exposures at the
selected potential health effect benchmarks are more frequent than that estimated for the
modeled as is air quality. Figure B-27 illustrates this using the simulated asthmatic
population for year 2002 data as an example. Many asthmatics that are exposed at or
above the selected levels are exposed more than one time. Repeated exposures above the
potential health effect benchmark levels are reduced however, when not including the
contribution from indoor sources. The percent of asthmatics exposed drops with
increasing benchmark level, with progressively fewer persons experiencing multiple
exposures for each benchmark level.
Estimated Number of
Repeated Exposures
in a Year
200 - with
indoor
soucrces
Health Effect Benchmark Level (ppb)
Figure B-27. Estimated percent of asthmatics in Philadelphia County with repeated exposures above
health effect benchmark levels, using modeled 2002 air quality just meeting the current standard,
with and without modeled indoor sources.
B-3.6.7 Additional Exposure Results
This section provides supplemental exposure and risk characterization results for two
subpopulations, all asthmatics and asthmatic children. The data are presented in series of
summary tables and figures across each of the scenarios investigated (i.e. with modeled
air quality as is and simulating just meeting the current standard), with and without
modeled indoor sources (i.e., gas stoves), for each of the potential health effect
benchmark levels (i.e., 200, 250, 300 ppb 1-hour), and across three years of modeled air
quality (i.e., 2001 to 2003). Repeated exposures are presented only for the lowest
potential health effect benchmark level (i.e., 200 ppb 1-hour).
B-83
-------
B-3.6.7.1
All Asthmatics
Table B-31. Estimated number of asthmatics in Philadelphia County exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using modeled air quality (as is) and with just
meeting the current standard (std), and with and without indoor sources.
Year (AQ)
2001 (as
is)
2001 (std)
2002 (as
is)
2002 (std)
2003 (as
is)
2003 (std)
Indoor
Source
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Level
(ppb)
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
Persons with Number of Repeated Exposures
1
49796
4867
1388
10544
2584
1013
128147
49632
16805
90211
40466
15100
47652
4430
1240
9505
2276
975
133524
53367
18828
98849
43972
16693
52639
14407
6568
26120
11142
5605
132640
73387
39283
109726
65437
35948
2
19544
1414
404
2577
765
344
96119
18322
4480
51600
14362
3590
17720
1173
393
2411
778
304
102861
20737
5220
60056
16367
4389
22084
5040
1892
10007
3927
1627
1 02034
38505
16213
73489
33096
14502
3
8959
658
157
1230
413
177
70079
8523
1828
31720
6155
1595
8056
530
147
1240
332
137
77512
9855
2324
36913
7370
1950
11950
2599
887
5857
2040
778
76909
22953
9280
51133
18948
8474
4
4516
381
108
795
295
98
50253
4808
1219
19805
3225
1003
4170
274
88
706
185
59
57152
5784
1447
23238
4066
1131
7441
1577
512
3783
1261
462
58857
15416
6175
36551
12710
5654
5
2666
265
59
520
186
39
35965
3095
866
12899
2141
755
2662
166
69
401
117
49
42473
3489
925
15850
2680
766
4863
935
335
2609
111
285
44719
11101
4374
27509
8964
4098
6
1732
157
39
422
118
29
26167
2152
638
8938
1414
569
1765
127
49
323
88
49
31800
2623
648
10875
1734
510
3457
650
245
1842
550
206
34990
8499
3259
21181
6862
2935
B-84
-------
Table B-32. Estimated percent of asthmatics in Philadelphia County exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using modeled air quality (as is) and with just
meeting the current standard (std), and with and without indoor sources.
Year (AQ)
2001
(as is)
2001
(std)
2002
(as is)
2002
(std)
2003
(as is)
2003
(std)
Indoor
Source
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Level
(ppb)
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
Percent (%) of Persons With Repeated Exposures
1
31
3
1
6
2
1
79
31
10
55
25
9
29
3
1
6
1
1
82
33
12
61
27
10
32
9
4
16
7
3
81
45
24
67
40
22
2
12
1
0
2
0
0
59
11
3
32
9
2
11
1
0
1
0
0
63
13
3
37
10
3
14
3
1
6
2
1
63
24
10
45
20
9
3
6
0
0
1
0
0
43
5
1
20
4
1
5
0
0
1
0
0
48
6
1
23
5
1
7
2
1
4
1
0
47
14
6
31
12
5
4
3
0
0
0
0
0
31
3
1
12
2
1
3
0
0
0
0
0
35
4
1
14
2
1
5
1
0
2
1
0
36
9
4
22
8
3
5
2
0
0
0
0
0
22
2
1
8
1
0
2
0
0
0
0
0
26
2
1
10
2
0
3
1
0
2
0
0
27
7
3
17
6
3
6
1
0
0
0
0
0
16
1
0
5
1
0
1
0
0
0
0
0
20
2
0
7
1
0
2
0
0
1
0
0
21
5
2
13
4
2
B-85
-------
200
250
2003 AQ (as is) - with indoor soucrces
2002 AQ (as is) - with indoor soucrces
2001 AQ (as is) -with indoor soucrces
Sim ulated Year - Scenario
Potential Health Effect Benchmark Level (ppb)
Figure B-28. Estimated percent of all asthmatics in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003 modeled ah- quality
(as is), with modeled indoor sources.
250
Potential Health Effect Benchmark Level (ppb)
2003 AQ (as is) - no indoor soucrces
2002 AQ (as is) - no indoor soucrces
2001 AQ (as is) - no indoor soucrces
Sim ulated Year - Scenario
Figure B-29. Estimated percent of all asthmatics in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003 modeled ah- quality
(as is), with no indoor sources.
B-86
-------
200
Potential Health Effect Benchmark Level (ppb)
2003 AQ (std) - with indoor soucrces
2002 AQ (std) - with indoor soucrces
2001 AQ (std) - with indoor soucrces
Simulated Year-Scenario
Figure B-30. Estimated percent of all asthmatics in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality
just meeting the current standard (std), with modeled indoor sources.
300
2003 AQ (std) - no indoor soucrces
2002 AQ (std) - no indoor soucrces
2001 AQ (std) - no indoor soucrces
Simulated Year-Scenario
Potential Health Effect Benchmark Level (ppb)
Figure B-31. Estimated percent of all asthmatics in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality
just meeting the current standard (std), with no indoor sources.
B-87
-------
2003 (as is)
Estimated Number of
Repeated Exposures to
200 ppb 1-hour in a Year
2002 (as is)
Simulated Year-Scenario
2001 (as is)
Figure B-32. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with modeled indoor
sources.
2003 (as is)
Estimated Number of
Repeated Exposures to
200 ppb 1-hour in a Year
2002 (as is)
Simulated Year-Scenario
2001 (as is)
Figure B-33. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), without indoor
sources.
B-88
-------
2003 (stti)
Estimated Number of
Repeated Exposures to
200 ppb 1 -hour in a Year
2002 (std)
Simulated Year - Scenario
2001 (std)
Figure B-34. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hour, using 2001-2003 modeled air quality just meeting the current
standard (std), with modeled indoor sources.
2003 (std)
Estimated Number of
Repeated Exposures to
200 ppb 1 -hour in a Year
2002 (std)
Simulated Year - Scenario
2001 (std)
Figure B-35. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hour, using 2001-2003 modeled air quality just meeting the current
standard (std), with no indoor sources.
B-89
-------
B-3.6.7.2
Asthmatic Children
Table B-33. Estimated number of asthmatic children in Philadelphia County exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using modeled air quality (as is) and
with Just meeting the current standard (std), and with and without indoor sources.
Year (AQ)
2001
(as is)
2001
(std)
2002
(as is)
2002
(std)
2003
(as is)
2003
(std)
Indoor
Source
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Level
(ppb)
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
Persons With Number of Repeated Exposures
1
11351
709
128
2329
393
97
36656
13543
3909
27511
11282
3440
10636
692
70
1771
158
30
38834
14855
4203
30548
12487
3736
12525
3541
1545
6724
2784
1368
37931
20044
10562
32066
18770
9547
2
3649
167
49
401
97
29
26353
4530
768
16067
3735
638
3338
139
10
315
49
10
28678
4887
947
18685
3775
670
4693
1240
423
2526
1032
355
28305
9893
4100
21662
8897
3704
3
1418
68
10
147
39
10
18272
1877
236
9890
1413
187
1439
49
0
158
20
0
20840
1978
336
11394
1288
276
2736
678
237
1515
531
208
20344
6016
2381
14938
4974
2223
4
709
49
10
98
20
10
12133
926
187
6094
500
128
800
30
0
79
10
0
14308
1086
228
7063
738
158
1712
423
138
984
335
119
15230
4088
1643
10326
3371
1496
5
424
20
0
58
0
0
8271
533
128
3757
333
109
494
0
0
10
0
0
10063
652
119
4336
493
99
1100
247
89
708
188
69
11013
2888
1211
7647
2388
1072
6
267
10
0
58
0
0
5783
295
88
2430
197
79
346
0
0
0
0
0
6996
514
79
2782
365
39
797
178
39
492
128
39
8483
2253
906
6018
1859
817
B-90
-------
Table B-34. Estimated percent of asthmatic children in Philadelphia County exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using modeled air quality (as is) and
with Just meeting the current standard (std), and with and without indoor sources.
Year (AQ)
2001
(as is)
2001
(std)
2002
(as is)
2002
(std)
2003
(as is)
2003
(std)
Indoor
Source
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Level
(ppb)
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
200
250
300
Percent (%) of Persons With Repeated Exposures
1
23
1
0
5
1
0
75
28
8
57
23
7
22
1
0
4
0
0
81
31
9
64
26
8
26
7
3
14
6
3
79
42
22
67
39
20
2
8
0
0
1
0
0
54
9
2
33
8
1
7
0
0
1
0
0
60
10
2
39
8
1
10
3
1
5
2
1
59
21
9
45
19
8
3
3
0
0
0
0
0
38
4
0
20
3
0
3
0
0
0
0
0
43
4
1
24
3
1
6
1
0
3
1
0
43
13
5
31
10
5
4
1
0
0
0
0
0
25
2
0
13
1
0
2
0
0
0
0
0
30
2
0
15
2
0
4
1
0
2
1
0
32
9
3
22
7
3
5
1
0
0
0
0
0
17
1
0
8
1
0
1
0
0
0
0
0
21
1
0
9
1
0
2
1
0
1
0
0
23
6
3
16
5
2
6
1
0
0
0
0
0
12
1
0
5
0
0
1
0
0
0
0
0
15
1
0
6
1
0
2
0
0
1
0
0
18
5
2
13
4
2
B-91
-------
200
250
2003 AQ (as is) - with indoor soucrces
2002 AQ (as is) - with indoor soucrces
2001 AQ (as is) -with indoor soucrces
Sim ulated Year - Scenario
Potential Health Effect Benchmark Level (ppb)
Figure B-36. Estimated percent of asthmatic children in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality (as
is), with modeled indoor sources.
250
Potential Health Effect Benchmark Level (ppb)
2003 AQ (as is) - no indoor soucrces
2002 AQ (as is) - no indoor soucrces
2001 AQ (as is) - no indoor soucrces
Sim ulated Year - Scenario
Figure B-37. Estimated percent of asthmatic children in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality (as
is), with no indoor sources.
B-92
-------
100
200
Potential Health Effect Benchmark Level (ppb)
2003 AQ (std) - with indoor soucrces
2002 AQ (std) - with indoor soucrces
2001 AQ (std) - with indoor soucrces
Simulated Year-Scenario
Figure B-38. Estimated percent of asthmatic children in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality
just meeting the current standard (std), with modeled indoor sources.
300
2003 AQ (std) - no indoor soucrces
2002 AQ (std) - no indoor soucrces
2001 AQ (std) - no indoor soucrces
Simulated Year-Scenario
Potential Health Effect Benchmark Level (ppb)
Figure B-39. Estimated percent of asthmatic children in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003 modeled air quality
just meeting the current standard (std), with no indoor sources.
B-93
-------
2003 (as is)
Estimated Number of
Repeated Exposures to
200 ppb 1-hour in a Year
2002 (as is)
Simulated Year-Scenario
2001 (as is)
Figure B-40. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with modeled indoor
sources.
2003 (as is)
Estimated Number of
Repeated Exposures to
200 ppb 1-hour in a Year
2002 (as is)
Simulated Year-Scenario
2001 (as is)
Figure B-41. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with no indoor
sources.
B-94
-------
T-
!
2003 (std)
Estimated Number of
Repeated Exposures to
200 ppb 1 -hour in a Year
2002 (std)
Simulated Year - Scenario
2001 (std)
Figure B-42. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality meeting the current
standard (std), with modeled indoor sources.
100
»
a g
11
•
I*
|8
II
2003 (std)
Estimated Number of
Repeated Exposures to
200 ppb 1 -hour in a Year
2002 (std)
Simulated Year - Scenario
2001 (std)
Figure B-43. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quah'ty meeting the current
standard (std), with no indoor sources.
B-95
-------
B-4 Atlanta Exposure Assessment Case-Study
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Sector borders are 43,104, and 255 degrees from geographic North. Atlanta city center is labeled.
B-96
-------
B-5 References
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B-101
-------
Attachment 1: Technical Memorandum on Longitudinal Diary
Construction Approach
B-102
-------
INTERNATIONAL
TECHNICAL MEMORANDUM
TO: Stephen Graham and John Langstaff, US EPA
FROM: Arlene Rosenbaum
DATE: February 29, 2008
SUBJECT: The Cluster-Markov algorithm in APEX
Background
The goals of population exposure assessment generally include an accurate estimate of
both the average exposure concentration and the high end of the exposure distribution. One of
the factors influencing the number of exposures at the high end of the concentration distribution
is time-activity patterns that differ from the average, e.g., a disproportionate amount of time
spent near roadways. Whether a model represents these exposure scenarios well depends on
whether the treatment of activity pattern data accurately characterizes differences among
individuals.
Human time-activity data for population exposure models are generally derived from
demographic surveys of individuals' daily activities, the amount of time spent engaged in those
activities, and the ME locations where the activities occur. Typical time-activity pattern data
available for inhalation exposure modeling consist of a sequence of location/activity
combinations spanning a 24-hour duration, with 1 to 3 records for any single individual. But
modeling assessments of exposure to air pollutants typically require information on activity
patterns over long periods of time, e.g., a full year. For example, even for pollutant health
effects with short averaging times (e.g., ozone 8-hour average) it may be desirable to know the
frequency of exceedances of a threshold concentration over a long period of time (e.g., the
annual number of exceedances of an 8-hour average ozone concentration of 0.07 ppm for each
simulated individual).
Long-term activity patterns can be estimated from daily ones by combining the daily
records in various ways, and the method used for combining them will influence the variability
of the long-term activity patterns across the simulated population. This in turn will influence the
ability of the model to accurately represent either long-term average high-end exposures, or the
number of individuals exposed multiple times to short-term high-end concentrations.
A common approach for constructing long-term activity patterns from short-term records
is to re-select a daily activity pattern from the pool of data for each day, with the implicit
assumption that there is no correlation between activities from day to day for the simulated
individual. This approach tends to result in long-term activity patterns that are very similar
across the simulated population. Thus, the resulting exposure estimates are likely to
underestimate the variability across the population, and therefore, underestimate the high-end
concentrations.
B-103
-------
A contrasting approach is to select a single activity pattern (or a single pattern for each
season and/or weekday-weekend) to represent a simulated individual's activities over the
modeling period. This approach has the implicit assumption that an individual's day to day
activities are perfectly correlated. This approach tends to result in long-term activity patterns
that are very different across the simulated population, and therefore may over-estimate the
variability across the population.
The Cluster-Markov Algorithm
Recently, a new algorithm has been developed and incorporated into APEX that attempts
to more realistically represent the day-to-day correlation of activities for individuals. The
algorithms first use cluster analysis to divide the daily activity pattern records into groups that
are similar, and then select a single daily record from each group. This limited number of daily
patterns is then used to construct a long-term sequence for a simulated individual, based on
empirically-derived transition probabilities. This approach is intermediate between the
assumption of no day-to-day correlation (i.e., re-selection for each time period) and perfect
correlation (i.e., selection of a single daily record to represent all days).
