Risk and Exposure Assessment to Support
the Review of the NC>2 Primary National
Ambient Air Quality Standard: Appendices
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EPA document # EPA-452/R-08-008b
November 2008
Risk and Exposure Assessment to Support
the Review of the NC>2 Primary National
Ambient Air Quality Standard
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina
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Disclaimer
This document has been prepared by staff from the Ambient Standards Group, Office of
Air Quality Planning and Standards, U.S. Environmental Protection Agency. Any
opinions, findings, conclusions, or recommendations are those of the authors and do not
necessarily reflect the views of the EPA. For questions concerning this document, please
contact Dr. Stephen Graham (919-541-4344; graham.stephen@epa.gov), Mr. Harvey
Richmond (919-541-5271; richmond.harvey@epa.gov), or Dr. Scott Jenkins (919-541-
1167; jenkins.scott@epa.gov).
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Appendix A. Supplement to the NO2 Air Quality Characterization
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Table of Contents
Appendix A. Supplement to the NO2 Air Quality Characterization i
A-l Overview 1
A-2 Air Quality Data Screen 2
A-2.1 Introduction 2
A-2.2 Approach 2
A-2.3 Results 2
A-3 Selection of Locations 5
A-3.1 Introduction 5
A-3.2 Approach 5
A-3.3 Results 5
A-4 Ambient Monitoring Site Characteristics 7
A-4.1 Introduction 7
A-4.2 Approach 7
A-4.3 Summary Results 7
A-4.4 Detailed Monitoring Site Characteristics 10
A-5 Spatial and Temporal Air Quality Analyses 25
A-5.1 Introduction 25
A-5.2 Approach 25
A-5.3 Summary Results by Locations 26
A-5.4 Summary Results by Year 31
A-5.5 Detailed Results by Year and Location 36
A-6 Technical Memorandum on Regression Modeling 80
A-6.1 Summary 80
A-6.2 Data Used 80
A-6.3 Regression Models 81
A-6.4 Conclusion 93
A-6.5 Detailed Regression Model Predictions 94
A-7 Adjustment of Air Quality to Just Meet the Current and Alternative Standards 100
A-7.1 Introduction 100
A-7.2 Approach 101
A-8 Method for Estimating On-Road Concentrations 110
A-8.1 Introduction 110
A-8.2 Derivation of On-Road Ratios Ill
A-8.3 Application of On-Road Factors 115
A-9 Supplemental Results Tables to the REA 117
A-9.1 Annual average NO2 concentration data for 2001-2003 117
A-9.2 Number of 1 -hour NO2 exceedances in a year, 2001-2003 139
A-9.3 Annual average NO2 concentration data for 2004-2006 183
A-9.4 Comparison of Historical and Recent Ambient Air Quality (As Is) 250
A-9.5 Comparison of On-Road Concentrations Derived From Historical and Recent
Ambient Air Quality (As Is) 257
A-9.6 Results Tables of Historical NO2 Ambient Monitoring Data (1995-2000) Adjusted to
Just Meeting the Current Standard 262
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A-9.7 Results Tables of Recent NO2 Ambient Monitoring Data (2001-2006) As Is and Just
Meeting the Current and Alternative Standards 266
A-10 References 267
in
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List of Tables
Table A-l. Example of ambient monitor years of operation, using the Boston CMS A 3
Table A-2. Counts of complete site-years of NC>2 monitoring data 4
Table A-3. Locations selected for NC>2 Air Quality Characterization, associated abbreviations,
and values of selection criteria 6
Table A-4. Distribution of the distance of ambient monitors to the nearest major road in selected
locations 8
Table A-5. Distribution of the distance of ambient monitors to stationary sources with NOX
emissions >5 tons per year and within a 10 kilometers radius 9
Table A-6. Distribution of NOX emissions from stationary sources within 10 kilometers of
monitoring site, where emissions were >5 tons per year 9
Table A-7. Attributes of location-specific ambient monitors used for air quality characterization
and the distance to nearest major roadway 11
Table A-8. Distance of location-specific ambient monitors to stationary sources emitting >5 tons
of NOX per year, within a 10 kilometer distance of monitoring site 20
Table A-9. Statistical test results for spatial comparisons of all location parameter distributions.
29
Table A-10. Statistical test results for spatial comparisons of within-location parameter
distributions 30
Table A-l 1. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Boston
CMSA 37
Table A-12. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Boston CMSA.37
Table A-13. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Boston CMSA set A, 1995-2006 38
Table A-14. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Boston CMSA
set A, 1995-2006 38
Table A-l 5. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Boston CMSA set B, 1995-2006 39
Table A-16. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Boston CMSA
setB, 1995-2006 39
Table A-17. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Chicago
CMSA 40
Table A-18. Distribution of hourly NO2 ambient concentrations (ppb) by year, Chicago CMSA.
40
Table A-19. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Chicago CMSA, 1995-2006 41
Table A-20. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Chicago
CMSA, 1995-2006 41
Table A-21. Distribution of annual average NC>2 ambient concentrations (ppb) by year,
Cleveland CMSA 42
Table A-22. Distribution of hourly NO2 ambient concentrations (ppb) by year, Cleveland
CMSA 42
Table A-23. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Cleveland CMSA, 1995-2006 43
IV
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Table A-24. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Cleveland
CMSA, 1995-2006 43
Table A-25. Temporal distribution of annual average NC>2 ambient concentrations (ppb) by year,
Denver CMSA 44
Table A-26. Temporal distribution of hourly NC>2 ambient concentrations (ppb) by year, Denver
CMSA 44
Table A-27. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Denver CMSA, 1995-2006 45
Table A-28. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Denver
CMSA, 1995-2006 45
Table A-29. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Detroit
CMSA 46
Table A-30. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Detroit CMSA.46
Table A-31. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Detroit CMSA, 1995-2006 47
Table A-32. Distribution of annual average NO2 ambient concentration (ppb) by monitor,
Detroit CMSA, 1995-2006 47
Table A-33. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Los
Angeles CMSA 48
Table A-34. Distribution of hourly NO2 ambient concentrations (ppb) by year, Los Angeles
CMSA 48
Table A-3 5. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, Los
Angeles CMSA set A, 1995-2006 49
Table A-36. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Los Angeles
CMSA set A, 1995-2006 49
Table A-37. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, Los
Angeles CMSA set B, 1995-2006 50
Table A-38. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Los Angeles
CMSA set B, 1995-2006 50
Table A-39. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, Los
Angeles CMSA set C, 1995-2006 51
Table A-40. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Los Angeles
CMSA set C, 1995-2006 51
Table A-41. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Miami
CMSA 52
Table A-42. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Miami CMSA. 52
Table A-43. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, Miami
CMSA, 1995-2006 53
Table A-44. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Miami CMSA,
1995-2006 53
Table A-45. Distribution of annual average NC>2 ambient concentrations (ppb) by year, New
York CMSA 54
Table A-46. Distribution of hourly NC>2 ambient concentrations (ppb) by year, New York
CMSA 54
Table A-47. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, New
York CMSA set A, 1995-2006 55
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Table A-48. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, New York
CMSA set A, 1995-2006 55
Table A-49. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, New
York CMSA set B, 1995-2006 56
Table A-50. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, New York
CMSA set B, 1995-2006 56
Table A-51. Distribution of annual average NC>2 ambient concentrations (ppb) by year,
Philadelphia CMSA 57
Table A-52. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Philadelphia
CMSA 57
Table A-53. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Philadelphia CMSA, 1995-2006 58
Table A-54. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Philadelphia
CMSA, 1995-2006 58
Table A-55. Distribution of annual average NC>2 ambient concentrations (ppb) by year,
Washington DC CMSA 59
Table A-56. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Washington DC
CMSA 59
Table A-57. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Washington DC CMSA set A, 1995-2006 60
Table A-58. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Washington
DC CMSA set A, 1995-2006 60
Table A-59. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Washington DC CMSA set B, 1995-2006 61
Table A-60. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Washington
DCCMSAsetB, 1995-2006 61
Table A-61. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Atlanta
MSA 62
Table A-62. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Atlanta MSA.. 62
Table A-63. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Atlanta MSA, 1995-2006 63
Table A-64. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Atlanta MSA,
1995-2006 63
Table A-65. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Colorado
Springs MSA 64
Table A-66. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Colorado Springs
MSA 64
Table A-67. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Colorado Springs MSA, 1995-2006 65
Table A-68. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Colorado
Springs MSA, 1995-2006 65
Table A-69. Distribution of annual average NC>2 ambient concentrations (ppb) by year, El Paso
MSA 66
Table A-70. Distribution of hourly NC>2 ambient concentrations (ppb) by year, El Paso MSA. 66
Table A-71. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, El
Paso MSA, 1995-2006 67
VI
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Table A-72. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, El Paso MSA,
1995-2006 67
Table A-73. Distribution of annual average NO2 ambient concentrations (ppb) by year,
Jacksonville MSA 68
Table A-74. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Jacksonville
MSA 68
Table A-75. Distribution of annual average NO2 ambient concentration (ppb) by monitor,
Jacksonville MSA, 1995-2006 69
Table A-76. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Jacksonville
MSA, 1995-2006 69
Table A-77. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Las
Vegas MSA 70
Table A-78. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Las Vegas MSA.
70
Table A-79. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, Las
Vegas MSA, 1995-2006 71
Table A-80. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Las Vegas
MSA, 1995-2006 71
Table A-81. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Phoenix
MSA 72
Table A-82. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Phoenix MSA. 72
Table A-83. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Phoenix MSA, 1995-2006 73
Table A-84. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Phoenix MSA,
1995-2006 73
Table A-85. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Provo
MSA 74
Table A-86. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Provo MSA.... 74
Table A-87. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, Provo
MSA, 1995-2006 75
Table A-88. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Provo MSA,
1995-2006 75
Table A-89. Distribution of annual average NC>2 ambient concentrations (ppb) by year, St. Louis
MSA 76
Table A-90. Distribution of hourly NC>2 ambient concentrations (ppb) by year, St. Louis MSA.
76
Table A-91. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, St.
Louis MSA, 1995-2006 77
Table A-92. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, St. Louis
MSA, 1995-2006 77
Table A-93. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Other
MSA/CMSA 78
Table A-94. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Other
MSA/CMSA 78
Table A-95. Distribution of annual average NO2 ambient concentrations (ppb) by year, Other
Not MSA 79
VII
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Table A-96. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Other Not MSA.
79
Table A-97. Goodness-of-fit statistics for eight generalized linear models 82
Table A-98. Parameters for Poisson exponential model stratified by location 86
Table A-99. Parameters for normal linear model stratified by location 87
Table A-100. Predicted number of exceedances of of 1-hour NC>2 concentrations of 150 ppb
using a Poisson exponential model for the as-is and current-standard scenarios 91
Table A-101. Predicted number of exceedances of of 1-hour NC>2 concentrations of 150 ppb
using a Normal linear model for the as-is and current-standard scenarios 92
Table A-102. Comparison of predicted exceedances of 150 ppb using McCurdy (1994) for
1988-1992 data and the Poisson exponential and normal linear models for 1995-2006
data 93
Table A-103. Predicted number of exceedances of of 1-hour NC>2 concentrations of 150 ppb
using a Poisson exponential model and at several annual average concentrations 94
Table A-104. Predicted number of exceedances of of 1-hour NC>2 concentrations of 150 ppb
using a Normal linear model and at several annual average concentrations 97
Table A-105. Maximum annual average NC>2 concentrations and air quality adjustment factors
(F) to just meet the current standard, historical monitoring data 104
Table A-106. Maximum annual average NC>2 concentrations and air quality adjustment factors
(F) to just meet the current standard, recent monitoring data 105
Table A-107. Air quality adjustment factors (F) to just meet the alternative 1-hour standards,
using recent monitoring data 106
Table A-108. Studies reviewed containing NC>2 concentrations at a distance from roadways.. Ill
Table A-109. Example data used to estimate on-road adjustment factor (m) obtained from
Tables 1 and 4 reported in Singer et. al (2004) 112
Table A-l 10. Estimated on-road adjustment factors (Cv/Cb or m) for two season groups and
potential influential factors 113
Table A-l 11. Estimated annual average NC>2 concentrations for monitors >100 m from a major
road using 2001-2003 air quality as is and air quality adjusted to just meet the current
and alternative standards 117
Table A-l 12. Estimated annual average NC>2 concentrations for monitors >20 m and <100 m
from a major road using 2001-2003 air quality as is and air quality adjusted to just
meet the current and alternative standards 124
Table A-l 13. Estimated annual average NC>2 concentrations for monitors <20 m from a major
road using 2001-2003 air quality as is and air quality adjusted to just meet the current
and alternative standards 128
Table A-l 14. Estimated annual average NC>2 concentrations on-roads using 2001-2003 air
quality as is, air quality adjusted to just meet the current and alternative standards, and
an on-road adjustment factor 132
Table A-l 15. Estimated number of exceedances of 1-hour concentration levels (100, 150, and
200 ppb) for monitors >100 m from a major road using 2001-2003 air quality as is and
air quality adjusted to just meet the current and alternative standards 139
Table A-l 16. Estimated number of exceedances of 1-hour concentration levels (250 and 300
ppb) for monitors >100 m from a major road using 2001-2003 air quality as is and air
quality adjusted to just meet the current and alternative standards 146
VIM
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Table A-l 17. Estimated number of exceedances of 1-hour concentration levels (100, 150 and
200 ppb) for monitors >20 m and <100 m from a major road using 2001-2003 air
quality as is and air quality adjusted to just meet the current and alternative standards.
151
Table A-l 18. Estimated number of exceedances of 1-hour concentration levels (250 and 300
ppb) for monitors >20 m and <100 m from a major road using 2001-2003 air quality
as is and air quality adjusted to just meet the current and alternative standards 155
Table A-l 19. Estimated number of exceedances of 1-hour concentration levels (100, 150 and
200 ppb) for monitors < 20 m from a major road using 2001-2003 air quality as is and
air quality adjusted to just meet the current and alternative standards 158
Table A-120. Estimated number of exceedances of 1-hour concentration levels (250 and 300
ppb) for monitors < 20 m from a major road using 2001-2003 air quality as is and air
quality adjusted to just meet the current and alternative standards 162
Table A-121. Estimated number of exceedances of 1-hour concentration levels (100, 150, and
200 ppb) on-roads using 2001-2003 air quality as is and air quality adjusted to just
meet the current and alternative standards and an on-road adjustment factor 165
Table A-122. Estimated number of exceedances of 1-hour concentration levels (250, and 300
ppb) on-roads using 2001-2003 air quality as is and air quality adjusted to just meet
the current and alternative standards and an on-road adjustment factor 178
Table A-123. Estimated annual average NC>2 concentrations for monitors >100 m from a major
road using 2004-2006 air quality as is and adjusted to just meet the current and
alternative standards 183
Table A-124. Estimated annual average NC>2 concentrations for monitors >20 m and <100 m
from a major road using 2004-2006 air quality as is and air quality adjusted to just
meet the current and alternative standards 189
Table A-125. Estimated annual average NC>2 concentrations for monitors < 20 m from a major
road using 2004-2006 air quality as is and air quality adjusted to just meet the current
and alternative standards 193
Table A-126. Estimated annual average NC>2 concentrations on-roads using 2004-2006 air
quality as is, air quality adjusted to just meet the current and alternative standards, and
an on-road adjustment factor 197
Table A-127. Estimated number of exceedances of 1-hour concentration levels (100, 150, and
200 ppb) for monitors >100 m from a major road following adjustment to just meeting
the current and alternative standards, 2004-2006 air quality 203
Table a-128. Estimated number of exceedances of 1-hour concentration levels (250 and 300
ppb) for monitors >100 m from a major road following adjustment to just meeting the
current and alternative standards, 2004-2006 air quality 211
Table A-129. Estimated number of exceedances of 1-hour concentration levels (100, 150, and
200 ppb) for monitors > 20 m and < 100 m from a major road following adjustment to
just meeting the current and alternative standards, 2004-2006 air quality 218
Table A-130. Estimated number of exceedances of 1-hour concentration levels (250 and 300
ppb) for monitors > 20 m and < 100 m from a major road following adjustment to just
meeting the current and alternative standards, 2004-2006 air quality 224
Table a-131. Estimated number of exceedances of 1-hour concentration levels (100, 150, and
200 ppb) for monitors < 20 m from a major road following adjustment to just meeting
the current and alternative standards, 2004-2006 air quality 228
IX
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Table A-132. Estimated number of exceedances of 1-hour concentration levels (250 and 300
ppb) for monitors < 20 m from a major road following adjustment to just meeting the
current and alternative standards, 2004-2006 air quality 234
Table A-133. Estimated number of exceedances of 1-hour concentration levels (100, 150, and
200 ppb) on-roads following adjustment to just meeting the current and alternative
standards, 2004-2006 air quality and an on-road road adjustment factor 238
Table A-134. Estimated number of exceedances of 1-hour concentration levels (250 and 300
ppb) on-roads following adjustment to just meeting the current and alternative
standards, 2004-2006 air quality and an on-road road adjustment factor 244
Table A-135. Monitoring site-years and annual average NC>2 concentrations for two monitoring
periods, historical and recent air quality data (as is) using monitors sited >100 m of a
major road 250
Table A-136. Monitoring site-years and annual average NC>2 concentrations for two monitoring
periods, historical and recent air quality data (as is) using monitors sited <100 m of a
major road 251
Table A-137. Total number of exceedances of short-term (1-hour) potential health effect
benchmark levels in a year, 1995-2000 historical NC>2 air quality (as is) using monitors
sited > 100 m of a major road 253
Table A-138. Total number of exceedances of short-term (1-hour) potential health effect
benchmark levels in a year, 2001-2006 recent NC>2 air quality (as is) using monitors
sited >100m of a major road 254
Table A-139. Total number of exceedances of short-term (1-hour) potential health effect
benchmark levels in a year, 1995-2000 historical NC>2 air quality (as is) using monitors
sited <100 m of a major road 255
Table A-140. Total number of exceedances of short-term (1-hour) potential health effect
benchmark levels in a year, 2001-2006 recent NC>2 air quality (as is) using monitors
sited <100 m of a major road 256
Table A-141. Estimated annual average on-road NC>2 concentrations for two monitoring periods,
historical and recent air quality data (as is) 258
Table A-142. Estimated total number of exceedances of short-term (1-hour) potential health
effect benchmark levels in a year on-roads, 1995-2000 historical NC>2 air quality (as
is) 260
Table A-143. Estimated total number of exceedances of short-term (1-hour) potential health
effect benchmark levels in a year on-roads, 2001-2006 recent NC>2 air quality (as is).
261
Table A-144. Total number of exceedances of short-term (1-hour) potential health effect
benchmark levels in a year, 1995-2000 historical NC>2 air quality adjusted to just
meeting the current annual average standard (0.053 ppm) using monitors sited >100 m
of a major road 263
Table 145. Total estimated number of exceedances of short-term (1-hour) potential health effect
benchmark levels in a year, 1995-2000 historical NC>2 air quality adjusted to just
meeting the current annual average standard (0.053 ppm) using monitors sited <100 m
of a major road 264
Table A-146. Total estimated number of exceedances of short-term (1-hour) potential health
effect benchmark levels in a year on-roads, 1995-2000 historical NC>2 air quality
adjusted to just meeting the current annual average standard (0.053 ppm) 265
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List of Figures
Figure A-l. Distributions of annual mean NC>2 ambient monitoring concentrations for selected
CMSA locations, years 1995-2006 27
Figure A-2. Distributions of annual mean NC>2 ambient monitoring concentrations for selected
MSA and grouped locations, years 1995-2006 27
Figure A-3. Distributions of hourly NC>2 ambient monitoring concentrations for selected CMSA
locations, years 1995-2006 28
Figure A-4. Distributions of hourly NC>2 ambient concentration for selected CMSA locations,
years 1995-2006 29
Figure A-5. Distributions of annual average NC>2 concentrations among 10 monitoring sites in
Philadelphia CMSA, years 1995-2006 30
Figure A-6. Distributions of annual mean NC>2 concentrations for all monitors, years 1995-2006.
32
Figure A-7. Distributions of annual mean NC>2 concentrations for the Philadelphia CMSA, years
1995-2006 33
Figure A-8. Distributions of hourly NC>2 concentrations in the Los Angeles CMSA, years 1995-
2006 34
Figure A-9. Distributions of hourly NC>2 concentrations in the Jacksonville MSA, years 1995-
2006, one monitor 35
Figure A-10. Distributions of annual average NC>2 concentrations in the Other Not MSA group
location, years 1995-2006 36
Figure A-l 1. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Boston
CMSA 37
Figure A-12. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Boston CMSA.
37
Figure A-13. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Boston CMSA set A, 1995-2006 38
Figure A-14. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Boston
CMSA set A, 1995-2006 38
Figure A-l 5. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Boston CMSA set B, 1995-2006 39
Figure A-16. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Boston
CMSA set B, 1995-2006 39
Figure A-17. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Chicago
CMSA 40
Figure A-18. Distribution of hourly NO2 ambient concentrations (ppb) by year, Chicago CMSA.
40
Figure A-19. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Chicago CMSA, 1995-2006 41
Figure A-20. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Chicago
CMSA, 1995-2006 41
Figure A-21. Distribution of annual average NC>2 ambient concentrations (ppb) by year,
Cleveland CMSA 42
Figure A-22. Temporal distribution of hourly NO2 ambient concentrations (ppb) by year,
Cleveland CMSA 42
XI
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Figure A-23. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Cleveland CMSA, 1995-2006 43
Figure A-24. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Cleveland
CMSA, 1995-2006 43
Figure A-25. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Denver
CMSA 44
Figure A-26. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Denver CMSA.
44
Figure A-27. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Denver CMSA, 1995-2006 45
Figure A-28. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Denver
CMSA, 1995-2006 45
Figure A-29. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Detroit
CMSA 46
Figure A-30. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Detroit CMSA.
46
Figure A-31. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Detroit CMSA, 1995-2006 47
Figure A-32. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Detroit CMSA, 1995-2006 47
Figure A-33. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Los
Angeles CMSA 48
Figure A-34. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Los Angeles
CMSA 48
Figure A-3 5. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, Los
Angeles CMSA set A, 1995-2006 49
Figure A-36. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Los Angeles
CMSA set A, 1995-2006 49
Figure A-37. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, Los
Angeles CMSA set B 1995-2006 50
Figure A-38. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Los Angeles
CMSA set B, 1995-2006 50
Figure A-39. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, Los
Angeles CMSA set C 1995-2006 51
Figure A-40. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Los Angeles
CMSA set C 1995-2006 51
Figure A-41. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Miami
CMSA 52
Figure A-42. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Miami CMSA.
52
Figure A-43. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Miami CMSA, 1995-2006 53
Figure A-44. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Miami
CMSA, 1995-2006 53
Figure A-45. Distribution of annual average NC>2 ambient concentrations (ppb) by year, New
York CMSA 54
XII
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Figure A-46. Distribution of hourly NC>2 ambient concentrations (ppb) by year, New York
CMSA 54
Figure A-47. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, New
York CMSA set a, 1995-2006 55
Figure A-48. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, New York
CMSA set a, 1995-2006 55
Figure A-49. Distribution of annual average NC>2 ambient concentration (ppb) by monitor, New
York CMSA set b, 1995-2006 56
Figure A-50. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, New York
CMSA set b, 1995-2006 56
Figure A-51. Distribution of annual average NC>2 ambient concentrations (ppb) by year,
Philadelphia CMSA 57
Figure A-52. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Philadelphia
CMSA 57
Figure A-53. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Philadelphia CMSA, 1995-2006 58
Figure A-54. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Philadelphia
CMSA, 1995-2006 58
Figure A-55. Distribution of annual average NC>2 ambient concentrations (ppb) by year,
Washington DC CMSA 59
Figure A-56. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Washington DC
CMSA 59
Figure A-57. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Washington DC CMSA set A, 1995-2006 60
Figure A-58. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Washington
DC CMSA set A, 1995-2006 60
Figure A-59. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Washington DC CMSA set B, 1995-2006 61
Figure A-60. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Washington
DCCMSAsetB, 1995-2006 61
Figure A-61. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Atlanta
MSA 62
Figure A-62. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Atlanta MSA. 62
Figure A-63. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Atlanta MSA, 1995-2006 63
Figure A-64. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Atlanta MSA,
1995-2006 63
Figure A-65. Distribution of annual average NC>2 ambient concentrations (ppb) by year,
Colorado Springs MSA 64
Figure A-66. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Colorado
Springs MSA 64
Figure A-67. Distribution of annual average NC>2 ambient concentration (ppb) by monitor,
Colorado Springs MSA, 1995-2006 65
Figure A-68. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, Colorado
Springs MSA, 1995-2006 65
XIII
-------
Figure A-69. Distribution of annual average NO2 ambient concentrations (ppb) by year, El Paso
MSA 66
Figure A-70. Distribution of hourly NO2 ambient concentrations (ppb) by year, El Paso MSA. 66
Figure A-71. Distribution of annual average NO2 ambient concentration (ppb) by monitor, El
Paso MSA, 1995-2006 67
Figure A-72. Distribution of hourly NO2 ambient concentration (ppb) by monitor, El Paso MSA,
1995-2006 67
Figure A-73. Distribution of annual average NO2 ambient concentrations (ppb) by year,
Jacksonville MSA 68
Figure A-74. Distribution of hourly NO2 ambient concentrations (ppb) by year, Jacksonville
MSA 68
Figure A-75. Distribution of annual average NO2 ambient concentration (ppb) by monitor,
Jacksonville MSA, 1995-2006 69
Figure A-76. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Jacksonville
MSA, 1995-2006 69
Figure A-77. Distribution of annual average NO2 ambient concentrations (ppb) by year, Las
Vegas MSA 70
Figure A-78. Distribution of hourly NO2 ambient concentrations (ppb) by year, Las Vegas
MSA 70
Figure A-79. Distribution of annual average NO2 ambient concentration (ppb) by monitor, Las
Vegas MSA, 1995-2006 71
Figure A-80. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Las Vegas
MSA, 1995-2006 71
Figure A-81. Distribution of annual average NO2 ambient concentrations (ppb) by year, Phoenix
MSA 72
Figure A-82. Distribution of hourly NO2 ambient concentrations (ppb) by year, Phoenix MSA.
72
Figure A-83. Distribution of annual average NO2 ambient concentration (ppb) by monitor,
Phoenix MSA, 1995-2006 73
Figure A-84. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Phoenix
MSA, 1995-2006 73
Figure A-85. Distribution of annual average NO2 ambient concentrations (ppb) by year, Provo
MSA 74
Figure A-86. Temporal distribution of hourly NO2 ambient concentrations (ppb) by year, Provo
MSA 74
Figure A-87. Distribution of annual average NO2 ambient concentration (ppb) by monitor,
Provo MSA, 1995-2006 75
Figure A-88. Distribution of hourly NO2 ambient concentration (ppb) by monitor, Provo MSA,
1995-2006 75
Figure A-89. Distribution of annual average NO2 ambient concentrations (ppb) by year, St.
Louis MSA 76
Figure A-90. Temporal distribution of hourly NO2 ambient concentrations (ppb) by year, St.
Louis MSA 76
Figure A-91. Distribution of annual average NO2 ambient concentration (ppb) by monitor, St.
Louis MSA, 1995-2006 77
XIV
-------
Figure A-92. Distribution of hourly NC>2 ambient concentration (ppb) by monitor, St. Louis
MSA, 1995-2006 77
Figure A-93. Distribution of annual average NC>2 ambient concentrations (ppb) by year, Other
MSA/CMSA 78
Figure A-94. Distribution of hourly NC>2 ambient concentrations (ppb) by year, Other
MSA/CMSA 78
Figure A-95. Distribution of annual average NO2 ambient concentrations (ppb) by year, Other
Not MSA 79
Figure A-96. Distribution of hourly NO2 ambient concentrations (ppb) by year, Other Not MSA.
79
Figure A-97. Measured number of exceedances of 1-hour NO2 concentrations of 150 ppb versus
annual meanNO2 concentrations (ppb) for CMSA locations 89
Figure A-98. Predicted and observed exceedances of 1-hour NO2 concentrations of 150 ppb for
CMSA locations using Poisson exponential model 89
Figure A-99. Predicted and observed exceedances of 1-hour NO2 concentrations of 150 ppb for
CMSA locations using normal linear model 90
Figure A-100. Trends in hourly and annual average NO2 ambient monitoring concentrations and
their associated coefficients of variation (COV) for all monitors, years 1995-2006. 101
Figure A-101. Distribution of estimated Cv/Cb ratios or m for two season groups 115
xv
-------
A-1 Overview
This appendix contains supplemental descriptions of the data and methods used in the NC>2
air quality characterization in support of the Risk and Exposure Assessment (REA) conducted
for the NC>2 NAAQS review. First, ambient monitoring data form years 1995 through 2006 have
been characterized based on siting characteristics, proximity to stationary source emissions, and
distance to roadways. Then, ambient NC>2 concentration trends were evaluated considering the
year of monitoring and distribution of monitors within a location.
The primary output of the air quality characterization was the numbers of exceedances of
potential health effect benchmark levels identified in the Integrated Science Assessment. The
ambient NO2 concentrations were evaluated for the numbers of exceedances of the selected
benchmarks in several locations and considering four scenarios. The first scenario considered as
is air quality as obtained from EPA's Air Quality System (US EPA, 2007a; 2007b). A second
scenario used a portion of the as is air quality to estimate on-road NO2 concentrations. A third
and fourth scenario followed in a similar manner, only these used air quality adjusted to just
meeting the current and potential alternative standards. Each of these scenarios, in addition to
the reasoning for the methods and data used, are described in detail in the sections that follow.
A-1
-------
A-2 Air Quality Data Screen
A-2.1 Introduction
The current NO2 standard of 53 ppb annual arithmetic average was set in 1971 and has been
retained since by subsequent reviews (i.e., 1985, 1995). Minor revisions to the standard made in
1985 included an explicit rounding convention, stated annual averages would be determined on a
calendar year basis, and indicated an explicit 75% completeness requirement for monitoring (60
FR 52874). Each of these components of the standard were considered in characterizing the air
quality monitoring data, beginning first with the selection of valid data.
A-2.2 Approach
NC>2 air quality data from years 1995 through 2006 and associated documentation were
downloaded from EPA's Air Quality System (US EPA, 2007a; 2007b). As of the date of the
first analyses were performed, hourly measurements for year 2006 were only available for
January 1 through October 31, 2006. A site was defined by the state, county, site code, and
parameter occurrence code (POC), which gives a 10-digit monitor ID code. The POC identifies
collocated measurements at the same monitoring location, so that each measuring instrument is
treated as a different site. Typically there was only one POC at a given monitoring location.
As required by the NO2 NAAQS, a valid year of monitoring data is needed to calculate the
annual average concentration. A valid year at a monitoring site is comprised of 75% of valid
days in a year, with at least 18 hourly measurements for a valid day (thus at least 274 or 275
valid days depending on presence of a leap year, a minimum of 4,932 or 4,950 hours). This
served as a screening criterion for data to be used for analysis.
Site-years of data are the total numbers of years the collective monitors in a location were in
operation. For example, from years 1995-2006, the Boston CMS A had 27 total monitors in
operation, some of which did not contain sufficient numbers of monitoring values, while others
contained upwards of 11 years (Table A-l). Thus in summing the number of operating years,
this particular location contained a total of 105 site-years of data across the monitoring period.
In all of the subsequent analyses, where hourly values were missing they were treated as such.
Reported values of zero (0) concentration were also retained as is. For certain illustrations,
values of zero were substituted with 0.5 ppb, derived from one-half the lowest recorded 1-hour
concentration (1 ppb).
A-2.3 Results
Of a total of 5,243 site-years of data in the entire NO2 1-hour concentration database, 1,039
site-years did not meet the above criterion and were excluded from any further analyses. In
addition, since shorter term average concentrations are of interest, the remaining site-years of
data were further screened for 75% completeness on hourly measures in a year (i.e., containing a
minimum of 6,570 or 6,588, depending on presence of a leap year). Twenty-seven additional
site-years were excluded, resulting in 4,177 complete site-years in the analytical database. Table
A-2
-------
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
250250021 1
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
-------
Table A-2. Counts of complete site-years of NO2 monitoring data.
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
Other MSA
Other Not MSA
Total
Com
1995-2000
24
58
47
11
26
26
12
14
6
16
193
24
93
46
22
6
56
69
1135
200
Number of
Dlete
2001-2006
29
47
36
11
ND
10
12
30
4
35
177
20
81
39
27
6
43
66
1177
243
4177
Site-Years
Incorr
1995-2000
5
16
20
2
4
10
4
11
0
4
16
1
12
6
8
0
3
21
249
112
iplete
2001-2006
1
34
22
2
4
4
1
0
2
9
19
4
24
8
25
0
9
18
235
141
1066
% Cor
1995-2000
83%
78%
70%
85%
87%
72%
75%
56%
100%
80%
92%
96%
89%
88%
73%
100%
95%
77%
82%
64%
iplete
2001-2006
97%
58%
62%
85%
ND
71%
92%
100%
67%
80%
90%
83%
77%
83%
52%
100%
83%
79%
83%
63%
80%
Notes:
ND no available monitoring data
A-4
-------
A-3 Selection of Locations
A-3.1 Introduction
The next step in this analysis was to identify similarities and differences in air quality among
locations for the purpose of either aggregating or segregating data using a combination of
descriptive statistics and health based criteria. Location in this context would include a
geographic area that encompasses more than a single air quality monitor (e.g., particular city,
consolidated metropolitan statistical area or CMSA).