The steps in the algorithm are as follows.
• For each demographic group (age, gender, employment status), temperature range,
and day-of-week combination, the associated time-activity records are partitioned into
3 groups using cluster analysis. The clustering criterion is a vector of 5 values: the
time spent in each of 5 microenvironment categories (indoors - residence; indoors -
other building; outdoors - near road; outdoors - away from road; in vehicle).
• For each simulated individual, a single time-activity record is randomly selected from
each cluster.
• Next the Markov process determines the probability of a given time-activity pattern
occurring on a given day based on the time-activity pattern of the previous day and
cluster-to-cluster transition probabilities. The cluster-to-cluster transition
probabilities are estimated from the available multi-day time-activity records. (If
insufficient multi-day time-activity records are available for a demographic group,
season, day-of-week combination, then the cluster-to-cluster transition probabilities
are estimated from the frequency of time-activity records in each cluster in the CHAD
data base.).
Figure 1 illustrates the Cluster-Markov algorithm in flow chart format.
B-104
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INTERNATIONAL
Demographic
Group 1
Weekday
Season 1
CHAD Data Base
Demographic
Group 1
Weekday
Season 2
TRANSITION
PROBABILITIES
Annual Time-Activity Sequence
Figure 1. Flow chart of Cluster-Markov algorithm used for constructing longitudinal time-activity diaries.
B-105
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INTERNATIONAL
Evaluation of modeled diary profiles versus observed diary profiles
The Cluster-Markov algorithm is also incorporated into the Hazardous Air
Pollutant Exposure Model (HAPEM). Rosebaum and Cohen (2004) incorporated the
algorithm in HAPEM and tested modeled longitudinal profiles with multi-day diary data
sets collected as part of the Harvard Southern California Chronic Ozone Exposure Study
(Xue et al. 2005, Geyh et al. 2000). In this study, 224 children in ages between 7 and 12
yr were followed for 1 year from June 1995 to May 1996, for 6 consecutive days each
month. The subjects resided in two separate areas of San Bernardino County: urban
Upland CA, and the small mountain towns of Lake Arrowhead, Crestline, and Running
Springs, CA.
For purposes of clustering the activity pattern records were characterized
according to time spent in each of 5 aggregate microenvironments: indoors-home,
indoors-school, indoors-other, outdoors, and in-transit. For purposes of defining diary
pools and for clustering and calculating transition probabilities the activity pattern
records were divided by day type (i.e., weekday, weekend), season (i.e., summer or ozone
season, non-summer or non-ozone season), age (7-10 and 11-12), and gender.
Week-long sequences (Wednesday through Tuesday) for each of 100 people in
each age/gender group for each season were simulated. To evaluate the algorithm the
following statistics were calculated for the predicted multi-day activity patterns and
compared them with the actual multi-day diary data.
• For each age/gender group for each season, the average time in each
mi croenvironment
• For each simulated person-week and microenvironment, the average of the
within-person variance across all simulated persons. (The within-person
variance was defined as the variance of the total time per day spent in the
microenvironment across the week.)
• For each simulated person-week the variance across persons of the mean time
spent in each microenvironment.
In each case the predicted statistic for the stratum was compared to the statistic for
the corresponding stratum in the actual diary data. The mean normalized bias for the
statistic, which is a common performance measure used in dispersion model performance
and was also calculated as follows.
100^ (predicted - observed)
N l observed
The predicted time-in-microenvironment averages matched well with the
observed values. For combinations of microenvironment/age/gender/season the
normalized bias ranges from -35% to +41%. Sixty percent of the predicted averages
have bias between -9% and +9%, and the mean bias across any microenvironment ranges
from -9% to +4%. Fourteen predictions have positive bias and 23 have negative bias.
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For the variance across persons for the average time spent in each
microenvironment, the bias ranged from -40% to +120% for any
microenvironment/age/gender/season. Sixty-five percent of the predicted variances had
bias between -22% and +24%. The mean normalized bias across any microenvironment
ranged from -10% to +28%. Eighteen predictions had positive bias and 20 had negative
bias.
For the within-person variance for time spent in each microenvironment, the bias
ranged from -47% to +150% for any microenvironment/age/gender/season. Seventy
percent of the predicted variances had bias between -25% and +30%. The mean
normalized bias across any microenvironment ranged from -11% to +47%. Twenty-eight
predictions had positive bias and 12 had negative bias, suggesting some tendency for
overprediction of this variance measure.
The overall conclusion was that the proposed algorithm appeared to be able to
replicate the observed data reasonably well. Although some discrepancies were rather
large for some of the "variance across persons" and "within-person variance" subsets,
about two-thirds of the predictions for each case were within 30% of the observed value.
A detailed description of the evaluation using HAPEM is presented in Attachment 1.
Comparison of Cluster-Markov approach with other algorithms
As part of the application of APEX in support of US EPA's recent review of the
ozone NAAQS several sensitivity analyses were conducted (US EPA, 2007). One of
these was to make parallel simulations using each of the three algorithms for constructing
multi-day time-activity sequences that are incorporated into APEX.
Table 1 presents the results for the number of persons in Atlanta population
groups with moderate exertion exposed to 8-hour average concentrations exceeding 0.07
ppm. The results show that the predictions made with alternative algorithm Cluster-
Markov algorithm are substantially different from those made with simple re-sampling or
with the Diversity-Autocorrelation algorithm ("base case"). Note that for the cluster
algorithm approximately 30% of the individuals with 1 or more exposure have 3 or more
exposures. The corresponding values for the other algorithms range from about 13% to
21%.
Table 2 presents the results for the mean and standard deviation of number of
days/person with 8-hour average exposures exceeding 0.07 ppm with moderate or greater
exertion. The results show that although the mean for the Cluster-Markov algorithm is
similar to the other approaches, the standard deviation is substantially higher, i.e., the
Cluster-Markov algorithm results in substantially higher inter-individual variability.
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Table 1. Sensitivity to longitudinal diary algorithm: 2002 simulated counts of Atlanta
general population and children (ages 5-18) with any or three or more 8-hour ozone
exposures above 0.07 ppm concomitant with moderate or greater exertion (after US EPA
2007).
Population
Group
General
Population
Children (5-18)
One or more exposures
Simple
re-sampling
979,533
411,429
Diversity-
Autocorrelation
939,663
(-4%)
389,372
(-5%)
Cluster-
Markov
668,004
(-32%)
295,004
(-28%)
Three or more exposures
Simple
re-sampling
124,687
71,174
Diversity-
Autocorrelation
144,470
(+16%)
83,377
(+17%)
Cluster-
Markov
188,509
(+51%)
94,216
(+32%)
Table 2. Sensitivity to longitudinal diary algorithm: 2002 days per person with 8-hour
ozone exposures above 0.07 ppm concomitant with moderate or greater exertion for
Atlanta general population and children (ages 5-18) (after US EPA 2007).
Population
Group
General
Population
Children (5-1 8)
Mean Days/Person
Simple
re-sampling
0.332
0.746
Base case
0.335
(+1%)
0.755
(+1%)
Cluster-
Markov
0.342
(+3%)
0.758
(+2%)
Standard Deviation
Simple re-
sampling
0.757
1.077
Base case
0.802
(+6%)
1.171
(+9%)
Cluster-
Markov
1.197
(+58%)
1.652
(+53%)
References
Geyh AS, Xue J, Ozkaynak H, Spengler JD. (2000). The Harvard Southern California
chronic ozone exposure study: Assessing ozone exposure of grade-school-age
children in two Southern California communities. Environ Health Persp. 108:265-
270.
Rosenbaum AS and Cohen JP. (2004). Evaluation of a multi-day activity pattern
algorithm for creating longitudinal activity patterns. Memorandum prepared for Ted
Palma, US EPA OAQPS, by ICF International.
US EPA. (2007). Ozone Population Exposure Analysis for Selected Urban Areas. EPA-
452/R-07-010. Available at:
http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007-01_o3_exposure_tsd.pdf
Xue J, Liu SV, Ozkaynak H, Spengler J. (2005). Parameter evaluation and model
validation of ozone exposure assessment using Harvard Southern California Chronic
Ozone Exposure Study Data. J. Air & Waste Manage Assoc. 55:1508-1515.
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Attachment 2: Detailed Evaluation Cluster-Markov Algorithm
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TECHNICAL MEMORANDUM
TO: Ted Palma, US EPA
FROM: Arlene Rosenbaum and Jonathan Cohen, ICF Consulting
DATE: November 4, 2004
SUBJECT: Evaluation of a multi-day activity pattern algorithm for creating longitudinal
activity patterns.
BACKGROUND
In previous work ICF reviewed the HAPEM4 modeling approach for developing
annual average activity patterns from the CHAD database and recommended an approach to
improve the model's pattern selection process to better represent the variability among
individuals. This section summarizes the recommended approach. (For details see
Attachment 2)
Using cluster analysis, first the CHAD daily activity patterns are grouped into either
two or three categories of similar patterns for each of the 30 combinations of day type
(summer weekday, non-summer weekday, and weekend) and demographic group (males or
females; age groups: 0-4, 5-11, 12-17, 18-64, 65+). Next, for each combination of day type
and demographic group, category-to-category transition probabilities are defined by the
relative frequencies of each second-day category associated with each given first-day
category, where the same individual was observed for two consecutive days. (Consecutive
day activity pattern records for a single individual constitute a small subset of the CHAD
data.)
To implement the proposed algorithm, for each day type and demographic group, one
daily activity pattern per category is randomly selected from the corresponding CHAD data
to represent that category. That is, if there are 3 cluster categories for each of 3 day types, 9
unique activity patterns are selected to be averaged together to create an annual average
activity pattern to represent an individual in a given demographic group and census tract.
The weighting for each of the 9 activity patterns used in the averaging process is
determined by the product of two factors. The first is the relative frequency of its day type,
i.e., 0.18 for summer weekdays, 0.54 for non-summer weekdays, and 0.28 for weekends.
The second factor in the weighting for the selected activity pattern is determined by
simulating a sequence of category-types as a one-stage Markov chain process using the
transition probabilities. The category for the first day is selected according to the relative
frequencies of each category. The category for the second day is selected according to the
category-to-category transition probabilities for the category selected for the first day. The
category for the third day is selected according to the transition probabilities for the category
selected for the second day. This is repeated for all days in the day type (65 for summer
weekdays, 195 for non-summer weekdays, 104 for weekends), producing a sequence of daily
categories. The relative frequency of the category-type in the sequence associated with the
selected activity pattern is the second factor in the weighting.
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PROPOSED ALGORITHM STEPS
The proposed algorithm is summarized in Figure 1. Each step is explained in this
section.
Data Preparation
Step 1: Each daily activity pattern in the CHAD data base is summarized by the total
minutes in each of five micro-environments: indoors - residence; indoors - other
building; outdoors - near road; outdoors - away from road; in vehicle. These five
numbers are assumed to represent the most important features of the activity pattern
for their exposure impact.
Step 2: All CHAD activity patterns for a given day-type and demographic group are
subjected to cluster analysis, resulting in 2 or 3 cluster categories. Each daily activity
pattern is tagged with a cluster category.
Step 3: For each day-type and demographic group, the relative frequency of each day-
type 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 category-
to-category transition frequencies in the CHAD data base. These transition
frequencies are used to calculate category-to-category transition probabilities.
For example, if there are 2 categories, A and B, then
PAA = the probability that a type A pattern is followed by a type A pattern,
PAB = the probability that a type A pattern is followed by a type B pattern (PAB = 1 -
PAA),
PBB = the probability that a type B pattern is followed by a type B pattern, and
PBA = the probability that a type B pattern is followed by a type A pattern (PBA = 1 -
PBB).
Activity Pattern Selection
For each day-type and demographic group in each census tract:
Step 5: One activity pattern is randomly selected from each cluster category group
(i.e., 2 to 3 activity patterns)
Creating Weights for Day-type Averaging
For each day-type and demographic group in each census tract:
Step 6: A cluster category is selected for the first day of the day-type sequence,
according to the relative frequency of the cluster category days in the CHAD data set.
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Step 7: A cluster category is selected for each subsequent day in the day-type
sequence day by day using the category-to-category transition probabilities.
Step 8: The relative frequency of each cluster category in the day-type sequence is
determined.
Step 9: The activity patterns selected for each cluster category (Step 5) are averaged
together using the cluster category frequencies (Step 8) as weights, to create a day-
type average activity pattern.
Creating Annual Average Activity Patterns
For each demographic group in each census tract:
Step 10: The day-type average activity patterns are averaged together using the
relative frequency of day-types as weights, to create an annual average activity
pattern.
Creating Replicates
For each demographic group in each census tract:
Step 11: Steps 5 through 10 are repeated 29 times to create 30 annual average activity
patterns.
EVALUATING THE ALGORITHM
The purpose of this study is to evaluate how well the proposed one-stage Markov
chain algorithm can reproduce observed multi-day activity patterns with respect to
demographic group means and inter-individual variability, while using one-day selection.
In order to accomplish this we propose to apply the algorithm to observed multi-day
activity patterns provided by the WAM, and compare the means and variances of the
predicted multi-day patterns with the observed patterns.
Current APEX Algorithm
Because the algorithm is being considered for incorporation into APEX, we would
like the evaluation to be consistent with the approach taken in APEX for selection of activity
patterns for creating multi-day sequences. The APEX approach for creating multi-day
activity sequences is as follows.
Stepl: A profile for a simulated individual is generated by selection of gender, race
(not implemented?), age group, and home sector from a given set of distributions
consistent with the population of the study area.
Step 2: A specific age within the age group is selected from a uniform distribution.
Step 3: The employment status is simulated as a function of the age.
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Step 4: For each simulated day, the user defines an initial pool of possible diary days
based on a user-specified function of the day type (e.g., weekday/weekend) and
temperature.
Step 5: The pool is further restricted to match the target gender and employment
status exactly and the age within 2A years for some parameter A. The diary days
within the pool are assigned a weight of 1 if the age is within A years of the target age
and a weight of w (user-defined parameter) if the age difference is between A and 2A
years. For each simulated day, the probability of selecting a given diary day is equal
to the age weight divided by the total of the age weights for all diary days in the pool
for that day.
Approach to Incorporation of Day-to-Day Dependence into APEX Algorithm
If we were going to incorporate day-to-day dependence of activity patterns into the
APEX model, we would propose preparing the data with cluster analysis and transition
probabilities as described in Steps 1-4 for the proposed HAPEM 5 algorithm, with the
following modifications.
• For Step 2 the activity patterns would be divided into groups based on day-type
(weekday, weekend), temperature, gender, employment status, and age, with
cluster analysis applied to each group. However, because the day-to-day
transitions in the APEX activity selection algorithm can cross temperature bins,
we would propose to use broad temperature bins for the clustering and transition
probability calculations so that the cluster definitions would be fairly uniform
across temperature bins. Thus we would probably define the bins according to
season (e.g., summer, non-summer).
• In contrast to HAPEM, the sequence of activity patterns may be important in
APEX. Therefore, for Step 4 transition probabilities would be specified for
transitions between days with the same day-type and season, as in HAPEM, and
also between days with different day-types and/or seasons. For example,
transition probabilities would be specified for transitions between summer
weekdays of each category and summer weekends of each category.
Another issue for dividing the CHAD activity records for the purposes of clustering
and calculating transition probabilities is that the diary pools specified for the APEX activity
selection algorithm use varying and overlapping age ranges. One way to address this
problem would be to simply not include consideration of age in the clustering process, under
the assumption that cluster categories are similar across age groups, even if the frequency of
each cluster category varies by age group. This assumption could be tested by examination
of the cluster categories stratified by age group that were developed for HAPEM5. If the
assumption is found to be valid, then the cluster categories could be pre-determined for input
to APEX, while the transition probabilities could be calculated within APEX during the
simulation for each age range specified for dairy pools.
If the assumption is found to be invalid, then an alternative approach could be
implemented that would create overlapping age groups for purposes of clustering as follows.