A-3.2 Approach
Criteria were established for selecting sites with high annual means and/or frequent
exceedances of potential health effect benchmarks. Selected locations were those that had a
maximum annual mean NC>2 level at a particular monitor greater than or equal to 25.7 ppb, which
represents the 90th percentile across all locations and site-years, and/or had at least one reported
1-hour NC>2 level greater than or equal to 200 ppb, the lowest level of the potential health effect
benchmarks. A location in this context would include a geographic area that encompasses more
than a single air quality monitor (e.g., particular city, metropolitan statistical area (MSA), or
consolidated metropolitan statistical area or CMSA). First, all monitors were identified as either
belonging to a CMSA, a MSA, or neither. Then, locations of interest were identified through
statistical analysis of the ambient NC>2 air quality data for each site within a location.
A-3.3 Results
Fifteen locations met both selection criteria, that is, having at least one site-year annual mean
above 25.7 ppb and at least one exceedance of 200 ppb. Upon further analysis of the more recent
ambient data (2001-2006), four additional locations were observed to have met at least one of the
criteria (either high annual mean and/or at least one exceedance of 200 ppb). New Haven, CT,
while meeting the earlier criteria, did not have any recent exceedances of 200 ppb and contained
one of the lowest maximum concentration-to-mean ratios, therefore was not separated out as a
specific location. Thus, 14 locations were retained from the initial selection and 4 locations
selected from a second screening to provide additional geographical representation. In addition
to these 18 specific locations, the remaining sites were grouped into two broad location
groupings. The Other CMSA location contains all the other sites that are in MSAs or CMSAs but
are not in any of the 18 specified locations. The Not MSA location contains all the sites that are
not in an MSA or CMSA. The selected locations are summarized in Table A-3.
The final database for analysis included air quality data from a total of 204 monitors within
the named locations, 332 monitors in the Other CMSA group, and 92 monitors in the Not MSA
group. Again, the monitors that were retained contained the criteria for estimating a valid annual
average concentration described above.
A-5
-------
Table A-3. Locations selected for NO2 Air Quality Characterization, associated abbreviations, and values of selection criteria.
Location
Type1 Code Description Abbreviation
MSA
CMSA
CMSA
CMSA
MSA
CMSA
CMSA
MSA
MSA
MSA
CMSA
CMSA
CMSA
CMSA
MSA
MSA
MSA
CMSA
MSA/CMSA
-
0520
1122
1602
1692
1720
2082
2162
2320
3600
4120
4472
4992
5602
6162
6200
6520
7040
8872
-
-
Atlanta, GA
Boston-Worcester-Lawrence, MA-NH-ME-CT
Chicago-Gary-Kenosha, IL-IN-WI
Cleveland-Akron, OH
Colorado Springs, CO
Denver-Boulder-Greeley, CO
Detroit-Ann Arbor-Flint, Ml
El Paso, TX
Jacksonville, FL
Las Vegas, NV-AZ
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
Phoenix-Mesa, AZ
Provo-Orem, UT
St, Louis, MO-IL
Washington-Baltimore, DC-MD-VA-WV
Other MSA/CMSA
Other Not MSA
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*
Other MSA
Other Not MSA
Maximum # of
Exceedances
of 200 ppb
1
1
0
1
69
2
12
2
2
11
5
3
3
3
37
0
8
2
10
2
Maximum
Annual Mean
(ppb)
26.6
31.1
33.6
28.1
34.8
36.8
25.9
35.1
15.9
27.1
50.6
16.8
42.2
34.00
40.5
28.9
27.2
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 first determined using a Tele-Atlas roads database
in a GIS application. For road-monitor pairs that showed particularly close distances, the values
were refined using GoogleEarth® to estimate the distance to road edge. Distances of monitoring
sites to stationary sources and those source's emissions were estimated using data within the 2002
National Emissions Inventory (NEI; US EPA, 2007d). The NEI database reports emissions of
NOX in tons per year (tpy) for 131,657 unique emission sources at various points of release. The
release locations were all taken from the latitude longitude values within the NEI. First, all NOX
emissions were summed for identical latitude and longitude entries while retaining source codes
for the emissions (e.g., Standard Industrial Code (SIC), or North American Industrial
Classification System (NAICS)). Therefore, any facility containing similar emission processes
were summed at the stack location, resulting in 40,855 observations. These data were then
screened for sources with emissions greater than 5 tpy, yielding 18,798 unique NOX emission
sources. Locations of these stationary source emissions were compared with ambient monitoring
locations using the following formula:
d = arccos(sin(toj) x sin(to2) + cos(latl) x cos(to2) x cos(/o«2 -lon^)) x r
where
d = distance (kilometers)
lat] = latitude of a monitor (radians)
Iat2 = latitude of source emission (radians)
lori] = longitude of monitor (radians)
Ion2 = 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.
A-4.3 Summary Results
Summary statistics for the monitoring site characteristics are presented in Tables A-4 through
A-6 for the selected locations. Detailed results for the distance to major roadways, the distance
A-7
-------
and emissions from stationary sources for each ambient monitor are provided in section A-4.4,
Tables A-7 and A-8.
The distribution of the nearest distance of the ambient monitors to major roads for each of the
named locations is summarized in Table A-4. On average, most monitors are placed at a distance
of 100 meters or greater from a major road, however in locations with a large monitoring network
such as Boston, Chicago, or New York CMSA, there may be one or two monitors sited within
close proximity (<20 meters) of a road. Since there is potential for roadway emissions to affect
concentrations at monitors sited close to major roads, the ambient monitors were further
categorized based on the monitor distance from major roads. Three proximity bins were
identified, the first containing those monitors sited at or within 20 meters, (<20 m), those between
20 m and 100 m, and those located at least 100 meters from a major road (>100 m).2
Table A-4. Distribution of the distance of ambient monitors to the nearest major road in selected locations.
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 >1 km from road are not included.
Table A-5 contains a summary of the distance of stationary source emissions to monitors
within each named location. There were a number of sources emitting >5 tpy of NOX and located
within a 10 km radius for many of the monitors. On average though, most monitors are placed at
greater distances from stationary source emissions than roads with most sources at a distance of
greater than 5 km. Most of the stationary source emissions of NOX within a 10 km radius of
monitors were less than 50 tpy (Table A-6). Details regarding individual monitors are provided in
Table A-8.
2 As part of our initial analysis, the historical data were separated into two-road distance categories, <100 m and >100
m from a major road. The recent data were separated into both the two- and three-road distance categories for
analysis.
A-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
A-9
-------
the source emissions.
A-4.4 Detailed Monitoring Site Characteristics
Detailed physical attributes of each monitor used within the named locations (i.e., 18 specific
locations were defined; it does not include the broadly grouped locations of "Other 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
Monitor 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
Latitude
33.68801
33.84568
33.77919
33.92855
33.59093
43.08333
42.06306
42.47467
42.79027
42.77077
42.31667
42.34887
42.37783
42.33333
42.33333
42.34025
42.31717
42.3294
42.40167
42.26722
42.26388
42.99278
43.00056
43.00056
Longitude
-84.2903
-84.21 34
-84.3958
-85.0455
-84.0654
-70.75
-71.1489
-70.9714
-70.8083
-71.1023
-71.1333
-71 .0972
-71 .0271
-71.1167
-71.1167
-71 .0383
-70.9662
-71 .0825
-71.0311
-71 .7989
-71 .7942
-71 .4594
-71 .4681
-71 .4681
Objective1
POPULATION
EXPOSURE
UNKNOWN
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
UNKNOWN
HIGHEST
CONCENTRATION
HIGHEST
CONCENTRATION
UNKNOWN
UNKNOWN
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
UNKNOWN
POPULATION
EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
Setting2
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
Land Use3
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
AGRICULTURAL
AGRICULTURAL
RESIDENTIAL
AGRICULTURAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
Scale4
URBAN SCALE
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
URBAN SCALE
NEIGHBORHOOD
URBAN SCALE
URBAN SCALE
URBAN SCALE
NEIGHBORHOOD
MICROSCALE
MICROSCALE
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
URBAN SCALE
URBAN SCALE
NEIGHBORHOOD
Monitor5
Years
10
9
12
10
12
7
2
10
5
2
1
11
8
1
1
11
1
6
5
8
3
4
1
5
Ht
(m)
5
5
5
4
5
4
4
5
4
4
5
4
4
6
5
4
3
4
5
5
Elev (m)
308
0
290
417
219
40
61
52
1
0
0
6
6
0
0
0
10
6
59
145
145
75
61
61
Roadway6
Dist
(m)
432
579
134
1000
809
70
17
158
15
337
144
7
7
158
158
37
1000
26
228
44
49
168
70
70
Type
3
2
3
3
2
3
3
3
3
3
2
3
3
3
3
3
4
3
3
3
3
3
A-11
-------
Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.
Location
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Colorado Springs
Monitor ID
330150009
330150013
330150014
330150015
170310037
1 7031 0063
1 7031 0064
1 7031 0075
1 7031 0076
170313101
1 7031 31 03
1 7031 4002
1 7031 4201
1 7031 8003
171971011
1 80890022
180891016
390350043
390350060
390350066
390350070
08041 6001
08041 6004
08041 6005
08041 6006
08041 6009
Latitude
43.07806
43
43.07528
43.0825
41 .97944
41 .87697
41 .79079
41.96417
41.7514
41 .96525
41.96519
41 .85524
42.14
41.63139
41.22154
41 .60667
41 .60028
41 .46278
41 .49396
41 .46278
41 .45694
38.63361
38.92139
38.76333
38.9225
38.64083
Longitude
-70.7628
-71.2
-70.7481
-70.7619
-87.67
-87.6343
-87.6016
-87.6586
-87.7135
-87.8763
-87.8763
-87.7525
-87.7992
-87.5681
-88.191
-87.3047
-87.3347
-81 .5792
-81 .6785
-81 .5803
-81 .5922
-104.716
-104.813
-104.757
-104.996
-104.714
Objective1
UNKNOWN
OTHER
POPULATION
EXPOSURE
POPULATION
EXPOSURE
UNKNOWN
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
HIGHEST
CONCENTRATION
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
GENERAL/BACKGR
OUND
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
Setting2
SUBURBAN
RURAL
URBAN AND
CENTER CITY
SUBURBAN
URBAN AND
CENTER CITY
URBAN AND
CENTER CITY
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
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
Land Use3
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
MOBILE
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
MOBILE
MOBILE
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
AGRICULTURAL
INDUSTRIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
RESIDENTIAL
AGRICULTURAL
RESIDENTIAL
INDUSTRIAL
Scale4
REGIONAL SCALE
NEIGHBORHOOD
NEIGHBORHOOD
MIDDLE SCALE
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
MIDDLE SCALE
MIDDLE SCALE
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
REGIONAL SCALE
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
Monitor5
Years
5
4
3
1
1
12
6
4
5
3
9
12
8
8
5
8
2
2
12
6
2
6
6
1
1
1
Ht
(m)
3
1
2
4
9
3
15
15
4
3
4
4
8
4
5
5
14
4
4
5
4
4
4
4
4
4
Elev (m)
3
0
4
3
183
181
180
180
186
197
195
184
198
179
181
183
183
287
206
287
278
1673
1931
1747
2313
1707
Roadway6
Dist
(m)
48
1000
266
38
17
68
346
136
2
20
20
118
239
2
1000
738
187
187
2
187
81
1000
150
79
199
1000
Type
3
3
3
3
3
3
3
3
2
2
3
2
3
1
3
2
4
2
3
1
3
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
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
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Monitor ID
080416011
080416013
080416018
08001 3001
080050003
08031 0002
080590006
080590008
080590009
080590010
260990009
261630016
261630019
481410027
481410028
481410037
481410044
481410055
481410057
481410058
120310032
320030022
320030023
320030073
320030078
320030539
Latitude
38.84667
38.81056
38.81139
39.83812
39.65722
39.75118
39.9129
39.87639
39.86194
39.89972
42.73139
42.35781
42.43084
31 .76308
31.75361
31 .76828
31 .76567
31 .74676
31.66219
31 .89393
30.35611
36.39078
36.80806
36.17306
35.46505
36.14444
Longitude
-104.827
-104.817
-104.751
-104.95
-104.998
-104.988
-105.189
-105.166
-105.203
-105.24
-82.7935
-83.096
-83.0001
-106.487
-106.404
-106.501
-106.455
-106.403
-106.303
-106.426
-81 .6356
-114.907
-114.061
-115.332
-114.92
-115.086
Objective1
UNKNOWN
UNKNOWN
UNKNOWN
POPULATION
EXPOSURE
HIGHEST
CONCENTRATION
HIGHEST
CONCENTRATION
UNKNOWN
GENERAL/BACKGR
OUND
GENERAL/BACKGR
OUND
UNKNOWN
UNKNOWN
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
GENERAL/BACKGR
OUND
SOURCE ORIENTED
POPULATION
EXPOSURE
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
UNKNOWN
SOURCE ORIENTED
POPULATION
EXPOSURE
POPULATION
EXPOSURE
REGIONAL
TRANSPORT
POPULATION
EXPOSURE
Setting2
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
RURAL
SUBURBAN
RURAL
SUBURBAN
Land Use3
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
AGRICULTURAL
COMMERCIAL
COMMERCIAL
INDUSTRIAL
INDUSTRIAL
INDUSTRIAL
AGRICULTURAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
INDUSTRIAL
RESIDENTIAL
RESIDENTIAL
DESERT
MOBILE
Scale4
URBAN SCALE
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
URBAN SCALE
MICROSCALE
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
REGIONAL SCALE
NEIGHBORHOOD
Monitor5
Years
6
1
4
11
1
9
3
4
3
5
2
11
11
4
1
11
8
7
7
6
10
7
4
7
1
8
Ht
(m)
3
3
3
4
4
5
4
4
4
4
4
5
4
4
5
5
5
3
3.5
4
3.5
4
3.5
Elev (m)
1832
1823
1795
1559
1654
1589
1774
1715
1848
1877
189
191
192
1140
1126
1143
1128
0
0
0
7
0
490
0
1094
533
Roadway6
Dist
(m)
198
386
163
748
138
18
65
31
99
63
415
393
339
33
718
128
38
127
450
478
144
122
303
515
25
11
Type
3
4
2
3
2
3
3
3
3
2
3
5
3
4
3
3
3
3
3
3
1
2
3
2
3
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
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
Monitor ID
320030557
320030563
320030601
320031 01 9
320032002
060370002
06037001 6
060370030
060370113
060370206
060371 002
060371103
060371201
060371301
060371601
060371701
060372005
060374002
060375001
060375005
060376002
06037601 2
060379002
060379033
060590001
060590007
Latitude
36.15889
36.17639
35.97889
35.78563
36.19111
34.1365
34.14435
34.03528
34.05111
33.95833
34.17605
34.06659
34.19925
33.92899
34.01407
34.06703
34.1326
33.82376
33.92288
33.9508
34.3875
34.38344
34.69
34.67139
33.83062
33.83062
Longitude
-115.11
-115.103
-1 1 4.844
-115.357
-115.122
-117.924
-117.85
-118.217
-118.456
-117.842
-118.317
-118.227
-118.533
-118.211
-118.061
-117.751
-118.127
-118.189
-118.37
-118.43
-118.534
-118.528
-118.132
-118.131
-117.938
-117.938
Objective1
UNKNOWN
POPULATION
EXPOSURE
POPULATION
EXPOSURE
GENERAL/BACKGR
OUND
HIGHEST
CONCENTRATION
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
Setting2
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
Land Use3
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
Scale4
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
URBAN SCALE
MIDDLE SCALE
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
MIDDLE SCALE
MIDDLE SCALE
MIDDLE SCALE
URBAN SCALE
URBAN SCALE
Monitor5
Years
2
3
5
7
7
12
12
1
12
1
11
11
12
12
10
12
12
11
9
2
2
5
6
5
5
4
Ht
(m)
3
4
4
4
3.5
2
6
5
5
5
13
6
7
6
6
4
6
4
5
3
5
4
Elev (m)
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
Roadway6
Dist
(m)
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
Type
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
A-14
-------
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
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Miami
Monitor ID
060591 003
060595001
06065001 2
060655001
060658001
060659001
060710001
060710012
060710014
060710015
060710017
060710306
060711004
060711234
060712002
060714001
060719004
061110005
061110007
061111003
061111004
061112002
061112003
061113001
120110003
120110031
120118002
120860027
120864002
Latitude
33.67464
33.92513
33.92086
33.85275
33.99958
33.67649
34.895
34.4261 1
34.5125
35.775
34.14194
34.51
34.10374
35.76389
34.10002
34.41806
34.10688
34.38694
34.21
34.44667
34.44833
34.2775
34.2804
34.255
26.28111
26.272
26.087
25.733
25.79833
Longitude
-117.926
-117.953
-116.858
-116.541
-117.416
-117.331
-117.024
-117.563
-117.33
-117.367
-116.055
-117.331
-117.629
-117.396
-117.492
-117.285
-117.274
-119.416
-118.869
-119.27
-119.23
-118.685
-119.314
-119.143
-80.2828
-80.295
-80.111
-80.162
-80.2103
Objective1
UNKNOWN
UNKNOWN
POPULATION
EXPOSURE
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
UNKNOWN
POPULATION
EXPOSURE
OTHER
UNKNOWN
UNKNOWN
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
UNKNOWN
UNKNOWN
POPULATION
EXPOSURE
HIGHEST
CONCENTRATION
GENERAL/BACKGR
OUND
POPULATION
EXPOSURE
HIGHEST
CONCENTRATION
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
POPULATION
EXPOSURE
HIGHEST
Setting2
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
URBAN AND
CENTER CITY
RURAL
SUBURBAN
SUBURBAN
URBAN AND
CENTER CITY
SUBURBAN
URBAN AND
CENTER CITY
RURAL
SUBURBAN
SUBURBAN
SUBURBAN
RURAL
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
SUBURBAN
RURAL
RURAL
SUBURBAN
SUBURBAN
SUBURBAN
URBAN AND
Land Use3
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
INDUSTRIAL
MOBILE
RESIDENTIAL
RESIDENTIAL
DESERT
INDUSTRIAL
RESIDENTIAL
COMMERCIAL
AGRICULTURAL
RESIDENTIAL
MOBILE
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
Scale4
MIDDLE SCALE
NEIGHBORHOOD
MIDDLE SCALE
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
URBAN SCALE
MIDDLE SCALE
URBAN SCALE
NEIGHBORHOOD
URBAN SCALE
URBAN SCALE
NEIGHBORHOOD
NEIGHBORHOOD
Monitor5
Years
12
11
9
12
12
12
12
2
5
2
3
7
11
9
12
3
12
7
9
1
7
12
9
12
3
8
11
11
11
Ht
(m)
6
82
4
6
4
8
4
4
4
6
1
5
5
1
5
4
4
2
4
6
4
4
16
4
Elev (m)
0
82
677
171
250
1440
690
4100
876
498
607
913
369
545
381
1006
0
320
244
231
262
314
3
43
3
3
3
2
5
Roadway6
Dist
(m)
202
570
432
75
133
522
64
30
18
42
64
38
349
1000
81
111
169
63
89
18
56
471
90
307
22
103
1000
15
87
Type
3
3
1
3
3
4
3
3
3
3
3
3
2
3
3
3
3
3
2
3
1
1
3
3
4
3
3
A-15
-------
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
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Monitor ID
090010113
09001 9003
090090027
090091123
340030001
340030005
340130011
340130016
340131003
340170006
34021 0005
34023001 1
340273001
340390004
340390008
360050080
360050083
360050110
36047001 1
360590005
360610010
36061 0056
36081 0097
36081 0098
Latitude
41.18361
41.11833
41.30111
41.31083
40.80833
40.89858
40.72667
40.72222
40.7575
40.67025
40.28319
40.46218
40.78763
40.64144
40.60083
40.83608
40.86586
40.81616
40.73277
40.74316
40.73944
40.75917
40.75527
40.7842
Longitude
-73.1903
-73.3367
-72.9028
-72.9169
-73.9928
-74.0299
-74.1442
-74.1469
-74.2005
-74.1261
-74.7422
-74.4294
-74.6763
-74.2084
-74.4419
-73.9202
-73.8808
-73.9021
-73.9472
-73.5855
-73.9861
-73.9665
-73.7586
-73.8476
Objective1
CONCENTRATION
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
POPULATION
EXPOSURE
UNKNOWN
UNKNOWN
POPULATION
EXPOSURE
UNKNOWN
POPULATION
EXPOSURE
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
UNKNOWN
HIGHEST
CONCENTRATION
POPULATION
EXPOSURE
HIGHEST
CONCENTRATION
UNKNOWN
UNKNOWN
HIGHEST
CONCENTRATION
HIGHEST
CONCENTRATION
HIGHEST
CONCENTRATION
HIGHEST
CONCENTRATION
GENERAL/BACKGR
OUND
UNKNOWN
Setting2
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
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
Land Use3
COMMERCIAL
FOREST
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
INDUSTRIAL
INDUSTRIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
AGRICULTURAL
AGRICULTURAL
INDUSTRIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
INDUSTRIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
Scale4
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
MIDDLE SCALE
Monitor5
Years
3
8
2
9
3
4
5
1
11
11
11
11
11
11
3
5
12
6
1
11
4
10
3
7
Ht
(m)
4
5
3.67
5
4
3
4
5
4
5
4
4
5
4
4
15
15
13
5
38
10
12
8
Elev (m)
3
4
11
18
61
6
3
3
48.45
3
30
21
274
5.4
0
15
24
0
9
27
38
15
0
6
Roadway6
Dist
(m)
8
508
237
14
82
172
232
6
25
266
442
298
227
37
99
122
132
76
171
32
55
62
197
9
Type
3
4
1
2
3
5
1
1
3
3
1
3
3
4
3
3
5
3
3
3
3
3
3
3
A-16
-------
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
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
Monitor ID
36081 01 24
361 030009
100031003
100031007
100032004
340070003
4201 7001 2
420450002
420910013
421010004
421010029
421010047
040130019
040133002
040133003
040133010
040134005
040134011
040139997
490490002
171630010
291830010
291831002
291 890001
291 890004
291 890006
291 893001
Latitude
40.7362
40.8275
39.76111
39.55111
39.73944
39.92304
40.10722
39.83556
40.11222
40.00889
39.95722
39.94472
33.48385
33.45793
33.47968
33.46093
33.4124
33.37005
33.50364
40.25361
38.61203
38.57917
38.8725
38.52167
38.5325
38.61361
38.64139
Longitude
-73.8232
-73.0569
-75.4919
-75.7308
-75.5581
-75.0976
-74.8822
-75.3725
-75.3092
-75.0978
-75.1731
-75.1661
-112.143
-112.046
-111.917
-112.117
-111.935
-112.621
-112.095
-111.663
-90.1605
-90.841 1
-90.2264
-90.3436
-90.3828
-90.4958
-90.3458
Objective1
POPULATION
EXPOSURE
UNKNOWN
POPULATION
EXPOSURE
OTHER
UNKNOWN
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
POPULATION
EXPOSURE
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
Setting2
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
Land Use3
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
Scale4
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
MIDDLE SCALE
URBAN SCALE
NEIGHBORHOOD
URBAN SCALE
Monitor5
Years
5
6
5
1
4
10
12
12
11
11
10
9
10
12
10
9
1
2
5
12
12
3
12
3
6
11
11
Ht
(m)
5
2
2
4
7
11
11
4.3
11.3
5.8
4.2
4
4
4
4
3
4
4
4
4
4
Elev (m)
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
Roadway6
Dist
(m)
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
Type
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-17
-------
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
Monitor ID
291 895001
291 897002
291 897003
2951 00072
295100080
295100086
110010017
110010025
110010041
110010043
240053001
2451 00040
2451 00050
51 01 30020
51 0590005
51 059001 8
51 0591 004
51 0591 005
510595001
511071005
511530009
51 51 00009
Latitude
38.7661 1
38.72722
38.72092
38.62422
38.68283
38.67227
38.90361
38.97528
38.89722
38.91889
39.31083
39.29806
39.31861
38.8575
38.89389
38.7425
38.86806
38.83752
38.93194
39.02444
38.85528
38.81083
Longitude
-90.2858
-90.3794
-90.367
-90.1987
-90.2468
-90.239
-77.0517
-77.0228
-76.9528
-77.0125
-76.4744
-76.6047
-76.5825
-77.0592
-77.4653
-77.0775
-77.1431
-77.1632
-77.1989
-77.49
-77.6356
-77.0447
Objective1
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
Setting2
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
Land Use3
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
COMMERCIAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
COMMERCIAL
AGRICULTURAL
RESIDENTIAL
COMMERCIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
RESIDENTIAL
Scale4
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
URBAN SCALE
NEIGHBORHOOD
NEIGHBORHOOD
REGIONAL SCALE
NEIGHBORHOOD
NEIGHBORHOOD
NEIGHBORHOOD
URBAN SCALE
Monitor5
Years
10
6
4
10
5
6
1
12
12
12
8
11
1
12
11
3
6
4
10
8
12
12
Ht
(m)
2
4
4
14
4
4
10
11
4.6
4.2
4
7
4
4
11
4
4
4
11
Elev (m)
168
168
0
154
152
0
20
91
8
50
5
12
49
171
77
11
110
83.9
106
0
111
23
Roadway6
Dist
(m)
421
59
112
43
116
133
54
106
141
278
186
14
338
80
315
54
84
50
18
75
196
83
Type
3
3
3
4
3
3
3
3
4
3
3
3
2
3
5
3
5
3
5
3
2
3
Notes:
1 Objective indicates the reason for measuring air quality by the monitor. Sites located to determine the highest concentration expected to occur in the area covered by the network (Highest
Concentration), sites located to measure typical concentrations in areas of high population (Population Exposure), sites located to determine the impact of significant sources or source categories on air
quality (Source Oriented), sites located to determine general background concentration levels (General Background), sites located to determine the extent of regional pollutant transport among populated
areas and in support of secondary standards (Regional Transport), sites located to measure air pollution impacts on visibility, vegetation damage, or other welfare-based impacts (Welfare Related
Impacts), sites are established to characterize upwind background and transported ozone and its precursor concentrations entering the area and will identify those areas which are subjected to transport
(Upwind Background), sites are established to monitor the magnitude and type of precursor emissions in the area where maximum precursor emissions are expected to impact and are suited for the
A-18
-------
Location
Monitor ID
Latitude
Longitude
Objective1
Setting2
Land Use3
Scale4
Monitor5
Years
Ht
(m)
Elev (m)
Roadway6
Dist
(m)
Type
Table A-7. Attributes of location-specific ambient monitors used for air quality characterization and the distance to nearest major roadway.
monitoring of urban air toxic pollutants (Max. Precursor Impact), sites are intended to monitor maximum ozone concentrations occurring downwind from the area of maximum precursor emissions (Max.
Ozone Concentration), and sites are established to characterize the downwind transported ozone and its precursor concentrations exiting the area and will identify those areas which are potentially
contributing to overwhelming transport in other areas (Extreme Downwind).
2 Setting is the description of the environmental setting within which the site is located
3 Land use indicates the prevalent land use within 1 /4 mile of that site.
4 Scale indicates what the data from a monitor can represent in terms of air volumes associated with area dimensions. Micro - 0 to 100 meters; Middle -100 to 500 meters; Neighborhood - 500 meters to
4 kilometers; Urban Scale - 4 to 50 kilometers; Regional Scale - 50 kilometers up to 1000km.
5 Years is the number of valid site-years available for the monitor. Monitor probe height (Ht) and site elevation (Elev) above sea level are given in meters (m).
6 Distances (Dist) to nearest major roadway are given in meters (m). Major road types were defined as: 1 =primary limited access or interstate, 2=primary US and State highways, 3=Secondary State
and County, 4=freeway ramp, 5=other ramps.
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
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
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
1 7031 4002
1 7031 4201
1 7031 8003
171971011
1 80890022
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
63
7
63
1
8
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
6.7
6.5
7.3
4.0
5.1
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
2.6
1.5
2.0
3.8
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
0.5
4.0
1.7
4.0
0.8
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
0.5
4.0
2.3
4.0
0.8
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
7.2
6.6
8.0
4.0
4.1
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
9.8
9.0
9.6
4.0
9.4
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
9.9
9.0
9.7
4.0
9.4
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
122
8
361
20
815
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
407
3
1201
1680
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
5
5
5
20
8
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
5
5
5
20
8
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
9
8
18
20
243
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
1677
14
6216
20
4936
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
2465
14
7141
20
4936
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
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
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Los Angeles
ID
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
320030022
320030023
320030073
320030078
320030539
320030557
320030563
320030601
320031019
320032002
060370002
n1
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
7
0
0
0
5
4
1
0
0
1
7
Distance (km) to Source emissions >5 tpy and within 10 km
mean
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
4.6
6.9
9.1
7.6
9.9
3.1
std
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
0.9
1.2
1.2
1.1
min
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
3.8
4.7
7.3
7.6
9.9
1.6
2.5
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
3.8
4.7
7.3
7.6
9.9
1.6
50
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
3.9
7.2
9.7
7.6
9.9
2.9
97.5
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
5.6
7.9
9.7
7.6
9.9
4.5
max
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
5.6
7.9
9.7
7.6
9.9
4.5
Emissions (tpy) of Sources within 10 km and >5 tpy
mean
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
175
816
807
84
84
10
std
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
222
760
877
4
min
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
30
18
18
84
84
5
2.5
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
30
18
18
84
84
5
50
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
77
851
772
84
84
9
97.5
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
650
1665
1665
84
84
16
max
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
650
1665
1665
84
84
16
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
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
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
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
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
n1
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
3
0
3
3
0
3
19
2
20
1
8
5
20
0
0
4
Distance (km) to Source emissions >5 tpy and within 10 km
mean
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
6.9
6.0
4.4
6.1
7.3
1.6
5.7
6.5
5.8
6.9
4.7
6.6
std
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
1.9
2.6
4.6
2.6
1.7
0.4
2.2
2.5
2.5
2.2
1.0
min
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
5.3
3.5
1.7
3.6
4.3
1.3
2.0
6.5
1.5
3.1
1.7
5.2
2.5
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
5.3
3.5
1.7
3.6
4.3
1.3
2.0
6.5
1.5
3.1
1.7
5.2
50
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
6.5
5.9
1.8
5.7
7.4
1.6
5.8
6.5
5.7
7.7
4.2
6.8
97.5
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
9.0
8.6
9.7
8.9
9.8
1.9
9.6
6.5
9.0
9.6
9.3
7.5
max
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
9.0
8.6
9.7
8.9
9.8
1.9
9.6
6.5
9.0
9.6
9.3
7.5
Emissions (tpy) of Sources within 10 km and >5 tpy
mean
12
23
15
32
47
18
10
22
28
22
12
76
205
224
29
26
22
22
14
14
65
19
119
11
209
199
752
199
57
1122
44
577
171
68
25
63
std
8
27
10
31
59
21
4
24
33
20
8
159
754
850
20
19
28
28
12
12
116
26
358
9
321
327
1045
327
120
1168
65
438
118
20
113
min
5
5
5
6
6
5
6
5
5
5
5
5
6
6
8
8
6
6
5
5
5
6
5
5
10
6
12
6
5
296
5
577
5
8
5
5
2.5
5
5
5
6
6
5
6
5
5
5
5
5
6
6
8
8
6
6
5
5
5
6
5
5
10
6
12
6
5
296
5
577
5
8
5
5
50
9
11
13
20
24
10
10
12
12
16
9
16
21
21
18
18
9
9
8
8
10
9
10
11
38
15
296
15
18
1122
17
577
10
19
18
7
97.5
29
115
36
109
215
86
15
86
115
70
30
744
4256
4256
54
54
64
64
46
46
434
109
1254
17
579
577
1948
577
492
1948
250
577
1254
278
76
232
max
29
115
36
109
215
86
15
115
115
70
30
789
4256
4256
54
54
64
64
46
46
434
109
1254
17
579
577
1948
577
492
1948
250
577
1254
278
76
232
A-22
-------
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
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
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
ID
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
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
n1
3
7
0
0
0
3
8
7
3
5
6
48
18
43
44
32
42
8
20
1
46
12
54
37
55
56
7
52
54
11
48
24
3
39
11
32
69
10
30
12
32
74
Distance (km) to Source emissions >5 tpy and within 10 km
mean
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
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
std
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
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
min
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
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
2.5
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
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
50
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
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
97.5
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
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
max
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
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
Emissions (tpy) of Sources within 10 km and >5 tpy
mean
18
35
31
22
538
127
280
234
468
53
273
267
77
369
115
95
20
134
23
241
171
236
296
372
494
470
65
262
436
537
282
323
223
87
85
504
89
58
74
std
4
51
19
15
711
179
484
447
1506
79
1372
1357
149
1420
244
175
341
36
776
725
769
787
500
1453
1429
77
820
1136
759
481
494
403
196
96
1055
232
111
148
min
14
5
14
8
48
12
14
7
6
6
5
5
5
5
8
6
20
5
5
6
6
6
7
7
5
7
13
6
8
40
5
6
5
5
11
5
5
5
5
2.5
14
5
14
8
48
12
14
7
7
6
5
5
5
6
8
6
20
6
5
6
6
6
7
7
7
7
13
7
8
40
5
6
5
5
11
5
5
5
5
50
20
13
27
18
192
37
86
64
31
21
18
18
22
24
32
36
20
21
10
29
21
29
42
223
50
50
26
31
26
161
62
63
45
24
57
73
12
20
19
97.5
22
146
51
51
1689
333
1144
1144
4440
307
640
640
640
2213
718
792
20
594
134
3676
4440
3676
3676
1451
4440
4440
246
3676
4440
1410
2058
1351
1312
477
275
4968
823
571
477
max
22
146
51
51
1689
333
1144
1144
9022
307
9022
9022
640
9022
718
792
20
2213
134
4440
4440
4440
4440
1451
9022
9022
246
4440
4440
1410
2058
1351
1312
1478
275
4968
823
571
1033
A-23
-------
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
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
St Louis
St Louis
St Louis
St Louis
St Louis
St Louis
St Louis
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
421 01 0047
040130019
040133002
040133003
040133010
040134005
040134011
040139997
490490002
171630010
291830010
291831002
291 890001
291 890004
291 890006
291 893001
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
73
11
6
10
10
11
1
10
7
48
1
9
10
6
8
16
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
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
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
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
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
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
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
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
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
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
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
9.6
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
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
9.8
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
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
95
106
21
50
115
81
18
115
60
112
7821
1868
24
38
25
22
46
28
24
77
98
94
557
40
124
109
1034
122
129
558
13
1104
80
94
30
14
809
std
221
313
19
80
328
116
328
38
178
4704
20
37
34
43
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
9
5
6
18
5
7
5
7821
7
5
7
6
5
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
9
5
6
18
5
7
5
7821
7
5
7
6
5
5
5
5
5
5
5
11
11
11
11
6
6
6
11
8
9
14
14
17
8
14
50
19
10
15
24
10
38
18
10
83
17
7821
8
15
28
11
11
15
15
15
16
17
17
34
26
66
46
45
56
56
46
13
13
19
19
22
12
156
97.5
1033
1049
56
272
1049
350
18
1049
102
538
7821
14231
60
105
105
181
181
143
143
508
848
848
6009
98
410
410
10756
1118
1118
6009
18
6009
571
571
58
27
6009
max
1478
1049
56
272
1049
350
18
1049
102
848
7821
14231
60
105
105
181
181
143
143
848
848
848
6009
98
410
410
10756
1118
1118
6009
18
6009
571
571
58
27
6009
Notes:
1 n is the number of sources emitting >5 tons per year (tpy) NOX within a 10 kilometer radius of a monitor in a particular location.
2 The min, 2.5, med, 97.5, and max represent the minimum, 2.5th, median, 97.5th, and maximum percentiles of the distribution for the distance in meters (m) to the source emission.
A-24
-------
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.
Box-plots were constructed to display the distribution across sites and years (or hours for the
hourly concentrations) for a single location. The box extends from the 25l 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-25
-------
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-26
-------
Annual Mean
45-
35-
25-
Los Angeles Miami New York Philadelphia Washington
.ocaiion
Boston Chicago Cleveland Denver Detroit
Figure A-1. Distributions of annual mean NO2 ambient monitoring concentrations for selected CMSA
locations, years 1995-2006.
Annual Mean
40-
30-
25-
20-
Atlanta Colorado r.l PaiK> Jacksonville t.us Vegas Phoenix Pnt\o St I.UUIK Other MSA Othr Non-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-27
-------
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 Cones
60-
55-
50-
45-
40-
35-
30-
15-
M-
10-
5-
Boston Chicago Cleveland Denver Detroit I.as Angeles Miami New York 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 box-plots for hourly concentrations were created using a different procedure than for the annual
statistics, because of the large number of hourly values and the inability of the graphing procedure to
allow frequency weights. Therefore, the appropriate weighted percentiles and means were calculated
and plotted as shown, but the vertical lines composing the sides of the box were omitted.
A-28
-------
I [nurly Ortnc
1000.0 H
lor
0.1-
location ""Boston
-3 -2
—Chicago "Cleveland
-Denver
\omnal Quantilc
Detroit Los Angeles Miami
345
—New York —Philadelphia —Washington
Zero values^ ere r
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 NCh 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-29
-------
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
.«
32
31
30
2')
21
25
23
21
20
19
18
17
16
15
14
-T-
•
•
— • — — | — — ' —
— 1 — I 1 i 1
S EE3 —
•
,
— i —
1000310031 1000310071 1000320041 3400700032 4201700121 4204500021 4209100131 4210100043 4210100292 4210100471
Monitor
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
Atlanta
Boston
Chicago
Cleveland
Colorado Springs
Denver
Detroit
El Paso
Las Vegas
Los Angeles
Miami
New York
Philadelphia
Phoenix
St. Louis
Means Comparison
F
Statistic p-value
119
47.3
123
12.1
8.7
85.3
13.2
36.0
137
49.0
111
106
48.9
20.4
51.5
0.001
0.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
Central Values
Comparison
Kruskal-
Wallis p-value
45.2
96.5
76.7
15.4
18.8
32.0
13.1
31.6
45.4
325
36.2
163
68.8
32.2
82.1
0.001
0.001
O.001
0.002
0.009
0.001
0.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
Scales Comparison
Mood p-value
28.6
79.9
68.5
7.5
8.7
23.0
7.8
35.3
35.2
240
29.9
151
33.0
23.6
69.0
0.001
0.001
O.001
0.058
0.273
0.001
0.020
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
A-30
-------
Concentration
Parameter
Hourly
Location
Washington DC
Other MSA
Other Not MSA
Atlanta
Boston
Chicago
Cleveland
Denver
Colorado Springs
Detroit
El Paso
Las Vegas
Los Angeles
Miami
New York
Philadelphia
Phoenix
St. Louis
Washington
Other MSA
Other Not MSA
Means Comparison
F
Statistic p-value
48.6
82.5
76.9
35917
17884
11611
4191
25130
5541
4125
10503
22567
27288
10669
20052
13759
5626
14807
14262
19557
17630
0.001
0.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
O.001
O.001
Central Values
Comparison
Kruskal-
Wallis p-value
104
2152
424
137022
312994
142034
14102
104800
48252
10442
57694
136455
1050310
68580
404234
112129
35645
178180
223040
6306431
1580139
0.001
0.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
O.001
O.001
Scales Comparison
Mood p-value
71.2
1934
372
17330
59896
37224
1985
2864
3921
424
18334
28972
269190
43090
91104
4903
6747
47842
30974
2164452
491390
0.001
0.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
O.001
O.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-31
-------
Annual Mean
100.0-
10.0-
-3
-2
-I
0 1 2
Normal Quantilc
year - I
-------
Q-Q Plot of Annual Mean
loc_type=CMSA loc_name=Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD CMSA
Annual Mean
100-
MB ~ 1995- 1996- 1997- 1998
Normal Qiiantili.1
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 NO2
concentration in the Los Angeles CMSA, comprising between 26 and 36 monitors in operation
per year. NO2 concentrations are decreased with increasing calendar year of monitoring with the
distribution of hourly concentrations lowest in the more recent years of monitoring. The pattern
of variability in NO2 concentration within a year at this location is also similar when comparing
across years based on similarities in the shape of each years respective curve.
A-33
-------
Q-Q Plot of Hourly Concentrations
loc_type=CMSA loc_ncime=Los Angeles-Riverside-Orange County, CA CMSA
Hourly Cone
1000.0-
-3-2-1012315
Ncrmal Quanlilc
year - 1W5 - ] 9% - 1997 - 1998 1999 2000 - 2001 - 2002 - 2003 - 2004 - 2005 - 2006
Zcrovflhics ucrc rcpliKcd In II S
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-34
-------
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-
01-
-4 -3 0-101:34
Normal Quantile
year - 1995- 1996- 1997- 1998 1999 2000 ~ 2002 ~ 2003 - 2004- 200S
Zero values ncre replaced bj' 0.5
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-35
-------
Annual Mean
20-
10-
18-
17-
16-
li-
14-
13-
12-
11-
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Year
Figure A-10. Distributions of annual average NO2 concentrations in the Other Not MSA group location,
years 1995-2006.
A-5.5 Detailed Results by Year and Location
This section contains the ambient air quality analysis results by year for each of the named
locations. Boxplots were constructed to display the annual average and hourly concentration
distributions across years for a single location. The box extends from the 25th to the 75th
percentile, with the median shown as the line inside the box. The whiskers extend from the box
to the 5th and 95th percentiles. The extreme values in the upper and lower tails beyond the 5th and
95th percentiles are not shown to allow for similar scaling along the y-axis for the plotted
independent variables. The mean is plotted as a dot; typically it would appear inside the box,
however it will fall outside the box if the distribution is highly skewed. All concentrations are
shown in parts per billion (ppb). The boxplots for hourly concentrations were created using a
different procedure than for the annual statistics, because of the large number of hourly values
and the inability of the graphing procedure to allow frequency weights. Therefore, the
appropriate weighted percentiles and means were calculated and plotted as shown, but the
vertical lines composing the sides of the box are essentially omitted. Tables are provided that
summarize the complete distribution, with percentiles given in segments of 10.
A-36
-------
T
I
I a
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 Coi
Table A-12. Distribution of hourly NO2 ambient concentrations (ppb) by year, Boston
CMSA.
IW5 IW6 IW I'm I'm 2000 2001 2002 2003 20W 2005 2IW>
Yetr
Figure 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
A-37
-------
Figure A-13. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Boston CMSAset A, 1995-2006.
Hourly Cones
50-
Figure A-14. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Boston CMSA set A, 1995-2006.
Table A-13. Distribution of annual average NO2 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
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-38
-------
Annual Mean
20-
I
2M270023I 330IIOOI6I 330IIOOI<>1 Ml 10020] 330ISOOO<>I 330150013] 330I500UI 330I500ISI
Monitor
Figure A-15. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Boston CMSA set B, 1995-2006.
2502700231 .1301100161 3301IOOW1 3301100201 33015000HI Itoilimll Xlrililnll.il 3301SOOI5I
Monitor
Figure A-16. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Boston CMSA set B, 1995-2006.
Table A-15. Distribution of annual average NO2 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
A-39
-------
I
Figure A-17. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Chicago CMSA.
Figure A-18. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Chicago CMSA.
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
A-40
-------
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-41
-------
IW5
IW8 I9W !000 200I MM !»3 MM MS ?«X>
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-42
-------
me-
-------
Figure A-25. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Denver CMSA.
Hourly Cones
1.0-
2000 :ooi MO:
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
o
J
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-44
-------
Annual Meal
.ui-
Figure A-27. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Denver CMSA, 1995-2006.
X>] OSOS'IOOOSI WWS'JWO'JI
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-45
-------
IWS l»6 IW7 IWE
5000 2001 2003 3003 30M 2005 J006
Figure A-29. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Detroit CMSA.
I»S l«6 IW7 l»8
2000 2001 2002 200) 2004 200! 2006
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-46
-------
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-47
-------
I
I
I
Figure A-33. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Los Angeles CMSA.
Hourly Cones
10-
l«5 IWd l«3 IWS
2000 2001 2002 200.1 2004 MOS 2006
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-48
-------
I
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
Hourly Cones
IIJO-
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-49
-------
Annual Meal
.KI-
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
Hourly Cones
70-
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-50
-------
Annual Mean
SO-
I
000011
77771 1
1 1 1 1 1 1
1 24900
200000
300000
421457
111111
)
)
1 1 1
1 1 1
1 1 1
000
000
2 j 1
1 1 1
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
Hourly Cones
70-
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-51
-------
Annual Mean
17-
il
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
]*>! l»4 IWT I«S IWI 3000 3001 2003 300! 3004 300!
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-52
-------
Annual Mean
17-
I
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
Figure A-43. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Miami CMSA, 1995-2006.
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-53
-------
I
I
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-54
-------
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
Hourly Cones
70-
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-55
-------
T
3600500801 3600500831 3600501101 3604700111 3605900052 3606100101 3606100561 360SI00971 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-56
-------
Annual Mean
J4-
13-
JZ-
II-
I
li« 10%
MOO MOI
!004 M05
•is..
Figure A-51. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Philadelphia CMSA.
Houily Cones
50-
Figure A-52. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Philadelphia CMSA.
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
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-57
-------
T
B
B
I
I
I0003IOIJ3I I0003I007I 100033004I 3400TO003! 420I700I2I 4!(MS«Xl2l 43NIMI3I 411(110004! 42IOI005V3 4IIOI0047]
VloniLoi
Figure A-53. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Philadelphia CMSA, 1995-2006.
HMiiiHiii 10003I007I I00032004I 34007000)2 420I700I2I j;i.Hi(K>:i:i 4209IOOI3I 42IOIM043 4210100242 42IOIM47I
Monitor
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-58
-------
T
T
Figure A-55. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Washington DC CMSA.
I lourly Cones
50-
||»5
IW IWB IW 2000
2002 2003 2ttH 20M 2006
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-59
-------
Annual Meal
id-
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
Hourly Cones
SO-
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-60
-------
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.
Hourly Cones
so-
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-61
-------
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
1*»S l«6
2000 2001 2002 2003 2004 2005 2006
Figure A-62. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Atlanta MSA.
A-62
-------
I
T
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
o
6
6
plO
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
plO
3
4
5
1
2
p20
5
6
8
1
o
6
p30
8
8
11
2
o
6
p40
10
10
14
o
J
4
p50
13
12
17
o
J
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-63
-------
I I
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.
II MI.IIH
50-
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-64
-------
OS041600I1 0804160041 0804160051 0804160061 0804160091 0804160111 0804160131 0804160181
Monitor
Figure A-67. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Colorado Springs MSA, 1995-
2006.
Hourly COT
i-1! QHMIM05I 0804160061
'H 080-1 1 Will 0804160)3) 0&041&0181
Figure A-68. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Colorado Springs MSA, 1995-2006.
Table A-67. Distribution of annual average NO2 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
plO
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
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
plO
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-65
-------
Table 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
Figure A-69. Distribution of annual average NO2 ambient
concentrations (ppb) by year, El Paso MSA.
Hourly Cones
60-
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
1*17 i*:>s
Figure A-70. Distribution of hourly NO2 ambient concentrations
(ppb) by year, El Paso MSA.
A-66
-------
Figure A-71. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, El Paso MSA, 1995-2006.
4SI4IU0271 M14I003SI 4814100471 48I4IIXH41 48I4IW55I 48HIW571 -1SMIW58I
Monitor
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
plO
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
plO
16
10
5
5
3
o
6
i
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-67
-------
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-68
-------
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
plO
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
plO
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-69
-------
I
I
I
IWT l*)8 1W !000 3001 200J 200J M04 MO!
Figure A-77. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Las Vegas MSA.
Hourly Cones
70-
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
I*)S I'M* IWJ IW8 1999 :«« MOI 300J 200.1 SOM 2M5
Figure A-78. Distribution of hourly NO2 ambient concentrations
(ppb) by year, Las Vegas MSA.
A-70
-------
3200300221 3200300231 3200300731 3200300781 5200305391 320030SJ7I 32003051.31 320030601] 3200310191 3200320021
Monitor
Figure A-79. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Las Vegas MSA, 1995-2006.
3200300221 3200300231 3200300731 3200300781 3200305311 320030J57I 3200305631 320030601] 32003IOW 3200320021
Moittlor
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
3200306011
3200310191
3200320021
n
7
4
7
1
8
2
o
J
5
1
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
plO
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
o
3
22
p70
5
9
8
9
25
27
19
8
o
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
plO
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-71
-------
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
IW MOO
Figure A-81. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Phoenix MSA.
Hourly Cones
70-
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-72
-------
I
040IJ.W3I !Xli| I.IOIOI 0401J400S1 owl.
Figure A-83. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Phoenix MSA, 1995-2006.
Hourly Cones
TO-
040I300IOI 040[3300:
-------
Figure A-85. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Provo MSA.
Hourly Cones
130-
IP
100
IW! IW6 l»7 I«S IWS MMP
2002 KiOJ !OM JMS MM
Figure A-86. Temporal distribution of hourly 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
A-74
-------
Annual Mem
in-
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
Figure A-87. Distribution of annual average NO2 ambient
concentration (ppb) by monitor, Provo MSA, 1995-2006.
Hourly Cones
50-
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
Figure A-88. Distribution of hourly NO2 ambient concentration
(ppb) by monitor, Provo MSA, 1995-2006.
A-75
-------
II
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
1«» MO 1001 2002 2001 WU MS !006
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-76
-------
Annual Meal
id-
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
Hourly Cows
50-
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
plO
8
1
2
7
4
3
8
6
8
4
11
7
6
p20
10
2
o
6
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
o
J
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-77
-------
I
I
I
Hill
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
Hourly Cones
40-
2000 2001 2002 2001 2004 2005 2:006
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-78
-------
Annual Mean
20-
5000 3001 MM 200)
MM 200!
Figure A-95. Distribution of annual average NO2 ambient
concentrations (ppb) by year, Other Not MSA.
2002 200J 2004 200S
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-79
-------
A-6 Technical Memorandum on Regression Modeling
This section provides a technical memorandum submitted to EPA by ICF International. The
memo has been formatted for consistency with the entire appendix. The work documented in
section A-6 was part of the initial exploratory analyses conducted to develop and evaluate the
relationship between the annual average concentration and the number of exceedances of
potential health effect benchmark levels. Conceptually, the approach was based on analyses
conducted for the last NO2 NAAQS review in 1995 (McCurdy, 1994).
Staff found that use of a regression model applied to the 1995-2006 ambient air quality
unsatisfactory, both because the models did not show a strong relationship between the annual
means and the number of exceedances, and because the predicted numbers of exceedances for
evaluating the current annual standard scenario were in many cases extremely high and
uncertain. In addition, due to the lack of data containing a number of values at or above 200 ppb
1-hour, staff decided to develop empirical exceedance estimates, as described in the REA.
There have been no modifications or re-analyses to the regression models or results since
they were first produced and documented in the 1st draft REA Technical Support Document
(TSD). They do not have any relationship to the estimated concentrations or numbers of
exceedances provided in the Final REA. Nevertheless, the regression models explored,
developed, and applied are described with the following to give the reader justification as to why
the regression model was not used and for why an empirical approach was ultimately used in the
Final REA to estimate the number of exceedances of the short-term (1-hour) potential health
effect benchmark levels.
A-6.1 Summary
This section describes the regression analyses of 1995 to 2006 NO2 hourly concentration
data. Regression was used to estimate the annual number of exceedances of 150 ppb from the
annual mean, in 20 locations (mostly large urban areas). Exposures to concentrations above
certain thresholds may be associated with adverse health effects. These models were applied in
an as-is scenario to estimate the annual exceedances at sites with annual means equal to the
1995-2006 current average for their location. These models were also applied in a current-
standard scenario to predict the annual exceedances at sites with annual means equal to the
current annual average NO2 standard of 0.053 ppm. The current-standard scenario is an
extrapolation to higher annual means than currently observed; the maximum annual mean across
all complete site-years was 51 ppb, in Los Angeles.
A-6.2 Data Used
All of the 1995 to 2006 NO2 hourly concentration data from AQS were compiled and annual
summary statistics for each site-year combination were computed. Of particular interest is the
long-term air quality measured by the annual mean and the short-term air quality measured by
the annual numbers of hourly exceedances of selected levels 150, 200, 250 and 300 ppb.
Exposures to concentrations above these thresholds may be associated with adverse health
effects. To make the results temporally representative, we restricted the analyses to the 20
percent of site-years that were 75 % complete, as defined by having data for 75 % of the hours in
a year and having data for at least 75 % of the hours in a day (i.e., 18 hours or more) on at least
A-80
-------
75 % of the days in a year. We also spatially grouped the data into 18 urban areas with high
annual means and high exceedances; these locations were all CMSAs or MSAs either with at
least one site-year annual mean above 25.7 ppb (the 90th percentile) or with at least one
exceedance of 200 ppb, as follows.
• Atlanta
• Boston
• Chicago
• Cleveland
• Colorado Springs
• Denver
• Detroit
• El Paso
• Jacksonville
• Las Vegas
• Los Angeles
• Miami
• New York
• Philadelphia
• Phoenix
• Provo
• St. Louis
• Washington DC
The remaining site-years were analyzed as two additional location groups: "Other
MSA/CMSA" site-years in an MSA or CMSA, and "Other Not MSA" site-years not in an MSA.
Thus we have a total of 20 "locations."
A-6.3 Regression Models
The regression modeling of the 1995-2006 NO2 data continues the analyses by McCurdy
(1994)4 of the 1988-1992 data. A regression model is used to estimate the mean number of
exceedances from the annual mean. McCurdy (1994) assumed normally distributed exceedances
and an exponential link function to estimate exceedances of 150, 200, 250, and 300 ppb based on
the 1988-1992 data. In this section we present the results of the regression analyses for
exceedances of 150 ppb using eight alternative models based on the 1995-2006 data.
Throughout this discussion, "exceedances" will refer to annual numbers of hourly exceedances
of 150 ppb, unless otherwise stated.
Of the eight models, the two selected regression models were the Poisson exponential model
and the normal linear model, stratified by location. The Poisson exponential model is of the
form:
• Number of exceedances has a Poisson distribution.
• Mean exceedances = exp(a + b x annual mean).
• The intercept a, and slope b, depend on the location.
4 McCurdy TR (1994). Analysis of high 1 hour NO2 values and associated annual averages using 1988-
1992 data. Report to the Office of Air Quality Planning and Standards, Durham NC.
A-81
-------
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.
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
Inverse Link
Linear
Linear
Exponential
Exponential
Strata (a
separate
model is
fitted in
each
stratum)
All
Location
All
Location
R squared
for all data
0.033
0.244
0.066
0.401
MinR
squared
among
locations
0.006
0.005
MaxR
squared
among
locations
0.616
0.981
Log-
Likelihood
-11527
-6065
-11438
-8734
Number of
strata in
final
model
1
13**
1
11***
A-82
-------
Poisson
Poisson
Poisson
Poisson
Linear
Linear
Exponential
Exponential
All
Location
All
Location
0.025
Not
Shown*
0.064
0.406
Not Shown*
0.004
Not Shown*
0.976
-4737
Not Shown*
-3660
-2694
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:
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
A-83
-------
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
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
A-84
-------
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-85
-------
Table A-98. Parameters for Poisson exponential model stratified by location.
Location
Type
MSA
MSA
MSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
MSA
MSA
MSA
CMSA
CMSA
CMSA
MSA
MSA
MSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
MSA
MSA
Location Name
Atlanta, GA
Atlanta, GA
Atlanta, GA
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
Colorado Springs, CO
Colorado Springs, CO
Colorado Springs, CO
Denver-Boulder-Greeley, CO CMSA
Denver-Boulder-Greeley, CO CMSA
Denver-Boulder-Greeley, CO CMSA
El Paso,TX
El Paso,TX
El Paso,TX
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
Phoenix-Mesa,AZ
Phoenix-Mesa,AZ
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
-5.081
0.140
1.000
-6.887
0.144
1.000
-14.209
0.548
1.000
-4.846
0.284
1.000
-4.399
0.137
1.000
-10.436
0.350
1.000
-5.628
0.181
1.000
-5.780
0.342
1.000
-6.800
0.147
1.000
-1.568
0.106
Standard
Error
1.917
0.099
0.000
2.832
0.116
0.000
4.374
0.164
0.000
0.401
0.012
0.000
1.186
0.038
0.000
2.455
0.074
0.000
0.253
0.006
0.000
1.641
0.114
0.000
1.269
0.037
0.000
0.400
0.013
Lower
Confidence
Bound
-9.975
-0.040
1.000
-14.693
-0.061
1.000
-25.210
0.283
1.000
-5.675
0.261
1.000
-7.182
0.070
1.000
-16.783
0.233
1.000
-6.134
0.169
1.000
-9.774
0.138
1.000
-9.560
0.079
1.000
-2.363
0.081
Upper
Confidence
Bound
-2.139
0.363
1.000
-2.757
0.430
1.000
-7.312
0.952
1.000
-4.097
0.309
1.000
-2.435
0.222
1.000
-6.664
0.538
1.000
-5.142
0.194
1.000
-3.068
0.606
1.000
-4.537
0.224
1.000
-0.798
0.131
P-
value
**
0.01
0.16
0.02
0.22
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
A-86
-------
Location
Type
MSA
CMSA
CMSA
CMSA
MSA/CMSA
MSA/CMSA
MSA/CMSA
Not MSA
Not MSA
Not MSA
Location Name
Phoenix-Mesa,AZ
Washington-Baltimore, DC-MD-VA-WV CMSA
Washington-Baltimore, DC-MD-VA-WV CMSA
Washington-Baltimore, DC-MD-VA-WV CMSA
Other MSA
Other MSA
Other MSA
Other Not MSA
Other Not MSA
Other Not MSA
Parameter*
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Estimate
1.000
-6.559
0.145
1.000
-5.137
0.152
1.000
-4.672
0.227
1.000
Standard
Error
0.000
3.054
0.135
0.000
0.222
0.010
0.000
0.467
0.036
0.000
Lower
Confidence
Bound
1.000
-14.610
-0.073
1.000
-5.580
0.132
1.000
-5.654
0.158
1.000
Upper
Confidence
Bound
1.000
-2.054
0.482
1.000
-4.711
0.172
1.000
-3.818
0.300
1.000
P-
value
**
0.03
0.28
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
MSA
MSA
MSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
MSA
MSA
MSA
CMSA
CMSA
Location Name
Atlanta, GA
Atlanta, GA
Atlanta, GA
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
Colorado Springs, CO
Colorado Springs, CO
Colorado Springs, CO
Denver-Boulder-Greeley, CO CMSA
Denver-Boulder-Greeley, CO CMSA
Parameter*
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Scale
Intercept
mean
Estimate
-0.041
0.008
0.226
-0.023
0.003
0.135
-3.259
0.176
1.755
-36.358
2.689
22.519
-0.439
0.044
Standard
Error
0.069
0.005
0.022
0.034
0.002
0.009
2.127
0.099
0.265
11.812
0.674
3.123
0.383
0.018
Lower
Confidence
Bound
-0.178
-0.002
0.189
-0.090
-0.001
0.119
-7.617
-0.027
1.341
-60.391
1.318
17.551
-1.211
0.008
Upper
Confidence
Bound
0.096
0.017
0.277
0.043
0.006
0.156
1.098
0.378
2.436
-12.326
4.061
30.362
0.332
0.080
P-value
**
0.55
0.11
0.49
0.17
0.13
0.08
0.00
0.00
0.25
0.01
A-87
-------
Location
Type
CMSA
MSA
MSA
MSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
CMSA
MSA
MSA
MSA
CMSA
CMSA
CMSA
MSA/CMSA
MSA/CMSA
MSA/CMSA
Not MSA
Not MSA
Not MSA
Location Name
Denver-Boulder-Greeley, CO CMSA
El Paso,TX
El Paso,TX
El Paso,TX
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
Phoenix-Mesa,AZ
Phoenix-Mesa,AZ
Phoenix-Mesa,AZ
Washington-Baltimore, DC-MD-VA-VW CMSA
Washington-Baltimore, DC-MD-VA-WV CMSA
Washington-Baltimore, DC-MD-VA-WV CMSA
Other MSA
Other MSA
Other MSA
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
1.097
-2.017
0.131
0.920
-3.301
0.194
4.723
-0.496
0.070
0.828
-0.230
0.013
0.407
-7.102
0.423
22.513
-0.032
0.003
0.208
-0.100
0.013
1.098
-0.064
0.021
0.549
Standard
Error
0.129
0.440
0.024
0.098
0.620
0.023
0.174
0.384
0.037
0.088
0.104
0.004
0.022
15.545
0.557
2.274
0.069
0.003
0.013
0.051
0.003
0.015
0.049
0.006
0.018
Lower
Confidence
Bound
0.885
-2.898
0.083
0.757
-4.519
0.148
4.402
-1.265
-0.005
0.681
-0.435
0.005
0.368
-38.177
-0.689
18.697
-0.167
-0.004
0.186
-0.201
0.006
1.069
-0.160
0.009
0.514
Upper
Confidence
Bound
1.408
-1.135
0.178
1.151
-2.083
0.240
5.085
0.273
0.144
1.036
-0.024
0.020
0.454
23.974
1.536
27.828
0.104
0.010
0.236
0.000
0.019
1.128
0.031
0.032
0.587
P-value
**
0.00
0.00
0.00
0.00
0.20
0.06
0.03
0.00
0.65
0.45
0.64
0.35
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-88
-------
SO
JLV
ji!
= » »»
»
* *% »
,- =
=
location "boston
t/lncayo
"(..'LeveJantl ™L>envei
Los AnyeJes "Miami
rMilyjL-JpJua
Figure A-97. Measured number of exceedances of 1-hour NO2 concentrations of 150 ppb versus annual
mean NO2 concentrations (ppb) for CMSA locations.
Predicted Exceedances
40
30
in
10
•10
location "Boston
20 30
Observed Exceedances
"C'le^eland "Denver ™Los Angeles "Miami "New York "Washington
50
Figure A-98. Predicted and observed exceedances of 1-hour NO2 concentrations of 150 ppb for CMSA
locations using Poisson exponential model.
A-89
-------
Predicted Exceedances
7
•
!
•
i
location ""Boston
""Cleveland ""Demer
Observed Exceedances
""Los Armeies Miami
""New York ""Washington
Figure A-99. Predicted and observed exceedances of 1-hour NO2 concentrations of 150 ppb for CMSA
locations using normal linear model
A-90
-------
Table A-100. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb using a
Poisson exponential model for the as-is and current-standard scenarios.
Location
Atlanta
Atlanta
Boston
Boston
Cleveland
Cleveland
Colorado
Springs
Colorado
Springs
Denver
Denver
El Paso
El Paso
Los Angeles
Los Angeles
Miami
Miami
New York
New York
Phoenix
Phoenix
Washington
Washington
Other MSA
Other MSA
Other Not
MSA
Other Not
MSA
Annual
Mean
(ppb)
53.0
12.9
53.0
16.8
53.0
21.2
53.0
16.3
53.0
18.7
53.0
17.7
53.0
24.3
53.0
9.7
53.0
25.5
53.0
27.3
53.0
19.4
53.0
13.9
53.0
7.0
Observed
Mean
Exceed-
ances
0.057
0.057
0.019
0.019
0.455
0.455
7.346
7.346
0.389
0.389
0.295
0.295
1.403
1.403
0.182
0.182
0.092
0.092
4.469
4.469
0.030
0.030
0.079
0.079
0.081
0.081
Observed
Max
Exceed-
ances
1
1
1
1
9
9
143
143
6
6
7
7
44
44
5
5
3
3
147
147
2
2
39
39
7
7
Predicted
Exceed-
ances
10.242
0.038
2.081
0.011
1000.000
0.073
1000.000
0.792
17.140
0.158
1000.000
0.015
53.244
0.293
1000.000
0.086
2.737
0.048
56.901
3.760
3.038
0.023
18.369
0.048
1000.000
0.046
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.012
0.008
0.002
0.001
578.253
0.011
1000.000
0.528
2.958
0.057
177.602
0.001
44.092
0.238
35.520
0.026
0.646
0.022
31.702
3.221
0.001
0.007
9.388
0.040
85.717
0.028
Upper
Bound
1000.000
0.181
1000.000
0.091
1000.000
0.474
1000.000
1.189
99.308
0.438
1000.000
0.142
64.297
0.360
1000.000
0.281
1 1 .604
0.104
102.130
4.389
1000.000
0.082
35.940
0.058
1000.000
0.075
95% Prediction
Interval for
Number of
Exceedances
Lower
Bound
0
0
0
0
364
0
1000
0
2
0
156
0
37
0
29
0
0
0
26
0
0
0
7
0
75
0
Upper
Bound
1000
1
1000
0
1000
1
1000
3
98
1
1000
1
73
2
1000
1
13
1
106
8
1000
0
41
1
1000
1
A-91
-------
Table A-101. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb using a
Normal linear model for the as-is and current-standard scenarios.