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APEX age group ranges and age window percentages would be constrained to some
maximum values. Then a set of overlapping age ranges that would be at least as large as the
largest possible dairy pool age ranges would be defined for the purposes of cluster analysis
and transition probability calculation. The resulting sets of cluster categories and transition
probabilities would be pre-determined for input into APEX and the appropriate set used by
APEX for each diary pool used during the simulation.
The actual activity pattern sequence selection would be implemented as follows. The
activity pattern for first day in the year would be selected exactly as is currently done in
APEX, as described above. For the selecting the second day's activity pattern, each age
weight would be multiplied by the transition probability PAB where A is the cluster for the
first day's activity pattern and B is the cluster for a given activity pattern in the available pool
of diary days for day 2. (Note that day 2 may be a different day-type and/or season than day
1). The probability of selecting a given diary day on day 2 is equal to the age weight times
PAB divided by the total of the products of age weight and PAB for all diary days in the pool
for day 2. Similarly, for the transitions from day 2 to day 3, day 3 to day 4, etc.
Testing the Approach with the Multi-day Data set
We tested this approach using the available multi-day data set. For purposes of
clustering we characterized the activity pattern records according to time spent in each of 5
microenvironments: indoors-home, indoors-school, indoors-other, outdoors (aggregate of the
3 outdoor microenvironments), and in-transit.
For purposes of defining diary pools and for clustering and calculating transition
probabilities we divided the activity pattern records by day type (i.e., weekday, weekend),
season (i.e., summer or ozone season, non-summer or non-ozone season), age (6-10 and 11-
12), and gender. Since all the subjects are 6-12 years of age and all are presumably
unemployed, we need not account for differences in employment status. For each day type,
season, age, and gender, we found that the activity patterns appeared to group in three
clusters.
In this case, we simulated week-long sequences (Wednesday through Tuesday) for
each of 100 people in each age/gender group for each season, using the transition
probabilities. To evaluate the algorithm we calculated the following statistics for the
predicted multi-day activity patterns for comparison with the actual multi-day diary data.
For each age/gender group for each season, the average time in each
microenvironment
For each age/gender group, season, and microenvironment, the average of the
within-person variance across all simulated persons (We defined the within-
person variance as the variance of the total time per day spent in the
microenvironment across the week.)
For each age/gender group, season, and microenvironment, the variance across
persons of the mean time spent in that microenvironment
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In each case we compared the predicted statistic for the stratum to the statistic for the
corresponding stratum in the actual diary data.20
We also calculated the mean normalized bias for the statistic, which is a common
performance measure used in dispersion model performance and which is calculated as
follows.
* m r., o 100 ^ ( predicted - observed} n /
NBIAS = > — %
N i observed
RESULTS
Comparisons of simulated and observed data for time in each of the 5
microenvironments are presented in Tables 1-3 and Figures 2-5.
Average Time in Microenvironment
Table 1 and Figure 2 show the comparisons for the average time spent in each of the
5 microenvironments for each age/gender group and season. Figure 3 shows the comparison
for all the microenvironments except indoor, home in order to highlight the lower values.
Table 1 and the figures show that the predicted time-in-microenvironment averages
match well with the observed values. For combinations of
microenvironment/age/gender/season the normalized bias ranges from -35% to +41%. Sixty
percent of the predicted averages have bias between -9% and +9%, and the mean bias across
any microenvironment ranges from -9% to +4%. Fourteen predictions have positive bias and
23 have negative bias. A Wilcoxon signed rank test that the median bias across the 40
combinations = 0 % was not significant (p-value = 0.40) supporting the conclusion of no
overall bias.
Variance Across Persons
Table 2 and Figure 4 show the comparisons for the variance across persons for the
average time spent in each microenvironment. In this case the bias ranges from -40% to
+120% for any microenvironment/age/gender/season. Sixty-five percent of the predicted
variances have bias between -22% and +24%. The mean normalized bias across any
microenvironment ranges from -10% to +28%. Eighteen predictions have positive bias and
20 have negative bias. Figure 4 suggests a reasonably good match of predicted to observed
variance in spite of 2 or 3 outliers. A Wilcoxon signed rank test that the median bias across
the 40 combinations = 0 % was not significant (p-value = 0.93) supporting the conclusion of
no overall bias.
Within-Person Variance for Persons
20 For the diary data, because the number of days per person varies, the average of the within-person variances
was calculated as a weighted average, where the weight is the degrees of freedom, i.e., one less than the number
of days simulated. Similarly, the variance across persons of the mean time was appropriately adjusted for the
different degrees of freedom using analysis of variance.
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Table 3 and Figure 5 show the comparisons for the within-person variance for time
spent in each microenvironment. In this case the bias ranges from -47% to +150% for any
microenvironment/age/gender/season. Seventy percent of the predicted variances have bias
between -25% and +30%. The mean normalized bias across any microenvironment ranges
from -11% to +47%. Twenty-eight predictions have positive bias and 12 have negative bias,
suggesting some tendency for overprediction of this variance measure. And indeed a
Wilcoxon signed rank test that the median bias across the 40 combinations = 0 % was very
significant (p-value = 0.01) showing that the within-person variance was significantly
overpredicted. Still, Figure 4 suggests a reasonably good match of predicted to observed
variance in most cases, with a few overpredicting outliers at the higher end of the
distribution. So although the positive bias is significant in a statistical sense (i.e., the variance
is more likely to be overpredicted than underpredicted), it is not clear whether the bias is
large enough to be important.
CONCLUSIONS
The proposed algorithm appears to be able to replicate the observed data reasonably
well, although the within-person variance is somewhat overpredicted.
It would be informative to compare this algorithm with the earlier alternative
approaches in order to gain perspective on the degree of improvement, if any, afforded by
this approach.
Two earlier approaches were:
1. Select a single activity pattern for each day-type/season combination from the
appropriate set, and use that pattern for every day in the multi-day sequence that
corresponds to that day-type and season.
2. Re-select an activity pattern for each day in the multi-day sequence from the
appropriate set for the corresponding day-type and season.
Goodness-of-fit statistics could be developed to compare the three approaches and find
which model best fits the data for a given stratum.
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Table 1. Average time spent in each microenvironment: comparison of predicted and observed.
Microenvironment
Indoor, home
Indoor, school
Indoor, other
Outdoors
In-vehicle
Demographic
Group
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Season
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Observed
(hours/day)
15.5
15.8
15.7
15.8
16.2
16.5
16.0
16.2
0.7
2.3
0.8
2.2
0.7
2.1
0.6
2.4
2.9
2.4
2.2
1.9
2.2
2.2
2.3
1.9
3.7
2.5
4.1
3.1
3.7
2.3
3.9
2.6
1.1
1.0
1.1
1.0
1.2
0.9
1.1
0.9
Predicted
(hours/day)
16.5
15.5
15.2
16.4
15.3
16.5
15.6
16.1
0.7
2.5
0.5
2.2
0.7
2.4
0.9
2.7
2.4
2.7
2.7
1.8
1.6
2.1
2.2
2.0
3.5
2.5
4.3
2.7
5.2
2.1
4.3
2.4
0.9
0.9
1.3
0.9
1.1
0.8
1.0
0.8
Normalized
Bias
6%
-2%
-3%
4%
-5%
0%
-3%
-1%
-1%
-9%
7%
-34%
0%
6%
13%
38%
11%
4%
-14%
13%
21%
-3%
-25%
-2%
-5%
4%
-2%
-6%
0%
4%
-12%
41%
-5%
9%
-7%
3%
-20%
-13%
13%
-16%
-12%
-15%
-5%
-7%
-9%
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Table 2. Variance across persons for time spent in each microenvironment: comparison of
predicted and observed.
Microenvironment
Indoor, home
Indoor, school
Indoor, other
Outdoors
In-vehicle
Demographic
Group
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Season
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Observed
(hours/day)2
70
67
54
35
56
42
57
39
6.0
9.5
5.6
5.3
4.9
5.4
5.6
9.2
46
44
34
23
21
28
33
30
17
9.3
17
8.3
22
9.0
13
10
1.9
1.8
2.5
1.5
3.5
2.8
3.2
1.3
Predicted
(hours/day)2
42
60
49
30
47
38
63
42
5.2
5.9
3.8
8.2
5.5
5.3
6.0
11
32
46.
33
16
18
22
31
30
23
6.8
18
7.6
22
9.1
29
11
2.3
1.6
4.7
1.6
4.7
2.0
5.4
1.7
Normalized
Bias
-40%
-9%
-9%
-12%
-17%
-10%
12%
8%
-10%
-13%
-38%
-32%
53%
11%
-1%
6%
23%
1%
-30%
6%
-4%
-27%
-15%
-22%
-6%
0%
-12%
37%
-27%
3%
-8%
0%
1%
120%
8%
17%
24%
-11%
93%
9%
34%
-28%
69%
35%
28%
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Table 3. Average within person variance for time spent in each microenvironment: comparison
of predicted and observed.
Microenvironment
Indoor, home
Indoor, school
Indoor, other
Outdoors
In-vehicle
Demographic
Group
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 8-10
Girls, 11-12
Boys, 11-12
MEAN
Girls, 6-10
Boys, 6-10
Girls, 11-12
Boys, 11-12
MEAN
Season
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Summer
Not Summer
Observed
(hours/day)2
20
18
17
15
22
22
21
17
2.3
7.3
2.0
6.7
1.7
7.4
1.4
7.3
14
14
12
10
10
14
11
12
8.4
3.4
6.7
3.4
10
4.0
9.2
4.3
1.0
0.90
1.1
0.81
1.3
1.3
2.4
0.85
Predicted
(hours/day)2
29
23
30
24
42
25
24
24
2.4
6.4
1.5
5.8
2.1
7.6
2.9
7.8
14
18
17
13
10
15
14
13
9.5
3.2
9.5
4.4
25
4.5
7.4
3.7
0.90
0.48
1.4
0.71
1.3
1.1
1.6
0.85
Normalized
Bias
49%
25%
75%
64%
93%
13%
16%
38%
47%
5%
-12%
-25%
-14%
29%
3%
101%
6%
12%
-4%
30%
42%
26%
1%
7%
26%
7%
17%
13%
-3%
42%
28%
150%
11%
-20%
-15%
26%
-13%
-47%
31%
-12%
4%
-16%
-34%
1%
-11%
B-119
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Consolidated Human Activity Database - CHAD (CHAD)
Winter Weekday
Pattern Group
Summer Weekday
Pattern Group
"••^..Cluster Analysis m..".V
Cluster Analysis
ransition
nalysi
Weekend
Pattern Group
(.luster Analysis
Transition
Probabilities
^
r
Average Winter
Weekday Pattern
0.54
Markov Selection
Individual Annual Average Activity Pattern
Figure 1. Flow diagram of proposed algorithm for creating annual average activity patterns for HAPEM5.
B-120
-------
1
2 I
3 f
4
on _,
re
5 l^
0
•c 10
•c
•a °
£
PL
n
*^
/^
/^
*^
• Indoor, home
• Indoor, school
indoor, other
Outdoor
x In chicle
0 5 10 15 20
Observed (hours/day)
"igure 2. Comparison of predicted and observed average time in each of 5 microenvironments
or age/gender groups and seasons.
5
6
7
Predicted (hours/day)
K
A
•*.
-\
n
^^
^^
^^
m Indoor, school
indoor, other
x Outdoor
x In chicle
012345
Observed (hours/day)
Figure 3. Comparison of predicted and observed average time in each of 4 microenvironments
for age/gender groups and seasons.
B-121
-------
2
3
4
5
6
80
70
60
~o 50
o ou
S 40
3 ^U
•- 30
W JU
20
10
0
c
/
*/.
/
-X * .
/".
\^
IZ*
J*
} 20 40 60 8
Observed
0
* girls, 6-10, summer
• girls, 6-10, winter
boys , 6-10, summer
boys, 6-10, winter
X. girls, 11-12, summer
• girls, 11-12, winter
+ boys, 11-12, summer
- boys, 11-12, winter
Figure 4. Comparison of predicted and observed variance across persons for time spent in each
of 5 microenvironments for age/gender groups and seasons.
7
8
9
30
95
90
O
« 15 -
3 ID
E
co 10
R
» /
X • /
\/
y*
j$
&
• girls, 6-10, summer
• girls, 6-10, winter
boys , 6-10, summer
boys, 6-10, winter
X girls, 11-12, summer
• girls, 11-12, winter
+ boys, 11-12, summer
-boys, 11-12, winter
iii
0 10 20 30
Observed
Figure 5. Comparison of predicted and observed the average within-person variance for time
spent in each of 5 microenvironments by age/gender groups and seasons.
B-122
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B-123
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Appendix C
Nitrogen Dioxide Health Risk Assessment
for Atlanta, GA
Draft Report
August 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
Work funded through
Contract No. EP-W-05-022
Work Assignment 2-62
Harvey Richmond, Work Assignment Manager
Catherine Turner, Project Officer
-------
DISCLAIMER
This report is being furnished to the U.S. Environmental Protection Agency
(EPA) by Abt Associates Inc. in partial fulfillment of Contract No. EP-W-05-022, Work
Assignment 2-62. Any opinions, findings, conclusions, or recommendations are those of
the authors and do not necessarily reflect the views of the EPA or Abt Associates. Any
questions concerning this document should be addressed to Harvey Richmond, U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, C504-
06, Research Triangle Park, North Carolina 27711 (email: richmond.harvey@epa.gov).
Abt Associates Inc. i August 2008
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Table of Contents
INTRODUCTION 1
PRELIMINARY CONSIDERATIONS 4
THE BROAD EMPIRICAL BASIS FOR A RELATIONSHIP BETWEEN NO2 AND ADVERSE HEALTH EFFECTS 4
BASIC STRUCTURE OF THE RISK ASSESSMENT 4
AIR QUALITY CONSIDERATIONS 5
METHODS 1
GENERAL APPROACH 1
SELECTION OF HEALTH ENDPOINT(S) 4
SELECTION OF URBAN AREA(S) AND EPIDEMIOLOGICAL STUDIES 4
SELECTION OF CONCENTRATION-RESPONSE FUNCTIONS 6
AlR QUALITY CONSIDERATIONS 7
BASELINE HEALTH EFFECTS INCIDENCE 8
SUMMARY OF DETERMINANTS OF THE NO2 RISK ASSESSMENT 8
ADDRESSING UNCERTAINTY AND VARIABILITY 9
Concentration-response functions 14
The air quality data 17
Baseline health effects incidence 18
RESULTS 20
REFERENCES 32
TOLBERT, P. 2008. PERSONAL COMMUNICATION (EMAIL) TO H. RICHMOND, U.S.