Location Name
Atlanta
Atlanta
Boston
Boston
Cleveland
Cleveland
Colorado
Springs
Colorado
Springs
Denver
Denver
El Paso
El Paso
Los Angeles
Los Angeles
Miami
Miami
New York
New York
Phoenix
Phoenix
Washington
Washington
Other MSA
Other MSA
Other Not MSA
Other Not MSA
Annual
Mean
(ppb)
53.0
12.9
53.0
16.8
53.0
21.2
53.0
16.3
53.0
18.7
53.0
17.7
53.0
24.3
53.0
9.7
53.0
25.5
53.0
27.3
53.0
19.4
53.0
13.9
53.0
7.0
Observed
Mean
Exceed-
ances
0.057
0.057
0.019
0.019
0.455
0.455
7.346
7.346
0.389
0.389
0.295
0.295
1.403
1.403
0.182
0.182
0.092
0.092
4.469
4.469
0.030
0.030
0.079
0.079
0.081
0.081
Observed
Max
Exceed-
ances
1
1
1
1
9
9
143
143
6
6
7
7
44
44
5
5
3
3
147
147
2
2
39
39
7
7
Predicted
Exceed-
ances
0.360
0.057
0.111
0.019
6.046
0.455
106.169
7.346
1.906
0.389
4.902
0.295
6.965
1.403
3.199
0.182
0.439
0.092
15.339
4.469
0.136
0.030
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
0.000
0.000
56.853
0.000
0.645
0.031
3.249
0.024
5.561
0.921
0.024
0.000
0.220
0.031
0.000
0.000
0.000
0.000
0.324
0.037
0.505
0.030
Upper
Bound
0.739
0.117
0.245
0.045
12.267
1.188
155.486
16.002
3.168
0.747
6.555
0.567
8.369
1.884
6.375
0.426
0.658
0.152
44.043
10.773
0.364
0.065
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
36.477
0.000
0.000
0.000
2.384
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
0.957
0.514
0.412
0.289
13.612
4.198
175.862
54.709
4.490
2.648
7.421
2.172
16.360
10.703
6.871
1.871
1.272
0.897
69.369
50.219
0.608
0.443
2.752
2.232
2.238
1.161
A-92
-------
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-93
-------
A-6.5
Detailed Regression Model Predictions
Table A-103. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb using a
Poisson exponential model and at several annual average concentrations.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Annual
Mean
(ppb)
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
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
6.8
16.3
Observed
Mean
Exceed-
ances
0.057
0.057
0.057
0.057
0.057
0.057
0.057
0.057
0.057
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
7.346
7.346
7.346
7.346
7.346
7.346
7.346
7.346
Observed
Max
Exceed-
ances
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
9
9
9
9
9
9
9
9
143
143
143
143
143
143
143
143
Predicted
Exceed-
ances
0.102
0.412
1.665
6.735
10.242
27.243
0.010
0.038
0.257
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
2.295
39.206
669.766
1000.000
1000.000
1000.000
0.054
0.792
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.032
0.034
0.023
0.014
0.012
0.008
0.000
0.008
0.037
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
1.662
33.759
526.509
1000.000
1000.000
1000.000
0.029
0.528
Upper
Bound
0.327
4.953
122.647
1000.000
1000.000
1000.000
0.230
0.181
1.770
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
3.168
45.531
852.001
1000.000
1000.000
1000.000
0.102
1.189
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
2
23
184
364
1000
0
0
0
0
26
523
1000
1000
1000
0
0
Upper
Bound
1
5
103
1000
1000
1000
0
1
3
1
1
14
680
1000
1000
0
0
1
1
32
1000
1000
1000
1000
0
1
9
6
53
870
1000
1000
1000
1
3
A-94
-------
Location
Springs
Colorado
Springs
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
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
New York
New York
New York
New York
New York
New York
New York
New York
Annual
Mean
(ppb)
34.8
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
8.2
17.7
35.1
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
30.0
40.0
50.0
53.0
60.0
9.7
25.5
42.2
Observed
Mean
Exceed-
ances
7.346
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.295
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
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.092
Observed
Max
Exceed-
ances
143
6
6
6
6
6
6
6
6
6
7
7
7
7
7
7
7
7
7
44
44
44
44
44
44
44
44
44
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
Predicted
Exceed-
ances
153.247
0.189
0.740
2.902
11.376
17.140
44.600
0.028
0.158
1.871
0.032
1.075
35.703
1000.000
1000.000
1000.000
0.001
0.015
6.447
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
0.086
0.970
0.021
0.092
0.403
1.760
2.737
7.677
0.005
0.048
0.557
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
130.906
0.074
0.438
1.201
2.426
2.958
4.659
0.004
0.057
0.925
0.005
0.536
11.290
95.081
177.602
757.520
0.000
0.001
3.454
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
0.026
0.380
0.007
0.052
0.211
0.507
0.646
1.121
0.001
0.022
0.260
Upper
Bound
179.401
0.482
1.251
7.014
53.350
99.308
426.973
0.186
0.438
3.786
0.230
2.156
112.906
1000.000
1000.000
1000.000
0.020
0.142
12.036
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
0.281
2.475
0.065
0.163
0.773
6.107
1 1 .604
52.548
0.028
0.104
1.193
95% Prediction
Interval for
Number of
Exceedances
Lower
Bound
121
0
0
0
1
2
4
0
0
0
0
0
11
94
156
634
0
0
1
0
0
1
20
37
138
0
0
22
0
2
7
33
29
40
0
0
0
0
0
0
0
0
0
0
0
0
Upper
Bound
189
2
3
9
53
98
454
1
1
6
1
4
119
1000
1000
1000
0
1
14
1
3
10
44
73
260
0
2
48
13
1000
1000
1000
1000
1000
1
1
4
0
1
2
7
13
53
0
1
3
A-95
-------
Location
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Annual
Mean
(ppb)
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
6.9
19.4
27.2
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
4.469
4.469
4.469
4.469
4.469
4.469
4.469
4.469
4.469
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.030
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
147
147
147
147
147
147
147
147
147
2
2
2
2
2
2
2
2
2
39
39
39
39
39
39
39
39
39
7
7
7
7
7
7
7
7
7
Predicted
Exceed-
ances
1.731
4.988
14.375
41 .422
56.901
119.362
0.673
3.760
15.110
0.026
0.109
0.463
1.968
3.038
8.368
0.004
0.023
0.072
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
1.287
4.367
10.922
24.843
31.702
55.901
0.404
3.221
11.361
0.008
0.011
0.004
0.001
0.001
0.000
0.000
0.007
0.014
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
2.329
5.698
18.919
69.066
102.130
254.864
1.119
4.389
20.098
0.081
1.044
55.438
1000.000
1000.000
1000.000
0.256
0.082
0.366
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
1
7
21
26
56
0
0
7
0
0
0
0
0
0
0
0
0
0
0
0
4
7
20
0
0
0
0
1
10
57
75
226
0
0
0
Upper
Bound
5
10
24
71
106
254
3
8
25
1
2
57
1000
1000
1000
1
0
1
1
2
6
25
41
116
0
1
4
3
32
573
1000
1000
1000
0
1
3
A-96
-------
Table A-104. Predicted number of exceedances of of 1-hour NO2 concentrations of 150 ppb using a
Normal linear model and at several annual average concentrations.
Location
Name
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Colorado
Springs
Annual
Mean
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
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
6.8
16.3
34.8
Observed
Mean
Exceed-
ances
0.057
0.057
0.057
0.057
0.057
0.057
0.057
0.057
0.057
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
7.346
7.346
7.346
7.346
7.346
7.346
7.346
7.346
7.346
Observed
Max
Exceed-
ances
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
9
9
9
9
9
9
9
9
9
143
143
143
143
143
143
143
143
143
Predicted
Exceed-
ances
0.110
0.186
0.262
0.337
0.360
0.413
0.000
0.057
0.161
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
17.426
44.318
71.210
98.102
106.169
124.994
0.000
7.346
57.235
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.020
0.015
0.001
0.000
0.000
0.000
0.000
0.000
0.019
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
7.454
24.197
38.662
52.682
56.853
66.550
0.000
0.000
31.241
Upper
Bound
0.201
0.357
0.522
0.689
0.739
0.857
0.092
0.117
0.303
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
27.398
64.439
103.758
143.522
155.486
183.438
0.000
16.002
83.228
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
13.462
31.411
36.477
47.873
0.000
0.000
3.296
Upper
Bound
0.573
0.672
0.787
0.916
0.957
1.055
0.452
0.514
0.637
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
65.075
95.397
128.958
164.793
175.862
202.115
31.109
54.709
111.173
A-97
-------
Location
Name
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
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
New York
New York
New York
New York
New York
New York
New York
New York
Phoenix
Phoenix
Phoenix
Annual
Mean
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
8.2
17.7
35.1
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
30.0
40.0
50.0
53.0
60.0
9.7
25.5
42.2
20.0
30.0
40.0
Observed
Mean
Exceed-
ances
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.389
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.295
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
0.092
0.092
0.092
0.092
0.092
0.092
0.092
0.092
4.469
4.469
4.469
Observed
Max
Exceed-
ances
6
6
6
6
6
6
6
6
6
7
7
7
7
7
7
7
7
7
44
44
44
44
44
44
44
44
44
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
147
147
147
Predicted
Exceed-
ances
0.446
0.888
1.331
1.773
1.906
2.216
0.000
0.389
1.189
0.594
1.900
3.205
4.511
4.902
5.816
0.000
0.295
2.567
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
0.149
0.275
0.401
0.439
0.527
0.000
0.092
0.302
1.367
5.601
9.835
95% Confidence
Interval for Mean
Number of
Exceedances
Lower
Bound
0.085
0.353
0.499
0.613
0.645
0.716
0.000
0.031
0.458
0.303
1.270
2.140
2.994
3.249
3.844
0.000
0.024
1.719
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
0.079
0.148
0.204
0.220
0.256
0.000
0.031
0.161
0.000
0.000
0.000
Upper
Bound
0.807
1.424
2.163
2.934
3.168
3.716
0.402
0.747
1.920
0.886
2.529
4.270
6.027
6.555
7.789
0.000
0.567
3.416
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
0.218
0.401
0.598
0.658
0.798
0.028
0.152
0.444
1 1 .546
12.546
25.027
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
1.049
2.085
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
0.000
0.000
0.000
Upper
Bound
2.706
3.185
3.720
4.306
4.490
4.933
2.136
2.648
3.543
2.474
3.866
5.361
6.936
7.421
8.568
0.981
2.172
4.619
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
0.955
1.088
1.228
1.272
1.375
0.707
0.897
1.118
47.846
51.449
57.734
A-98
-------
Location
Name
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Annual
Mean
50.0
53.0
60.0
11.1
27.3
40.5
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
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
4.469
4.469
4.469
4.469
4.469
4.469
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.030
0.030
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
147
147
147
147
147
147
2
2
2
2
2
2
2
2
2
39
39
39
39
39
39
39
39
39
7
7
7
7
7
7
7
7
7
Predicted
Exceed-
ances
14.069
15.339
18.303
0.000
4.469
10.035
0.032
0.063
0.095
0.127
0.136
0.158
0.000
0.030
0.054
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
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
0.100
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
39.591
44.043
54.495
16.406
10.773
25.696
0.067
0.143
0.237
0.335
0.364
0.432
0.081
0.065
0.117
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
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
66.390
69.369
76.880
46.824
50.219
58.093
0.445
0.483
0.531
0.589
0.608
0.654
0.412
0.443
0.471
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-99
-------
A-7 Adjustment of Air Quality to Just Meet the Current and
Alternative Standards
A-7.1 Introduction
This section provides supplemental data and discussion on the approach used in adjusting air
quality to just meet the current and alternative standards. As a reminder, every location across
the U.S. meets the current NO2 annual standard (US EPA, 2007e). Even considering air quality
data as far back as 1995, no location/monitoring site exceeded the current standard. Therefore,
simulation of air quality data was required to evaluate just meeting the current standard or
standards that are more stringent.
In developing a simulation approach to adjust air quality to meet a particular standard level,
policy-relevant background (PRB) levels in the U.S. were first considered. Policy-relevant
background is defined as the distribution of NO2 concentrations that would be observed in the
U.S. in the absence of anthropogenic (man-made) emissions of NO2 precursors in the U.S.,
Canada, and Mexico. Estimates of PRB have been reported in the draft ISA (Section 1.5.5) and
the Annex (AX2.9), and for most of the continental U.S. the PRB is estimated to be less than 300
parts per trillion (ppt). In the Northeastern U.S. where present-day NC>2 concentrations are
highest, this amounts to a contribution of about 1% percent of the total observed ambient NC>2
concentration (AX2.9). This low contribution of PRB to NC>2 concentrations provides support
for a proportional method to adjust air quality, i.e., an equal adjustment of air quality values
across the entire air quality distribution to just meet a target value.
Next, the variability in NC>2 concentrations was evaluated to determine whether a
proportional approach would be reasonable if applied broadly across all years of data. Because
the adjustment factor to meet the current standard would likely increase with increasing year, it
was of interest to determine the trend in both the hourly concentrations and variability by year.
Figure A-100 presents a summary of the annual average and hourly mean concentrations, as well
as the coefficient of variation (COV, standard deviation as a percent of the mean) for each
respective mean. Sample size for the annual average concentrations was about 350 per year,
while hourly concentrations numbered about 3 million per year.
As expected, there was no observed difference in the mean concentrations when comparing
each concentration metric within a year. The mean of the annual averages of all monitors is
nearly identical to the mean of the hourly concentrations. However, statistically significant
decreases in concentration are evident from year-to-year (p<0.0001), with concentrations
decreasing by about 30% across the monitoring period. Contrary to this, there is no apparent
trend in the COV for the annual average concentrations across the 12 years of data, generally
centered about 53%. The COV of the hourly concentrations is larger than the annual COV as
expected, however it increases with increasing year. The hourly COV ranges from a low of 84%
in 1998 to a high of 92% in 2006, amounting to a relative percent difference of only 10% across
the entire monitoring period. A non-parametric Mann-Whitney U-test indicates that there is a
significant difference in the COVs when comparing each year-group (p=0.004). This may result
in a small upward bias in the number of estimated exceedances of short-term (1-hour) potential
health benchmark levels if using a proportional roll-up on the more recent monitoring data
relative to that estimated by rolling up the historical data to just meet the current standard. While
A-100
-------
the trend of increasing COV is apparent across the entire monitoring period, based on the limited
difference in COV from year-to-year for both the annual and hourly concentration data within
each year-group (each is <4%), it was concluded that a proportional method could be broadly
applied to each data set. Additional analyses by Rizzo (2008) on the trends within six selected
locations also support the findings here.
20
19
18
I "
O
16
O
O
15 -
14
i 13
0)
12
11 -
10
-•—Annual Mean
-•—Hourly Mean
-D--COV-Annual
-o--COV-Hourly
••a-.
•a'
••H-
"•o-
100
95
90
85
80
-- 75
60
55
50
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Year
c
O
70 &
65 O
Figure A-100. Trends in hourly and annual average NO2 ambient monitoring concentrations and their
associated coefficients of variation (COV) for all monitors, years 1995-2006.
A-7.2 Approach
For the air quality characterization, data were first separated into two groups, an historical set
of monitoring data (1995-2000) and one containing the most recent air quality (2001-2006).
This grouping would further reduce any potential influential monitoring data affecting the
variability in hourly concentrations that may exist in one year to the next within a location. The
following air quality scenarios were considered for these sets of data:
• "as is" representing the historical and recent ambient monitoring hourly concentration
data as reported by US EPA's Air Quality System (AQS).
• "simulated" concentrations to just meet the current NC>2 NAAQS (53 ppb annual
average).
A-101
-------
Based on the form of the current standard and observed trends in ambient monitoring, such as
the retention of similar hourly and annual COVs over time while annual average concentrations
significantly decrease over the same time period, NO2 concentrations were proportionally
modified at each location using the maximum annual average concentration that occurred in each
year. To just meet the current standard adjustment factors F for each location (/') and year (/')
were derived by the following
Fi}. = S/CmaxiJ equation (1)
where,
FJJ = NO2 concentration adjustment factor (unitless) in location /' given the annual
average standard and for each yeary
S = Current standard level (i.e., 53 ppb, annual average NO2 concentration)
Cmax,ij = The maximum annual average NO2 concentration at a monitor in each
location / and for each yeary (ppb)
Values for each air quality adjustment factor used for each location to simulate just meeting
the current standard are given in Tables A-105 and A-106. It should be noted that a different
monitor could have been used for each year to estimate F, the selection dependent only on
whether the monitor contained the highest annual concentration for that year in the particular
location. For each location and calendar year, all the hourly concentrations were multiplied by
the same constant value F to make the highest annual mean equal to 53 ppb for that location and
year. For example, for Boston in 1995, the maximum annual mean was 30.5 ppb, giving an
adjustment factor ofF= 53/30.5 = 1.74 using equation 1. All hourly concentrations in Boston in
1995 were multiplied by 1.74. Then, using the adjusted hourly concentrations, the distributions
of the annual means and annual number of exceedances are computed in the same manner as the
as-is scenario.5
Following review of the NO2 ISA and summarization of relevant epidemiological and
clinical health studies, alternative NO2 standards of differing averaging time, form, and level
were also considered. Much of the discussion regarding the selection of each of these
components of the standard is provided in Chapter 5 of the Final NO2 REA, with only the broad
conclusions provided here. For averaging time, the epidemiological evidence does not provide
clear guidance in choosing between 1-hour and 24-hour averaging times, and given that the
experimental literature provides support for the occurrence of effects following exposures of
shorter duration than 24-hours (e.g., 1-hour), staff evaluated standards with 1-hour averaging
times. For the form, we have focused on standards with statistical, concentration-based forms.
Staff selected the 98th and 99th percentiles daily maximum concentration averaged over 3 years to
balance the desire to provide a stable regulatory target with the desire to limit the occurrence of
peak concentrations. Concentration levels ranging from 50 ppb to 200 ppb in increments of 50
5 Because of the large database, we did not implement this procedure exactly as stated. For the annual means we
computed and applied the adjustment factors directly to each annual mean. For the hourly concentrations we used
the frequency distributions of the rounded hourly values, so that, in effect, we applied the adjustment factors to the
hourly values after rounding them to the nearest integer. This has a negligible impact on the calculated number of
exceedances.
A-102
-------
ppb were selected by staff based largely on the observed concentrations from both epidemiologic
and controlled human exposure studies. Using these criteria for the investigated alternative
standards, the following scenarios were considered using the most recent years of data (i.e.,
2001-2006) and divided into two three-year groups for analysis (years 2001-2003 and 2004-
2006):
• "as is" representing the recent ambient monitoring hourly concentration data as reported
by US EPA's Air Quality System (AQS).
• "simulated' concentrations to just meet the current NO2 NAAQS (53 ppb annual average
as described above) and alternative 1-hour standards.
Proportional adjustment factors were also derived considering the form, averaging time,
and levels of the potential alternative standards under consideration. Discussion regarding the
staff selection of each of these components is provided in chapter 5 of this document. The 98th
and 99th percentile 1-hour NO2 daily maximum concentrations averaged across three years of
monitoring were used in calculating the adjustment factors at each of four standard levels as
follows:
f 3
Flkl =
equation (6-2)
J max,;
Fjki = NO2 concentration adjustment factor (unitless) in location / given alternative
standard percentile form k and standard level / across a 3-year period
Si = Standard level / (i.e., 50, 100, 150, 200 ppb 1-hour NO2 concentration (ppb))
Cjjk = Selected percentile k (i.e., 98th or 99th) 1-hour daily maximum NO2
concentration at a monitor in location /' (ppb) for each year7
Values for each air quality adjustment factor used for each location and year-group to
simulate just meeting the alternatives standards are given in Tables A-107 and A-108. It should
be noted that a different monitor could have been used for each year group to estimate F, the
selection dependent only on whether the monitor contained the highest 98th or 99th daily
maximum 1-hour concentration averaged across the three year period in the particular location.
For each location and year-group, all monitor hourly concentrations were multiplied by the same
constant value F, whereas the monitor used to develop the adjustment factor would have a 3-year
averaged daily maximum 1-hour concentration at the selected percentile equivalent to the level
of the alternative standard. For example, for Atlanta in years 2001-2003, the maximum 3-year
average 98th percentile of daily maximum 1-hour NO2 concentrations was 81.7 ppb, giving an
adjustment factor F = 100/81.7=1.224 for the 1 -hour alternative standard level of 200 ppb using
equation (2). All hourly concentrations in Atlanta for each year in 2001-2003 were multiplied by
1.224. Then, using the adjusted hourly concentrations, the distributions of the annual number of
exceedances are computed in the same manner as the as-is scenario.
A-103
-------
Table A-105. Maximum annual average NO2 concentrations and air quality adjustment factors (F) to just
meet the current standard, historical monitoring data.
Location
Atlanta
Atlanta
Boston
Boston
Chicago
Chicago
Cleveland
Cleveland
Colorado Springs
Colorado Springs
Denver
Denver
Detroit
Detroit
El Paso
El Paso
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Los Angeles
Los Angeles
Miami
Miami
New York
New York
Philadelphia
Philadelphia
Phoenix
Phoenix
Provo
Provo
St. Louis
St. Louis
Washington DC
Washington DC
Other MSA
Other MSA
Other Not MSA
Other 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
18.8
2.81
30.5
1.74
32.2
1.64
27.3
1.94
23.2
2.28
34.8
1.52
21.6
2.45
23.3
2.27
15.8
3.36
27.1
1.96
46.2
1.15
14.7
3.60
41.7
1.27
31.8
1.67
32.6
1.63
22.6
2.35
26.2
2.02
26.2
2.02
31.9
1.66
19.1
2.78
1996
26.6
1.99
31.0
1.71
32.0
1.66
25.9
2.04
23.6
2.24
33.1
1.60
21.5
2.47
35.1
1.51
14.9
3.55
26.7
1.99
42.3
1.25
16.0
3.30
42.2
1.26
33.9
1.56
31.6
1.68
24.3
2.18
24.8
2.14
26.9
1.97
30.3
1.75
14.5
3.66
1997
25.2
2.10
30.4
1.74
33.6
1.58
28.1
1.89
19.8
2.68
33.9
1.56
25.9
2.05
33.6
1.58
14.4
3.69
43.2
1.23
16.6
3.19
41.1
1.29
32.4
1.63
32.0
1.66
23.3
2.27
24.8
2.14
25.9
2.05
29.4
1.80
19.7
2.69
1998
24.1
2.20
30.7
1.73
32.2
1.64
27.3
1.94
20.5
2.59
35.3
1.50
22.9
2.31
30.7
1.72
15.0
3.52
25.3
2.09
43.4
1.22
15.2
3.49
41.9
1.26
34.0
1.56
35.0
1.52
23.9
2.22
25.8
2.05
27.2
1.95
31.0
1.71
18.8
2.82
1999
23.8
2.22
29.7
1.79
31.5
1.68
24.5
2.16
19.3
2.75
19.4
2.73
18.0
2.94
27.7
1.91
15.9
3.34
26.6
1.99
50.6
1.05
16.8
3.15
41.5
1.28
31.7
1.67
40.5
1.31
24.1
2.20
27.2
1.95
25.4
2.09
29.3
1.81
19.7
2.69
2000
22.9
2.31
29.0
1.83
32.0
1.66
23.1
2.30
34.8
1.52
14.9
3.55
23.9
2.22
24.3
2.18
15.4
3.45
25.1
2.12
43.9
1.21
15.7
3.37
40.6
1.31
27.9
1.90
36.3
1.46
23.6
2.25
26.3
2.02
23.5
2.26
26.5
2.00
18.7
2.83
A-104
-------
Table A-106. Maximum annual average NO2 concentrations and air quality adjustment factors (F) to just
meet the current standard, recent monitoring data.
Location
Atlanta
Atlanta
Boston
Boston
Chicago
Chicago
Cleveland
Cleveland
Colorado Springs
Colorado Springs
Denver
Denver
Detroit
Detroit
El Paso
El Paso
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Los Angeles
Los Angeles
Miami
Miami
New York
New York
Philadelphia
Philadelphia
Phoenix
Phoenix
Provo
Provo
St. Louis
St. Louis
Washington DC
Washington DC
Other MSA
Other MSA
Other Not MSA
Other 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
23.3
2.27
29.7
1.79
31.9
1.66
23.6
2.25
36.8
1.44
23.2
2.29
21.7
2.45
22.5
2.35
41.2
1.29
15.8
3.35
40.3
1.32
29.9
1.77
37.1
1.43
24.1
2.20
24.7
2.15
24.3
2.18
26.5
2.00
16.5
3.21
2002
19.4
2.73
25.3
2.10
32.4
1.63
22.3
2.38
35.4
1.50
21.4
2.47
21.4
2.48
14.6
3.62
22.3
2.38
40.2
1.32
14.3
3.71
39.7
1.33
29.5
1.80
34.7
1.53
24.8
2.14
22.9
2.32
24.8
2.14
27.4
1.93
16.4
3.23
2003
16.4
3.23
22.5
2.36
30.9
1.72
21.7
2.45
21.4
2.47
22.0
2.41
19.9
2.66
14.3
3.70
21.4
2.48
35.3
1.50
12.9
4.12
32.0
1.65
24.7
2.15
34.3
1.54
21.8
2.43
20.3
2.60
26.0
2.04
26.4
2.01
15.5
3.42
2004
17.0
3.12
25.0
2.12
29.3
1.81
22.2
2.38
27.2
1.95
18.9
2.80
18.0
2.94
13.7
3.88
19.7
2.69
33.7
1.57
13.0
4.08
30.5
1.74
25.6
2.07
31.4
1.69
22.3
2.37
22.3
2.37
24.0
2.20
25.3
2.09
15.8
3.36
2005
17.4
3.05
23.4
2.26
29.6
1.79
21.5
2.46
27.6
1.92
19.6
2.71
17.3
3.06
13.3
3.97
19.9
2.67
30.9
1.72
13.5
3.92
36.5
1.45
26.3
2.02
31.5
1.68
20.5
2.58
16.8
3.15
24.1
2.20
24.0
2.21
17.1
3.11
2006
17.9
2.96
22.5
2.35
30.6
1.73
18.2
2.91
29.1
1.82
15.9
3.34
18.0
2.94
29.7
1.78
34.2
1.55
17.8
2.98
30.6
1.73
28.9
1.83
15.0
3.52
19.6
2.70
18.5
2.87
15.6
3.39
A-105
-------
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
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
1-hour
Standard
Level
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
1312100481
1312100481
1312100481
1312100481
1312100481
1312100481
1312100481
1312100481
2502500401
2502500401
2502500401
2502500401
2502500401
2502500401
2502500401
2502500401
1703100631
1703100631
1703100631
1703100631
1703100631
1703100631
1703100631
1703100631
3903500601
3903500601
3903500601
3903500601
3903500601
3903500601
3903500601
3903500601
0803100021
0803100021
0803100021
0803100021
0800130011
0800130011
0800130011
0800130011
2616300192
2616300192
2616300192
Adjustment
Factor1
0.612
1.224
1.837
2.449
0.703
1.405
2.108
2.810
0.688
1.376
2.064
2.752
0.719
1.439
2.158
2.878
0.577
1.154
1.731
2.308
0.570
1.141
1.711
2.281
0.711
1.422
2.133
2.844
0.765
1.531
2.296
3.061
0.518
1.036
1.554
2.073
0.658
1.316
1.974
2.632
0.554
1.107
1.661
99th Percentile
Maximum
Monitor
1312100481
1312100481
1312100481
1312100481
1312100481
1312100481
1312100481
1312100481
2502500401
2502500401
2502500401
2502500401
2502500401
2502500401
2502500401
2502500401
1703131031
1703131031
1703131031
1703131031
1703100631
1703100631
1703100631
1703100631
3903500601
3903500601
3903500601
3903500601
3903500601
3903500601
3903500601
3903500601
0803100021
0803100021
0803100021
0803100021
0800130011
0800130011
0800130011
0800130011
2616300192
2616300192
2616300192
Adjustment
Factor1
0.564
1.128
1.692
2.256
0.641
1.282
1.923
2.564
0.622
1.245
1.867
2.490
0.613
1.227
1.840
2.454
0.512
1.024
1.536
2.048
0.538
1.075
1.613
2.151
0.664
1.327
1.991
2.655
0.691
1.382
2.074
2.765
0.459
0.917
1.376
1.835
0.584
1.167
1.751
2.335
0.374
0.748
1.122
A-106
-------
Year Group
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
Location
Detroit
Detroit
Detroit
Detroit
Detroit
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Jacksonville
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
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
1-hour
Standard
Level
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
150
200
98th Percentile
Maximum
Monitor
2616300192
2616300161
2616300161
2616300161
2616300161
4814100441
4814100441
4814100441
4814100441
4814100551
4814100551
4814100551
4814100551
1203100322
1203100322
1203100322
1203100322
1203100322
1203100322
1203100322
1203100322
3200305391
3200305391
3200305391
3200305391
3200320021
3200320021
3200320021
3200320021
0603700301
0603700301
0603700301
0603700301
0603716012
0603716012
0603716012
0603716012
1208640022
1208640022
1208640022
1208640022
1208640022
1208640022
1208640022
1208640022
Adjustment
Factor1
2.214
0.915
1.829
2.744
3.659
0.655
1.310
1.965
2.620
0.743
1.485
2.228
2.970
0.901
1.802
2.703
3.604
0.962
1.923
2.885
3.846
0.718
1.435
2.153
2.871
0.820
1.639
2.459
3.279
0.394
0.787
1.181
1.575
0.549
1.099
1.648
2.198
0.929
1.858
2.786
3.715
0.877
1.754
2.632
3.509
99th Percentile
Maximum
Monitor
2616300192
2616300161
2616300161
2616300161
2616300161
4814100441
4814100441
4814100441
4814100441
4814100371
4814100371
4814100371
4814100371
1203100322
1203100322
1203100322
1203100322
1203100322
1203100322
1203100322
1203100322
3200305391
3200305391
3200305391
3200305391
3200305391
3200305391
3200305391
3200305391
0603700301
0603700301
0603700301
0603700301
0603711031
0603711031
0603711031
0603711031
1208640022
1208640022
1208640022
1208640022
1208640022
1208640022
1208640022
1208640022
Adjustment
Factor1
1.496
0.862
1.724
2.586
3.448
0.573
1.145
1.718
2.290
0.664
1.327
1.991
2.655
0.840
1.681
2.521
3.361
0.658
1.316
1.974
2.632
0.652
1.304
1.957
2.609
0.758
1.515
2.273
3.030
0.379
0.758
1.136
1.515
0.505
1.010
1.515
2.020
0.817
1.635
2.452
3.270
0.826
1.653
2.479
3.306
A-107
-------
Year Group
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
Location
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
1-hour
Standard
Level
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
200
50
98th Percentile
Maximum
Monitor
3403900042
3403900042
3403900042
3403900042
3403900042
3403900042
3403900042
3403900042
4210100471
4210100471
4210100471
4210100471
1000320041
1000320041
1000320041
1000320041
0401330101
0401330101
0401330101
0401330101
0401330101
0401330101
0401330101
0401330101
4904900021
4904900021
4904900021
4904900021
4904900021
4904900021
4904900021
4904900021
2951000861
2951000861
2951000861
2951000861
2951000722
2951000722
2951000722
2951000722
2451000401
2451000401
2451000401
2451000401
2451000401
Adjustment
Factor1
0.542
1.083
1.625
2.166
0.613
1.227
1.840
2.454
0.694
1.389
2.083
2.778
0.758
1.515
2.273
3.030
0.577
1.154
1.731
2.308
0.598
1.195
1.793
2.390
0.785
1.571
2.356
3.141
0.532
1.064
1.596
2.128
0.769
1.538
2.308
3.077
0.820
1.639
2.459
3.279
0.701
1.402
2.103
2.804
0.758
99th Percentile
Maximum
Monitor
3403900042
3403900042
3403900042
3403900042
3401310031
3401310031
3401310031
3401310031
4210100471
4210100471
4210100471
4210100471
1000320041
1000320041
1000320041
1000320041
0401330101
0401330101
0401330101
0401330101
0401330101
0401330101
0401330101
0401330101
4904900021
4904900021
4904900021
4904900021
4904900021
4904900021
4904900021
4904900021
2951000861
2951000861
2951000861
2951000861
2951000722
2951000722
2951000722
2951000722
1100100411
1100100411
1100100411
1100100411
1100100411
Adjustment
Factor1
0.476
0.952
1.429
1.905
0.532
1.064
1.596
2.128
0.637
1.274
1.911
2.548
0.610
1.220
1.829
2.439
0.526
1.053
1.579
2.105
0.538
1.075
1.613
2.151
0.735
1.471
2.206
2.941
0.521
1.042
1.563
2.083
0.704
1.408
2.113
2.817
0.794
1.587
2.381
3.175
0.633
1.266
1.899
2.532
0.617
A-108
-------
Year Group
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
2001-2003
2001-2003
2001-2003
2001-2003
2004-2006
2004-2006
2004-2006
2004-2006
Location
Washington DC
Washington DC
Washington DC
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
1-hour
Standard
Level
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
50
100
150
200
98th Percentile
Maximum
Monitor
2451000401
2451000401
2451000401
4905700021
4905700021
4905700021
4905700021
0607320071
0607320071
0607320071
0607320071
0602500061
0602500061
0602500061
0602500061
5600508921
5600508921
5600508921
5600508921
Adjustment
Factor1
1.515
2.273
3.030
0.508
1.015
1.523
2.030
0.578
1.156
1.734
2.312
0.547
1.095
1.642
2.190
0.535
1.070
1.604
2.139
99th Percentile
Maximum
Monitor
1100100411
1100100411
1100100411
4905700021
4905700021
4905700021
4905700021
0607320071
0607320071
0607320071
0607320071
0602500061
0602500061
0602500061
0602500061
5600508921
5600508921
5600508921
5600508921
Adjustment
Factor1
1.235
1.852
2.469
0.439
0.877
1.316
1.754
0.532
1.064
1.596
2.128
0.466
0.932
1.398
1.863
0.429
0.858
1.288
1.717
Notes:
1 The selected percentile (98th or 99th) in 1 -hour daily maximum NO2 concentration at each monitor was
averaged across the 3-years of data (either 2001 -2003 or 2004-2006), with the highest concentration
monitor retained for use in calculating the adjustment to just meet the alternative standard.
A-109
-------
A-8 Method for Estimating On-Road Concentrations
A-8.1 Introduction
As an additional step in the air quality characterization, the potential impact of motor
vehicles on the surrogate exposure metrics was evaluated. Several studies have shown that
concentrations of NC>2 are at elevated levels when compared to ambient concentrations measured
at increasing distances from the roadway (e.g., Rodes and Holland, 1981; Gilbert et al., 2003;
Cape et al., 2004; Pleijel et al., 2004; Singer et al., 2004). On average, concentrations on or near
a roadway can be from 2 to 3 times greater than ambient concentrations (ISA, section 2.5.4), but
on occasion, as high as 7 times greater (Bell and Ashenden, 1997; Bignal et al., 2007). A strong
relationship between measured on-road NC>2 concentrations and those with increasing distance
from the road has been reported under a variety of conditions (e.g., variable traffic counts,
different seasons, wind direction) and can be described (e.g., Cape et al., 2004) with an
exponential decay equation of the form
Ci=Cfe + CvŁTfa equation (3)
where,
Cx = NC>2 concentration at a given distance (x) from a roadway (ppb)
Cb = NC>2 concentration (ppb) at a distance from a roadway, not directly influenced
by road or non-road source emissions
Cv = NC>2 concentration contribution from vehicles on a roadway (ppb)
k = Removal rate constant describing NC>2 combined formation/decay with
perpendicular distance from roadway (meters'1)
x = Distance from roadway (meters)
As a function of reported concentration measurements and the derived relationship, much of
the decline in NC>2 concentrations with distance from the road has been shown to occur within
the first few meters (by approximately 90% within a 10 meter distance), returning to near
ambient levels between 200 to 500 meters (Rodes and Holland, 1981; Bell and Ashenden, 1997;
Gilbert et al., 2003; Pleijel et al., 2004). At a distance of 0 meters, referred to here as on-road,
the equation reduces to the sum of the non-source influenced NC>2 concentration and the
concentration contribution expected from vehicle emissions on the roadway using
Cr =Ca(l+ m) equation (4)
where,
Cr = 1-hour on-road NC>2 concentration (ppb)
Ca = 1-hour ambient monitoring NO2 concentration (ppb) either as is or modified to
just meet the current or alternative standards
m = Ratio derived from estimates of Cv/Cb (from eq (1))
A-110
-------
and assuming that Ca = Cb.6
A-8.2 Derivation of On-Road Ratios
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 years, roadway locations,
seasons, wind directions, and averaging times (Table A-108). The final data set contained 501
data points, encompassing multiple NC>2 measurements at a distance from a total of 56 individual
roads, some of which were collected within 10m of the road.
Table A-108. Studies reviewed 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
Averaging
time
7 days
14 days
14 days
7 days
14 days
7 days
7 days
30 days
> 1 day
14 days
7 days
Although there were, on occasion, data from several roads within a particular study, data for
factors thought to influence on-road concentrations were very limited or were not distinctly
defined for all studies. Factors that were reported and already noted as influential are the
individual roadway (where the study included multiple roads) and the time of the year. Wind
direction (upwind versus downwind) was also indicated as an important factor influencing
concentrations at a distance from a roadway (Singer, 2004). Note however that the averaging
time for measurements of 11 of the 12 studies is a week or more in length (Table A-108). Even
for where the wind direction is reported as either downwind or upwind it is possible that the
wind direction was variable (combined downwind and upwind) over the monitoring period.
The relationship noted in equation (3) was iteratively solved using the data collected from the
above reviewed literature and employing the SAS procedure proc nlin, generally as follows,
proc nlin data=no2 maxiter=1000 noprint NOITPRINT;
parms Cb=0 to 80 by 1
Cv= 0 to 80 by 1
k= 0 to 1 by .025;
Note that Ca differs from Cb since Ca may include the influence of on-road as well as non-road sources. However,
it is expected that for most monitors the influence of on-road emissions is minimal so that Ca = Cfr
A-111
-------
model Cx=Cb + Cv*exp (-k*distance);
by author road season wind;
output out=outdata parms=Cb Cv k;
run;
As an example, data were compiled from Table 1 and Table 4 reported in Singer et al.
(2004) for NC>2 measurements collected at six outdoor locations with varying distance from
Interstate-880 in San Francisco, CA as follows:
Table A-109. Example data used to estimate on-road adjustment factor (m) obtained from Tables 1 and 4
reported in Singer et. al (2004).
Distance to
Road (m)
60
130
200
230
1200
1400
Measured
Concentration
Cx (ppb)
30
26
26
24
21
21
Season
SPR-
FALL
SPR-
FALL
SPR-
FALL
SPR-
FALL
SPR-
FALL
SPR-
FALL
Wind
D
D
D
D
D
D
Road
I-880
I-880
I-880
I-880
I-880
I-880
The non-linear procedure was applied to these data and all other individual roads identified
within each study location listed in Table A-108. The results of this analysis were screened for
data that yielded no unique solutions (lack of model convergence) or irrational parameters.
Criteria for censoring data included the following, as well as the number of individual roads
censored:
• Model did not converge (number (n) of roads = 5)
• Ł<0 (n=l)
• k>\ (n=2)
• Both k=0 and Cv=0(n=l)
• Extremely large Cv (>8,000 ppb; n=2)
• Cb<0 (n=l)
These data were then evaluated for trends using the limited influential factors reported in the
collection of studies, considering the number of values of m available for potential groupings,
and how the data were to be applied to the ambient monitoring data. In general, the
measurements reported in the summer and resultant parameter estimates were observed as
distinct from the measures and parameter estimates from other seasons, including data for where
only annual averages were reported. The data were then grouped accordingly into two seasonal
groups, summer and not summer, containing 23 and 21 samples, respectively. These two groups
were also further censored for unusual parameter estimates. Resulting criteria for censoring the
grouped data included the following:
A-112
-------
• An extreme value ofk (0.354) in the summer category compared with others in group
(mean=0.020, std=0.014)
• Extreme values of estimated m (24.4 and 59.0) in the not summer category due to
combined low estimated Cb (<1 ppb) relative to high estimated Cv in comparison with
the other derived m (mean=0.73, std=0.38)
Therefore the final data set contained 19 and 22 values for use in the not summer and summer
categories, respectively (Table A-110).
Table A-110. Estimated on-road adjustment factors (Cv/Cb or m) for two season groups and potential
influential factors.
Author
Bell
Bell
Bell
Bell
Bell
Signal
Bignal
Cape
Cape
Cape
Cape
Cape
Cape
Cape
Cape
Cape
Cape
Cape
Gilbert
Gilbert
Gilbert
Maruo
Monn
Location
WAL
WAL
WAL
WAL
WAL
ENG
ENG
SCT
SCT
SCT
SCT
SCT
SCT
SCT
SCT
SCT
SCT
SCT
QUE
QUE
QUE
JAP
SWZ
Road
Name
A5
A5
A5
A5
B4547
M40
M62
d1
d3
d5
01
o3
o4
o5
t1
t2
t4
t5
HW15
HW15
HW15
L1L2
1
Season1
SU
su
Wl
Wl
su
su
FA
AN
AN
AN
AN
AN
AN
AN
AN
AN
AN
AN
SU
SU
SU
SU
SU
Wind
Direction2
D
U
D
U
D
B
B
B
B
B
B
B
B
B
B
B
B
B
B
D
U
B
B
Area
Type3
R
R
R
R
R
U*
U*
U*
U
U*
R
U
R*
R
R
R*
R*
R
U
U
U
U
U
Traffic
Count
5000
5000
2500
2500
3500
94000
74000
57786
85623
20134
3433
NR
240
1299
11997
3551
9373
5052
185000
185000
185000
24000
8800
Season2
Summer
Summer
Not
Summer
Not
Summer
Summer
Summer
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Summer
Summer
Summer
Summer
Summer
cvcfc
orm
2.45
1.32
1.14
0.58
0.90
2.70
0.64
0.78
0.86
0.59
0.75
0.25
1.50
0.36
0.79
1.08
0.79
0.82
0.78
0.75
0.94
0.92
0.74
A-113
-------
Author
Nitta
Nitta
Nitta
Nitta
Pleijel
Pleijel
Pleijel
Pleijel
Pleijel
Rodes
Rodes
Roorda-
Knape
Roorda-
Knape
Roorda-
Knape
Roorda-
Knape
Roorda-
Knape
Roorda-
Knape
Singer
Location
JAP
JAP
JAP
JAP
SWE
SWE
SWE
SWE
SWE
CAL
CAL
HOL
HOL
HOL
HOL
HOL
HOL
CAL
Road
Name
a
b
e
f
1
2
3
4
5
HI03
LowOS
1
1p2
1p3
1p4
2
2p2
I880D
Season1
NR
NR
NR
NR
SU
SU
SU
SU
SU
SU
SU
SU
SU
SU
SU
SU
SU
SPFA
Wind
Direction2
B
B
B
B
D
D
D
D
D
D
D
B
B
B
B
B
B
D
Area
Type3
U*
U*
U*
U*
R
R
R
R
R
U
U
U
U
U
U
U
U
U*
Traffic
Count
106000
106000
60000
60000
32500
32500
18900
18900
18900
200000
200000
131907
131907
131907
131907
142512
142512
200000
Season2
Not
Summer
Not
Summer
Not
Summer
Not
Summer
Summer
Summer
Summer
Summer
Summer
Summer
Summer
Summer
Summer
Summer
Summer
Summer
Summer
Not
Summer
cvcfc
orm
0.36
0.22
0.42
0.47
1.21
1.19
0.51
1.13
0.79
0.93
2.43
0.78
0.67
0.70
0.49
0.52
1.95
1.54
Notes:
1 Season: AN - Annual, SP - Spring, FA - Fall, SU - Summer, Wl - Winter
2 Wind: B - Both, D - Downwind, U - Upwind
3 Type: R - Rural, U - Urban, * Inferred by staff using traffic count data
NR- Not reported
Two approaches were considered for estimating m from the Cv and Cb pairs in each season.
The first approach considered was to regress Cb on Cv (either with or without an intercept) and
use the fitted slope to estimate m. Ignoring meteorological effects, equation 3 implies that Cv
results solely from on-road emission sources and that Cb results solely from non-road emission
sources. Since these two source types are likely to have quite different diurnal profiles, we
expect the hourly Cv and Cb values to be approximately independent.7 Regressing Cb against Cv
would imply that there is some correlation between the values, which would be inconsistent with
the conceptual model underlying equation (3). Further, if Cb were regressed against Cv using an
intercept, the physical meaning of the intercept would be unclear.
7 Although the fact that Cv and Cb are subject to the same meteorology introduces some correlation, because
meteorology tends to vary on a longer time scale than hourly, it is likely to have less influence than the emissions on
the correlation between hourly concentrations.
A-114
-------
An empirical method was selected for the approach to characterize m based on the two
seasonal sets of ratios of CVQ,. The resulting cumulative distribution for each group is depicted
by Figure A-101 using the data from Table A-l 10. In applying the factors for use in equation
(4), the selection criteria for a given m are based on equivalent probability (e.g., 1/19 for the not
summer data).
Means from the two seasons were tested for significant difference using a Student's t
(p=0.026), while the season distributions were compared using a Kolmogorov-Smirnov test (p=
0.196). It was decided to retain the season-groups as separate to allow for some apportioning of
variability resulting from an apparent seasonal influence, even though the statistical test results
were mixed.
100% T
90%
80%
.2 70%
'c
o 60%
Q.
-------
using the appropriate distribution. Because the influence of on-road and non-road sources is
likely different in each location and at each monitor, it would be expected that the empirical
relationship between the two values Cv and Cb to vary from place to place. If source category
emissions data for each study location were available to derive an equation (3) regression, that
could have been used to match each of the study locations here, or, perhaps, each of the
monitoring sites, to a similar equation (3) study area for assigning an appropriate ratio.
However, since this information was not available, an empirical approach was used to randomly
match the literature-derived ratios to the NO2 site-seasons.
A particular summer on-road factor has a 1/22 chance of selection, while a specific not
summer value has a 1/19 probability of selection, based on respective sample sizes. This random
assignment was repeated for all site-years of data. Hourly NO2 concentrations were estimated
for each site-year of data in a location using equation (4) and the randomly assigned on-road
factors. Finally, the process was simulated 100 times for each site-year of hourly data. For
example, the Boston CMS A location had 210 random selections from the on-road distributions
applied independently to the total site-years of data (105). Following 100 simulations, a total of
10,500 site-years of data were generated using this procedure (along with 21,000 randomly
assigned on-road values selected from the appropriate empirical distribution).
Simulated on-road NO2 concentrations were used to generate concentration distributions for
the annual average concentrations and distributions for the number of exceedances of short-term
potential health effect benchmark levels. Means and median values are reported to represent the
central tendency of each parameter estimate. Since there were multiple simulations performed at
each location using all available site-years of data, results for the upper percentiles were
expanded to the 95th, 98th and 99th percentiles of the distribution. It is more appropriate to apply
the parameter estimates outside the central tendencies to particular sites, areas within locations,
or for certain conditions. Minimum values for the annual mean and annual number of
exceedances were also estimated. One approach would have been to use the minimum values
across the 100 simulations. However, that approach may not give the lowest possible value,
because it is unlikely that in 100 simulations for a site-year there is a simulation where both
seasonal adjustment factors are chosen to be the lowest values of 1 + m. To obtain the lowest
value, two simulations were conducted for each site-year. The Summer adjustment factor was set
to the lowest possible value (1.49) and the Not-Summer adjustment factor was the lowest
possible value (1.22). The annual means and exceedances for those two separate simulations
were used to compute the minimum values for each distribution.
As part of the air quality characterization, these data were used to estimate the number of
short-term concentrations above selected levels that might occur on roadways using the
estimated hourly Cr values, associated with air quality as is. For evaluating just meeting the
current annual and alternative standards, the approach described in Section A-7 to adjust the
ambient concentrations was applied before estimating on-road NO2 concentrations.
A-116
-------
A-9 Supplemental Results Tables to the REA
A-9.1
Annual average NO2 concentration data for 2001-2003
Table A-lll. Estimated annual average NO2 concentrations for monitors >100 m from a major road using
2001-2003 air quality as is and air quality adjusted to Just meet the current and alternative standards.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Scenario1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
Percentile2
98
99
98
99
98
99
98
99
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
14
14
14
14
6
6
6
6
6
6
6
6
6
6
6
6
6
6
9
9
9
9
9
9
9
9
9
9
Annual Average NO2 (ppb)3
Mean
12
33
8
7
15
14
23
21
30
28
38
35
46
42
10
19
7
6
13
12
20
18
26
24
33
30
39
36
22
36
12
11
25
22
37
33
50
44
Min
4
9
3
2
5
5
8
7
10
9
13
12
15
14
5
11
4
3
7
7
11
10
15
13
18
17
22
20
17
27
10
9
19
17
29
26
39
34
Med
15
39
9
9
19
17
28
26
37
34
46
43
56
51
11
21
7
7
15
14
22
20
30
27
37
34
45
41
20
34
12
10
23
20
35
31
46
41
p98
23
53
14
13
29
26
43
39
57
53
71
66
86
79
12
26
8
7
16
15
24
22
32
29
40
36
48
44
28
47
16
14
32
28
48
43
64
57
p99
23
53
14
13
29
26
43
39
57
53
71
66
86
79
12
26
8
7
16
15
24
22
32
29
40
36
48
44
28
47
16
14
32
28
48
43
64
57
A-117
-------
Location
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Scenario1
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
9
9
9
9
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
6
6
6
6
6
6
6
6
6
6
6
6
6
Annual Average NO2 (ppb)3
Mean
62
55
75
66
18
42
13
12
25
24
38
35
51
47
63
59
76
71
24
45
12
11
24
22
37
33
49
43
61
54
73
65
21
49
11
8
23
15
34
23
46
31
57
39
68
Min
48
43
58
51
17
41
12
12
25
23
37
35
49
46
62
58
74
69
21
37
11
10
22
20
33
30
44
39
56
49
67
59
19
44
10
7
21
14
31
21
41
28
51
35
62
Med
58
51
69
61
17
42
12
12
25
23
37
35
50
46
62
58
75
70
24
45
12
11
24
22
37
33
49
43
61
54
73
65
20
50
11
8
23
15
34
23
45
31
56
38
68
p98
80
71
96
85
19
43
13
12
26
25
40
37
53
49
66
61
79
74
26
53
13
12
27
24
40
35
53
47
67
59
80
71
23
53
13
9
26
17
38
26
51
35
64
43
77
p99
80
71
96
85
19
43
13
12
26
25
40
37
53
49
66
61
79
74
26
53
13
12
27
24
40
35
53
47
67
59
80
71
23
53
13
9
26
17
38
26
51
35
64
43
77
A-118
-------
Location
Detroit
El Paso
El Paso
El Paso
El Paso
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
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Los Angeles
Los Angeles
Scenario1
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
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
12
12
12
12
12
12
12
12
12
12
12
12
12
12
2
2
2
2
2
2
2
2
2
2
2
2
2
2
16
16
16
16
16
16
16
16
16
16
16
16
16
16
51
51
Annual Average NO2 (ppb)3
Mean
46
15
38
10
9
20
17
30
26
40
35
49
43
59
52
14
53
13
12
26
24
39
36
52
49
65
61
78
73
10
25
8
7
15
14
23
21
30
27
38
34
45
41
22
31
Min
42
10
26
7
6
14
12
21
18
27
24
34
30
41
36
14
53
13
12
26
24
39
36
52
48
64
60
77
72
2
5
2
1
3
3
5
4
6
6
8
7
9
8
5
7
Med
46
16
40
11
9
21
18
32
28
42
37
53
46
63
55
14
53
13
12
26
24
39
36
52
49
65
61
78
73
7
18
5
5
11
10
16
15
21
19
27
24
32
29
24
32
p98
52
18
48
12
10
24
21
36
31
47
41
59
52
71
62
15
53
13
12
26
25
40
37
53
49
66
61
79
74
22
53
16
14
32
29
48
43
64
58
79
72
95
87
36
48
p99
52
18
48
12
10
24
21
36
31
47
41
59
52
71
62
15
53
13
12
26
25
40
37
53
49
66
61
79
74
22
53
16
14
32
29
48
43
64
58
79
72
95
87
37
52
A-119
-------
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
Miami
Miami
Miami
Miami
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
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Scenario1
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
51
51
51
51
51
51
51
51
51
51
51
51
6
6
6
6
6
6
6
6
6
6
6
6
6
6
26
26
26
26
26
26
26
26
26
26
26
26
26
26
14
14
14
14
14
Annual Average NO2 (ppb)3
Mean
9
8
18
17
26
25
35
34
44
42
53
51
9
32
8
7
16
14
24
21
32
29
41
36
49
43
20
29
11
10
22
19
32
29
43
38
54
48
65
57
20
37
14
13
27
Min
2
2
4
4
6
5
8
7
9
9
11
11
7
26
6
6
13
11
19
17
26
22
32
28
38
34
11
15
6
5
12
11
18
16
24
21
30
26
36
32
15
26
10
9
20
Med
9
9
19
18
28
27
37
36
47
45
56
54
9
34
8
7
16
15
25
22
33
29
41
36
49
44
18
27
10
9
20
18
30
26
40
35
50
44
60
53
18
35
13
12
25
p98
14
14
29
28
43
41
57
55
71
69
86
83
10
37
9
8
19
17
28
25
38
33
47
41
56
50
31
44
17
15
34
30
51
45
68
59
84
74
101
89
28
53
20
18
39
p99
15
14
29
28
44
42
59
57
73
71
88
85
10
37
9
8
19
17
28
25
38
33
47
41
56
50
31
44
17
15
34
30
51
45
68
59
84
74
101
89
28
53
20
18
39
A-120
-------
Location
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
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
Scenario1
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
5
5
5
5
5
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
3
3
3
3
9
9
9
9
9
9
9
9
Annual Average NO2 (ppb)3
Mean
25
41
38
55
50
68
63
82
75
27
40
16
14
31
28
47
43
62
57
78
71
94
85
24
53
19
17
37
35
56
52
74
69
93
87
111
104
17
41
13
12
27
24
40
36
Min
19
30
28
40
37
50
46
61
56
22
32
13
12
26
23
38
35
51
47
64
58
77
70
22
53
17
16
34
32
51
48
68
64
86
80
103
96
14
36
11
10
22
20
33
30
Med
23
38
35
51
46
63
58
76
70
29
41
17
15
33
30
50
45
66
60
83
75
99
90
24
53
19
18
38
35
57
53
76
71
95
89
114
106
17
38
13
12
26
24
39
36
p98
36
59
54
79
72
98
90
118
108
29
45
17
15
34
31
51
46
68
62
85
77
102
93
25
53
19
18
39
37
58
55
78
73
97
91
117
110
21
49
16
14
32
29
48
43
p99
36
59
54
79
72
98
90
118
108
29
45
17
15
34
31
51
46
68
62
85
77
102
93
25
53
19
18
39
37
58
55
78
73
97
91
117
110
21
49
16
14
32
29
48
43
A-121
-------
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
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not MSA
Other Not MSA
Scenario1
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
9
9
9
9
9
9
18
18
18
18
18
18
18
18
18
18
18
18
18
18
612
612
612
612
612
612
612
612
612
612
612
612
612
612
127
127
127
127
127
127
127
127
127
127
127
Annual Average NO2 (ppb)3
Mean
53
49
66
61
80
73
18
39
13
12
26
23
39
35
52
47
64
58
77
70
13
25
6
6
13
11
19
17
25
22
32
28
38
33
7
22
4
3
7
6
11
9
15
12
18
Min
44
40
55
50
66
60
9
19
6
6
12
11
18
17
25
22
31
28
37
33
1
1
0
0
1
0
1
1
1
1
1
1
2
1
1
3
1
0
1
1
2
1
2
2
3
Med
52
47
65
59
78
71
21
44
15
13
29
26
44
40
59
53
73
66
88
79
13
25
7
6
13
11
20
17
26
23
33
28
39
34
6
20
3
3
7
6
10
8
13
11
16
p98
63
58
79
72
95
87
25
53
17
16
35
31
52
47
70
63
87
78
104
94
22
45
11
10
23
20
34
30
46
39
57
49
68
59
15
53
8
7
17
14
25
22
34
29
42
p99
63
58
79
72
95
87
25
53
17
16
35
31
52
47
70
63
87
78
104
94
24
48
12
11
25
21
37
32
49
42
61
53
74
64
16
53
9
8
18
15
27
23
36
31
45
A-122
-------
Location
Other Not MSA
Other Not MSA
Other Not MSA
Scenario1
250
300
300
Percentile2
99
98
99
Site-
Years
127
127
127
Annual Average NO2 (ppb)3
Mean
16
22
19
Min
2
3
3
Med
14
20
17
p98
36
51
43
p99
38
54
46
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual
standard, All others - air quality that just meets 1-hour concentration level given percentile form of
alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 Annual means for each monitor were first calculated based on all simulated hourly values in a year.
Then the mean of the annual means was estimated as the sum of all the annual means in a particular
location divided by the number of simulated site-years across the monitoring period. The min, med, p98,
p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual means.
A-123
-------
Table A-112. Estimated annual average NO2 concentrations for monitors >20 m and <100
road using 2001-2003 air quality as is and air quality adjusted to just meet the current and
standards.
m from a major
alternative
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Scenario1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
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
14
14
14
14
6
6
6
6
6
6
6
6
6
6
6
6
6
6
3
3
3
3
3
3
3
3
3
3
3
3
3
3
Annual Average NO2 (ppb)3
Mean
17
35
12
11
23
21
35
32
47
42
58
53
70
63
31
51
18
16
35
31
53
47
71
63
88
78
106
94
21
53
14
12
27
24
41
36
55
48
69
60
82
72
Min
9
16
6
5
12
11
18
16
24
22
30
27
36
32
28
47
16
15
33
29
49
44
66
58
82
73
99
87
20
53
13
11
26
23
39
34
52
46
65
57
78
68
Med
19
36
13
12
26
23
39
35
51
46
64
58
77
70
31
52
18
16
35
31
53
47
70
63
88
78
106
94
21
53
14
12
28
25
42
37
56
49
70
61
84
74
p98
25
53
17
16
35
32
52
47
70
63
87
79
105
95
32
53
19
17
37
33
56
50
75
66
94
83
112
100
22
53
14
12
28
25
43
37
57
50
71
62
85
74
p99
25
53
17
16
35
32
52
47
70
63
87
79
105
95
32
53
19
17
37
33
56
50
75
66
94
83
112
100
22
53
14
12
28
25
43
37
57
50
71
62
85
74
A-124
-------
Las Vegas
Las Vegas
Las Vegas
Las Vegas
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
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
Scenario1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
3
3
3
3
3
35
35
35
35
35
35
35
35
35
35
35
35
35
35
3
3
3
3
3
3
3
3
3
3
3
3
3
3
13
13
13
13
Annual Average NO2 (ppb)3
Mean
6
14
4
4
8
8
13
11
17
15
21
19
25
23
24
33
10
9
19
18
29
28
38
37
48
46
57
55
14
53
13
12
27
23
40
35
53
47
67
59
80
70
31
44
17
15
Min
3
7
2
2
4
4
6
6
9
8
11
10
13
12
4
5
2
2
3
3
5
5
7
6
8
8
10
10
13
53
12
11
24
21
36
32
48
42
60
53
72
63
21
30
11
10
Med
6
14
4
4
8
8
12
11
17
15
21
19
25
23
24
33
9
9
19
18
28
27
38
36
47
46
57
55
14
53
13
12
27
23
40
35
53
47
66
58
80
70
30
46
16
14
p98
9
21
6
6
12
11
19
17
25
23
31
28
37
34
41
53
16
16
32
31
49
47
65
62
81
78
97
94
16
53
15
13
29
26
44
39
59
52
74
65
88
78
40
53
22
19
p99
9
21
6
6
12
11
19
17
25
23
31
28
37
34
41
53
16
16
32
31
49
47
65
62
81
78
97
94
16
53
15
13
29
26
44
39
59
52
74
65
88
78
40
53
22
19
A-125
-------
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
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
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
Scenario1
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
13
13
13
13
13
13
13
13
13
13
7
7
7
7
7
7
7
7
7
7
7
7
7
7
2
2
2
2
2
2
2
2
2
2
2
2
2
2
11
11
11
11
11
11
11
11
Annual Average NO2 (ppb)3
Mean
33
29
50
44
66
58
83
73
100
88
24
46
17
16
34
31
51
47
68
62
85
78
102
93
23
33
13
12
26
24
39
36
52
47
65
59
78
71
14
34
11
10
22
20
33
30
Min
23
20
34
30
45
40
57
50
68
60
19
34
13
12
26
24
39
36
53
48
66
60
79
72
22
31
12
11
25
23
37
34
50
45
62
57
74
68
9
21
7
6
13
12
20
18
Med
33
29
49
43
65
57
82
72
98
86
24
45
17
15
33
31
50
46
67
61
84
77
100
92
23
33
13
12
26
24
39
36
52
47
65
59
78
71
12
27
9
8
18
16
27
24
p98
44
38
65
58
87
77
109
96
131
115
30
53
21
19
42
38
62
57
83
76
104
95
125
114
24
36
14
12
27
25
41
37
54
50
68
62
81
74
25
53
19
17
38
35
57
52
p99
44
38
65
58
87
77
109
96
131
115
30
53
21
19
42
38
62
57
83
76
104
95
125
114
24
36
14
12
27
25
41
37
54
50
68
62
81
74
25
53
19
17
38
35
57
52
A-126
-------
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
Scenario1
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
11
11
11
11
11
11
10
10
10
10
10
10
10
10
10
10
10
10
10
10
Annual Average NO2 (ppb)3
Mean
44
40
55
50
66
60
20
43
14
13
29
26
43
39
57
52
71
65
86
77
Min
26
24
33
30
40
36
14
30
10
9
20
18
30
27
40
36
49
45
59
54
Med
36
33
45
41
54
49
22
47
16
14
31
28
47
42
62
56
78
70
93
84
p98
76
70
95
87
114
104
26
53
18
16
36
33
55
49
73
66
91
82
109
99
p99
76
70
95
87
114
104
26
53
18
16
36
33
55
49
73
66
91
82
109
99
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual
standard, All others - air quality that just meets 1-hour concentration level given percentile form of
alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 Annual means for each monitor were first calculated based on all simulated hourly values in a year.
Then the mean of the annual means was estimated as the sum of all the annual means in a particular
location divided by the number of simulated site-years across the monitoring period. The min, med,
p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual
means.
A-127
-------
Table A-113. Estimated annual average NO2 concentrations for monitors <20 m from a major road using
2001-2003 air quality as is and air quality adjusted to Just meet the current and alternative standards.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Scenario1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
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
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
Annual Average NO2 (ppb
Mean
21
41
15
13
29
26
44
39
58
53
73
66
87
79
22
37
13
11
26
23
39
34
52
46
65
57
78
69
23
53
16
15
32
30
48
45
64
60
80
75
96
90
36
53
Min
7
13
5
4
10
9
14
13
19
17
24
22
29
26
22
36
13
11
25
22
38
34
50
45
63
56
76
67
22
53
15
14
31
29
46
43
62
57
77
72
92
86
35
53
Med
23
48
16
14
32
29
48
43
63
57
79
72
95
86
22
37
13
11
26
23
38
34
51
46
64
57
77
68
22
53
16
15
32
30
48
44
63
59
79
74
95
89
36
53
p98
30
53
20
18
41
37
61
55
82
74
102
92
122
111
24
39
14
12
27
24
41
36
54
48
68
60
81
72
24
53
17
16
34
31
50
47
67
63
84
78
101
94
37
53
3
p99
30
53
20
18
41
37
61
55
82
74
102
92
122
111
24
39
14
12
27
24
41
36
54
48
68
60
81
72
24
53
17
16
34
31
50
47
67
63
84
78
101
94
37
53
A-128
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Las Vegas
Las Vegas
Las Vegas
Las Vegas
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
Miami
Miami
Miami
Miami
Miami
Scenario1
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
9
9
9
9
9
9
9
9
9
9
9
9
9
9
3
3
3
3
3
Annual Average NO2 (ppb
Mean
19
17
37
33
56
50
75
66
93
83
112
99
22
53
16
14
32
29
47
43
63
57
79
72
95
86
30
41
12
11
23
23
35
34
47
45
59
56
70
68
6
23
6
5
12
Min
18
16
37
32
55
49
73
65
92
81
110
97
21
53
15
14
31
28
46
42
61
56
77
70
92
84
23
30
9
9
18
17
27
26
36
35
45
44
54
52
6
19
5
5
11
Med
19
17
37
33
56
50
75
66
93
83
112
99
22
53
16
15
32
29
48
44
64
58
80
73
96
87
29
39
12
11
23
22
35
33
46
45
58
56
69
67
6
23
6
5
12
p98
19
17
38
34
57
51
76
68
95
84
114
101
23
53
16
15
32
29
48
44
65
59
81
73
97
88
37
53
15
14
29
28
44
42
58
56
73
70
87
84
7
27
6
5
12
3
p99
19
17
38
34
57
51
76
68
95
84
114
101
23
53
16
15
32
29
48
44
65
59
81
73
97
88
37
53
15
14
29
28
44
42
58
56
73
70
87
84
7
27
6
5
12
A-129
-------
Location
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
New York
New York
New York
New York
Phoenix
Phoenix
Phoenix
Phoenix
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
Scenario1
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
7
7
7
7
7
7
7
7
7
7
7
7
7
7
3
3
3
3
3
3
3
3
3
3
3
3
3
3
6
6
6
6
6
6
6
6
Annual Average NO2 (ppb
Mean
10
17
15
23
20
29
26
35
31
28
39
15
13
30
26
45
39
60
53
75
66
90
79
35
53
20
19
41
37
61
56
82
74
102
93
122
112
18
43
14
13
28
26
42
39
Min
9
16
14
22
19
27
24
32
28
25
34
13
12
27
23
40
35
53
47
67
59
80
70
34
53
20
18
40
36
59
54
79
72
99
90
119
108
16
40
13
11
25
23
38
34
Med
10
18
16
24
21
29
26
35
31
28
38
15
13
30
26
45
40
60
53
75
66
90
79
35
53
20
18
40
37
60
55
80
73
100
91
120
110
19
42
14
13
29
26
43
39
p98
11
18
16
25
22
31
27
37
33
30
49
16
14
33
29
49
43
65
57
81
72
98
86
37
53
21
20
43
39
64
59
86
78
107
98
128
117
20
48
15
14
30
28
45
41
3
p99
11
18
16
25
22
31
27
37
33
30
49
16
14
33
29
49
43
65
57
81
72
98
86
37
53
21
20
43
39
64
59
86
78
107
98
128
117
20
48
15
14
30
28
45
41
A-130
-------
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
Scenario1
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
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
4
4
4
4
4
4
4
4
4
4
4
4
4
4
Annual Average NO2 (ppb
Mean
56
51
70
64
84
77
23
50
16
15
33
30
49
44
66
59
82
74
98
89
Min
50
46
63
57
75
69
20
43
14
12
28
25
42
37
55
50
69
62
83
75
Med
57
52
71
65
86
78
24
52
17
15
34
30
50
46
67
61
84
76
101
91
p98
60
55
75
69
90
83
26
53
18
16
36
33
54
49
72
65
91
82
109
98
3
p99
60
55
75
69
90
83
26
53
18
16
36
33
54
49
72
65
91
82
109
98
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual
standard, All others - air quality that just meets 1-hour concentration level given percentile form of
alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 Annual means for each monitor were first calculated based on all simulated hourly values in a year.
Then the mean of the annual means was estimated as the sum of all the annual means in a particular
location divided by the number of simulated site-years across the monitoring period. The min, med,
p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual
means.