EPA - "ATLANTA EMERGENCY DEPARTMENT VISIT AND AIR QUALITY DATA USED
IN TOLBERT ET AL. (2007)," MAY 30 32
Abt Associates Inc. ii August 2008
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List of Tables
Table 3-1. Mean and 98th and 99th Percentiles of the Distributions of 1-Hour Daily
Maximum NOi Concentrations (in ppm) at the Georgia Tech Monitor: 2005,
2006, and 2007 7
Table 3-2. Key Uncertainties in the NOi Risk Assessment 12
Table 4-1. Estimated Incidence of Respiratory Emergency Department Visits Associated
with "As Is" NOi Concentrations and NOi Concentrations that Just Meet
Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NOi
Concentrations 21
Table 4-2. Estimated Incidence of Respiratory Emergency Department Visits Associated
with "As Is" NOi Concentrations and NOi Concentrations that Just Meet the
Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006
NOi Concentrations 22
Table 4-3. Estimated Incidence of Respiratory Emergency Department Visits Associated
with "As Is" NOi Concentrations and NOi Concentrations that Just Meet the
Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007
NOi Concentrations 23
Table 4-4. Estimated Incidence of Respiratory Emergency Department Visits per
100,000 Population Associated with "As Is" NOi Concentrations and NOi
Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on
Adjusting 2005 NO2 Concentrations 24
Table 4-5. Estimated Incidence of Respiratory Emergency Department Visits per
100,000 Population Associated with "As Is" NOi Concentrations and NOi
Concentrations that Just Meet the Current and Alternative Standards in Atlanta,
GA, Based on Adjusting 2006 NOi Concentrations 25
Table 4-6. Estimated Incidence of Respiratory Emergency Department Visits per
100,000 Population Associated with "As Is" NOi Concentrations and NOi
Concentrations that Just Meet the Current and Alternative Standards in Atlanta,
GA, Based on Adjusting 2007 NOi Concentrations 26
Table 4-7. Estimated Percent of Total Incidence of Respiratory Emergency Department
Visits Associated with "As Is" NOi Concentrations and NOi Concentrations
that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005
NOi Concentrations 27
Table 4-8. Estimated Percent of Total Incidence of Respiratory Emergency Department
Visits Associated with "As Is" NOi Concentrations and NOi Concentrations
that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on
Adjusting 2006 NO2 Concentrations 28
Table 4-9. Estimated Percent of Total Incidence of Respiratory Emergency Department
Visits Associated with "As Is" NOi Concentrations and NOi Concentrations
that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on
Adjusting 2007 NO2 Concentrations 29
Abt Associates Inc. iii August 2008
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List of Figures
Figure 3-1. Major Components of NOi Health Risk Assessment Based on Epidemiology
Studies 1
Figure 4-1. Incidence of Respiratory-Related Emergency Department Visits in Atlanta,
GA Under Different Air Quality Scenarios, Based on Adjusting 2005, 2006,
and 2007 NO2 Concentrations 30
Abt Associates Inc. iv August 2008
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Nitrogen Dioxide Health Risk Assessment for Atlanta, GA
INTRODUCTION
The U.S. Environmental Protection Agency (EPA) is presently conducting a
review of the national ambient air quality standards (NAAQS) for nitrogen dioxide
(NC>2). Sections 108 and 109 of the Clean Air Act (Act) govern the establishment and
periodic review of the NAAQS. These standards are established for pollutants that may
reasonably be anticipated to endanger public health and welfare, and whose presence in
the ambient air results from numerous or diverse mobile or stationary sources. The
NAAQS are to be based on air quality criteria, which are to accurately reflect the latest
scientific knowledge useful in indicating the kind and extent of identifiable effects on
public health or welfare that may be expected from the presence of the pollutant in
ambient air. The EPA Administrator is to promulgate and periodically review, at five-
year intervals, "primary" (health-based) and "secondary" (welfare-based) NAAQS for
such pollutants.1 Based on periodic reviews of the air quality criteria and standards, the
Administrator is to make revisions in the criteria and standards, and promulgate any new
standards, as may be appropriate. The Act also requires that an independent scientific
review committee advise the Administrator as part of this NAAQS review process, a
function performed by the Clean Air Scientific Advisory Committee (CASAC).
EPA's plan and schedule for this NC>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 discusses the preparation of two key
components in the NAAQS review process: an Integrated Science Assessment (ISA) and
risk/exposure assessments. The ISA critically evaluates and integrates scientific
information on the health effects associated with exposure to oxides of nitrogen (NOx) in
the ambient air. The risk/exposure assessments develop qualitative characterization and
quantitative estimates, where judged appropriate, of human exposure and health risk and
related variability and uncertainties, drawing upon the information summarized in the
ISA.
In early March 2008, EPA's National Center for Environmental Assessment
released a second draft of the "Integrated Science Assessment for Oxides of Nitrogen -
Health Criteria (Second External Review Draft)," henceforth referred to as the draft ISA
(U.S. EPA, 2008a). EPA's Office of Air Quality Planning and Standards (OAQPS) in
early April released a first draft of its "Risk and Exposure Assessment to Support the
Review of the NO2 Primary National Ambient Air Quality Standard," henceforth referred
Section 109(b)(l) [42 U.S.C. 7409] of the Act defines a primary standard as one "the attainment
and maintenance of which in the judgment of the Administrator, based on such criteria and allowing an
adequate margin of safety, are requisite to protect the public health."
Abt Associates Inc. 1 August 2008
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to as the 1st draft REA (U.S. EPA, 2008b). Both of these documents were reviewed by
the CAS AC NO2 Panel on May 1-2, 2008.
As a result of the May 2008 CAS AC NO2 Panel review and in response to advice
offered by the CASAC Panel, OAQPS decided to expand the health risk assessment to
include a quantitative assessment of respiratory-related emergency department (ED)
visits estimated to be associated with exposures to NO2 for the Atlanta metropolitan
statistical area (MSA).
NO2is one of a group of substances known as nitrogen oxides (NOX), which
include multiple gaseous (e.g., NO2, NO) and particulate (e.g., nitrate) species. As in past
NAAQS reviews, NO2 is considered as the surrogate for the gaseous NOX species for the
purpose of this assessment, with particulate species addressed as part of the particulate
matter (PM) NAAQS review.
Previous reviews of the NO2 primary NAAQS completed in 1985 and 1994 did
not include quantitative health risk assessments. Thus, the risk assessment described in
this document builds upon the methodology and lessons learned from the risk assessment
work conducted for the recently concluded PM and Os NAAQS reviews (Abt Associates,
2005; Abt Associates, 2007a). Many of the same methodological issues are present for
each of these criteria air pollutants where epidemiological studies provided the basis for
the concentration-response (C-R) relationships used in the quantitative risk assessment.
In July 2008, EPA issued the final ISA, "Integrated Science Assessment for
Oxides of Nitrogen - Health Criteria (Final Report), henceforth referred to as the final
ISA (U.S. EPA, 2008c). The risk assessment described in this document is also based on
the information and evaluation contained in the final ISA. In August 2008, EPA is
releasing its 2nd draft REA, henceforth referred to as the draft REA (U.S. EPA, 2008d).
The NO2 health risk assessment described in this document estimates the
incidence of respiratory-related ED visits associated with short-term exposures to NO2
under recent ("as is") air quality levels and upon just meeting the current NO2 standard of
0.053 ppm annual average and several alternative NO2 primary NAAQS in the Atlanta
MSA.2 The alternative standards considered are daily maximum 1-hour standards, with
levels of 0.05, 0.10, 0.15, and 0.20 ppm, using a 98th percentile form and also using a
99th percentile form, using a three-year period.3 The risk assessment is intended as a tool
that, together with other information on this health endpoint and other health effects
evaluated in the final ISA, can aid the Administrator in judging whether the current
primary standard protects public health with an adequate margin of safety, or whether
revisions to the standard are appropriate.
2 The current NO2 standard refers to a two-year period and requires that the annual average NO2 level be
less than or equal to 0.053 ppm in each of the two years.
3 For the alternative standards using, say, the 98th percentile form, the standard is met when the average of
the three annual 98th percentile daily maximum 1-hr concentrations for the 3-year period is at or below the
specified standard level.
Abt Associates Inc. 2 August 2008
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Preliminary considerations and the basic structure of the risk assessment are
described in section 2. Section 3 describes the methods used, and section 4 presents the
results of the risk assessment.
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PRELIMINARY CONSIDERATIONS
The health risk assessment described in this document estimates the incidence of
respiratory-related ED visits associated with NC>2 exposures for recent ("as is") NC>2 levels,
based on 2005, 2006, and 2007 air quality data, as well as the risks associated with just
meeting the current standard and the reduced risks associated with just meeting each of
several alternative NC>2 NAAQS.4 In this section we address preliminary considerations.
Section 2.1 briefly discusses the broad empirical basis for a relationship between NC>2
exposures and adverse health effects. Section 2.2 describes the basic structure of the risk
assessment. Finally, section 2.3 addresses air quality considerations.
THE BROAD EMPIRICAL BASIS FOR A RELATIONSHIP BETWEEN
NO2 AND ADVERSE HEALTH EFFECTS
The final ISA concludes that there is a broad empirical basis supporting the inference
of a likely causal relationship between short-term NC>2 exposure and respiratory effects:
Taken together, the findings of epidemiologic, human clinical, and animal
toxicological studies provided evidence that is sufficient to infer a likely
causal relationship for respiratory effects with short-term NC>2 exposure.
The body of evidence from epidemiologic studies has grown substantially
since the 1993 AQCD and provided scientific evidence that short-term
exposure to NC>2 is associated with a broad range of respiratory morbidity
effects, including altered lung host defense, inflammation, airway
hyperresponsiveness, respiratory symptoms, lung function decrements, and
ED visits and hospital admissions for respiratory diseases" (final ISA,
section 3.1.7, p. 3-41).
BASIC STRUCTURE OF THE RISK ASSESSMENT
The general approach used in this risk assessment, as in the risk assessment that was
part of the recent PM NAAQS review, relies upon C-R functions that have been estimated in
epidemiological studies. Since these studies estimate C-R functions using ambient air quality
data from fixed-site, population-oriented monitors, the appropriate application of these
functions in aNC>2 risk assessment similarly requires the use of ambient air quality data at
fixed-site, population-oriented monitors. The NC>2 health risk model combines information
about NC>2 air quality for a specific urban area with C-R functions derived from an
epidemiological study and baseline health incidence data for a specific health endpoint to
derive estimates of the annual incidence of the specified health effect attributable to ambient
NC>2 concentrations. The analyses have been conducted for both "as is" air quality and for air
4 The current NO2 standard is met in all locations in the United States. The risks associated with just meeting
the current standard are therefore greater than the risks associated with "as is" NO2 concentrations, which are
lower than NO2 concentrations simulated to just meet the current standard.
Abt Associates Inc. 4 August 2008
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quality simulated to reflect attainment of the current and alternative NO2 ambient standards.
As described more fully below, a risk assessment based on epidemiological studies requires
baseline incidence data or baseline incidence rates and population data for the risk assessment
locations.
The characteristics that are relevant to carrying out a risk assessment based on
epidemiology studies can be summarized as follows:
A risk assessment based on epidemiology studies uses C-R functions, and therefore
requires as input (monitored) ambient NO2 concentrations.
Epidemiological studies are carried out in specific real world locations (e.g., specific
urban areas). A risk assessment focused on locations in which the epidemiologic
studies providing the C-R functions were carried out will minimize uncertainties.
A risk assessment based on epidemiological studies requires baseline incidences or
baseline incidence rates and population data for the risk assessment locations.
The methods for the NO2 risk assessment are discussed in section 3 below. The risk
assessment was implemented within a new probabilistic version of TRDVI.Risk, the
component of EPA's Total Risk Integrated Methodology (TRIM) model that estimates human
health risks.5
AIR QUALITY CONSIDERATIONS
The risk assessment includes risk estimates for three recent years of air quality ("as is"
air quality) and for air quality adjusted so that it simulates just meeting the current or
alternative NO2 standards based on that recent three-year period (2005-2007). This period
was selected to represent the most recent air quality for which complete data were available.
In order to estimate health risks associated with just meeting the current and alternative
NO2 standards, it is necessary to estimate the distribution of hourly NO2 concentrations that
would occur under any given standard. Since all locations in the United States are in
attainment of the current NO2 standard, and since compliance with the current NO2 standard is
based on examining a 2-year period, air quality data from 2006 to 2007 were used to
determine the amount of increase in NO2 concentrations that would occur if the current
standard were just met in the risk assessment location. Estimated design values were used to
determine the (upward) adjustment necessary to just meet the current NO2 standard. The
adjustment was then applied to each year of data (2006 and 2007) to estimate risks in each of
these individual years. For alternative 1-hour daily maximum standards, staff specified the
form as being the 3-year average of the 98th (or 99th) percentile of the daily maximum 1-hour
concentrations. Thus, the three-year period including 2005 to 2007 was used for analyses
involving alternative 1-hour standards. Estimated design values were used to determine the
5 TRIM.Risk was most recently applied to EPA's O3 health risk assessment. A User's Guide for the Application
of TRIM.Risk to the O3 health risk assessment (Abt Associates, 2007b) is available online at:
http://epa.gov/ttn/fera/data/trinVtrimrisk ozone ra userguide 8-6-07.pdf.
Abt Associates Inc. 5 August 2008
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upward (or downward) adjustments necessary to just meet alternative NC>2 standards, and the
adjustments were then applied to each year of data to estimate risks in each of these individual
years.
As described in section 6.2.1 of the draft REA, EPA concluded that the proportional
(linear) air quality adjustment procedure adequately represented the pattern of reductions
across the NC>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
NC>2 monitoring data, based on the 2-year period (2006-2007) NC>2 design value for the
current standard, to generate new time series of hourly NC>2 concentrations for 2006 and 2007
that simulate air quality levels that just meet the current NC>2 standard of 0.053 ppm annual
average. Because every location across the U.S. meets the current NO2 standard (see U.S.
EPA, 2007b, Figure 1), simulation of just meeting the current standard required rolling up air
quality.
The proportional air quality adjustment procedure was similarly applied in the Atlanta
MSA to the filled in 2005, 2006, and 2007 NO2 monitoring data, based on the 3-year period
(2005-2007) NC>2 design values for the alternative 1-hour standards, to generate new time
series of hourly NC>2 concentrations for 2005, 2006, and 2007 that simulate air quality levels
that just meet each of the alternative NO2 standards considered in the risk assessment over this
three year period.
Because compliance with the alternative 1-hour daily maximum standards is based on
the 3-year average of the values for the chosen metric, the air quality distribution in each of
the 3 years can and generally does vary. As a result, the risk estimates associated with air
quality just meeting a standard also will vary depending on the year chosen for the analysis.
The risk assessment for the alternative 1-hour standards includes risk estimates involving
adjustment of 2005, 2006, and 2007 air quality data to illustrate the magnitude of this year-to-
year variability in the estimates.
The risk estimates developed for the recently concluded PM and Os NAAQS reviews
represented risks associated with PM and 63 levels in excess of estimated policy-relevant
background (PRB) levels in the U.S. PRB levels of NC>2 are defined as the distribution of
NC>2 concentrations that would be observed in the U.S. in the absence of anthropogenic (man-
made) emissions of NC>2 precursors in the U.S., Canada, and Mexico. Estimates of NC^PRB
are reported in section 2.4.6 of the final ISA, and for most of the continental U.S. the PRB is
estimated to be less than 300 parts per trillion (ppt). In the Northeastern U.S., where present-
day NC>2 concentrations are highest, this amounts to a contribution of about 1% percent of the
total observed ambient NC>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 with PRB concentrations in the current NC>2 health risk
assessment.
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METHODS
The major components of the NC>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 NC>2 is associated
with a range of respiratory effects. The evidence is judged to be sufficient to infer a
likely causal relationship between short-term NC>2 exposure and adverse effects on the
respiratory system. This finding is supported by a large body of epidemiologic evidence,
in combination with findings from human and animal experimental studies (final ISA,
sections 3.1.6 and 3.1.7).
GENERAL APPROACH
As in the PM risk assessment (Abt Associates, 2005) and part of the recently
completed 63 risk assessment (Abt Associates, 2007a), the general approach used in the
NC>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, the appropriate application of
these functions in a risk assessment similarly requires the use of ambient air quality data
at fixed-site, ambient monitors. The NC>2 health risk model combines information about
NC>2 air quality for a specific urban area with C-R functions derived from
epidemiological studies and baseline incidence data for a specific health endpoint to
derive estimates of the incidence of the health endpoint attributable to ambient NC>2
concentrations during the period examined.
In the first part of the risk assessment, we estimate health effects incidence
associated with "as is" NC>2 levels. In the second part, we estimate the (increased) health
effects incidence associated with NC>2 concentrations simulated to just meet the current
NC>2 annual standard and the health effects incidence associated with NC>2 concentrations
simulated to just meet alternative 1-hour daily maximum NO2 standards in the assessment
location. In both parts, we consider the incidence of health effects associated with NC>2
concentrations in excess of 0 ppm (as opposed to in excess of PRB, as explained in
section 2.3).