A-131
-------
Table A-114. Estimated annual average NO2 concentrations on-roads using 2001-2003 air quality as is, air
quality adjusted to Just meet the current and alternative standards, and an on-road adjustment factor.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Standard1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
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
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
600
600
600
600
600
600
600
600
600
600
600
600
600
600
900
900
900
900
900
900
900
900
900
900
900
900
900
900
300
300
Annual Average NO2 (ppb)3
Mean
22
60
14
13
27
25
41
38
55
50
68
63
82
75
17
34
12
11
24
22
36
32
48
43
59
54
71
65
39
65
23
20
45
40
68
60
91
80
113
100
136
121
32
76
Min
5
12
3
3
6
6
9
9
13
12
16
14
19
17
7
14
5
4
9
8
14
13
19
17
23
21
28
25
21
35
12
11
25
22
37
33
49
44
62
55
74
66
22
53
Med
24
62
15
13
29
27
44
40
58
54
73
67
87
80
18
36
12
11
24
22
37
33
49
44
61
55
73
66
37
62
22
19
43
38
65
57
86
77
108
96
129
115
32
75
p98
47
127
29
26
57
53
86
79
115
106
144
132
172
159
29
60
20
18
40
36
59
54
79
72
99
89
119
107
65
111
37
33
75
66
112
100
150
133
187
166
224
199
43
102
p99
53
130
32
30
65
60
97
90
130
120
162
149
195
179
30
61
21
19
41
37
62
56
82
74
103
93
123
112
68
114
39
35
78
69
117
104
156
138
195
173
234
207
45
106
A-132
-------
Location
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
El Paso
El Paso
El Paso
El Paso
El Paso
Standard1
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
300
300
300
300
300
300
300
300
300
300
300
300
200
200
200
200
200
200
200
200
200
200
200
200
200
200
600
600
600
600
600
600
600
600
600
600
600
600
600
600
1200
1200
1200
1200
1200
Annual Average NO2 (ppb)3
Mean
23
21
46
43
69
64
92
86
115
107
138
129
42
80
22
19
44
39
66
58
88
78
110
97
131
116
37
89
21
14
41
28
62
42
83
56
103
70
124
84
27
69
18
16
36
Min
16
15
32
29
47
44
63
59
79
74
95
88
27
48
14
12
28
25
42
37
56
50
70
62
84
75
24
56
13
9
26
18
39
27
52
35
66
44
79
53
13
32
9
8
17
Med
23
21
45
42
68
64
91
85
113
106
136
127
40
81
21
19
42
37
63
56
84
74
105
93
126
111
36
87
20
13
40
27
60
40
80
54
100
67
119
81
27
68
18
15
35
p98
30
28
61
57
91
85
122
114
152
142
183
171
63
127
33
29
65
58
98
87
131
116
163
145
196
174
54
130
30
20
59
40
89
60
119
80
149
100
178
120
43
111
28
24
56
p99
32
30
64
60
96
90
128
120
160
150
192
180
64
129
33
30
67
59
100
89
134
118
167
148
200
178
57
131
32
21
63
43
95
64
126
85
158
107
189
128
44
116
29
25
58
A-133
-------
Location
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
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
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
Standard1
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
1200
1200
1200
1200
1200
1200
1200
1200
1200
200
200
200
200
200
200
200
200
200
200
200
200
200
200
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
1600
5100
5100
5100
5100
5100
5100
5100
5100
Annual Average NO2 (ppb)3
Mean
31
54
47
71
62
89
78
107
94
26
96
24
22
47
44
71
66
95
88
118
110
142
132
19
46
14
12
27
25
41
37
54
50
68
62
82
74
41
56
16
15
32
31
48
46
Min
15
26
23
34
30
43
38
52
45
18
68
16
15
33
31
49
46
66
61
82
77
99
92
3
7
2
2
4
4
6
5
8
7
10
9
12
11
6
8
2
2
5
5
7
7
Med
31
53
46
70
61
88
77
105
92
26
94
23
22
47
43
70
65
93
87
117
109
140
130
14
33
10
9
20
18
29
27
39
36
49
45
59
54
40
55
16
15
32
31
48
46
p98
49
84
73
112
98
140
122
168
147
36
130
32
30
64
60
96
90
128
119
160
149
192
179
48
117
35
32
69
63
104
95
139
126
174
158
208
189
77
106
31
29
61
59
92
88
p99
51
87
76
116
101
145
126
174
152
37
135
34
31
67
63
101
94
134
125
168
157
201
188
51
124
36
33
73
66
109
99
146
133
182
166
219
199
82
113
32
31
65
62
97
94
A-134
-------
Location
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
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
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Standard1
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
5100
5100
5100
5100
5100
5100
600
600
600
600
600
600
600
600
600
600
600
600
600
600
2600
2600
2600
2600
2600
2600
2600
2600
2600
2600
2600
2600
2600
2600
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
Annual Average NO2 (ppb)3
Mean
64
62
80
77
97
93
16
59
15
13
29
26
44
39
59
52
73
65
88
78
36
52
20
17
39
35
59
52
79
69
98
86
118
104
36
67
25
23
50
46
74
68
99
91
124
Min
10
9
12
12
14
14
9
33
8
7
16
14
24
22
33
29
41
36
49
43
14
18
8
7
15
13
23
20
30
27
38
33
45
40
18
33
13
12
26
23
38
35
51
47
64
Med
63
61
79
76
95
92
15
58
14
13
29
25
43
38
57
50
71
63
86
75
34
49
18
16
37
33
55
49
74
65
92
81
111
98
33
63
23
21
46
42
69
63
92
85
115
p98
122
117
153
147
183
176
24
87
22
20
45
39
67
59
89
78
111
98
134
117
65
98
35
31
70
62
105
93
140
123
175
154
211
185
64
119
44
41
89
81
133
122
177
163
222
p99
130
125
162
156
194
187
25
92
23
20
46
40
68
60
91
80
114
100
137
121
73
103
39
35
79
69
118
104
158
139
197
173
237
208
66
126
46
42
92
84
138
127
184
169
230
A-135
-------
Location
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
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
St. Louis
St. Louis
St. Louis
St. Louis
Standard1
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
1400
1400
1400
500
500
500
500
500
500
500
500
500
500
500
500
500
500
300
300
300
300
300
300
300
300
300
300
300
300
300
300
900
900
900
900
900
900
900
900
900
900
900
900
900
900
Annual Average NO2 (ppb)3
Mean
114
149
137
49
72
28
26
56
51
84
77
113
103
141
128
169
154
43
96
33
31
67
63
100
94
134
125
167
156
200
188
31
74
24
22
48
44
72
66
96
88
120
110
145
132
Min
59
77
70
28
40
16
15
33
30
49
45
65
59
81
74
98
89
28
67
22
20
44
41
65
61
87
82
109
102
131
123
18
45
14
13
28
26
42
38
56
51
70
64
84
77
Med
106
138
127
47
69
27
25
55
50
82
75
109
100
137
125
164
150
41
93
32
30
65
61
97
91
129
121
162
151
194
182
30
71
23
21
47
43
70
64
94
86
117
107
141
129
p98
203
266
244
72
110
42
38
83
76
125
114
166
152
208
190
250
228
61
132
48
45
96
89
143
134
191
179
239
224
287
268
48
114
37
34
75
68
112
102
149
137
186
171
224
205
p99
211
276
253
77
114
44
40
88
81
133
121
177
161
221
202
265
242
64
144
50
47
101
94
151
141
201
188
252
236
302
283
50
118
38
35
76
70
114
105
152
140
190
174
229
209
A-136
-------
Location
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
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Standard1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
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
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
61200
61200
61200
61200
61200
61200
61200
61200
61200
61200
61200
61200
61200
61200
12700
12700
12700
12700
12700
12700
12700
12700
12700
12700
12700
12700
12700
12700
Annual Average NO2 (ppb)3
Mean
33
71
23
21
47
42
70
63
93
84
117
105
140
126
23
45
12
10
23
20
35
30
46
40
58
50
69
60
12
40
7
6
13
11
20
17
26
22
33
28
40
34
Min
11
24
8
7
15
14
23
21
31
28
39
35
46
42
1
1
0
0
1
1
1
1
1
1
2
1
2
2
1
4
1
1
1
1
2
2
3
2
4
3
4
4
Med
34
73
24
22
48
43
72
65
96
87
120
108
144
130
22
44
11
10
23
20
34
30
46
39
57
49
68
59
11
35
6
5
12
10
17
15
23
20
29
25
35
30
p98
58
125
41
37
82
74
123
111
163
148
204
185
245
221
47
93
24
21
48
41
71
62
95
82
119
103
143
124
31
101
17
14
34
29
51
43
67
57
84
72
101
86
p99
63
133
44
40
88
80
133
120
177
160
221
199
265
239
50
99
25
22
51
44
76
66
102
88
127
110
153
132
33
109
18
16
37
31
55
47
73
62
91
78
110
93
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual
standard, All others - air quality that just meets 1-hour concentration level given percentile form of
alternative standard.
A-137
-------
Location
Standard1
Percentile2
Site-
Years
Annual Average NO2 (ppb)3
Mean
Min
Med
p98
p99
* Percentile: 98tn or 99tn percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 Annual means for each monitor were first calculated based on all simulated hourly values in a year.
Then the mean of the annual means was estimated as the sum of all the annual means in a particular
location divided by the number of simulated site-years across the monitoring period. The min, med,
p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual
means.
A-138
-------
A-9.2 Number of 1-hour NO2 exceedances in a year, 2001-2003
Table A-115. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) for monitors >100 m from a major road using
2001-2003 air quality as is and air quality adjusted to just meet the current and alternative standards.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Standard1
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
Percentile2
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
6
6
6
6
6
6
6
6
6
6
9
9
9
9
9
9
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
0
80
0
0
2
1
20
13
65
48
0
3
0
0
0
0
3
2
34
17
0
23
0
0
1
0
Min
0
2
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
7
3
0
1
0
0
0
0
Med
0
85
0
0
0
0
9
4
66
39
0
1
0
0
0
0
2
1
35
14
0
15
0
0
0
0
p98
3
215
1
0
15
8
84
57
196
162
0
12
0
0
1
1
10
6
62
34
2
82
0
0
4
2
p99
3
215
1
0
15
8
84
57
196
162
0
12
0
0
1
1
10
6
62
34
2
82
0
0
4
2
> 150 ppb
Mean
0
18
0
0
0
0
2
1
10
7
0
0
0
0
0
0
0
0
1
1
0
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
Med
0
8
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
p98
1
75
0
0
1
1
15
8
53
42
0
0
0
0
0
0
1
1
5
3
0
4
0
0
1
0
p99
1
75
0
0
1
1
15
8
53
42
0
0
0
0
0
0
1
1
5
3
0
4
0
0
1
0
> 200 ppb
Mean
0
3
0
0
0
0
0
0
2
1
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
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
p98
0
18
0
0
1
0
1
1
15
8
0
0
0
0
0
0
0
0
1
1
0
1
0
0
0
0
p99
0
18
0
0
1
0
1
1
15
8
0
0
0
0
0
0
0
0
1
1
0
1
0
0
0
0
A-139
-------
Location
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
Standard1
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
Percentile2
98
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
9
9
9
9
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
6
6
6
6
6
6
6
Number of Daily Maximum Exceedances3
Ł100 ppb
Mean
31
12
106
72
0
70
0
0
2
1
49
31
133
117
2
99
0
0
2
1
37
17
149
89
3
99
1
1
3
2
17
Min
4
1
51
30
0
59
0
0
1
1
32
20
120
108
1
24
0
0
1
0
29
13
104
62
0
83
0
0
1
0
7
Med
24
7
104
57
0
68
0
0
3
1
54
32
129
109
2
99
0
0
2
1
37
17
149
89
2
96
1
0
3
1
18
p98
86
45
193
146
1
82
0
0
3
2
60
41
151
135
2
174
0
0
2
2
44
21
193
116
7
115
4
3
7
7
23
p99
86
45
193
146
1
82
0
0
3
2
60
41
151
135
2
174
0
0
2
2
44
21
193
116
7
115
4
3
7
7
23
> 150 ppb
Mean
1
0
14
4
0
5
0
0
0
0
2
1
27
14
0
21
0
0
0
0
2
1
17
5
1
14
1
0
2
1
3
Min
0
0
1
0
0
4
0
0
0
0
1
1
16
12
0
1
0
0
0
0
1
0
13
4
0
4
0
0
0
0
1
Med
0
0
8
3
0
5
0
0
0
0
3
1
28
13
0
21
0
0
0
0
2
1
17
5
1
15
0
0
1
0
3
p98
4
2
51
16
0
7
0
0
0
0
3
2
38
17
0
41
0
0
0
0
2
2
21
6
5
20
3
1
7
4
7
p99
4
2
51
16
0
7
0
0
0
0
3
2
38
17
0
41
0
0
0
0
2
2
21
6
5
20
3
1
7
4
7
> 200 ppb
Mean
0
0
1
0
0
1
0
0
0
0
0
0
2
1
0
2
0
0
0
0
1
0
2
1
1
4
0
0
1
1
2
Min
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
Med
0
0
0
0
0
1
0
0
0
0
0
0
3
1
0
2
0
0
0
0
1
0
2
1
0
4
0
0
1
0
1
p98
1
1
4
2
0
1
0
0
0
0
1
1
3
2
0
4
0
0
0
0
1
0
2
2
4
7
2
0
4
3
7
p99
1
1
4
2
0
1
0
0
0
0
1
1
3
2
0
4
0
0
0
0
1
0
2
2
4
7
2
0
4
3
7
A-140
-------
Location
Detroit
Detroit
Detroit
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
Standard1
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
Percentile2
99
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
12
12
12
12
12
12
12
12
12
12
2
2
2
2
2
2
2
2
2
2
16
16
16
16
16
16
16
16
Number of Daily Maximum Exceedances3
Ł100 ppb
Mean
3
73
10
0
115
0
0
3
1
35
14
125
76
1
152
1
1
7
4
74
55
152
132
0
69
0
0
1
1
41
22
Min
1
46
2
0
48
0
0
0
0
11
3
59
30
0
142
0
0
3
3
70
50
142
128
0
0
0
0
0
0
0
0
Med
3
70
11
0
125
0
0
2
0
34
13
129
77
1
152
1
1
7
4
74
55
152
132
0
21
0
0
0
0
5
3
p98
7
99
14
1
189
1
0
8
5
60
28
174
118
1
161
1
1
10
5
77
60
161
135
1
218
0
0
7
2
143
79
p99
7
99
14
1
189
1
0
8
5
60
28
174
118
1
161
1
1
10
5
77
60
161
135
1
218
0
0
7
2
143
79
> 150 ppb
Mean
2
9
3
0
13
0
0
0
0
3
1
16
6
1
44
1
1
1
1
7
4
41
29
0
6
0
0
0
0
1
1
Min
0
2
0
0
3
0
0
0
0
0
0
4
2
0
35
0
0
0
0
3
3
35
22
0
0
0
0
0
0
0
0
Med
1
10
2
0
12
0
0
0
0
2
0
14
7
1
44
1
1
1
1
7
4
41
29
0
1
0
0
0
0
0
0
p98
7
14
7
1
27
0
0
1
1
8
5
30
13
1
53
1
1
1
1
10
5
46
35
0
34
0
0
0
0
7
2
p99
7
14
7
1
27
0
0
1
1
8
5
30
13
1
53
1
1
1
1
10
5
46
35
0
34
0
0
0
0
7
2
> 200 ppb
Mean
1
3
2
0
2
0
0
0
0
0
0
3
1
1
7
1
1
1
1
1
1
7
4
0
0
0
0
0
0
0
0
Min
0
1
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
1
0
3
3
0
0
0
0
0
0
0
0
Med
1
3
1
0
2
0
0
0
0
0
0
2
0
1
7
1
1
1
1
1
1
7
4
0
0
0
0
0
0
0
0
p98
4
7
7
0
7
0
0
1
0
1
1
8
5
1
11
1
1
1
1
1
1
10
5
0
1
0
0
0
0
1
1
p99
4
7
7
0
7
0
0
1
0
1
1
8
5
1
11
1
1
1
1
1
1
10
5
0
1
0
0
0
0
1
1
A-141
-------
Location
Las Vegas
Las Vegas
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
Standard1
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
Percentile2
98
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
16
16
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
Number of Daily Maximum Exceedances3
Ł100 ppb
Mean
114
87
4
21
0
0
1
0
9
8
37
32
0
106
0
0
4
2
41
23
107
72
0
8
0
0
1
0
16
7
72
Min
1
0
0
0
0
0
0
0
0
0
0
0
0
75
0
0
0
0
9
2
59
35
0
0
0
0
0
0
0
0
9
Med
61
31
1
16
0
0
0
0
4
3
31
27
0
102
0
0
3
2
35
15
111
75
0
6
0
0
0
0
7
2
68
p98
293
252
17
67
0
0
5
3
43
36
110
98
0
157
0
0
14
7
80
53
145
111
3
39
0
0
5
3
42
25
141
p99
293
252
18
78
0
0
10
9
46
39
128
117
0
157
0
0
14
7
80
53
145
111
3
39
0
0
5
3
42
25
141
> 150 ppb
Mean
19
9
0
2
0
0
0
0
1
0
5
4
0
23
0
0
0
0
4
2
23
12
0
0
0
0
0
0
1
0
7
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
Med
3
2
0
0
0
0
0
0
0
0
1
1
0
15
0
0
0
0
3
2
15
7
0
0
0
0
0
0
0
0
2
p98
70
38
1
10
0
0
0
0
5
3
26
19
0
52
0
0
2
0
14
7
53
37
0
3
0
0
0
0
5
3
25
p99
70
38
8
11
0
0
5
4
10
9
27
19
0
52
0
0
2
0
14
7
53
37
0
3
0
0
0
0
5
3
25
> 200 ppb
Mean
1
1
0
0
0
0
0
0
0
0
1
0
0
4
0
0
0
0
1
0
4
2
0
0
0
0
0
0
0
0
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
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
3
2
0
0
0
0
0
0
0
0
0
p98
7
2
0
1
0
0
0
0
0
0
5
3
0
12
0
0
0
0
3
2
14
7
0
1
0
0
0
0
1
0
5
p99
7
2
4
7
0
0
0
0
5
5
10
9
0
12
0
0
0
0
3
2
14
7
0
1
0
0
0
0
1
0
5
A-142
-------
Location
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Standard1
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
Percentile2
99
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
26
14
14
14
14
14
14
14
14
14
14
5
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
Number of Daily Maximum Exceedances3
Ł100 ppb
Mean
40
0
29
0
0
3
1
53
30
171
121
0
32
0
0
1
0
89
45
213
171
0
162
0
0
6
4
200
144
327
316
Min
3
0
4
0
0
0
0
18
7
99
52
0
2
0
0
0
0
14
7
115
71
0
148
0
0
4
3
156
107
295
281
Med
30
0
21
0
0
2
1
41
19
176
120
0
29
0
0
1
0
111
56
227
190
0
160
0
0
6
4
221
160
341
332
p98
87
1
75
1
1
15
5
140
91
265
215
0
65
0
0
2
1
146
88
272
235
0
177
0
0
9
5
223
164
345
335
p99
87
1
75
1
1
15
5
140
91
265
215
0
65
0
0
2
1
146
88
272
235
0
177
0
0
9
5
223
164
345
335
> 150 ppb
Mean
3
0
1
0
0
0
0
3
1
27
16
0
0
0
0
0
0
1
0
41
17
0
4
0
0
0
0
6
4
112
65
Min
0
0
0
0
0
0
0
0
0
6
1
0
0
0
0
0
0
0
0
4
2
0
3
0
0
0
0
4
3
81
42
Med
2
0
1
0
0
0
0
2
1
16
9
0
0
0
0
0
0
1
0
52
22
0
4
0
0
0
0
6
4
122
63
p98
15
1
4
1
1
1
1
15
5
83
58
0
0
0
0
0
0
2
1
79
31
0
6
0
0
0
0
9
5
133
91
p99
15
1
4
1
1
1
1
15
5
83
58
0
0
0
0
0
0
2
1
79
31
0
6
0
0
0
0
9
5
133
91
> 200 ppb
Mean
0
0
0
0
0
0
0
1
0
3
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
6
4
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
4
3
Med
0
0
0
0
0
0
0
1
0
2
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
6
4
p98
3
1
1
0
0
1
1
1
1
15
5
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
9
5
p99
3
1
1
0
0
1
1
1
1
15
5
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
9
5
A-143
-------
Location
St. Louis
St. Louis
St. Louis
St. Louis
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
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other Not MSA
Standard1
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
Percentile2
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
9
9
9
9
9
9
9
9
18
18
18
18
18
18
18
18
18
18
612
612
612
612
612
612
612
612
612
612
127
Number of Daily Maximum Exceedances3
Ł100 ppb
Mean
0
65
0
0
3
1
60
37
175
141
0
61
0
0
4
1
58
34
153
117
0
16
0
0
0
0
3
1
18
8
0
Min
0
22
0
0
0
0
20
7
109
76
0
0
0
0
0
0
0
0
10
4
0
0
0
0
0
0
0
0
0
0
0
Med
0
44
0
0
1
0
51
24
171
133
0
66
0
0
3
1
67
34
196
150
0
8
0
0
0
0
0
0
8
2
0
p98
1
128
0
0
15
5
118
81
245
215
1
146
0
0
11
5
131
87
251
214
1
78
0
0
1
1
25
9
81
50
5
p99
1
128
0
0
15
5
118
81
245
215
1
146
0
0
11
5
131
87
251
214
4
94
0
0
4
1
30
14
102
56
5
> 150 ppb
Mean
0
5
0
0
0
0
3
1
31
15
0
4
0
0
0
0
4
1
30
15
0
1
0
0
0
0
0
0
1
0
0
Min
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
0
Med
0
1
0
0
0
0
1
0
18
7
0
3
0
0
0
0
3
1
28
16
0
0
0
0
0
0
0
0
0
0
0
p98
0
18
0
0
1
1
15
5
70
38
0
16
0
0
0
0
11
5
78
43
0
9
0
0
0
0
1
1
10
4
1
p99
0
18
0
0
1
1
15
5
70
38
0
16
0
0
0
0
11
5
78
43
0
12
0
0
0
0
4
1
16
6
1
> 200 ppb
Mean
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
4
1
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
0
0
0
0
Med
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
0
0
0
p98
0
1
0
0
0
0
1
1
15
5
0
1
0
0
0
0
1
0
11
5
0
1
0
0
0
0
0
0
1
1
0
p99
0
1
0
0
0
0
1
1
15
5
0
1
0
0
0
0
1
0
11
5
0
3
0
0
0
0
1
0
4
1
1
A-144
-------
Location
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
Standard1
Current Std
50
50
100
100
150
150
200
200
Percentile2
98
99
98
99
98
99
98
99
Site-
Years
127
127
127
127
127
127
127
127
127
Number of Daily Maximum Exceedances3
Ł100 ppb
Mean
37
0
0
0
0
1
1
6
3
Min
0
0
0
0
0
0
0
0
0
Med
12
0
0
0
0
0
0
0
0
p98
170
1
0
6
3
23
14
78
49
p99
192
1
0
6
5
32
16
79
52
> 150 ppb
Mean
6
0
0
0
0
0
0
1
0
Min
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
p98
74
0
0
1
1
6
3
16
10
p99
80
0
0
1
1
6
5
18
11
> 200 ppb
Mean
1
0
0
0
0
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
p98
21
0
0
1
0
2
1
6
3
p99
34
0
0
1
0
2
1
6
5
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual standard, All others - air quality that just
meets 1-hour concentration level given percentile form of alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1 -hour concentration averaged over three years at maximum monitor in location.
3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location divided
by the number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles
of the distribution for the number of daily maximum exceedances in any one year within the monitoring period.
A-145
-------
Table A-116. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for
monitors >100 m from a major road using 2001-2003 air quality as is and air quality adjusted to just meet the
current and alternative standards.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
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
Standard1
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
Percentile2
98
99
98
99
98
99
98
99
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
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
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
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
1
0
0
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
1
0
0
1
p98
0
4
0
0
0
0
1
1
2
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
1
1
0
2
p99
0
4
0
0
0
0
1
1
2
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
1
1
0
2
> 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
0
0
0
0
0
0
0
0
0
0
0
0
0
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
1
p98
0
1
0
0
0
0
1
0
1
1
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
1
p99
0
1
0
0
0
0
1
0
1
1
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
1
A-146
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
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
Standard1
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
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
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
2
2
2
2
2
2
6
6
6
6
6
6
6
6
6
6
12
12
12
12
12
12
12
12
12
12
2
2
2
2
2
2
2
2
2
2
16
16
16
16
16
16
16
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
0
0
0
0
0
1
0
1
2
0
0
1
0
1
1
2
1
0
0
0
0
0
0
0
0
1
0
1
3
1
0
1
1
1
1
2
1
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
2
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
0
0
0
1
3
1
0
1
1
1
1
2
1
0
0
0
0
0
0
0
p98
0
0
0
0
0
0
1
0
4
7
0
0
4
2
5
4
7
5
0
3
0
0
0
0
1
1
3
1
1
3
1
0
1
1
1
1
2
1
0
0
0
0
0
0
0
p99
0
0
0
0
0
0
1
0
4
7
0
0
4
2
5
4
7
5
0
3
0
0
0
0
1
1
3
1
1
3
1
0
1
1
1
1
2
1
0
0
0
0
0
0
0
> 300 ppb
Mean
0
0
0
0
0
0
0
0
0
2
0
0
1
0
1
1
2
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
1
1
1
1
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
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
p98
0
0
0
0
0
0
0
0
2
7
0
0
3
1
4
3
7
4
0
1
0
0
0
0
1
0
1
1
0
1
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
p99
0
0
0
0
0
0
0
0
2
7
0
0
3
1
4
3
7
4
0
1
0
0
0
0
1
0
1
1
0
1
0
0
1
1
1
1
1
1
0
0
0
0
0
0
0
A-147
-------
Location
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
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
Phoenix
Phoenix
Standard1
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
Percentile2
99
98
99
98
99
98
99
98
99
98
99
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
16
16
16
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
14
14
14
14
14
14
14
5
5
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
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
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
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
0
p98
0
1
1
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
6
3
0
0
0
0
0
0
0
0
1
1
1
1
0
0
1
1
1
1
1
1
0
0
p99
0
1
1
0
4
0
0
0
0
3
2
7
6
0
3
0
0
0
0
0
0
6
3
0
0
0
0
0
0
0
0
1
1
1
1
0
0
1
1
1
1
1
1
0
0
> 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
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
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
p98
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
0
0
0
1
0
0
1
1
1
1
1
1
0
0
p99
0
0
0
0
1
0
0
0
0
0
0
5
4
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
1
1
1
1
1
0
0
A-148
-------
Location
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
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Standard1
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
Percentile2
98
99
98
99
98
99
98
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
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
18
18
18
18
18
18
18
18
18
Number of Daily Maximum Exceedances3
> 250 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
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
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
p98
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
1
2
1
0
0
0
0
0
0
0
0
2
p99
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
1
1
2
1
0
0
0
0
0
0
0
0
2
> 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
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
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
p98
0
0
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
0
0
0
0
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
1
0
0
0
0
0
0
0
0
0
A-149
-------
Location
Washington
DC
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not
MSA
Standard1
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
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-Years
18
612
612
612
612
612
612
612
612
612
612
127
127
127
127
127
127
127
127
127
127
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
0
0
0
0
0
0
0
0
0
0
0
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
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
p98
1
0
1
0
0
0
0
0
0
1
0
0
12
0
0
0
0
1
1
3
1
p99
1
0
1
0
0
0
0
0
0
1
1
0
12
0
0
0
0
1
1
3
1
> 300 ppb
Mean
0
0
0
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
Med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
1
0
1
1
p99
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
1
0
1
1
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual standard, All
others -air quality that just meets 1 -hour concentration level given percentile form of alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1 -hour concentration averaged over three years at
maximum monitor in location.
3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors
in a particular location divided by the number of site-years across the monitoring period. The min, med, p98, and
p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the number of daily
maximum exceedances in any one year within the monitoring period.
A-150
-------
1 Table A-117. Estimated number of exceedances of 1
2 2003 air quality as is and air quality adjusted to Just
-hour concentration levels (100,150 and 200 ppb) for monitors >20 m and <100 m from a major road using 2001-
meet the current and alternative standards.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Standard1
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
Percentile2
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
14
14
14
14
14
14
14
14
14
14
6
6
6
6
6
6
6
6
6
6
3
3
3
3
3
3
3
3
3
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
0
38
0
0
3
1
35
20
116
82
2
74
0
0
7
2
90
50
212
164
2
170
0
0
6
4
77
38
178
Min
0
0
0
0
0
0
2
0
21
11
0
34
0
0
1
0
48
20
147
108
0
128
0
0
2
1
54
23
141
Med
0
24
0
0
2
0
27
14
120
79
1
74
0
0
4
1
86
48
216
163
2
187
0
0
7
5
71
34
174
p98
0
145
0
0
11
6
89
64
226
182
6
111
0
0
21
7
144
95
280
242
3
196
0
0
9
5
105
57
218
p99
0
145
0
0
11
6
89
64
226
182
6
111
0
0
21
7
144
95
280
242
3
196
0
0
9
5
105
57
218
> 150 ppb
Mean
0
3
0
0
0
0
3
1
19
9
0
5
0
0
1
0
7
2
53
24
0
32
0
0
0
0
6
4
40
Min
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
24
6
0
17
0
0
0
0
2
1
25
Med
0
1
0
0
0
0
2
0
13
6
0
4
0
0
0
0
4
1
51
21
0
35
0
0
0
0
7
5
34
p98
0
20
0
0
0
0
11
6
59
28
0
9
0
0
3
2
21
7
99
51
0
44
0
0
0
0
9
5
62
p99
0
20
0
0
0
0
11
6
59
28
0
9
0
0
3
2
21
7
99
51
0
44
0
0
0
0
9
5
62
> 200 ppb
Mean
0
0
0
0
0
0
0
0
3
1
0
1
0
0
0
0
1
1
7
2
0
6
0
0
0
0
1
0
6
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
2
0
0
0
0
0
0
2
Med
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
4
1
0
7
0
0
0
0
1
0
7
p98
0
4
0
0
0
0
1
0
11
6
0
5
0
0
0
0
5
3
21
7
0
8
0
0
0
0
2
0
9
p99
0
4
0
0
0
0
1
0
11
6
0
5
0
0
0
0
5
3
21
7
0
8
0
0
0
0
2
0
9
A-151
-------
Location
El Paso
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
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Standard1
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
Percentile2
99
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
3
3
35
35
35
35
35
35
35
35
35
35
3
3
3
3
3
3
3
3
3
3
Number of Daily Maximum Exceedances3
>100ppb
Mean
137
0
24
0
0
1
1
11
5
46
32
6
31
0
0
1
1
15
13
53
48
0
152
0
0
8
2
73
42
155
114
Min
104
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
137
0
0
4
0
48
27
113
80
Med
137
0
4
0
0
0
0
0
0
25
10
1
25
0
0
0
0
2
2
63
47
0
154
0
0
6
1
75
38
166
122
p98
169
0
67
0
0
4
2
34
16
113
86
31
82
1
0
7
4
59
49
131
124
0
166
0
0
15
6
97
62
187
140
p99
169
0
67
0
0
4
2
34
16
113
86
31
82
1
0
7
4
59
49
131
124
0
166
0
0
15
6
97
62
187
140
>150ppb
Mean
16
0
2
0
0
0
0
1
1
5
3
0
3
0
0
0
0
1
1
8
7
0
42
0
0
0
0
8
2
42
25
Min
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
38
0
0
0
0
4
0
27
19
Med
16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
43
0
0
0
0
6
1
38
20
p98
19
0
5
0
0
0
0
4
2
14
9
2
21
0
0
1
1
7
4
36
33
0
44
0
0
1
0
15
6
62
35
p99
19
0
5
0
0
0
0
4
2
14
9
2
21
0
0
1
1
7
4
36
33
0
44
0
0
1
0
15
6
62
35
> 200 ppb
Mean
4
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
1
0
8
0
0
0
0
1
0
8
2
Min
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
4
0
Med
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
0
1
0
6
1
p98
5
0
1
0
0
0
0
0
0
4
2
1
2
0
0
1
0
2
2
7
4
0
11
0
0
0
0
3
1
15
6
p99
5
0
1
0
0
0
0
0
0
4
2
1
2
0
0
1
0
2
2
7
4
0
11
0
0
0
0
3
1
15
6
A-152
-------
Location
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
Phoenix
Phoenix
Phoenix
St. Louis
Standard1
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
Percentile2
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
13
13
13
13
13
13
13
13
13
13
7
7
7
7
7
7
7
7
7
7
2
2
2
2
2
2
2
2
2
2
11
Number of Daily Maximum Exceedances3
>100ppb
Mean
1
26
0
0
3
1
50
25
154
102
0
44
0
0
5
2
84
47
216
168
0
4
0
0
0
0
49
15
210
162
0
Min
0
1
0
0
0
0
10
2
66
23
0
10
0
0
0
0
36
18
155
107
0
3
0
0
0
0
39
13
190
140
0
Med
0
16
0
0
1
0
42
20
148
95
0
48
0
0
4
1
80
32
219
172
0
4
0
0
0
0
49
15
210
162
0
p98
8
48
0
0
13
5
121
67
258
197
1
73
0
0
11
4
136
89
275
231
0
5
0
0
0
0
59
17
229
184
0
p99
8
48
0
0
13
5
121
67
258
197
1
73
0
0
11
4
136
89
275
231
0
5
0
0
0
0
59
17
229
184
0
>150ppb
Mean
0
1
0
0
0
0
3
1
25
12
0
2
0
0
0
0
5
2
42
22
0
0
0
0
0
0
0
0
11
2
0
Min
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
16
6
0
0
0
0
0
0
0
0
6
0
0
Med
0
1
0
0
0
0
1
0
20
8
0
2
0
0
0
0
4
1
30
15
0
0
0
0
0
0
0
0
11
2
0
p98
0
4
0
0
2
0
13
5
67
33
0
5
0
0
0
0
11
4
82
51
0
0
0
0
0
0
0
0
15
3
0
p99
0
4
0
0
2
0
13
5
67
33
0
5
0
0
0
0
11
4
82
51
0
0
0
0
0
0
0
0
15
3
0
> 200 ppb
Mean
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
5
2
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
0
0
0
0
Med
0
0
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
0
0
0
p98
0
0
0
0
0
0
3
2
13
5
0
1
0
0
0
0
1
0
11
4
0
0
0
0
0
0
0
0
0
0
0
p99
0
0
0
0
0
0
3
2
13
5
0
1
0
0
0
0
1
0
11
4
0
0
0
0
0
0
0
0
0
0
0
A-153
-------
Location
St. Louis
St. Louis
St. Louis
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
Standard1
Current Std
50
50
100
100
150
150
200
200
As is
Current Std
50
50
100
100
150
150
200
200
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
11
11
11
11
11
11
11
11
11
10
10
10
10
10
10
10
10
10
10
Number of Daily Maximum Exceedances3
>100ppb
Mean
41
0
0
2
1
37
24
119
90
0
57
0
0
2
1
57
31
168
124
Min
0
0
0
0
0
0
0
28
11
0
5
0
0
0
0
4
0
56
29
Med
26
0
0
0
0
24
11
90
63
0
67
0
0
2
0
64
34
201
149
p98
120
0
0
11
5
120
82
265
235
0
103
0
0
5
2
111
69
271
221
p99
120
0
0
11
5
120
82
265
235
0
103
0
0
5
2
111
69
271
221
>150ppb
Mean
3
0
0
0
0
2
1
21
11
0
2
0
0
0
0
2
1
28
12
Min
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
10
4
0
2
0
0
0
0
2
0
30
12
p98
13
0
0
0
0
11
5
74
42
0
6
0
0
0
0
5
2
65
34
p99
13
0
0
0
0
11
5
74
42
0
6
0
0
0
0
5
2
65
34
> 200 ppb
Mean
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
2
1
Min
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
2
0
p98
2
0
0
0
0
1
0
11
5
0
0
0
0
0
0
0
0
5
2
p99
2
0
0
0
0
1
0
11
5
0
0
0
0
0
0
0
0
5
2
Notes:
1 Scenario: As is - unadjusted air quality, Current Std -air quality that just meets the current annual standard, All others - air quality that just meets 1-hour
concentration level given percentile form of alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at maximum monitor in location.
3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location divided by the number
of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the
number of daily maximum exceedances in any one year within the monitoring period.
A-154
-------
Table A-118. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for
monitors >20 m and <100 m from a major road using 2001-2003 air quality as is and air quality adjusted to
just meet the current and alternative standards.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
El Paso
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
Los Angeles
Los Angeles
Standar
d1
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
Percentile
2
98
99
98
99
98
99
98
99
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
6
6
6
6
6
6
6
6
6
6
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
35
35
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
2
0
0
0
0
0
0
3
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
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
2
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
2
0
5
5
0
5
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
1
1
p99
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
2
0
5
5
0
5
0
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
1
1
> 300 ppb
Mean
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
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
p98
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
0
0
0
0
0
0
0
0
0
0
1
p99
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
0
0
0
0
0
0
0
0
0
0
1
A-155
-------
Location
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
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Standar
d1
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
2
98
99
98
99
98
99
98
99
98
99
98
99
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
35
35
35
35
35
35
35
35
3
3
3
3
3
3
3
3
3
3
13
13
13
13
13
13
13
13
13
13
7
7
7
7
7
7
7
7
7
7
2
2
2
2
2
2
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
2
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
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
Med
0
0
0
0
0
0
0
0
0
1
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
0
0
0
0
0
p98
0
0
0
0
1
1
2
2
0
1
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
4
3
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
p99
0
0
0
0
1
1
2
2
0
1
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
4
3
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
> 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
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
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
p98
0
0
0
0
1
0
1
1
0
1
0
0
0
0
0
0
1
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
p99
0
0
0
0
1
0
1
1
0
1
0
0
0
0
0
0
1
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
A-156
-------
Location
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
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
Standar
d1
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
2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
Site-
Years
2
2
2
2
11
11
11
11
11
11
11
11
11
11
10
10
10
10
10
10
10
10
10
Number of Daily Maximum Exceedances3
> 250 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
Min
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
p98
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
p99
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
> 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
Min
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
p98
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
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Washington
DC 200 99 10 0 0 0 0 0 0 0 0 0 0
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual standard, All
others- air quality that just meets 1 -hour concentration level given percentile form of alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all
monitors in a particular location divided by the number of site-years across the monitoring period. The min, med,
p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the number of daily
maximum exceedances in any one year within the monitoring perod.
A-157
-------
1
2
3
Table A-119. Estimated number of exceedances of 1-hour concentration levels (100,150 and 200 ppb) for monitors < 20 m from a major road using 2001-2003 air
quality as is and air quality adjusted to just meet the current and alternative standards.
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
Standard1
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
Percentile2
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
5
5
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
0
19
0
0
2
1
29
17
104
72
0
21
0
0
2
0
28
14
119
76
0
111
0
0
7
4
79
57
187
Min
0
0
0
0
0
0
0
0
9
3
0
14
0
0
0
0
19
7
107
72
0
103
0
0
3
2
68
46
177
Med
0
26
0
0
0
0
22
16
60
46
0
23
0
0
1
0
30
14
118
76
0
107
0
0
7
4
77
55
179
p98
1
38
0
0
6
4
91
46
249
196
0
26
0
0
6
1
35
20
132
80
0
122
0
0
10
7
92
69
204
p99
1
38
0
0
6
4
91
46
249
196
0
26
0
0
6
1
35
20
132
80
0
122
0
0
10
7
92
69
204
> 150 ppb
Mean
0
1
0
0
0
0
2
1
15
8
0
1
0
0
0
0
2
0
15
7
0
14
0
0
0
0
7
4
50
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
4
0
13
0
0
0
0
3
2
40
Med
0
0
0
0
0
0
0
0
15
7
0
1
0
0
0
0
1
0
14
6
0
15
0
0
0
0
7
4
48
p98
0
3
0
0
1
0
6
4
39
23
0
4
0
0
0
0
6
1
23
13
0
15
0
0
0
0
10
7
62
p99
0
3
0
0
1
0
6
4
39
23
0
4
0
0
0
0
6
1
23
13
0
15
0
0
0
0
10
7
62
> 200 ppb
Mean
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
2
0
0
1
0
0
0
0
0
0
7
Min
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
3
Med
0
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
7
p98
0
1
0
0
0
0
2
1
6
4
0
0
0
0
0
0
0
0
6
1
0
2
0
0
0
0
0
0
10
p99
0
1
0
0
0
0
2
1
6
4
0
0
0
0
0
0
0
0
6
1
0
2
0
0
0
0
0
0
10
A-158
-------
Location
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
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
Miami
Standard1
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
Percentile2
99
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
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
9
9
9
9
9
9
9
9
9
9
3
Number of Daily Maximum Exceedances3
>100ppb
Mean
166
7
48
0
0
7
4
68
33
224
157
1
211
0
0
7
2
141
85
280
248
6
42
0
0
1
0
21
17
72
62
0
Min
161
3
43
0
0
3
2
54
24
224
144
0
203
0
0
1
0
112
65
261
229
0
25
0
0
0
0
7
5
46
35
0
Med
167
7
48
0
0
7
4
68
33
224
157
0
212
0
0
10
2
142
89
261
236
6
37
0
0
0
0
20
18
55
49
0
p98
171
10
53
0
0
10
6
81
42
224
170
2
218
0
0
11
4
168
102
319
278
9
77
0
0
3
2
32
28
118
110
0
p99
171
10
53
0
0
10
6
81
42
224
170
2
218
0
0
11
4
168
102
319
278
9
77
0
0
3
2
32
28
118
110
0
>150ppb
Mean
30
1
5
0
0
2
0
7
4
33
18
0
22
0
0
0
0
7
2
76
46
0
4
0
0
0
0
1
0
7
6
0
Min
19
1
3
0
0
1
0
3
2
24
11
0
15
0
0
0
0
1
0
59
36
0
0
0
0
0
0
0
0
3
1
0
Med
28
1
5
0
0
2
0
7
4
33
18
0
22
0
0
0
0
10
2
78
40
0
2
0
0
0
0
0
0
9
6
0
p98
43
1
7
0
0
3
0
10
6
42
24
0
28
0
0
0
0
11
4
92
63
0
9
0
0
0
0
3
2
11
10
0
p99
43
1
7
0
0
3
0
10
6
42
24
0
28
0
0
0
0
11
4
92
63
0
9
0
0
0
0
3
2
11
10
0
> 200 ppb
Mean
4
0
2
0
0
0
0
2
2
7
4
0
1
0
0
0
0
1
0
7
2
0
0
0
0
0
0
0
0
1
0
0
Min
2
0
1
0
0
0
0
1
1
3
2
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
Med
4
0
2
0
0
0
0
2
2
7
4
0
0
0
0
0
0
0
0
10
2
0
0
0
0
0
0
0
0
0
0
0
p98
7
0
3
0
0
0
0
3
3
10
6
0
4
0
0
0
0
2
1
11
4
0
1
0
0
0
0
0
0
3
2
0
p99
7
0
3
0
0
0
0
3
3
10
6
0
4
0
0
0
0
2
1
11
4
0
1
0
0
0
0
0
0
3
2
0
A-159
-------
Location
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
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
Standard1
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
Percentile2
98
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
3
3
3
3
3
3
3
3
3
7
7
7
7
7
7
7
7
7
7
3
3
3
3
3
3
3
3
3
3
6
6
6
Number of Daily Maximum Exceedances3
>100ppb
Mean
78
0
0
1
0
28
16
76
53
1
18
0
0
1
0
38
18
143
88
2
72
0
0
6
4
160
96
293
260
0
56
0
Min
56
0
0
0
0
25
13
68
46
0
6
0
0
0
0
18
4
107
53
1
58
0
0
4
2
135
79
266
236
0
28
0
Med
79
0
0
1
0
30
15
79
52
1
17
0
0
1
0
42
17
141
96
1
67
0
0
4
4
170
102
297
259
0
47
0
p98
100
0
0
1
0
30
19
80
61
1
52
0
0
2
1
55
31
168
117
4
90
0
0
11
6
174
107
316
284
1
112
0
p99
100
0
0
1
0
30
19
80
61
1
52
0
0
2
1
55
31
168
117
4
90
0
0
11
6
174
107
316
284
1
112
0
>150ppb
Mean
17
0
0
0
0
1
0
16
8
0
1
0
0
0
0
1
0
18
7
0
2
0
0
0
0
6
4
88
39
0
3
0
Min
14
0
0
0
0
0
0
13
5
0
0
0
0
0
0
0
0
4
3
0
1
0
0
0
0
4
2
67
31
0
0
0
Med
15
0
0
0
0
1
0
15
6
0
0
0
0
0
0
1
0
17
6
0
1
0
0
0
0
4
4
97
40
0
2
0
p98
23
0
0
0
0
1
0
19
13
0
2
0
0
0
0
2
1
31
12
0
4
0
0
0
0
11
6
100
46
0
6
0
p99
23
0
0
0
0
1
0
19
13
0
2
0
0
0
0
2
1
31
12
0
4
0
0
0
0
11
6
100
46
0
6
0
> 200 ppb
Mean
1
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
6
4
0
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
4
2
0
0
0
Med
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
4
4
0
1
0
p98
2
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
1
0
11
6
0
2
0
p99
2
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
1
0
11
6
0
2
0
A-160
-------
Location
St. Louis
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
Standard1
50
100
100
150
150
200
200
As is
Current Std
50
50
100
100
150
150
200
200
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-Years
6
6
6
6
6
6
6
4
4
4
4
4
4
4
4
4
4
Number of Daily Maximum Exceedances3
>100ppb
Mean
0
3
1
46
26
175
136
0
78
0
0
6
2
73
43
209
154
Min
0
1
0
21
16
126
95
0
61
0
0
3
0
47
25
158
106
Med
0
3
1
50
23
192
149
0
81
0
0
6
2
77
45
217
162
p98
0
6
4
68
40
209
169
1
88
0
0
9
4
92
55
243
184
p99
0
6
4
68
40
209
169
1
88
0
0
9
4
92
55
243
184
>150ppb
Mean
0
0
0
3
1
23
10
0
7
0
0
0
0
6
2
38
22
Min
0
0
0
1
0
15
6
0
6
0
0
0
0
3
0
22
15
Med
0
0
0
3
1
22
9
0
7
0
0
0
0
6
2
38
20
p98
0
1
1
6
4
36
15
0
8
0
0
1
0
9
4
53
32
p99
0
1
1
6
4
36
15
0
8
0
0
1
0
9
4
53
32
> 200 ppb
Mean
0
0
0
1
1
3
1
0
0
0
0
0
0
0
0
6
2
Min
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
3
0
Med
0
0
0
1
1
3
1
0
0
0
0
0
0
0
0
6
2
p98
0
0
0
2
2
6
4
0
1
0
0
0
0
1
1
9
4
p99
0
0
0
2
2
6
4
0
1
0
0
0
0
1
1
9
4
Notes:
1 Scenario: As is- unadjusted air quality, Current Std - air quality that just meets the current annual standard, All others- air quality that just meets 1-hour
concentration level given percentile form of alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1 -hour concentration averaged over three years at maximum monitor in location.
3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location divided by the number of
site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the number of
daily maximum exceedances in any one year within the monitoring period.
A-161
-------
Table A-120. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for
monitors < 20 m from a major road using 2001-2003 air quality as is and air quality adjusted to just meet the
current and alternative standards.
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
Las Vegas
Standard1
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
Percentile2
98
99
98
99
98
99
98
99
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
5
5
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
3
Number of Daily Maximum Exceedances3
> 250 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
1
0
0
0
0
0
0
0
0
0
3
2
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
1
1
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
1
0
0
0
0
0
0
0
0
0
3
2
0
p98
0
0
0
0
0
0
0
0
2
1
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
4
3
0
p99
0
0
0
0
0
0
0
0
2
1
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
4
3
0
> 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
0
0
0
0
0
0
0
0
0
0
2
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
1
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
2
0
0
p98
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
3
0
0
p99
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
3
0
0
A-162
-------
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
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
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Standard1
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
Percentile2
98
99
98
99
98
99
98
99
98
99
98
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
3
3
3
3
3
3
3
3
3
9
9
9
9
9
9
9
9
9
9
3
3
3
3
3
3
3
3
3
3
7
7
7
7
7
7
7
7
7
7
3
3
3
3
3
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
1
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
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
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
p98
2
0
0
0
0
0
0
2
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
0
0
0
p99
2
0
0
0
0
0
0
2
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
0
0
0
> 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
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
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
p98
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
A-163
-------
Location
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
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Standard1
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
Percentile2
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
6
6
6
6
6
6
6
6
6
6
4
4
4
4
4
4
4
4
4
4
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
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
Med
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
p98
0
0
0
1
0
0
1
0
0
0
0
1
1
2
2
0
0
0
0
0
0
0
0
2
1
p99
0
0
0
1
0
0
1
0
0
0
0
1
1
2
2
0
0
0
0
0
0
0
0
2
1
> 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
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
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
p98
0
0
0
0
0
0
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
0
p99
0
0
0
0
0
0
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
1
0
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual
standard, All others - air quality that just meets 1-hour concentration level given percentile form of
alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all
monitors in a particular location divided by the number of site-years across the monitoring period. The
min, med, p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for
the number of daily maximum exceedances in any one year within the monitoring period.
A-164
-------
Table A-121. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) on-roads using 2001-2003 air quality as is and air quality
adjusted to Just meet the current and alternative standards and an on-road adjustment factor.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Standard1
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
Percentile2
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
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
600
600
600
600
600
600
600
600
600
600
900
900
900
900
900
900
900
900
900
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
23
183
2
2
46
36
117
102
170
156
5
86
0
0
26
16
95
76
163
139
52
193
4
2
83
56
205
167
281
Min
0
12
0
0
0
0
0
0
5
2
0
1
0
0
0
0
3
0
27
15
0
24
0
0
2
0
45
18
129
Med
5
228
0
0
22
13
123
96
202
178
1
83
0
0
13
4
87
67
166
141
35
192
0
0
68
40
207
161
285
p98
130
341
26
20
190
166
295
282
339
331
36
209
3
3
125
89
219
200
283
263
180
328
43
31
238
188
331
316
354
p99
169
348
38
25
229
203
312
298
344
340
40
227
6
3
131
99
248
220
293
279
191
332
49
34
257
197
336
321
356
> 150 ppb
Mean
4
110
0
0
10
7
46
36
95
80
0
20
0
0
3
1
26
16
72
53
9
74
0
0
18
10
83
56
168
Min
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
0
18
Med
0
103
0
0
1
0
22
13
83
62
0
7
0
0
0
0
13
4
60
38
2
56
0
0
7
2
68
40
163
p98
36
290
4
1
70
53
190
166
273
254
3
96
0
0
22
13
125
89
196
180
62
235
4
1
92
65
238
188
316
p99
51
299
5
3
104
76
229
203
289
275
5
105
0
0
25
18
131
99
220
188
68
250
7
3
106
74
257
197
322
> 200 ppb
Mean
1
58
0
0
2
2
17
12
46
36
0
4
0
0
0
0
6
3
26
16
2
26
0
0
4
2
29
18
83
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
2
Med
0
29
0
0
0
0
3
1
22
13
0
1
0
0
0
0
1
0
13
4
0
13
0
0
0
0
16
7
68
p98
8
228
1
0
26
20
106
79
190
166
1
31
0
0
3
3
40
22
125
89
24
123
0
0
43
31
126
90
238
p99
13
238
1
1
38
25
146
113
229
203
1
41
0
0
6
3
44
25
131
99
29
135
0
0
49
34
142
104
257
A-165
-------
Location
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
Detroit
Detroit
Detroit
Standard1
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
Percentile2
99
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
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
600
600
600
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
253
31
264
7
5
104
84
235
212
305
291
89
267
4
2
99
66
232
197
288
270
41
282
5
1
59
14
178
62
265
143
Min
91
0
154
0
0
19
12
114
84
220
181
8
137
0
0
8
4
90
55
209
158
1
181
0
0
4
1
52
6
131
35
Med
257
21
277
2
1
98
80
240
214
314
301
74
286
1
1
82
52
238
192
288
276
30
285
3
1
46
8
183
50
271
134
p98
344
83
332
37
30
209
179
320
307
348
342
242
315
36
14
252
209
317
311
325
323
130
345
26
7
170
57
301
176
334
284
p99
350
102
335
38
30
225
192
324
313
348
344
259
316
37
15
269
221
318
312
326
323
141
349
28
7
186
59
308
191
343
287
> 150 ppb
Mean
130
5
134
1
0
24
19
104
84
196
174
17
157
0
0
19
11
99
66
198
157
9
166
1
1
13
3
59
14
140
41
Min
9
0
31
0
0
0
0
19
12
66
50
0
8
0
0
0
0
8
4
55
29
0
52
0
0
1
0
4
1
31
1
Med
118
1
134
0
0
15
10
98
80
198
173
5
162
0
0
6
3
82
52
192
143
5
167
1
0
8
2
46
8
131
30
p98
291
30
241
7
3
73
62
209
179
298
283
80
282
1
1
86
66
252
209
313
293
44
299
7
4
56
16
170
57
278
125
p99
301
30
264
8
3
86
70
225
192
304
295
94
284
1
1
103
73
269
221
315
303
46
307
7
5
57
18
186
59
286
137
> 200 ppb
Mean
56
1
58
0
0
7
5
40
30
104
84
3
76
0
0
4
2
33
19
99
66
3
77
1
0
5
1
20
5
59
14
Min
0
0
8
0
0
0
0
2
0
19
12
0
0
0
0
0
0
0
0
8
4
0
6
0
0
0
0
1
0
4
1
Med
40
0
51
0
0
2
1
32
21
98
80
1
79
0
0
1
1
18
6
82
52
2
63
0
0
3
1
13
3
46
8
p98
188
10
137
2
1
37
30
107
82
209
179
25
227
1
1
36
14
130
86
252
209
16
210
5
4
26
7
70
26
170
57
p99
197
10
154
2
1
38
30
116
98
225
192
26
238
1
1
37
15
135
103
269
221
18
219
6
4
28
7
84
28
186
59
A-166
-------
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
Los Angeles
Standard1
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
Percentile2
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
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
5100
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
32
272
3
1
89
56
216
174
278
255
13
290
7
5
119
100
233
214
289
279
23
189
5
3
82
64
167
148
218
201
71
Min
0
122
0
0
4
1
47
15
135
97
0
227
0
0
26
19
135
111
227
200
0
2
0
0
0
0
0
0
5
3
0
Med
19
284
0
0
79
42
222
176
288
265
7
278
4
3
118
98
232
212
278
273
4
194
0
0
43
27
161
127
237
212
57
p98
136
339
23
10
231
190
313
297
342
334
55
338
34
25
217
201
323
314
338
336
171
335
63
49
298
276
330
325
343
337
231
p99
145
348
24
12
249
201
327
306
348
342
56
340
34
25
235
212
325
317
340
337
194
338
71
58
307
292
333
329
344
339
251
> 150 ppb
Mean
4
168
0
0
17
8
89
56
180
136
1
205
1
1
32
23
119
100
201
182
4
106
0
0
19
13
82
64
143
123
17
Min
0
13
0
0
0
0
4
1
15
10
0
103
0
0
2
1
26
19
96
81
0
0
0
0
0
0
0
0
0
0
0
Med
1
168
0
0
8
3
79
42
180
133
1
206
1
1
25
18
118
98
201
182
0
71
0
0
3
1
43
27
118
89
6
p98
24
291
3
1
80
46
231
190
298
274
6
307
3
2
97
79
217
201
306
290
54
309
6
2
149
102
298
276
324
318
94
p99
26
301
3
1
91
49
249
201
313
288
7
310
3
2
114
86
235
212
310
297
62
316
7
3
172
120
307
292
328
321
108
> 200 ppb
Mean
0
79
0
0
3
1
30
15
89
56
1
123
1
1
7
5
51
39
119
100
0
49
0
0
5
3
32
21
82
64
5
Min
0
2
0
0
0
0
0
0
4
1
0
26
0
0
0
0
3
2
26
19
0
0
0
0
0
0
0
0
0
0
0
Med
0
69
0
0
0
0
17
7
79
42
1
122
1
1
4
3
44
31
118
98
0
18
0
0
0
0
8
3
43
27
0
p98
5
224
0
0
23
10
127
76
231
190
1
217
1
1
34
25
127
115
217
201
8
250
0
0
63
49
199
158
298
276
41
p99
6
236
1
0
24
12
136
83
249
201
1
240
1
1
34
25
147
128
235
212
9
278
0
0
71
58
230
182
307
292
48
A-167
-------
Location
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
Standard1
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
Percentile2
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
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
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
152
1
1
33
29
111
100
191
181
7
232
5
3
78
53
174
144
232
208
42
129
3
1
57
35
169
129
249
216
37
222
Min
0
0
0
0
0
0
0
0
0
0
147
0
0
2
0
44
21
139
84
0
0
0
0
0
0
5
0
40
20
0
29
Med
152
0
0
19
16
101
89
204
189
2
233
1
0
74
48
178
150
228
202
28
124
0
0
43
22
170
119
266
229
19
231
p98
319
15
13
142
132
280
266
338
334
50
316
37
21
181
151
267
237
317
299
177
298
33
19
212
156
316
294
344
332
149
339
p99
330
20
17
160
148
299
288
346
344
64
323
48
31
189
173
276
245
322
307
201
310
34
22
226
179
324
307
351
340
172
347
> 150 ppb
Mean
55
0
0
6
5
33
29
81
73
1
146
0
0
19
11
78
53
145
114
7
36
0
0
10
6
57
35
132
94
6
87
Min
0
0
0
0
0
0
0
0
0
0
25
0
0
0
0
2
0
21
9
0
0
0
0
0
0
0
0
0
0
0
0
Med
40
0
0
1
0
19
16
69
59
0
147
0
0
10
4
74
48
150
115
1
22
0
0
3
1
43
22
125
80
1
71
p98
196
1
0
46
42
142
132
246
236
6
243
5
2
92
67
181
151
242
210
55
157
3
1
67
48
212
156
294
263
49
250
p99
225
3
2
55
49
160
148
262
254
10
253
7
3
110
84
189
173
253
225
63
196
4
2
73
56
226
179
307
279
62
280
> 200 ppb
Mean
19
0
0
1
1
10
8
33
29
0
78
0
0
5
3
31
18
78
53
2
10
0
0
3
1
17
10
57
35
1
29
Min
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
8
0
0
0
0
2
1
19
16
0
74
0
0
1
0
21
9
74
48
0
2
0
0
0
0
8
2
43
22
0
15
p98
101
0
0
15
13
66
58
142
132
1
191
0
0
37
21
114
89
181
151
24
71
0
0
33
19
89
67
212
156
11
127
p99
117
0
0
20
17
77
68
160
148
2
200
1
0
48
31
141
104
189
173
24
82
0
0
34
22
110
72
226
179
24
150
A-168
-------
Location
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
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
Standard1
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
Percentile2
98
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
1400
1400
1400
1400
1400
1400
1400
1400
500
500
500
500
500
500
500
500
500
500
300
300
300
300
300
300
300
300
300
300
900
900
900
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
7
4
116
91
254
228
312
299
101
245
6
3
153
118
293
269
332
325
61
338
18
13
257
229
343
337
351
350
25
262
7
Min
0
0
1
0
65
42
192
152
1
23
0
0
2
1
92
48
245
199
1
293
0
0
60
38
289
264
321
320
0
112
0
Med
1
1
102
75
261
235
317
304
83
266
0
0
152
103
297
283
345
334
38
347
4
2
277
248
352
347
362
360
12
268
1
p98
54
41
284
256
346
335
364
359
280
345
44
19
319
290
356
351
362
361
248
363
86
75
353
352
364
363
365
365
128
346
45
p99
68
56
294
275
350
347
364
359
315
352
48
24
337
323
360
356
363
362
289
364
106
82
358
355
364
364
365
365
139
350
51
> 150 ppb
Mean
1
0
27
18
116
91
217
187
16
96
0
0
35
22
153
118
262
231
9
235
2
1
75
56
257
229
331
322
3
131
1
Min
0
0
0
0
1
0
31
21
0
1
0
0
0
0
2
1
41
22
0
55
0
0
1
1
60
38
236
204
0
4
0
Med
0
0
12
5
102
75
223
191
2
77
0
0
14
4
152
103
278
250
0
258
0
0
51
33
277
248
342
329
0
124
0
p98
8
4
118
93
284
256
332
325
113
286
2
1
182
135
319
290
350
343
62
351
18
9
273
235
353
352
363
362
28
276
7
p99
20
10
137
105
294
275
342
332
124
299
3
1
206
156
337
323
354
349
64
355
18
9
301
283
358
355
364
363
37
295
10
> 200 ppb
Mean
0
0
7
4
44
31
116
91
2
29
0
0
6
3
58
40
153
118
1
100
0
0
18
13
121
93
257
229
0
51
0
Min
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
1
0
4
0
0
0
0
4
2
60
38
0
0
0
Med
0
0
1
1
27
15
102
75
0
8
0
0
0
0
37
19
152
103
0
77
0
0
4
2
97
71
277
248
0
37
0
p98
1
1
54
41
169
132
284
256
17
174
0
0
44
19
230
195
319
290
11
297
2
0
86
75
318
300
353
352
5
200
1
p99
1
1
68
56
196
155
294
275
20
191
0
0
48
24
254
218
337
323
11
327
2
0
106
82
342
327
358
355
6
207
1
A-169
-------
Location
St. Louis
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
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Standard1
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
Percentile2
99
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
900
900
900
900
900
900
900
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
61200
61200
61200
61200
61200
61200
61200
61200
61200
61200
12700
12700
12700
12700
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
4
125
96
258
233
316
301
36
222
7
5
109
83
221
194
279
262
16
133
0
0
17
8
73
47
138
104
4
126
0
0
Min
0
2
1
92
63
211
166
0
6
0
0
0
0
4
1
38
20
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
118
85
262
237
322
306
17
268
0
0
99
65
269
227
325
313
3
129
0
0
4
1
52
25
136
91
0
104
0
0
p98
35
274
256
345
335
358
356
169
348
56
38
287
260
347
339
360
357
105
320
5
2
110
68
258
208
324
298
43
333
4
2
p99
41
288
263
351
347
361
357
205
353
63
45
310
291
352
344
362
360
129
336
8
3
133
85
287
238
338
320
62
343
8
4
> 150 ppb
Mean
0
28
19
125
96
224
195
6
111
0
0
27
18
109
83
190
161
2
47
0
0
2
1
17
8
51
30
1
63
0
0
Min
0
0
0
2
1
52
30
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
15
6
118
85
229
197
0
100
0
0
10
4
99
65
221
176
0
25
0
0
0
0
4
1
29
12
0
26
0
0
p98
4
144
105
274
256
329
315
46
293
6
2
139
102
287
260
336
326
22
208
0
0
23
11
110
68
216
161
9
255
1
1
p99
4
153
114
288
263
341
329
54
312
8
2
168
123
310
291
343
334
32
239
1
0
34
17
133
85
247
191
15
282
1
1
> 200 ppb
Mean
0
7
4
47
33
125
96
1
46
0
0
7
5
44
29
109
83
0
15
0
0
0
0
4
2
17
8
0
28
0
0
Min
0
0
0
0
0
2
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
Med
0
1
0
32
18
118
85
0
25
0
0
0
0
23
12
99
65
0
3
0
0
0
0
0
0
4
1
0
6
0
0
p98
1
45
35
189
156
274
256
9
199
0
0
56
38
186
146
287
260
4
102
0
0
5
2
40
20
110
68
2
172
0
0
p99
1
51
41
204
166
288
263
14
230
0
0
63
45
225
176
310
291
7
124
0
0
8
3
53
30
133
85
5
200
1
0
A-170
-------
Location
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Standard1
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
Percentile2
98
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
12700
12700
12700
12700
12700
12700
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
6
3
28
16
63
42
17
193
3
2
58
45
133
116
181
167
2
95
0
0
19
8
84
52
153
114
36
189
2
1
59
Min
0
0
0
0
0
0
0
4
0
0
0
0
0
0
3
1
0
4
0
0
0
0
4
1
32
14
0
25
0
0
0
Med
0
0
6
2
26
12
2
259
0
0
39
21
162
131
241
219
0
91
0
0
8
2
78
42
151
111
20
187
0
0
43
p98
64
33
172
127
254
207
114
337
29
21
206
185
295
276
328
319
18
207
1
0
92
54
189
150
249
215
148
329
24
15
201
p99
86
44
199
149
280
235
120
341
31
23
218
198
304
288
334
326
21
221
2
1
99
65
207
162
267
238
161
339
30
24
220
> 150 ppb
Mean
1
0
6
3
19
10
2
126
0
0
13
8
58
45
111
94
0
25
0
0
1
0
19
8
59
34
5
69
0
0
10
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
1
0
0
0
0
0
0
Med
0
0
0
0
3
1
0
148
0
0
1
0
39
21
123
91
0
15
0
0
0
0
8
2
50
24
0
54
0
0
2
p98
12
6
64
33
139
92
26
285
4
1
93
64
206
185
266
249
1
104
0
0
12
3
92
54
161
119
41
225
0
0
63
p99
17
13
86
44
164
118
27
300
5
2
100
75
218
198
281
262
1
109
0
0
13
5
99
65
174
129
53
241
0
0
78
> 200 ppb
Mean
0
0
2
1
6
3
0
72
0
0
3
2
22
15
58
45
0
6
0
0
0
0
3
1
19
8
1
22
0
0
2
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
Med
0
0
0
0
0
0
0
57
0
0
0
0
3
1
39
21
0
1
0
0
0
0
0
0
8
2
0
8
0
0
0
p98
4
2
19
11
64
33
6
225
0
0
29
21
126
103
206
185
0
44
0
0
1
0
26
10
92
54
7
117
0
0
24
p99
8
4
27
16
86
44
7
237
0
0
31
23
141
110
218
198
0
50
0
0
2
1
33
12
99
65
18
126
0
0
30
A-171
-------
Location
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
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Standard1
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
Percentile2
99
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
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
1200
1200
1200
1200
1200
1200
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