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Figure 0-1. Major Components of NO2 Health Risk Assessment Based on Epidemiology Studies
Air Quality
Ambient Monitoring for
Selected Urban Areas
Air Quality Adjustment
Procedures
Current and Alternative
Proposed Standards
Concentration-Response
Human Epidemiological
Studies
Estimates of City-specific
Baseline Health Effects
Incidence
Recent ("As Is") Ambient
NO2 Levels
Changes in
Distribution of
NO2Air
Quality
Concentration -
Response
Relationships
Health
Risk
Model
Risk Estimates:
• Recent Air
Quality
• Current
Standard
• Alternative
Standards
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August 2008
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Both parts of the risk assessment may be viewed as assessing the change in
incidence of the health effect associated with a change in NC>2 concentrations from some
upper levels to specified (lower) levels - in the NO2 risk assessment, the lower level is 0
ppm in both cases. The important operational difference between the two parts is in the
upper NC>2 levels. In the first part, the upper NC>2 levels are "as is" concentrations. In
contrast, the upper NC>2 levels in the second part are the estimated NC>2 levels that would
occur when the current NO2 standard of 0.053 ppm annual average is just met in the
assessment location or when one of several alternative 1-hour daily maximum NC>2
standards is just met in this location. The second part therefore requires that a method be
developed to simulate just meeting the current or alternative standards. This method is
described in chapter 6 of the draft REA.
To estimate the incidence of a given health effect associated with "as is" ambient
NC>2 concentrations or NC>2 concentrations that just meet the current or an alternative
standard in an assessment location, the following analysis inputs are necessary:
• Air quality information including: (1) "as is" air quality data for NC>2 from
ambient monitors in the assessment location, and (2) "as is" concentrations
adjusted to simulate just meeting the specified standard. (These air quality inputs
are discussed in more detail in chapter 2 of this report and in chapter 6 of the draft
REA.)
• Concentration-response function(s), which provide an estimate of the
relationship between the health endpoint of interest and NC>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"
NC>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 (divided by 10,000, if the rate is per 10,000
population). Alternatively, if an estimate of the incidence in the location of
interest is available, that can be used instead.
These inputs are combined to estimate health effect incidence changes associated
with specified changes in NC>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 NC>2 risk assessment) is the exponential (or log-linear)
form:
y = Beftc, (3-1)
where x is the ambient NC>2 level, y is the incidence of the health endpoint of interest at
NC>2 level x, ft is the coefficient of ambient NC>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 NC>2. The relationship between a specified ambient NC>2 level, XQ, for example,
Abt Associates Inc. 2 August 2008
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and the incidence of a given health endpoint associated with that level (denoted asyo) is
then
y0=Be^. (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 XQ denote the baseline (upper) NC>2 level, and xj denote the lower NC>2
level, and.yoand.y7 denote the corresponding incidences of the health effect, we can
derive the following relationship between the change in x, Ax= (XQ- xj), and the
corresponding change in^, Ay, from equation (3-I)6:
*y = (y0-yi) = y0V-e-f&*]. (3-3)
Alternatively, the difference in health effects incidence can be calculated
indirectly using relative risk. Relative risk (RR) is a measure commonly used by
epidemiologists to characterize the comparative health effects associated with a particular
air quality comparison. The risk of ED visits for respiratory illness at ambient NC>2 level
XQ relative to the risk of ED visits for respiratory illness at ambient NC>2 level xj, for
example, may be characterized by the ratio of the two rates: the rate of ED visits for
respiratory illness among individuals when the ambient NC>2 level is x0 and the rate of ED
visits for respiratory illness among (otherwise identical) individuals when the ambient
NC>2 level is xj. This is the RR for ED visits for respiratory illness associated with the
difference between the two ambient NO2 levels, x0 and x}. Given a C-R function of the
form shown in equation (3-1) and a particular difference in ambient NC>2 levels, Ax, the
RR associated with that difference in ambient NC>2, denoted as RR-Ax, is equal to e'3Ax.
The difference in health effects incidence, Ay, corresponding to a given difference in
ambient NO2 levels, Ax, can then be calculated based on this RR-Ax as
&y = (y0-yl) = y0V-(VRR^)]. (3-4)
Equations (3-3) and (3-4) are simply alternative ways of expressing the relationship
between a given difference in ambient NC>2 levels, Ax > 0, and the corresponding
difference in health effects incidence, Ay. These health impact equations are the key
equations that combine air quality information, C-R function information, and baseline
health effects incidence information to estimate health risks related to changes in ambient
NC>2 concentrations.
If Ax < 0 - i.e., if Ax = (x! - x0) - then the relationship between Ax and Ay can be shown to be
Ay = (yl - y0) = y0 [epfa - 1]. If Ax < 0, Ay will similarly be negative. However, the magnitude of Ay
will be the same whether Ax > 0 or Ax < 0 - i.e., the absolute value of Ay does not depend on which
equation is used.
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SELECTION OF HEALTH ENDPOINT(S)
As discussed in section 3.1.6 of the final ISA, many studies have observed positive
associations between ambient NO2 concentrations and ED visits and hospitalizations for
all respiratory diseases and asthma, and these associations appear to be generally robust
and independent of the effects of ambient particles or gaseous copollutants. Noting that
exposure to NO2 has been found to result in host defense and immune system changes,
airway inflammation, and airway responsiveness, the final ISA concludes that "while not
providing specific mechanistic data linking exposure to ambient NO2 and respiratory
hospitalization or ED visits for asthma, these findings provide plausibility and coherence
for such a relationship" (section 3.1.6.5, p. 3-41).
In summarizing the evidence for a relationship between short-term exposure to NO2
and respiratory health effects, the final ISA notes that "the body of evidence from
epidemiologic studies has grown substantially since the 1993 AQCD and provided
scientific evidence that short-term exposure to NO2 is associated with a broad range of
respiratory morbidity effects, including altered lung host defense, inflammation, airway
hyperresponsiveness, respiratory symptoms, lung function decrements, and ED visits and
hospital admissions for respiratory diseases" (section 3.1.7, p. 3-41). For this risk
assessment, we are focusing on respiratory ED visits.
SELECTION OF URBAN AREA(S) AND EPIDEMIOLOGICAL
STUDIES
Several objectives were considered in selecting potential urban areas for which to
conduct the risk assessment. An urban area was considered if:
• it had sufficient air quality data for the 3-year period under consideration;
• it was a location where at least one C-R function for the selected health
endpoint had been estimated by a study that satisfied the study selection
criteria; and
• it had available relatively recent location-specific baseline incidence data,
specific to International Classification of Disease (ICD) codes, or an
equivalent illness classification system.
C-R functions for respiratory ED visits have been estimated in two urban areas in
the United States - Atlanta and New York City. The selection of an urban area to include
in the risk assessment depends in part on the decision of which epidemiological studies to
use. An epidemiological study was considered if:
• it was a published, peer-reviewed study that had been evaluated in the final ISA
for the pollutant of interest and judged adequate by EPA staff for purposes of
inclusion in the risk assessment based on that evaluation;
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• it directly measured, rather than estimated, the pollutant of interest on a reasonable
proportion of the days in the study; and
• it either did not rely on Generalized Additive Models (GAMs) using the S-Plus
software to estimate C-R functions or it appropriately re-estimated these functions
using revised methods.7
• it preferably included both single- and multi-pollutant models.
Six U.S. studies focused on ED visits and/or hospital admissions. Three of these
(Peel et al., 2005 and Tolbert et al., 2007 in Atlanta; Ito et al., 2007 in New York City)
evaluated associations with NO2 using multi-pollutant models as well as single-pollutant
models. Tolbert et al. (2007), which updated Peel et al. (2005), evaluated ED visits
among all ages in Atlanta, GA during the period of 1993 to 2004. Using single pollutant
models, the authors reported a 2% (95% CI: 1%, 2.9%) increase in respiratory ED visits
associated with a 23-ppb increase in 1-h maximum NO2 levels. In a two-pollutant model
with CO, NO2 was positive and still statistically significant (RR = 1.017, 95% CI =1.006,
1.029). In two-pollutant models with PMio and O3, and in a three-pollutant model with
both PMio and O3, NO2 was still positively associated with respiratory ED visits albeit no
longer statistically significant (RR = 1.007, 95% CI = 0.996, 1.018 in the model with
PMio; RR = 1.010, 95% CI = 0.999, 1.020 in the model with O3; and RR = 1.004, 95% CI
= 0.992, 1.015 in the model with both PMio and O3) (Tolbert, 2008).
The Atlanta study (Peel et al., 2005 and Tolbert et al., 2007) spanned 12 years,
and collected NO2 monitor data on 4,351 out of a possible 4,384 days - over 99 percent
of the days. It satisfies all of the criteria listed above for study selection.
In the study by Ito and colleagues, investigators evaluated ED visits for asthma in
New York City during the years 1999 to 2002. The authors found a 12 % (95% CI: 7%,
15%) increase in risk per 20 ppb increase in 24-hour ambient NO2. Risk estimates were
robust and remained statistically significant in multi-pollutant models that included
PM2.5, O3, CO, and SO2.
Due to time and resource constraints, EPA staff selected the Atlanta area and the
study by Tolbert et al. to conduct a focused risk assessment for ED visits. Considerations
that influenced this choice were the longer time period and more comprehensive coverage
of emergency departments in the Tolbert et al. study, the ready availability of baseline
incidence data from the authors of this study, and the EPA staffs objective of conducting
the risk assessment for the same urban area selected for the population exposure analysis.
7 The GAM S-Plus problem was discovered prior to the recent final PM risk assessment carried out as part
of the PM NAAQS review. It is discussed in the PM Criteria Document (EPA, 2004), PM Staff Paper
(EPA, 2005), and PM Health Risk Assessment Technical Support Document (Abt Associates, 2005).
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SELECTION OF CONCENTRATION-RESPONSE FUNCTIONS
Studies often report more than one estimated C-R function for the same location
and health endpoint. Sometimes models including different sets of co-pollutants are
estimated in a study; sometimes different lags are estimated.
Tolbert et al. (2007) estimated C-R functions in which NO2 was the only pollutant
entered into the health effects model (i.e., single pollutant models) as well as other C-R
functions in which NO2 and one or two co-pollutants (PMio, Os, CO) were entered into
the health effects model (i.e., multi-pollutant models). To the extent that any of the co-
pollutants present in the ambient air may have contributed to the health effects attributed
to NO2 in single pollutant models, risks attributed to NO2 might be overestimated where
C-R functions are based on single pollutant models. However, if co-pollutants are highly
correlated with NO2, their inclusion in an NO2 health effects model can lead to
misleading conclusions in identifying a specific causal pollutant. When collinearity
exists, inclusion of multiple pollutants in models often produces unstable and statistically
insignificant effect estimates for both NO2 and the co-pollutants. Given that single and
multi-pollutant models each have both potential advantages and disadvantages, with
neither type clearly preferable over the other in all cases, we report risk estimates based
on both single- and multi-pollutant models.
All of the models in Tolbert et al. (2007) used a 3-day moving average of
pollution levels (i.e., the average of 0-, 1-, and 2-day lags), so the issue of which of
several different lag structures to select does not arise. The issue of how well a given lag
structure captures the actual relationship between the pollutant and the health effect,
however, is still relevant. Models in which the pollutant-related incidence on a given day
depends only on same-day or previous-day pollutant concentration (or some variant of
those, such as a two- or three-day average concentration) necessarily assume that the
longer pattern of pollutant levels preceding the pollutant concentration on a given day
does not affect incidence of the health effect on that day. To the extent that a pollutant -
related health effect on a given day is affected by pollutant concentrations over a longer
period of time, then these models would be mis-specified, and this mis-specification
would affect the predictions of daily incidence based on the model. The extent to which
short-term NO2 exposure studies may not capture the possible impact of long-term
exposures to NO2 is not known. A number of epidemiologic studies have examined the
effects of long-term exposure to NO2 and observed associations with decrements in lung
function and partially irreversible decrements in lung function growth. The final ISA
concludes, however, that "overall, the epidemiological evidence was suggestive but not
sufficient to infer a causal relationship between long-term NO2 exposure and respiratory
morbidity" (section 3.4). Currently, there is insufficient information to adequately adjust
for the potential impact of longer-term exposure on respiratory ED visits associated with
NO2 exposures, if any, and this uncertainty should be kept in mind as one considers the
results from the short-term exposure NO2 risk assessment.
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AIR QUALITY CONSIDERATIONS
Air quality considerations are discussed briefly in section 2 of this document and
in chapter 6 of the draft REA. Here we describe those air quality considerations that are
directly relevant to the estimation of health risks in the NO2 risk assessment.
In the first part of the risk assessment, we estimate the incidence of the health
effect associated with "as is" levels of NO2 (or equivalently, the change in health effect
incidence, Ay, associated with a change in NO2 concentrations from "as is" levels of NO2
to 0 ppm). In the second part, we estimate the incidence of the health effect associated
with NO2 concentrations simulated to just meet a standard (i.e., the current NO2 standard
of 0.053 ppm annual average as well as each of several alternative 1-hour daily maximum
standards).
To estimate the incidence of a health effect associated with "as is" NO2 levels in a
location, we need a time series of hourly "as is" NO2 concentrations for that location. We
use monitor data from the Georgia Tech monitor (monitor id =131210048), the monitor
that was used in Tolbert et al. (2007), the epidemiology study from which we obtained C-
R functions (see section 3.3 above).
For the Georgia Tech monitor site, complete hourly data were available on over
93 percent of the days - 348 days in 2005, 345 days in 2006, and 340 days in 2007.
Missing air quality data were estimated by the following procedure. Where there were
consecutive strings of missing values (data gaps of less than 6 hours), missing values
were estimated by linear interpolation between the valid values at the ends of the gap.
Remaining missing values at a monitor were estimated by fitting linear regression models
for each hour of the day, with each of the other monitors, and choosing the model which
maximizes R2 for each hour of the day, subject to the constraints that R2 be greater than
0.5 and the number of regression data values is at least 50. If there were any remaining
missing values at this point, for gaps of less than 9 hours, missing values were estimated
by linear interpolation between the valid values at the ends of the gap. Any remaining
missing values were replaced with the regional mean for that hour. The annual mean,
and the 98th and 99th percentiles of daily 1-hr maximum concentrations are shown in
Table 3-1, separately for 2005, 2006, and 2007.
Table 0-1. Mean and 98th and 99th Percentiles of the Distributions of 1-Hour Daily Maximum NO2
Concentrations (in ppm) at the Georgia Tech Monitor: 2005,2006, and 2007
Year Mean 98th 99th
Percentil Percentil
e e
2005 0.0351 0.0764 0.0794
2006 0.0364 0.0660 0.0694
2007 0.0327 0.0684 0.0780
Because Tolbert et al. (2007) estimated a relationship between daily respiratory-
related ED visits and the 3-day moving average (i.e., NO2 levels on the same day, the
previous day, and the day before that) of daily 1-hour maximum NO2 concentrations, we
calculated daily 1-hour maximum NO2 concentrations at the monitor. Because our lower
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bound NC>2 concentration is 0 ppm in all cases, for each day Ax in equation (3-3) equals
the 3-day moving average of the 1-hour maximum "as is" NC>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 NC>2 levels simulated to just meet the current and alternative
standards were done analogously, using the monitor-specific series of adjusted hourly
concentrations rather than the monitor-specific series of "as is" hourly concentrations.
BASELINE HEALTH EFFECTS INCIDENCE
The most common epidemiologically-based health risk model expresses the
reduction in health risk (Ay) associated with a given reduction in NC>2 concentrations
(Ax) as a percentage of the baseline incidence (y). To accurately assess the impact of
changes in NC>2 air quality on health risk in the selected urban area, information on the
baseline incidence of the health effect (i.e., the incidence under "as is" air quality
conditions) in the selected location is therefore needed.
We obtained an estimate of the baseline incidence of respiratory ED visits in
Atlanta, GA (Tolbert, 2008). The study notes that there are 42 hospitals with EDs in the
20-county Atlanta MSA. Of these, 41 were able to provide incidence data for at least part
of the study period (1993 - 2004). For purposes of the NC>2 risk assessment, we need
incidences for the years of the risk assessment (2005 - 2007). Assuming that baseline
incidence of respiratory ED visits does not change appreciably in the span of a few years,
we used the incidence of respiratory ED visits for the most recent year in the Tolbert
study, 2004 - 121,818 respiratory ED visits (Tolbert, 2008).8 Because this baseline
incidence estimate is based on 36 hospitals, rather than the total 42 hospitals with EDs in
Atlanta, this is an underestimate of baseline incidence. This is thus a source of
downward bias in our estimates of NO2-related risk.
The specific definition of "respiratory-related" ED visits used in Tolbert et al.
(2007) included ED visits with the following respiratory illnesses as the primary
diagnosis (specified by ICD-9 diagnostic codes): asthma (493, 786.07, and 786.09),
COPD (491, 492, and 496), upper respiratory illness (460 - 465, 460.0, and 477),
pneumonia (480 - 486), and bronchiolitis (466.1, 466.11, and 466.19). The baseline
incidence given above - 121,818 - is thus a count of all ED visits with one of these ICD-
9 codes as the primary diagnosis at the 36 hospitals in the Atlanta MSA that contributed
2004 baseline incidence data to the Tolbert study.
SUMMARY OF DETERMINANTS OF THE NO2 RISK
ASSESSMENT
8 2004 was not only the most recent year for which a baseline incidence estimate was available from the
study, but it also had the most hospitals reporting - 36 out of 42 hospitals.
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The determinants of the NC>2 risk assessment can be summarized as follows:
1 Health endpoint: respiratory ED visits among all ages
1 Assessment location: Atlanta MSA
1 Epidemiological study: Tolbert et al. (2007)
1 C-R functions:
o a single-pollutant C-R function,
o two-pollutant C-R functions (with CO, PMio, and Os), and
o a three-pollutant C-R function (with both PMio and 63).
In all C-R functions the count of ED visits on a given day is related to a 3-day
moving average of NC>2 1-hour maxima (i.e., NC>2 1-hour maxima on the same
day, the previous day, and the day before that).
1 Air quality data: 1-hour maximum "as is" NC>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.
1 Baseline incidence: an estimate of the baseline incidence of respiratory ED visits
in Atlanta, GA in 2004 (the most recent year in the study) was obtained (Tolbert,
2008). The estimate, 121,818 respiratory ED visits in 2004, was based on 36 (out
of 42) hospitals that reported data.
ADDRESSING UNCERTAINTY AND VARIABILITY
Any estimation of risk associated with "as is" NC>2 concentrations, with just
meeting the current NC>2 standard, or with just meeting alternative NC>2 standards should
address both the variability and uncertainty that generally underlie such an analysis.
Uncertainty refers to the lack of knowledge regarding the actual values of model input
variables (parameter uncertainty) and of physical systems or relationships (model
uncertainty - e.g., the shapes of C-R functions). The goal of the analyst is to reduce
uncertainty to the maximum extent possible. Uncertainty can be reduced by improved
measurement and improved model formulation. In a health risk assessment, however,
significant uncertainty often remains. The degree of uncertainty can be characterized,
sometimes quantitatively. For example, the statistical uncertainty surrounding the
estimated NO2 coefficients in the C-R functions is reflected in confidence intervals
provided for the risk estimates.
Variability refers to the heterogeneity in a population or parameter. Even if there
is no uncertainty surrounding inputs to the analysis, there may still be variability. For
example, there may be variability among C-R functions describing the relationship
between NC>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, reflecting differences in the populations and/or
other factors that may affect the relationship between NC>2 and respiratory ED visits. In
general, it is possible to have uncertainty but no variability (if, for instance, there is a
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single parameter whose value is uncertain) or variability but little or no uncertainty (for
example, people's heights vary considerably but can be accurately measured with little
uncertainty).
The NC>2 risk assessment addresses variability-related concerns by using location-
specific inputs (i.e., location-specific C-R function, baseline incidence data and air
quality data). Because the NC>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 incidence data. However, future changes in
inputs have not been predicted (e.g., future baseline incidences). To address the impact
of variability in NC>2 concentrations from one year to another, we carried out the risk
assessment for the years in the three-year period under consideration - 2005, 2006, and
2007 - separately.
A number of important sources of uncertainty in the NC>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 NC>2 and the
health endpoint actually reflects a causal relationship.
o uncertainty surrounding estimates of NC>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 whether or not there is a threshold
below which no response occurs.
o uncertainty related to the transferability of NC>2 C-R functions from the
study time period to the time period selected for the risk assessment.9 A
C-R function in a study time period may not provide an accurate
representation of the C-R relationship in the analysis time period because
of
9 Uncertainty about transferability of C-R functions often results not only from differences between the
study and risk assessment time periods, but also between the study and risk assessment locations. Because
the NO2 risk assessment is being conducted in the same location as the study from which the C-R functions
were obtained, this is not a problem here.
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• the possible role of associated co-pollutants, which may vary over
time, in influencing NC>2 risk,
• temporal variation in the relationship of total ambient exposure
(both outdoor and ambient contributions to indoor exposure) to
ambient monitoring (e.g., due to changes in air conditioning usage
over time),
• changes in population characteristics (e.g., the proportions of
members of sensitive subpopulations) and population behavior
patterns over time.
• Uncertainties related to the air quality data, including the adjustment procedure
that was used to simulate just meeting the current and alternative NC>2 standards.
• Uncertainties associated with use of baseline health effects incidence information
- e.g., the extent to which the baseline incidence estimate is downward biased by
the lack of data for 6 of the 42 hospitals in the Atlanta MSA.
The specific sources of uncertainty in the NO2 risk assessment are described in detail
below and are summarized in Table 3-2.
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Table 0-2. Key Uncertainties in the NO2 Risk Assessment
Uncertainty
Causality
Empirically estimated C-R relations
Functional form of C-R relation
Lag structure of C-R relation
Transferability of C-R relations
Extrapolation of C-R relations
beyond the range of observed NO2
data
Adequacy of ambient NO2 monitors
as surrogate for population
exposure
Adjustment of air quality
Comments
Statistical association does not prove causation. However, the risk assessment considers only a
health endpoint for which the overall weight of the evidence supports the assumption that NO2 is
likely causally related based on the totality of the health effects evidence.
Because C-R functions are empirically estimated, there is uncertainty surrounding these
estimates. Omitted confounding variables could cause bias in the estimated NO2 coefficients.
However, including potential confounding variables that are highly correlated with one another
can lead to unstable estimators. Because both single- and multi-pollutant models were available,
both were used.
Statistical significance of coefficients in an estimated C-R function does not necessarily mean
that the mathematical form of the function is the best model of the true C-R relation.
The actual lag structure for short-term NO2 exposures is uncertain. Omitted lags could cause an
underestimation in the predicted incidence associated with a given reduction in NO2
concentrations.
C-R functions may not provide an adequate representation of the C-R relationship in times and
places other than those in which they were estimated. For example, populations in the
assessment location/time period may have more or fewer members of sensitive subgroups than
the location/time period in which functions were derived, which would introduce additional
uncertainty related to the use of a given C-R function in the analysis. This problem was
minimized in the NO2 risk assessment, however, because it relies on C-R functions estimated in a
recent study conducted in the assessment location.
A C-R relationship estimated by an epidemiological study may not be valid at concentrations
outside the range of concentrations observed during the study. This problem should be minimal
in the NO2 risk assessment, however, because the NO2 concentrations observed in the study from
which C-R functions were obtained covered a wide range - from 1 ppb to 181 ppb.
Possible differences in how the spatial variation in ambient NO2 levels across an urban area are
characterized in the original epidemiological study compared to the more recent ambient NO2
data used to characterize current air quality would contribute to uncertainty in the health risk
estimates. The NO2 risk assessment uses the same monitor used in the epidemiological study
from which the C-R functions were obtained, which should minimize this source of uncertainty.
The pattern and extent of daily reductions in NO2 concentrations that would result if the current
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Uncertainty
Comments
distributions to simulate just
meeting current and alternative NO2
standards.
NC>2 standard or alternative NC>2 standards were just met is not known. There remains
uncertainty about the shape of the air quality distribution of hourly levels upon just meeting an
NC>2 standard that will depend on future air quality control strategies.
Baseline health effects data
Data on baseline incidence may be uncertain for a variety of reasons. For example, location- and
age-group-specific baseline rates may not be available in all cases. Baseline incidence may
change over time for reasons unrelated to NC>2. This source of uncertainty is relatively minor in
the NC>2 risk assessment, however, because a baseline incidence estimate has been obtained from
the study authors for the assessment area. There is a known downward bias to this estimate,
however, because it is based on an incomplete set of hospitals providing ED data (36 out of 42)
in the Atlanta MSA.
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We handled uncertainties in the risk assessment as follows:
• Limitations and assumptions in estimating risks and reduced risks are clearly
stated and explained.
• The uncertainty resulting from the statistical uncertainty associated with the
estimate of the NC>2 coefficient in a C-R function was characterized by confidence
intervals around the corresponding point estimate of risk. Confidence intervals
express the range within which the true risk is likely to fall if the uncertainty
surrounding the NC>2 coefficient estimate were the only uncertainty in the
analysis. They do not, for example, reflect the uncertainty concerning whether
the NC>2 coefficients in the study period and the assessment period are the same.
Not all health effects that may result from NC>2 exposure were included. We
focused on respiratory ED visits because it was judged that there was sufficient
epidemiological and other evidence to support the hypothesis of a causal relationship.
Other health effects reported to be associated with exposure to NO2 (e.g., increased
respiratory illnesses and symptoms) are considered qualitatively in the draft REA. Thus,
it is important to recognize that the NC>2 risk assessment represents only a portion of the
health risks associated with NC>2 exposures.
Concentration-response functions
The C-R function is a key element of the NC>2 risk assessment. The quality of the
risk assessment depends, in part, on (1) whether the C-R functions used in the risk
assessment are good estimates of the relationship between the population health response
and ambient NO2 concentration in the study location (which, in this case, is the same as
the assessment location), (2) how applicable these functions are to the analysis period,
and (3) the extent to which these relationships apply beyond the range of the NC>2
concentrations from which they were estimated. These issues are discussed in the
subsections below.
Uncertainty associated with the appropriate model form
The relationship between a health endpoint and NC>2 can be characterized in terms
of the form of the function describing the relationship - e.g., linear, log-linear, or logistic
- and the value of the NO2 coefficient in that function. Although most epidemiological
studies estimated NC>2 coefficients in log-linear models, there is still substantial
uncertainty about the correct functional form of the relationship between NC>2 and
respiratory ED visits - especially at the low end of the range of NC>2 values, where data
are generally too sparse to discern possible thresholds. While there are likely biological
thresholds in individuals for specific health responses, the available epidemiological
studies generally have not supported or refuted the existence of thresholds at the
population level for NC>2 exposures within the range of air quality observed in the studies.
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Uncertainty associated with the estimated concentration-response functions in the
study location
The uncertainty associated with an estimate of the NO2 coefficient in a C-R
function reported by a study depends on the sample size and the study design. The final
ISA has evaluated the substantial body of NO2 epidemiological studies. In general,
critical considerations in evaluating the design of an epidemiological study include the
adequacy of the measurement of ambient NO2, the adequacy of the health effects
incidence data, and the consideration of potentially important health determinants and
potential confounders and effect modifiers such as:
• other pollutants;
• weather variables (e.g., temperature extremes);
• exposure to other health risks, such as smoking and occupational exposure; and
• demographic characteristics, including age, sex, socioeconomic status, and access to
medical care.
The possible confounding effects of copollutants, including other criteria air
pollutants, has often been noted as a problem in air pollutant risk assessments,
particularly when these other pollutants are highly correlated with the pollutant of
interest. NC>2 was only moderately correlated with the other pollutants considered in the
models that produced the C-R functions that are used in the risk assessment (see Tolbert
et al., 2007, Table 3), although it was fairly highly correlated (corr.=0.7) with CO. The
issue of possible confounding by copollutants is discussed in more detail in the final ISA.
One of the criteria for selecting studies addresses the adequacy of the
measurement of ambient NO2. This criterion was that NO2 was directly measured, rather
than estimated, on a reasonable proportion of the days in the study. This criterion was
designed to minimize error in the estimated NO2 coefficients in the C-R functions used in
the risk assessment. NO2 was measured in the Tolbert study on over 93 percent of the
days of the study period, so this criterion was well satisfied.
Ambient concentrations at central monitors, however, may not provide a good
representation of personal exposures. The final ISA identifies the following three
components to exposure measurement error: (1) the use of average population rather than
individual exposure data; (2) the difference between average personal ambient exposure
and ambient concentrations at central monitoring sites; and (3) the difference between
true and measured ambient concentrations (final ISA, section 1.3.2, p. 1-5). While a C-R
function may understate the effect of personal exposures to NO2 on the incidence of a
health effect, however, it will give an unbiased estimate of the effect of ambient
concentrations on the incidence of the health effect, if the ambient concentrations at
monitoring stations provide an unbiased estimate of the ambient concentrations to which
the population is exposed. In this case, if NO2 is actually the causal agent, the
understatement of the impact of personal exposures isn't an issue (since EPA regulates
ambient concentrations rather than personal exposures). If NO2 is not the causal agent,
however, then there is a problem of confounding copollutants or other factors, so that
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reducing ambient NC>2 concentrations might not result in the expected reductions in the
health effect. A more comprehensive discussion of exposure measurement error and its
potential impact on the NC>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 associated with the C-R function
estimated in that study, beyond that reflected in the confidence or credible interval. It
may result in either over- or underestimates of risk associated with ambient NC>2
concentrations in the location in which the study was carried out. Techniques for
addressing the problem of confounding factors and other study design issues have
improved over the years, however, and the epidemiological studies currently available for
use in the NC>2 risk assessment provide a higher level of confidence in study quality than
ever before.
When a study is conducted in a single location, the problem of possible
confounding co-pollutants may be particularly difficult, if co-pollutants are highly
correlated in the study location. Single-pollutant models, which omit co-pollutants, may
produce overestimates of the NC>2 effect, if some of the effects of other pollutants
(omitted from the model) are falsely attributed to NO2. Statistical estimates of an NO2
effect based on a multi-pollutant model can be more uncertain, and even statistically
insignificant, if the co-pollutants included in the model are highly correlated with NC>2.
As a result of these considerations, we report risk estimates based on both the single-
pollutant and multi-pollutant models from Tolbert et al. (2007).
Applicability of concentration-response functions in different locations and/or time
periods
The relationship between ambient NC>2 concentration and the incidence of a given
health endpoint in the population (the population health response) depends on (1) the
relationship between ambient NC>2 concentration and personal exposure to ambient
generated NC>2 and (2) the relationship between personal exposure to ambient-generated
NC>2 and the population health response. Both of these are likely to vary to some degree
from one location and/or time period to another. The relationship between ambient NC>2
concentration and personal exposure to ambient-generated NC>2 will depend on patterns
of behavior, such as the amount of time spent outdoors, as well as on factors affecting the
extent to which ambient-generated NC>2 infiltrates into indoor environments. The
relationship between personal exposure to ambient-generated NC>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 NC>2 air pollution. For instance, people with preexisting conditions
such as asthma are probably more susceptible to the adverse effects of exposure to NC>2,
and populations may vary from one location and/or time period to another in the
prevalence of specific diseases. Also, some age groups may be more susceptible than
others, and population age distributions may also vary both spatially and temporally. In
the NC>2 risk assessment we avoid the uncertainty associated with inter-locational
Abt Associates Inc. 16 August 2008
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differences, however, by using C-R functions that were estimated in the assessment area.
In addition, although we cannot completely eliminate possible temporal changes, we
minimize the uncertainty associated with such changes by using relatively recent baseline
incidence data.
Extrapolation beyond observed air quality levels
Although a C-R function describes the relationship between ambient NO2 and a
given health endpoint for all possible NO2 levels (potentially down to zero), the
estimation of a C-R function is based on real ambient NO2 values that are limited to the
range of NO2 concentrations in the location in which the study was conducted. Thus,
uncertainty in the shape of the estimated C-R function increases considerably outside the
range of NO2 concentrations observed in the study.
Because we are interested in the effects of NO2 down to 0 ppm, the NO2 risk
assessment assumes that the estimated C-R functions adequately represent the true C-R
relationship down to 0 ppm in the assessment location. However, although the observed
NO2 concentrations in Tolbert et al. (2007) did not go down to 0 ppm, the study authors
reported the minimum 1-hour NO2 level observed in their study to be 1 ppb (or 0.001
ppm) (and the maximum to be 181 ppb), so the uncertainty resulting from extrapolation
to levels below those air quality levels observed in the study should be minimal.
The C-R relationship may also be less certain towards the upper end of the
concentration range being considered in a risk assessment, particularly if the NO2
concentrations in the assessment location/time period exceed the NO2 concentrations
observed in the study location/time period. Even though it may be reasonable to model
the C-R relationship as log-linear over the ranges of NO2 concentrations typically
observed in epidemiological studies, it may not be log-linear over the entire range of NO2
levels at the location considered in the NO2 risk assessment. However, because the study
was carried out in the risk assessment location and is relatively recent, the uncertainty
resulting from extrapolation to levels above those air quality levels observed in the study
should similarly be minimal.
The air quality data
Adequacy ofNO2 air quality data
Ideally, the measurement of average hourly ambient NO2 concentrations in the
study location is unbiased. In this case, unbiased risk predictions in the assessment
location depend, in part, on an unbiased measurement of average hourly ambient NO2
concentrations in the assessment location as well. If, however, the measurement of
average hourly ambient NO2 concentrations in the study location is biased, unbiased risk
predictions in the assessment location are still possible if the measurement of average
hourly ambient NO2 concentrations in the assessment location incorporates the same bias
as exists in the study location measurements. Because the NO2 risk assessment is using
the same NO2 monitor as was used in Tolbert et al. (2007), the estimates of risk should
Abt Associates Inc. 17 August 2008
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avoid any bias as a result of the monitor estimates of average hourly ambient NO2
concentrations in the risk assessment location.
Another potential source of uncertainty is missing air quality data. Although NO2
concentrations were not available for all hours of the 3 -year period chosen for the NO2
risk assessment in the assessment location, they were available for all hours on most
days. In particular, complete hourly data were available on over 93 percent of the days -
348 days in 2005, 345 days in 2006, and 340 days in 2007. Missing NO2 concentrations
were filled in, as described above in section 3.5.
The results of the risk assessment are generalizable to other years only to the
extent that ambient NO2 levels in the available data are similar to ambient NO2 levels in
those other years. A substantial difference between NO2 levels in the years used in the
risk assessment and NO2 levels in the other years could imply a substantial difference in
predicted incidences of health effects.
Simulation of reductions in NO2 concentrations to just meet the current or an
alternative standard
The pattern of hourly NO2 concentrations that would result if the current NO2
standard or an alternative standard were just met in the assessment location is, of course,
not known. This therefore adds uncertainty to estimates of risk when NO2 concentrations
just meet a specified standard.
Although the health risk assessment uses air quality data from three years, 2005,
2006, and 2007, it simulates just attaining a standard in each year separately, since we are
estimating annual reduced health risks. Design values based on the most recent three-
year period available are used to determine the amount of adjustment to apply to each of
these years. As can be seen in Table 3-1, the distributions of NO2 concentrations in the
three years are similar.
Baseline health effects incidence
The C-R functions used in the NO2 risk assessment are log-linear (see equation 3-
1 in section 3.1). Given this functional form, the percent change in incidence of a health
effect corresponding to a change in NO2 depends only on the change in NO2 levels (and
not the actual value of either the initial or final NO2 concentration). This percent change
is multiplied by a baseline incidence, yo, in order to determine the change in health effects
incidence, as shown in equation (3-3) in section 3.1:
Predicted changes in incidence therefore depend on the baseline incidence of the health
effect.
Abt Associates Inc. 18 August 2008
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Quality of incidence data
As noted in section 3.7 above, we obtained an estimate of the baseline incidence
of respiratory ED visits in Atlanta, GA (Tolbert, 2008). There are 42 hospitals with EDs
in the 20-county Atlanta MSA, but not all 42 contributed incidence data in all of the years
of the Tolbert study (1993 - 2004). The most recent year of the study (2004) had an
estimate of baseline incidence of respiratory ED visits in Atlanta based on data from 36
hospitals. Although this was the largest number of hospitals reporting in any single year
of the study, it is still not the entire 42 hospitals with EDs in the study (and risk
assessment) area. The estimate of baseline incidence in 2004, which is used as the
estimate of baseline incidence in the NC>2 risk assessment for 2005 - 2007, is thus an
underestimate. This underestimate of baseline incidence is therefore a source of
downward bias in the estimates of NO2-related respiratory ED visits.
A minor uncertainty surrounding hospital or ED visit baseline incidence estimates
sometimes arises if these estimates are based on the reporting of hospitals within an
assessment area. Hospitals report the numbers of ICD code-specific discharges in a given
year. If people from outside the assessment area use these hospitals or EDs, and/or if
residents of the assessment area use hospitals or EDs outside the assessment area, these
rates will not accurately reflect the numbers of residents of the assessment area who were
admitted to the hospital or ED for specific illnesses during the year, the rates that are
desired for the risk assessment. This problem is partially avoided in Tolbert et al. (2007)
because only residents of the Atlanta MSA, determined by residential zip code at the time
of the ED visit, were included in the study. To the extent that residents of the Atlanta
MSA visited EDs outside the Atlanta MSA, this would tend to downward bias the
estimates of NCVrelated risk of respiratory-related ED visits. However, this is likely to
be a very minor problem because emergency visits are likely to be made to the closest ED
available, which, for residents of the Atlanta MSA are likely to be within that MSA.
Regardless of the data source, if actual incidences are higher than the incidences
used, risks will be underestimated. If actual incidences are lower than the incidences
rates used, then risks will be overestimated.
Both morbidity and mortality rates change over time for various reasons. One of
the most important of these is that population age distributions change over time. The old
and the extremely young are more susceptible to many health problems than is the
population as a whole. The most recent available data were used in the NO2 risk
assessment. However, the average age of the population in the assessment location will
increase as post-World War II children age. Alternatively, if Atlanta experiences rapid
in-migration, as is currently occurring in much of the South and West, it may tend to have
a decreasing mean population age and corresponding changes in incidence rates and risk.
Consequently, to the extent that respiratory-related ED visits are age-related, the baseline
incidence rate may change over time. However, recent data were used in all cases, so
temporal changes are not expected to be a large source of uncertainty.
Abt Associates Inc. 19 August 2008
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Lack of daily health effects incidences
Both ambient NO2 levels and the daily health effects incidence rates
corresponding to ambient NC>2 levels vary somewhat from day to day. Those analyses
based on C-R functions estimated by short-term exposure studies calculate daily changes
in incidence and sum them over the days of the year to predict a total change in health
effect incidence during the year. However, only annual baseline incidence is available.
Average daily baseline incidences, necessary for short-term daily C-R functions, were
calculated by dividing the annual incidence by the number of days in the year for which
the baseline incidences were obtained. To the extent that NC>2 affects health, however,
actual incidence rates would be expected to be somewhat higher than average on days
with high NC>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 lower than average on days with low NC>2
concentrations; using an average daily incidence would therefore result in overestimating
the changes in incidence on low NC>2 days. Both effects would be expected to be small,
however, and should largely cancel one another out.
RESULTS
Results are expressed as (1) incidence of respiratory-related ED visits, (2)
incidence of respiratory-related ED visits per 100,000 population, and (3) percent of total
incidence of respiratory-related ED visits. Each form of result is shown in three tables,
one for each of the three years (2005, 2006, and 2007) of air quality data used in the
analysis. As noted in section 2.3, because the current annual average standard is based on
two years, the adjustment to simulate just meeting the current standard was applied only
to two years, 2006 and 2007. Therefore, results tables for 2005 do not include results
associated with just meeting the current standard. The alternative 1-hour daily maximum
standards, in contrast, have the form of the 3-year average of the 98th (or 99th) percentile
of the daily maximum 1-hour concentrations. Thus, the adjustment to simulate just
meeting these alternative 1-hour daily maximum standards was applied to each of the
three years, 2005, 2006 and 2007. Therefore, results tables for 2006 and 2007 include
results associated with just meeting the alternative 1-hour daily maximum standards as
well as results associated with just meeting the current standard. All results tables
include results associated with "as is" NC>2 concentrations.
Tables 4-1 through 4-3 show results expressed as incidence of respiratory-related
ED visits for 2005, 2006, and 2007, respectively. Tables 4-4 through 4-6 show results
expressed as incidence of respiratory-related ED visits per 100,000 population for each of
the three years; and Tables 4-7 through 4-9 show results expressed as percent of total
incidence of respiratory-related ED visits for each of the three years. Figure 4-1 shows
the trends over both years and air quality scenarios, based on the single-pollutant model.
Abt Associates Inc. 20 August 2008
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Table 0-1. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2 Concentrations
that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations*
Other
Pollutants in
Model
none
CO
03
PM10
PM10, O3
Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concentrations that Just Meet
Alternative Standards**
"as is"
3600
(1900-5300)
3100
(1000-5100)
1800
(-100-3700)
1300
(-700 - 3300)
700
(-1400-2800)
Alternative 98th percentile 1-hr daily maximum standards
(ppm)
0.05***
2600
(1400-3800)
2200
(700-3600)
1300
(-100-2600)
900
(-500-2300)
500
(-1000-2000)
0.1
5100
(2700 - 7400)
4300
(1500-7200)
2600
(-100-5200)
1800
(-1000-4600)
1000
(-2000 - 4000)
0.15
7500
(4100-10900)
6400
(2200-10500)
3900
(-200 - 7700)
2700
(-1600-6800)
1600
(-3000 - 5900)
0.2
9900
(5400-14300)
8500
(2900-13800)
5100
(-200-10200)
3600
(-2100-9000)
2100
(-4000 - 7800)
Alternative 99th percentile 1-hr daily maximum standards
(ppm)
0.05
2400
(1300-3500)
2000
(700 - 3400)
1200
(-100-2500)
800
(-500 - 2200)
500
(-900-1900)
0.1
4700
(2500-6900)
4000
(1400-6700)
2400
(-100-4900)
1700
(-1000-4300)
1000
(-1800-3700)
0.15
7000
(3800-10200)
6000
(2000-9800)
3600
(-200 - 7200)
2500
(-1500-6400)
1500
(-2800 - 5500)
0.2
9300
(5000-13300)
7900
(2700-12900)
4800
(-200 - 9500)
3400
(-1900-8400)
1900
(-3700 - 7300)
*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results
corresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the
daily 1-hr, maximum NO2 concentration and apply to all ages.
**lncidence was quantified down to 0 ppb. Incidences are rounded to the nearest 100.
***Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual
nth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.
Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.
Abt Associates Inc.
21
August 2008
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Table 0-2. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2 Concentrations
that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations*
Other
Pollutants in
Model
none
CO
03
PM10
PM10, 03
Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concentrations that Just Meet the Current and
Alternative Standards**
"as is"
3800
(2000 - 5500)
3200
(1100-5300)
1900
(-100-3900)
1300
(-800 - 3400)
800
(-1500-2900)
current annual
standard
10900
(5900-15700)
9400
(3200-15200)
5600
(-300 - 1 1 200)
4000
(-2300 - 9900)
2300
(-4400 - 8600)
Alternative 98th percentile 1-hr daily maximum standards
(ppm)
0.05***
2700
(1 400 - 3900)
2300
(800 - 3800)
1400
(-100-2700)
900
(-500 - 2400)
500
(-1000-2100)
0.1
5300
(2800 - 7700)
4500
(1 500 - 7400)
2700
(-100-5400)
1900
(-1100-4800)
1100
(-2100-4100)
0.15
7800
(4200 - 1 1 300)
6700
(2300 - 1 1 000)
4000
(-200 - 8000)
2800
(-1600-7100)
1600
(-3100-6200)
0.2
10300
(5600-14800)
8800
(3000-14400)
5300
(-200 - 1 0600)
3700
(-2200 - 9400)
2200
(-4200-8100)
Alternative 99th percentile 1-hr daily maximum standards
(ppm)
0.05
2500
(1300-3600)
2100
(700 - 3500)
1300
(-100-2600)
900
(-500 - 2300)
500
(-1000-1900)
0.1
4900
(2600 - 7200)
4200
(1400-6900)
2500
(-1 00 - 51 00)
1800
(-1000-4500)
1000
(-1900-3900)
0.15
7300
(3900 - 1 0600)
6200
(2100-10200)
3700
(-200 - 7500)
2600
(-1500-6600)
1500
(-2900 - 5700)
0.2
9600
(5200 - 1 3900)
8200
(2800-13400)
4900
(-200 - 9900)
3500
(-2000 - 8700)
2000
(-3900 - 7600)
*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results corresponding
to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the daily 1-hr, maximum NO2
concentration and apply to all ages.
"Incidence was quantified down to 0 ppb. Incidences are rounded to the nearest 100.
""""Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual nth percentile 1-
hr daily maxima over a 3-year period be at or below m ppm.
Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.
Abt Associates Inc.
22
August 2008
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Table 0-3. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2 Concentrations
that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations*
Other
Pollutants in
Model
none
CO
03
PM10
PM10, 03
Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concentrations that Just Meet the Current and
Alternative Standards**
"as is"
3400
(1800-4900)
2900
(1000-4800)
1700
(-100-3500)
1200
(-700 - 3000)
700
(-1300-2600)
current annual
standard
9800
(5300-14200)
8400
(2900-13700)
5100
(-200-10100)
3600
(-21 00 - 8900)
2100
(-4000 - 7800)
Alternative 98th percentile 1-hr daily maximum standards
(ppm)
0.05***
2400
(1 300 - 3500)
2000
(700 - 3400)
1200
(-100-2500)
800
(-500 - 2200)
500
(-900-1900)
0.1
4700
(2500 - 6900)
4000
(1300-6700)
2400
(-100-4900)
1700
(-1000-4300)
1000
(-1800-3700)
0.15
7000
(3800-10200)
6000
(2000 - 9900)
3600
(-200 - 7200)
2500
(-1 500 - 6400)
1500
(-2800 - 5500)
0.2
9300
(5000-13400)
7900
(2700-12900)
4800
(-200 - 9500)
3400
(-1900-8400)
1900
(-3700 - 7300)
Alternative 99th percentile 1-hr daily maximum standards
(ppm)
0.05
2200
(1200-3300)
1900
(600 - 3200)
1100
(-100-2300)
800
(-400 - 2000)
500
(-900-1700)
0.1
4400
(2400 - 6400)
3800
(1300-6200)
2200
(-1 00 - 4500)
1600
(-900 - 4000)
900
(-1700-3500)
0.15
6500
(3500 - 9500)
5600
(1900-9200)
3300
(-200 - 6700)
2400
(-1400-5900)
1400
(-2600 - 51 00)
0.2
8600
(4700 - 1 2500)
7400
(2500-12100)
4400
(-200 - 8900)
3100
(-1800-7800)
1800
(-3400 - 6800)
*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results corresponding
to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the daily 1-hr, maximum NO2
concentration and apply to all ages.
"Incidence was quantified down to 0 ppb. Incidences are rounded to the nearest 100.
""""Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual nth percentile 1-
hr daily maxima over a 3-year period be at or below m ppm.
Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.
Abt Associates Inc.
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August 2008
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Table 0-4. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO2 Concentrations and
NO2 Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations*
Other
Pollutants in
Model
none
CO
03
PM10
PMio, O3
Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As is" NO2 Concentrations and NO2
Concentrations that Just Meet Alternative Standards**
"as is"
240
(130-360)
210
(70-340)
120
(-10-250)
90
(-50 - 220)
50
(-90-190)
Alternative 98th percentile 1-hr daily maximum standards
(ppm)
0.05***
170
(90-250)
150
(50-250)
90
(0-180)
60
(-40-160)
40
(-70-140)
0.1
340
(180-500)
290
(100-480)
170
(-10-350)
120
(-70-310)
70
(-130-270)
0.15
510
(270-730)
440
(150-710)
260
(-10-520)
180
(-110-460)
110
(-200 - 400)
0.2
670
(360 - 960)
570
(190-930)
340
(-20 - 690)
240
(-140-610)
140
(-270 - 530)
Alternative 99th percentile 1-hr daily maximum standards
(ppm)
0.05
160
(90-240)
140
(50-230)
80
(0-170)
60
(-30-150)
30
(-60-130)
0.1
320
(170-460)
270
(90-450)
160
(-10-330)
110
(-70-290)
70
(-120-250)
0.15
470
(250 - 690)
410
(140-660)
240
(-10-490)
170
(-100-430)
100
(-190-370)
0.2
620
(340 - 900)
540
(180-870)
320
(-10-640)
230
(-130-570)
130
(-250 - 490)
*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results
corresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the
daily 1-hr, maximum NO2 concentration and apply to all ages.
**lncidence was quantified down to 0 ppb. Incidences per 100,000 population are rounded to the nearest ten.
***Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual
nth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.
Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.
Abt Associates Inc.
24
August 2008
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Table 0-5. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO2 Concentrations and
NO2 Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations*
Other
Pollutants in
Model
none
CO
03
PM10
PM10, 03
Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As is" NO2 Concentrations and NO2 Concentrations that Just Meet
the Current and Alternative Standards**
"as is"
250
(1 40 - 370)
220
(70 - 360)
130
(-10-260)
90
(-50 - 230)
50
(-1 00 - 200)
current annual
standard
740
(400-1060)
630
(210-1030)
380
(-20 - 760)
270
(-1 60 - 670)
150
(-300 - 580)
Alternative 98th percentile 1-hr daily maximum standards
(ppm)
0.05***
180
(100-260)
150
(50 - 260)
90
(0 - 1 90)
60
(-40-160)
40
(-70-140)
0.1
360
(190-520)
300
(100-500)
180
(-1 0 - 370)
130
(-70 - 320)
70
(-140-280)
0.15
530
(280 - 760)
450
(150-740)
270
(-10-540)
190
(-110-480)
110
(-210-420)
0.2
700
(380 - 1 000)
600
(200 - 970)
360
(-20-710)
250
(-150-630)
150
(-280 - 550)
Alternative 99th percentile 1-hr daily maximum standards
(ppm)
0.05
170
(90 - 250)
140
(50 - 240)
80
(0-170)
60
(-30-150)
30
(-60-130)
0.1
330
(1 80 - 480)
280
(90 - 470)
170
(-10-340)
120
(-70 - 300)
70
(-130-260)
0.15
490
(260-710)
420
(140-690)
250
(-10-510)
180
(-1 00 - 450)
100
(-190-390)
0.2
650
(350 - 940)
560
(190-910)
330
(-20 - 670)
240
(-140-590)
140
(-260 - 51 0)
*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results corresponding
to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the daily 1-hr, maximum NO2
concentration and apply to all ages.
"Incidence was quantified down to 0 ppb. Incidences per 100,000 population are rounded to the nearest ten.
"""Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual nth percentile 1-
hr daily maxima over a 3-year period be at or below m ppm.
Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.
Abt Associates Inc.
25
August 2008
-------
Table 0-6. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO2 Concentrations and
NO2 Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations*
Other
Pollutants in
Model
none
CO
03
PM10
PM10, 03
Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As is" NO2 Concentrations and NO2 Concentrations that Just Meet
the Current and Alternative Standards**
"as is"
230
(1 20 - 330)
190
(60 - 320)
120
(-10-230)
80
(-50-210)
50
(-90-180)
current annual
standard
660
(360 - 960)
570
(190-930)
340
(-20 - 680)
240
(-1 40 - 600)
140
(-270 - 520)
Alternative 98th percentile 1-hr daily maximum standards
(ppm)
0.05***
160
(90 - 240)
140
(50 - 230)
80
(0 - 1 70)
60
(-30-150)
30
(-60-130)
0.1
320
(170-470)
270
(90 - 450)
160
(-1 0 - 330)
110
(-70 - 290)
70
(-120-250)
0.15
470
(260 - 690)
410
(140-670)
240
(-10-490)
170
(-100-430)
100
(-190-370)
0.2
630
(340 - 900)
540
(180-870)
320
(-10-640)
230
(-130-570)
130
(-250 - 490)
Alternative 99th percentile 1-hr daily maximum standards
(ppm)
0.05
150
(80 - 220)
130
(40-210)
80
(0-150)
50
(-30-140)
30
(-60-120)
0.1
300
(1 60 - 430)
250
(80 - 420)
150
(-10-310)
110
(-60 - 270)
60
(-120-230)
0.15
440
(240 - 640)
380
(1 30 - 620)
230
(-10-450)
160
(-90 - 400)
90
(-170-350)
0.2
580
(310-840)
500
(170-820)
300
(-10-600)
210
(-120-530)
120
(-230 - 460)
*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results corresponding
to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the daily 1-hr, maximum NO2
concentration and apply to all ages.
"Incidence was quantified down to 0 ppb. Incidences per 100,000 population are rounded to the nearest ten.
"""Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual nth percentile 1-
hr daily maxima over a 3-year period be at or below m ppm.
Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.
Abt Associates Inc.
26
August 2008
-------
Table 0-7. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2
Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations*
Other
Pollutants in
Model
none
CO
03
PM10
PMio, O3
Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concentrations that
Just Meet Alternative Standards**
"as is"
3%
(1.6% -4. 3%)
2.5%
(0.8% - 4.2%)
1.5%
(-0.1% -3.1%)
1.1%
(-0.6% -2.7%)
0.6%
(-1.1% -2. 3%)
Alternative 98th percentile 1-hr daily maximum standards
(ppm)
0.05***
2.1%
(1.1% -3.1%)
1.8%
(0.6% - 3%)
1.1%
(0% - 2.2%)
0.8%
(-0.4% -1.9%)
0.4%
(-0.8% -1.7%)
0.1
4.2%
(2.2% -6.1%)
3.6%
(1.2% -5.9%)
2.1%
(-0.1% -4. 3%)
1.5%
(-0.9% -3. 8%)
0.9%
(-1.6% -3. 3%)
0.15
6.2%
(3.3% - 8.9%)
5.3%
(1.8% -8.7%)
3.2%
(-0.1% -6. 3%)
2.2%
(-1.3% -5.6%)
1.3%
(-2.5% -4. 9%)
0.2
8.1%
(4.4% -11. 7%)
7%
(2.4% -11. 3%)
4.2%
(-0.2% - 8.4%)
3%
(-1.7% -7.4%)
1.7%
(-3.3% -6.4%)
Alternative 99th percentile 1-hr daily maximum standards
(ppm)
0.05
2%
(1%-2.9%)
1.7%
(0.6% - 2.8%)
1%
(0% - 2%)
0.7%
(-0.4% -1.8%)
0.4%
(-0.8% -1.5%)
0.1
3.9%
(2.1% -5. 7%)
3.3%
(1.1% -5. 5%)
2%
(-0.1% -4%)
1.4%
(-0.8% - 3.5%)
0.8%
(-1.5% -3%)
0.15
5.8%
(3.1% -8. 3%)
4.9%
(1.7% -8.1%)
2.9%
(-0.1% -5.9%)
2.1%
(-1.2% -5.2%)
1.2%
(-2. 3% -4. 5%)
0.2
7.6%
(4.1% -10. 9%)
6.5%
(2.2% -10.6%)
3.9%
(-0.2% -7.8%)
2.8%
(-1.6% -6.9%)
1 .6%
(-3.1% -6%)
*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results
corresponding to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the
daily 1-hr, maximum NO2 concentration and apply to all ages.
**lncidence was quantified down to 0 ppb. Percents are rounded to the nearest tenth.
***Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual
nth percentile 1-hr daily maxima over a 3-year period be at or below m ppm.
Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.
Abt Associates Inc.
27
August 2008
-------
Table 0-8. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2
Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations*
Other
Pollutants in
Model
none
CO
03
PM10
PM10, 03
Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concentrations that Just Meet the
Current and Alternative Standards**
"as is"
3.1%
(1 .6% - 4.5%)
2.6%
(0.9% - 4.4%)
1 .6%
(-0.1% -3.2%)
1.1%
(-0.6% - 2.8%)
0.6%
(-1 .2% - 2.4%)
current annual
standard
9%
(4.9% -12.9%)
7.7%
(2.6% -12.5%)
4.6%
(-0.2% - 9.2%)
3.3%
(-1 .9% - 8.2%)
1 .9%
(-3.6% -7.1%)
Alternative 98th percentile 1-hr daily maximum standards
(ppm)
0.05***
2.2%
(1 .2% - 3.2%)
1 .9%
(0.6% -3.1%)
1.1%
(-0.1% -2.3%)
0.8%
(-0.4% - 2%)
0.4%
(-0.8% - 1 .7%)
0.1
4.3%
(2.3% - 6.3%)
3.7%
(1.2% -6.1%)
2.2%
(-0.1% -4.5%)
1 .6%
(-0.9% - 3.9%)
0.9%
(-1 .7% - 3.4%)
0.15
6.4%
(3.5% - 9.3%)
5.5%
(1 .8% - 9%)
3.3%
(-0.2% - 6.6%)
2.3%
(-1 .3% - 5.8%)
1 .3%
(-2.5% -5.1%)
0.2
8.5%
(4.6% -12.2%)
7.3%
(2.5% - 1 1 .8%)
4.4%
(-0.2% - 8.7%)
3.1%
(-1 .8% - 7.7%)
1 .8%
(-3.4% - 6.7%)
Alternative 99th percentile 1-hr daily maximum standards
(ppm)
0.05
2%
(1.1% -3%)
1 .7%
(0.6% - 2.9%)
1%
(0%-2.1%)
0.7%
(-0.4% - 1 .8%)
0.4%
(-0.8% - 1 .6%)
0.1
4%
(2.2% - 5.9%)
3.4%
(1 .2% - 5.7%)
2.1%
(-0.1% -4.1%)
1 .4%
(-0.8% - 3.7%)
0.8%
(-1 .6% - 3.2%)
0.15
6%
(3.2% - 8.7%)
5.1%
(1 .7% - 8.4%)
3.1%
(-0.1% -6.2%)
2.2%
(-1 .2% - 5.4%)
1 .2%
(-2.4% - 4.7%)
0.2
7.9%
(4.3% - 1 1 .4%)
6.8%
(2.3% -11%)
4.1%
(-0.2% -8.1%)
2.9%
(-1 .7% - 7.2%)
1 .6%
(-3.2% - 6.2%)
*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results corresponding
to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the daily 1-hr, maximum NO2
concentration and apply to all ages.
"Incidence was quantified down to 0 ppb. Percents are rounded to the nearest tenth.
"""Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual nth percentile 1-
hr daily maxima over a 3-year period be at or below m ppm.
Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.
Abt Associates Inc.
28
August 2008
-------
Table 0-9. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2
Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations*
Other
Pollutants in
Model
none
CO
03
PM10
PM10, 03
Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As is" NO2 Concentrations and NO2 Concentrations that Just Meet the
Current and Alternative Standards**
"as is"
2.8%
(1 .5% - 4%)
2.4%
(0.8% - 3.9%)
1 .4%
(-0.1% -2.8%)
1%
(-0.6% - 2.5%)
0.6%
(-1.1% -2.2%)
current annual
standard
8.1%
(4.4% - 1 1 .6%)
6.9%
(2.3% - 1 1 .3%)
4.1%
(-0.2% - 8.3%)
2.9%
(-1 .7% - 7.3%)
1 .7%
(-3.2% - 6.4%)
Alternative 98th percentile 1-hr daily maximum standards
(ppm)
0.05***
2%
(1%-2.9%)
1 .7%
(0.6% - 2.8%)
1%
(0% - 2%)
0.7%
(-0.4% - 1 .8%)
0.4%
(-0.8% - 1 .5%)
0.1
3.9%
(2.1% -5.7%)
3.3%
(1.1% -5.5%)
2%
(-0.1% -4%)
1 .4%
(-0.8% - 3.5%)
0.8%
(-1 .5% - 3%)
0.15
5.8%
(3.1% -8.4%)
4.9%
(1.7% -8.1%)
2.9%
(-0.1% -5.9%)
2.1%
(-1 .2% - 5.2%)
1 .2%
(-2.3% - 4.5%)
0.2
7.6%
(4.1% -11%)
6.5%
(2.2% -10.6%)
3.9%
(-0.2% - 7.8%)
2.8%
(-1 .6% - 6.9%)
1 .6%
(-3% - 6%)
Alternative 99th percentile 1-hr daily maximum standards
(ppm)
0.05
1 .8%
(1%-2.7%)
1 .6%
(0.5% - 2.6%)
0.9%
(0% - 1 .9%)
0.6%
(-0.4% - 1 .7%)
0.4%
(-0.7% - 1 .4%)
0.1
3.6%
(1 .9% - 5.3%)
3.1%
(1%-5.1%)
1 .8%
(-0.1% -3.7%)
1 .3%
(-0.7% - 3.3%)
0.7%
(-1 .4% - 2.8%)
0.15
5.4%
(2.9% - 7.8%)
4.6%
(1 .5% - 7.5%)
2.7%
(-0.1% -5.5%)
1 .9%
(-1.1% -4.9%)
1.1%
(-2.1% -4.2%)
0.2
7.1%
(3.8% -10.2%)
6.1%
(2% - 9.9%)
3.6%
(-0.2% - 7.3%)
2.6%
(-1 .5% - 6.4%)
1 .5%
(-2.8% - 5.6%)
*Estimated incidences of respiratory emergency department visits are based on the concentration-response functions estimated in Tolbert et al. (2007) [results corresponding
to Figure 2 in Tolbert et al. (2007) were obtained via personal communication with P. Tolbert]. All models use a 3-day moving average of the daily 1-hr, maximum NO2
concentration and apply to all ages.
"Incidence was quantified down to 0 ppb. Percents are rounded to the nearest tenth.
"""Alternative 1-hr daily maximum standards are characterized by a concentration of m ppm and an nth percentile, requiring that the average of the 3 annual nth percentile 1-
hr daily maxima over a 3-year period be at or below m ppm.
Note: Numbers in parentheses are 95% confidence intervals based on statistical uncertainty surrounding the NO2 coefficient.
Abt Associates Inc.
29
August 2008
-------
Figure 0-1. Incidence of Respiratory-Related Emergency Department Visits in Atlanta, GA Under Different Air Quality Scenarios, Based on Adjusting
2005,2006, and 2007 NO2 Concentrations*
12000
I/)
•^
'in
Q
LLJ
1
jo
o
EC
10000
8000
2 6000
Q.
I
•5
8
I
4000
2000
"as is" 0.05/98 0.1/98 0.15/98 0.2/98 0.05/99 0.1/99 0.15/99 0.2/99 current
std.
Air Quality Scenario
*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 the
standard level and n is the percentile. So, for example, 0.05/98 denotes a 98* percentile standard of 0.05 ppm. See section 1 for more detail. All results shown
are based on the single-pollutant model in Tolbert et al. (2007).
Abt Associates Inc.
August 2008
-------
As can be seen in Figure 4-1, the greatest incidence of respiratory-related ED
visits in Atlanta is estimated to occur if the current annual standard were just met -
almost three times as high as the incidence associated with "as is" NC>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 are estimated to reduce the incidence of
respiratory-related ED visits from the estimated levels associated with "as is" NC>2
concentrations are the two 1-hour daily maximum standards based on 0.05 ppm. The 98th
percentile 0.05 ppm standard is estimated to reduce the incidence of respiratory-related
ED visits by from 28 percent (in 2005) to 29 percent (in 2007); the 99th percentile 0.05
ppm standard is estimated to reduce the incidence of respiratory-related ED visits by 33
to 35 percent.
In general, the impact of changing the level of the alternative 1-hour daily
maximum standards is substantially greater than the impact of changing from a 98th to a
99th percentile standard. For example, changing from a 98th percentile 1-hour daily
maximum standard based on 0.05 ppm to one based on 0.1 ppm reduces the estimated
incidence of respiratory-related ED visits in Atlanta by about 49 percent in 2007 (from
4700 to 2400); however, changing from a 98th percentile 1-hour daily maximum standard
based on 0.05 ppm to a 99th percentile 1-hour daily maximum standard based on 0.05
ppm reduces the incidence in 2007 by only about 8 percent (from 2400 to 2200). The
corresponding results for 2006 and 2005 are similar.
Abt Associates Inc. 31 August 2008
-------
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12
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16
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30
Abt Associates Inc. 33 August 2008
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United States Office of Air Quality Planning and Standards EPA-452/P-08-004b
Environmental Protection Air Quality Strategies and Standards Division August 2008
Agency Research Triangle Park, NC
------- |