47
176
157
259
244
63
257
9
5
148
106
263
239
296
286
20
287
13
10
165
146
273
261
313
307
24
281
5
3
108
75
Min
0
20
14
94
64
2
135
0
0
20
10
170
114
241
217
0
166
0
0
29
18
163
141
237
234
0
137
0
0
3
2
Med
31
169
148
261
248
49
259
2
1
151
100
264
243
308
287
9
286
4
2
166
143
271
260
318
309
12
285
1
0
102
62
p98
181
320
313
352
348
190
314
41
23
263
236
315
309
330
323
90
350
57
52
293
275
341
337
355
354
114
349
32
20
254
225
p99
193
329
320
355
353
195
320
45
25
269
242
321
313
332
327
103
352
66
53
296
284
346
342
359
357
143
354
37
23
263
234
> 150 ppb
Mean
8
59
47
138
119
10
134
1
0
38
23
148
106
239
204
2
189
1
1
50
40
165
146
249
235
3
198
0
0
23
13
Min
0
0
0
9
7
0
12
0
0
0
0
20
10
122
66
0
35
0
0
0
0
29
18
117
90
0
22
0
0
0
0
Med
1
43
31
127
108
4
133
0
0
23
11
151
100
243
209
0
193
0
0
37
27
166
143
251
237
0
205
0
0
11
5
p98
54
201
181
302
291
47
260
5
2
132
87
263
236
309
294
20
300
12
9
165
146
293
275
331
327
20
309
4
2
113
72
p99
67
220
193
309
296
52
264
9
3
150
95
269
242
313
299
24
315
18
14
180
163
296
284
337
332
23
320
4
2
137
86
> 200 ppb
Mean
1
18
14
59
47
2
52
0
0
9
5
61
38
148
106
0
95
0
0
13
10
77
62
165
146
0
109
0
0
5
3
Min
0
0
0
0
0
0
0
0
0
0
0
1
0
20
10
0
3
0
0
0
0
1
0
29
18
0
3
0
0
0
0
Med
0
6
4
43
31
0
41
0
0
2
1
48
23
151
100
0
86
0
0
4
2
66
50
166
143
0
102
0
0
1
0
p98
15
98
80
201
181
11
164
0
0
41
23
180
132
263
236
3
222
1
0
57
52
203
186
293
275
4
255
0
0
32
20
p99
24
109
93
220
193
14
174
0
0
45
25
192
150
269
242
6
258
1
1
66
53
221
204
296
284
4
265
1
0
37
23
A-172
-------
Location
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
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Standard1
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
Percentile2
98
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
1200
1200
1200
1200
200
200
200
200
200
200
200
200
200
200
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
5400
5400
5400
5400
5400
5400
5400
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
229
199
281
264
11
295
9
2
127
39
241
135
293
218
15
177
6
4
83
70
161
145
210
198
38
160
2
1
54
40
155
Min
63
26
137
97
0
229
0
0
30
1
145
33
229
118
0
2
0
0
0
0
1
0
6
3
0
2
0
0
0
0
2
Med
235
205
286
271
5
295
5
2
124
27
243
131
294
223
0
171
0
0
39
24
146
118
224
205
25
163
0
0
41
27
155
p98
323
309
349
340
48
341
39
5
256
145
320
265
339
309
133
343
77
71
305
288
342
337
350
348
150
314
18
12
188
153
314
p99
327
317
353
346
59
342
50
5
269
158
327
279
340
318
148
346
79
73
312
304
343
339
350
349
169
326
24
15
208
176
325
> 150 ppb
Mean
108
75
197
162
2
216
2
1
35
6
127
39
211
102
2
99
1
0
22
16
83
70
138
123
6
58
0
0
10
6
54
Min
3
2
26
12
0
114
0
0
0
0
30
1
104
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
102
62
205
166
2
223
2
1
23
4
124
27
214
95
0
55
0
0
1
0
39
24
108
83
1
45
0
0
2
1
41
p98
254
225
308
289
5
309
5
4
138
25
256
145
302
232
54
317
17
6
174
142
305
288
335
329
43
187
1
1
62
44
188
p99
263
234
317
296
6
318
5
4
151
29
269
158
312
253
64
324
19
8
187
151
312
304
336
333
55
215
2
1
75
55
208
> 200 ppb
Mean
39
23
108
75
1
134
1
0
9
2
55
10
127
39
0
50
0
0
6
4
36
26
83
70
1
18
0
0
2
1
17
Min
0
0
3
2
0
31
0
0
0
0
1
0
30
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
23
12
102
62
1
133
1
0
5
2
42
5
124
27
0
11
0
0
0
0
5
2
39
24
0
8
0
0
0
0
6
p98
164
113
254
225
4
260
4
3
39
5
169
47
256
145
6
260
0
0
77
71
219
191
305
288
11
90
0
0
18
12
85
p99
178
137
263
234
4
279
4
3
50
5
193
59
269
158
8
275
2
0
79
73
239
206
312
304
14
105
0
0
24
15
104
A-173
-------
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
Standard1
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
Percentile2
99
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
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
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
132
227
208
6
202
3
2
56
46
128
117
182
171
35
147
3
2
75
45
192
149
264
232
22
232
5
1
112
53
237
172
Min
0
13
8
0
100
0
0
0
0
24
17
75
63
0
0
0
0
0
0
2
0
38
20
0
54
0
0
5
0
76
24
Med
127
240
217
1
201
0
0
47
34
135
123
188
178
23
148
0
0
62
32
204
151
284
250
10
242
1
0
102
39
245
178
p98
296
349
342
46
284
24
18
144
134
194
187
260
248
149
308
32
17
225
175
326
305
350
339
101
321
37
14
258
187
325
297
p99
310
352
348
46
286
24
21
148
140
199
192
262
250
171
321
34
23
248
196
333
312
353
343
130
326
42
14
276
214
329
311
> 150 ppb
Mean
40
122
100
0
130
0
0
13
9
56
46
106
94
5
45
0
0
14
7
75
45
157
115
2
110
0
0
24
8
112
53
Min
0
0
0
0
30
0
0
0
0
0
0
12
7
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
5
0
Med
27
115
91
0
139
0
0
4
2
47
34
111
96
1
31
0
0
6
2
62
32
162
108
0
99
0
0
10
1
102
39
p98
153
285
258
6
197
1
1
72
62
144
134
178
170
40
188
3
1
73
49
225
175
310
278
20
269
4
0
109
49
258
187
p99
176
304
281
6
204
1
1
77
65
148
140
183
174
45
202
4
1
88
53
248
196
317
294
22
280
4
1
139
53
276
214
> 200 ppb
Mean
11
54
40
0
77
0
0
3
2
21
16
56
46
1
13
0
0
3
2
25
13
75
45
0
43
0
0
5
1
39
14
Min
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
3
41
27
0
76
0
0
0
0
11
7
47
34
0
4
0
0
0
0
13
4
62
32
0
27
0
0
1
0
27
4
p98
68
188
153
1
160
1
0
24
18
97
81
144
134
13
75
0
0
32
17
120
67
225
175
3
188
0
0
37
14
155
77
p99
79
208
176
1
163
1
0
24
21
101
92
148
140
15
89
1
0
34
23
135
80
248
196
3
202
0
0
42
14
189
83
A-174
-------
Location
Philadelphia
Philadelphia
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
Standard1
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
Percentile2
98
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
1200
1200
900
900
900
900
900
900
900
900
900
900
300
300
300
300
300
300
300
300
300
300
400
400
400
400
400
400
400
400
400
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
295
256
77
284
4
2
146
103
299
268
338
328
51
306
13
13
63
60
209
201
298
294
15
233
4
3
107
98
226
217
287
Min
186
105
0
127
0
0
3
0
151
95
227
204
0
153
0
0
0
0
38
30
217
204
0
82
0
0
3
2
96
82
166
Med
302
264
53
296
0
0
140
84
309
280
347
337
44
331
0
0
48
46
214
210
300
290
5
228
0
0
100
90
221
211
291
p98
339
329
275
357
26
12
320
293
358
353
363
361
160
360
45
44
187
182
328
328
356
354
76
341
35
31
238
227
331
328
357
p99
345
332
293
359
35
14
332
312
360
357
364
363
160
361
45
45
187
183
335
334
359
356
83
347
41
35
249
240
340
340
357
> 150 ppb
Mean
204
134
10
124
0
0
28
16
146
103
264
225
17
192
4
4
20
19
63
60
160
151
1
121
0
0
23
20
107
98
194
Min
51
7
0
2
0
0
0
0
3
0
92
39
0
44
0
0
0
0
0
0
7
6
0
1
0
0
0
0
3
2
62
Med
213
130
1
111
0
0
7
2
140
84
277
236
2
187
0
0
5
3
48
46
157
147
0
113
0
0
12
10
100
90
190
p98
313
277
57
307
1
0
159
101
320
293
352
345
68
331
42
42
78
78
187
182
306
299
20
280
9
4
105
96
238
227
308
p99
319
296
71
325
1
0
171
107
332
312
356
352
70
340
42
42
79
79
187
183
314
306
24
300
10
9
108
101
249
240
320
> 200 ppb
Mean
112
53
1
38
0
0
4
2
49
30
146
103
12
87
1
1
13
13
26
24
63
60
0
50
0
0
4
3
39
33
107
Min
5
0
0
0
0
0
0
0
0
0
3
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
Med
102
39
0
11
0
0
0
0
19
7
140
84
0
61
0
0
0
0
17
15
48
46
0
33
0
0
0
0
28
22
100
p98
258
187
7
186
0
0
26
12
222
165
320
293
44
260
20
15
45
44
95
91
187
182
2
198
0
0
35
31
135
124
238
p99
276
214
8
211
0
0
35
14
245
184
332
312
44
278
20
15
45
45
95
91
187
183
5
221
0
0
41
35
142
133
249
A-175
-------
Location
St. Louis
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not MSA
Standard1
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
Percentile2
99
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
400
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
56500
56500
56500
56500
56500
56500
56500
56500
56500
56500
11600
11600
11600
11600
11600
11600
11600
11600
11600
11600
Number of Daily Maximum Exceedances3
> 100 ppb
Mean
282
21
207
5
2
96
50
200
148
260
220
10
143
0
0
20
14
80
63
143
125
4
124
0
0
6
2
28
13
60
34
Min
162
0
10
0
0
0
0
2
0
42
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
283
7
238
1
0
81
29
232
157
299
256
1
143
0
0
5
2
62
43
144
119
0
100
0
0
0
0
4
1
22
6
p98
355
119
340
37
15
269
209
337
309
358
346
79
322
5
2
123
96
264
237
322
311
43
331
2
2
61
23
173
113
249
193
p99
356
143
345
42
16
283
226
345
322
362
352
100
336
8
5
152
121
288
263
337
328
65
339
6
3
86
35
198
134
269
216
> 150 ppb
Mean
186
2
102
0
0
22
8
96
50
171
117
1
59
0
0
2
1
20
14
57
44
1
62
0
0
1
0
6
2
19
8
Min
49
0
0
0
0
0
0
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
181
0
88
0
0
8
1
81
29
191
108
0
36
0
0
0
0
5
2
36
23
0
24
0
0
0
0
0
0
2
0
p98
305
20
270
5
2
126
52
269
209
323
288
12
233
0
0
26
17
123
96
228
201
7
257
1
0
10
3
61
23
141
80
p99
317
22
289
6
2
150
66
283
226
335
298
18
257
1
0
37
25
152
121
255
228
13
272
2
2
17
7
86
35
164
104
> 200 ppb
Mean
98
0
41
0
0
5
2
36
14
96
50
0
22
0
0
0
0
5
3
20
14
0
29
0
0
0
0
1
0
6
2
Min
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
90
0
21
0
0
1
0
17
3
81
29
0
5
0
0
0
0
0
0
5
2
0
4
0
0
0
0
0
0
0
0
p98
227
4
176
1
0
37
15
173
85
269
209
2
138
0
0
5
2
44
30
123
96
2
180
0
0
2
2
18
6
61
23
p99
240
6
202
1
0
42
16
192
110
283
226
3
163
0
0
8
5
60
43
152
121
5
201
1
1
6
3
26
11
86
35
A-176
-------
Location
Standard
Percentile
Site-
Years
Number of Daily Maximum Exceedances
> 100 ppb
Mean
Min
Med
p98
p99
> 150 ppb
Mean
Min
Med
p98
p99
> 200 ppb
Mean
Min
Med
p98
p99
Notes:
1 Scenario: As is- unadjusted air quality, Current Std - air quality that just meets the current annual standard, All others - air quality that just meets 1-hour
concentration level given percentile form of alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at maximum monitor in location.
3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all monitors in a particular location divided by the
number of site-years across the monitoring period. The min, med, p98, and p99 represent the minimum, median, 98
for the number of daily maximum exceedances in any one year within the monitoring period.
and 99 percentiles of the distribution
A-177
-------
Table A-122. Estimated number of exceedances of 1-hour concentration levels (250, and 300 ppb) on-roads
using 2001-2003 air quality as is and air quality adjusted to just meet the current and alternative standards
and an on-road adjustment factor.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
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
Standard
1
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
Percentile
2
98
99
98
99
98
99
98
99
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
1400
1400
1400
1400
1400
1400
1400
1400
1400
1400
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
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
29
0
0
1
0
6
4
21
15
0
1
0
0
0
0
1
1
9
5
0
9
0
0
1
0
11
6
38
23
0
24
0
0
3
2
15
11
50
40
1
36
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
4
2
0
0
Med
0
7
0
0
0
0
0
0
4
2
0
0
0
0
0
0
0
0
1
1
0
2
0
0
0
0
2
1
23
11
0
15
0
0
0
0
8
4
44
32
0
22
p98
3
157
0
0
8
6
47
39
122
101
0
11
0
0
1
1
13
7
59
33
5
64
0
0
15
8
68
49
150
109
3
70
0
0
19
15
57
48
123
105
5
156
p99
4
189
0
0
10
8
70
53
165
137
0
12
0
0
1
1
18
13
67
37
12
69
0
0
21
13
76
56
164
125
3
83
0
0
19
16
63
54
136
116
5
163
> 300 ppb
Mean
0
15
0
0
0
0
2
2
10
7
0
0
0
0
0
0
0
0
3
1
0
4
0
0
0
0
4
2
18
10
0
10
0
0
1
0
7
5
24
19
0
16
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
1
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
7
2
0
3
0
0
0
0
2
1
15
10
0
7
p98
1
102
0
0
4
1
26
20
70
53
0
3
0
0
0
0
3
3
22
13
1
37
0
0
4
1
43
31
92
65
1
44
0
0
7
3
37
30
73
62
1
94
p99
1
125
0
0
5
3
38
25
104
76
0
3
0
0
0
0
6
3
25
18
3
45
0
0
7
3
49
34
106
74
1
48
0
0
8
3
38
30
86
70
1
97
A-178
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
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
Standard
1
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
2
98
99
98
99
98
99
98
99
98
99
98
99
98
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
200
200
200
200
200
200
200
200
600
600
600
600
600
600
600
600
600
600
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
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
0
1
0
12
6
43
26
2
34
1
0
2
1
9
2
26
7
0
33
0
0
1
0
10
5
39
21
1
67
1
1
2
1
20
14
64
50
0
20
0
0
1
1
12
Min
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
3
3
0
0
0
0
0
0
0
Med
0
0
0
0
3
1
27
10
1
25
0
0
1
1
5
1
18
3
0
20
0
0
0
0
4
1
24
11
1
64
1
1
1
1
11
8
60
42
0
3
0
0
0
0
1
p98
1
0
5
2
71
50
162
105
7
109
4
2
11
5
43
11
80
33
1
160
0
0
6
3
51
27
159
104
1
153
1
1
11
6
68
55
145
127
1
155
0
0
24
13
97
p99
1
0
5
2
77
52
165
121
7
120
4
3
12
7
46
12
100
37
1
168
0
0
7
3
56
31
171
118
1
168
1
1
11
7
74
56
168
147
1
172
0
0
30
15
116
> 300 ppb
Mean
0
0
0
0
4
2
19
11
1
17
0
0
1
1
5
1
13
3
0
14
0
0
0
0
3
1
17
8
1
34
1
1
1
1
7
5
32
23
0
9
0
0
0
0
5
Min
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
Med
0
0
0
0
1
1
6
3
1
11
0
0
1
0
3
1
8
2
0
7
0
0
0
0
0
0
8
3
1
26
1
1
1
1
4
3
25
18
0
0
0
0
0
0
0
p98
0
0
1
1
36
14
86
66
7
66
4
2
7
4
26
7
56
16
0
76
0
0
3
1
23
10
80
46
1
103
1
1
3
2
34
25
97
79
0
76
0
0
6
2
63
p99
0
0
1
1
37
15
103
73
7
72
4
2
7
5
28
7
57
18
1
86
0
0
3
1
24
12
91
49
1
114
1
1
3
2
34
25
114
86
0
90
0
0
7
3
71
A-179
-------
Location
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
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
Phoenix
Phoenix
Standard
1
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
Percentile
2
99
98
99
98
99
98
99
98
99
98
99
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
1600
1600
1600
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
500
500
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
8
42
28
1
7
0
0
0
0
3
3
14
11
0
40
0
0
1
0
12
7
40
24
0
4
0
0
1
0
6
3
23
13
0
10
0
0
2
1
17
11
56
40
0
8
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
Med
0
13
7
0
1
0
0
0
0
0
0
3
2
0
33
0
0
0
0
5
2
31
14
0
0
0
0
0
0
1
0
12
4
0
2
0
0
0
0
4
2
38
24
0
0
p98
73
231
186
16
53
0
0
5
4
33
28
79
71
0
143
0
0
12
5
68
47
127
105
6
38
0
0
11
4
53
36
117
73
3
69
1
1
26
14
90
69
198
158
4
58
p99
82
264
215
23
59
0
0
7
6
39
34
92
84
0
154
0
0
18
9
85
61
156
123
9
47
0
0
14
7
58
40
137
87
5
75
1
1
40
27
100
83
225
184
5
69
> 300 ppb
Mean
3
19
13
0
3
0
0
0
0
1
1
6
5
0
19
0
0
0
0
5
3
19
11
0
1
0
0
0
0
3
1
10
6
0
4
0
0
1
0
7
4
27
18
0
2
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
Med
0
3
1
0
0
0
0
0
0
0
0
1
0
0
10
0
0
0
0
1
0
10
4
0
0
0
0
0
0
0
0
3
1
0
1
0
0
0
0
1
1
12
5
0
0
p98
49
149
102
7
29
0
0
1
0
15
13
46
42
0
97
0
0
5
2
37
21
92
67
1
19
0
0
3
1
33
19
67
48
1
41
1
1
8
4
54
41
118
93
0
18
p99
58
172
120
10
34
0
0
3
2
20
17
55
49
0
103
0
0
7
3
48
31
110
84
2
27
0
0
4
2
34
22
73
56
1
46
1
1
20
10
68
56
137
105
0
22
A-180
-------
Location
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
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Standard
1
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
2
98
99
98
99
98
99
98
99
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
500
500
500
300
300
300
300
300
300
300
300
300
300
900
900
900
900
900
900
900
900
900
900
1800
1800
1800
1800
1800
1800
1800
1800
1800
1800
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
0
1
0
20
11
76
52
0
37
0
0
7
5
47
34
151
121
0
19
0
0
2
1
17
11
61
44
0
18
0
0
2
1
18
11
56
38
Min
0
0
0
0
0
0
1
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
0
0
0
0
0
Med
0
0
0
0
3
1
54
27
0
16
0
0
0
0
24
14
128
97
0
7
0
0
0
0
6
2
47
28
0
4
0
0
0
0
4
1
36
18
p98
0
0
11
5
135
81
259
221
2
174
0
0
52
38
208
161
331
318
1
103
1
1
22
12
99
68
215
181
1
107
0
0
22
10
100
72
213
175
p99
0
0
12
5
147
88
284
244
2
204
0
0
56
40
253
203
348
342
1
122
1
1
27
18
103
70
230
200
1
128
0
0
28
14
123
84
251
211
> 300 ppb
Mean
0
0
0
0
6
3
35
22
0
15
0
0
2
1
18
13
75
56
0
7
0
0
1
0
7
4
28
19
0
8
0
0
0
0
7
5
27
18
Min
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
Med
0
0
0
0
0
0
14
4
0
2
0
0
0
0
4
2
51
33
0
1
0
0
0
0
1
0
15
6
0
0
0
0
0
0
0
0
10
4
p98
0
0
2
1
44
19
182
135
0
74
0
0
18
9
86
75
273
235
1
50
0
0
7
4
45
35
144
105
0
60
0
0
6
2
56
38
139
102
p99
0
0
3
1
48
24
206
156
0
90
0
0
18
9
106
82
301
283
1
59
1
1
10
4
51
41
153
114
0
65
0
0
8
2
63
45
168
123
A-181
-------
Location
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not
MSA
Standard
1
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
2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-Years
61200
61200
61200
61200
61200
61200
61200
61200
61200
61200
12700
12700
12700
12700
12700
12700
12700
12700
12700
12700
Number of Daily Maximum Exceedances3
> 250 ppb
Mean
0
5
0
0
0
0
1
0
6
3
0
13
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
Med
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
p98
1
47
0
0
1
0
13
6
51
28
1
107
0
0
2
1
9
5
25
14
p99
2
62
0
0
2
1
22
9
66
39
2
130
0
0
3
1
14
8
35
18
> 300 ppb
Mean
0
2
0
0
0
0
0
0
2
1
0
6
0
0
0
0
0
0
1
0
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
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
21
0
0
0
0
5
2
23
11
1
63
0
0
1
1
4
2
12
6
p99
0
30
0
0
1
0
8
3
34
17
1
82
0
0
1
1
8
4
17
13
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual standard, All
others -air quality that just meets 1-hour concentration level given percentile form of alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 The mean number of exceedances represents the sum of daily maximum exceedances occurring at all
monitors in a particular location divided by the number of site-years across the monitoring period. The min, med,
p98, and p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the number of daily
maximum exceedances in any one year within the monitoring period.
A-182
-------
1
2
3
A-9.3
Table A-123.
2004-2006 air
Annual average NO2 concentration data for 2004-2006
Estimated annual average NO2 concentrations for monitors >100 m from a major road using
quality as is and adjusted to just meet the current and alternative standards.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Scenario1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
Percentile2
98
99
98
99
98
99
98
99
98
99
98
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
15
15
15
15
15
15
15
15
15
15
15
15
15
15
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Annual Average NO2(ppb)3
Mean
11
34
8
7
16
14
24
22
32
29
39
36
47
43
9
20
6
5
12
11
19
16
25
21
31
27
37
32
19
35
11
10
22
21
33
31
44
42
56
52
67
Min
3
10
2
2
5
4
7
7
10
9
12
11
14
13
7
15
5
4
10
9
16
13
21
18
26
22
31
27
16
28
9
9
18
17
27
26
36
34
45
43
54
med
14
44
10
9
20
18
30
28
40
37
51
46
61
55
9
20
6
5
13
11
19
16
25
21
31
27
38
32
18
32
10
10
21
19
31
29
41
39
51
48
62
p98
18
53
13
11
25
23
38
34
50
46
63
57
75
69
10
23
7
6
14
12
21
18
29
24
36
30
43
36
24
44
14
13
28
26
42
39
55
52
69
65
83
p99
18
53
13
11
25
23
38
34
50
46
63
57
75
69
10
23
7
6
14
12
21
18
29
24
36
30
43
36
24
44
14
13
28
26
42
39
55
52
69
65
83
A-183
-------
Location
Chicago
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Scenario1
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
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
3
3
3
3
3
3
3
3
3
3
3
3
3
3
6
6
6
6
6
6
6
6
6
6
6
6
6
6
12
12
12
12
12
12
12
12
12
12
12
12
12
12
2
2
Annual Average NO2(ppb)3
Mean
63
20
38
13
12
26
23
39
35
53
47
66
58
79
70
17
49
15
14
31
29
46
43
61
58
77
72
92
87
14
42
10
9
21
19
31
28
42
37
52
46
62
56
14
53
Min
51
18
33
12
11
24
21
36
32
48
42
59
53
71
63
14
42
13
12
26
24
38
36
51
48
64
60
77
72
8
24
6
5
12
11
18
16
24
22
30
27
36
33
13
53
med
58
20
39
13
12
27
24
40
36
53
47
67
59
80
71
17
50
15
14
30
29
46
43
61
57
76
72
91
86
15
45
11
10
22
20
33
30
45
40
56
50
67
60
14
53
p98
78
21
42
14
13
28
25
42
38
57
50
71
63
85
75
20
53
18
17
36
34
54
51
72
68
90
84
107
101
18
53
13
12
27
24
40
36
54
48
67
60
80
72
14
53
p99
78
21
42
14
13
28
25
42
38
57
50
71
63
85
75
20
53
18
17
36
34
54
51
72
68
90
84
107
101
18
53
13
12
27
24
40
36
54
48
67
60
80
72
14
53
A-184
-------
Location
Jacksonville
Jacksonville
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
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
Miami
Miami
Miami
Miami
Miami
Scenario1
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
2
2
2
2
2
2
2
2
2
2
11
11
11
11
11
11
11
11
11
11
11
11
11
11
54
54
54
54
54
54
54
54
54
54
54
54
54
54
4
4
4
4
4
Annual Average NO2(ppb)3
Mean
13
9
26
18
39
27
52
36
65
44
78
53
9
24
7
7
15
14
22
20
29
27
37
34
44
41
18
30
10
9
20
18
30
27
40
37
50
46
60
55
8
31
7
6
13
Min
13
9
26
18
38
26
51
35
64
44
77
53
1
4
1
1
2
2
3
3
5
4
6
5
7
6
5
8
2
2
5
5
7
7
10
9
12
11
15
14
7
28
6
6
13
med
13
9
26
18
39
27
52
36
65
44
78
53
6
16
5
5
10
9
15
14
20
18
25
23
30
28
18
31
10
9
20
18
29
27
39
36
49
45
59
54
8
31
7
6
14
p98
13
9
26
18
39
27
53
36
66
45
79
54
20
53
16
15
32
30
48
45
65
60
81
75
97
90
31
48
17
15
34
31
50
46
67
62
84
77
101
92
8
32
7
7
14
p99
13
9
26
18
39
27
53
36
66
45
79
54
20
53
16
15
32
30
48
45
65
60
81
75
97
90
31
53
17
16
34
31
51
47
68
62
85
78
102
94
8
32
7
7
14
A-185
-------
Location
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
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Scenario1
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
4
4
4
4
4
4
4
4
4
22
22
22
22
22
22
22
22
22
22
22
22
22
22
12
12
12
12
12
12
12
12
12
12
12
12
12
12
9
9
9
9
9
9
9
9
Annual Average NO2(ppb)3
Mean
13
20
19
27
25
34
32
40
38
19
30
12
10
23
20
35
30
47
41
59
51
70
61
17
39
13
11
26
21
39
32
52
42
65
53
79
63
24
41
14
13
29
26
43
38
Min
12
19
18
25
24
32
30
38
36
10
16
6
5
12
10
18
15
24
21
30
26
36
31
14
29
11
9
21
17
32
26
43
34
53
43
64
52
21
36
12
11
25
22
37
33
med
13
20
19
27
26
34
32
41
39
20
32
12
11
25
21
37
32
49
43
62
54
74
64
16
39
12
10
25
20
37
30
50
40
62
50
75
60
24
40
14
13
29
26
43
39
p98
13
21
20
28
26
35
33
42
39
27
43
16
14
33
28
49
42
65
57
82
71
98
85
25
51
19
15
37
30
56
45
75
60
94
75
112
90
26
44
16
14
31
28
47
42
p99
13
21
20
28
26
35
33
42
39
27
43
16
14
33
28
49
42
65
57
82
71
98
85
25
51
19
15
37
30
56
45
75
60
94
75
112
90
26
44
16
14
31
28
47
42
A-186
-------
Location
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
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
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
Scenario1
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
9
9
9
9
9
9
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
17
17
17
17
17
17
17
17
17
17
17
Annual Average NO2(ppb)3
Mean
57
51
71
64
86
77
24
53
13
12
25
25
38
37
51
50
64
62
76
75
15
38
12
12
24
23
36
35
48
46
60
58
72
69
15
36
12
9
23
19
35
28
46
38
58
Min
50
45
62
56
74
67
21
53
11
11
22
21
33
32
44
43
55
53
65
64
12
29
10
10
20
20
30
29
40
39
50
49
61
59
7
19
5
4
10
8
16
13
21
17
26
med
57
51
71
64
86
77
22
53
12
12
24
23
36
35
48
47
59
58
71
70
14
36
12
11
23
23
35
34
47
45
58
56
70
68
16
42
12
10
24
20
36
30
48
39
60
p98
63
56
78
70
94
85
29
53
15
15
31
30
46
45
62
60
77
75
92
90
18
49
14
14
29
28
43
42
58
56
72
70
87
84
22
51
17
14
33
27
50
41
67
54
84
p99
63
56
78
70
94
85
29
53
15
15
31
30
46
45
62
60
77
75
92
90
18
49
14
14
29
28
43
42
58
56
72
70
87
84
22
51
17
14
33
27
50
41
67
54
84
A-187
-------
Location
Washington DC
Washington DC
Washington DC
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario1
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
99
98
99
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
17
17
17
565
565
565
565
565
565
565
565
565
565
565
565
565
565
116
116
116
116
116
116
116
116
116
116
116
116
116
116
Annual Average NO2(ppb)3
Mean
47
69
56
11
26
6
6
13
12
19
18
26
24
32
29
38
35
7
21
3
3
7
6
10
8
14
11
17
14
21
17
Min
21
31
25
1
2
0
0
1
1
1
1
2
2
2
2
3
3
1
3
0
0
1
1
1
1
2
1
2
2
3
2
med
49
72
59
11
26
6
6
13
12
19
18
26
24
32
30
39
36
6
19
3
3
7
5
10
8
13
10
16
13
20
16
p98
68
100
82
21
49
12
11
24
22
36
33
48
44
59
55
71
66
16
53
8
7
17
13
25
20
33
27
42
34
50
40
p99
68
100
82
23
52
13
12
27
25
40
37
54
50
67
62
81
74
16
53
8
7
17
14
25
20
34
27
42
34
51
41
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual
standard, All others - air quality that just meets 1-hour concentration level given percentile form of
alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 Annual means for each monitor were first calculated based on all simulated hourly values in a year.
Then the mean of the annual means was estimated as the sum of all the annual means in a particular
location divided by the number of simulated site-years across the monitoring period. The min, med,
p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual
means.
A-188
-------
2 Table A-124. Estimated annual average NO2 concentrations for monitors >20 m and <100 m from a major
3 road using 2004-2006 air quality as is and air quality adjusted to just meet the current and alternative
4 standards.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Scenario1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
Percentile2
98
99
98
99
98
99
98
99
98
99
98
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
11
11
11
11
11
11
11
11
11
11
11
11
11
11
6
6
6
6
6
6
6
6
6
6
6
6
6
6
2
2
2
2
2
2
2
2
2
2
2
2
2
Annual Average NO2 (ppb)3
Mean
15
34
11
9
22
19
33
28
44
37
55
47
66
56
29
52
17
16
33
31
50
47
66
62
83
78
99
93
15
41
12
11
24
21
36
32
47
43
59
54
71
Min
10
24
7
6
15
12
22
19
29
25
36
31
44
37
28
48
16
15
31
30
47
44
63
59
79
74
94
89
14
41
11
10
22
20
33
29
43
39
54
49
65
med
16
35
11
10
23
19
34
29
45
38
56
48
68
58
29
52
16
16
33
31
49
47
66
62
82
78
99
93
15
41
12
11
24
21
36
32
47
43
59
54
71
p98
19
44
13
11
27
23
40
34
54
46
67
57
81
69
31
53
17
16
35
33
52
49
70
66
87
82
105
99
17
41
13
12
26
23
39
35
51
46
64
58
77
p99
19
44
13
11
27
23
40
34
54
46
67
57
81
69
31
53
17
16
35
33
52
49
70
66
87
82
105
99
17
41
13
12
26
23
39
35
51
46
64
58
77
A-189
-------
Location
Cleveland
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
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
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
Scenario1
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
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
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
22
22
22
22
22
22
22
22
22
22
22
22
22
22
2
2
2
2
2
2
2
2
2
2
2
2
2
2
11
11
Annual Average NO2 (ppb)3
Mean
64
15
44
11
10
22
20
33
29
44
39
55
49
66
59
25
41
14
12
27
25
41
37
54
50
68
62
81
75
13
53
12
11
23
22
35
33
46
44
58
55
70
66
28
43
Min
59
13
39
10
9
19
17
29
26
39
35
48
43
58
52
9
15
5
4
9
9
14
13
19
17
23
22
28
26
13
53
11
11
23
21
34
32
46
43
57
54
68
64
18
28
med
64
13
40
10
9
20
17
29
26
39
35
49
44
59
52
27
47
15
14
30
27
44
41
59
54
74
68
89
82
13
53
12
11
23
22
35
33
46
44
58
55
70
66
29
42
p98
70
18
53
13
12
27
24
40
36
53
48
67
60
80
72
34
53
19
17
37
34
56
51
74
68
93
85
111
102
14
53
12
11
24
22
36
34
47
45
59
56
71
67
36
53
p99
70
18
53
13
12
27
24
40
36
53
48
67
60
80
72
34
53
19
17
37
34
56
51
74
68
93
85
111
102
14
53
12
11
24
22
36
34
47
45
59
56
71
67
36
53
A-190
-------
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
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Scenario1
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
11
11
11
11
11
11
11
11
11
11
11
11
6
6
6
6
6
6
6
6
6
6
6
6
6
6
3
3
3
3
3
3
3
3
3
3
3
3
3
3
8
8
8
8
8
Annual Average NO2 (ppb)3
Mean
17
15
34
30
51
44
68
59
85
74
102
89
22
48
17
13
33
27
50
40
66
54
83
67
100
80
19
33
12
10
23
21
35
31
47
42
58
52
70
63
12
32
9
9
19
Min
11
10
22
20
34
29
45
39
56
49
67
59
18
36
13
11
27
22
40
32
54
43
67
54
80
65
19
33
12
10
23
21
35
31
46
41
58
52
69
62
8
19
7
6
13
med
18
15
35
31
53
46
71
61
88
77
106
92
22
50
17
14
34
27
50
41
67
54
84
68
101
81
19
33
12
10
23
21
35
31
47
42
58
52
70
63
10
30
8
8
16
p98
22
19
45
39
67
58
90
78
112
97
134
116
26
53
20
16
40
32
60
48
80
64
100
80
120
96
20
33
12
11
24
21
35
32
47
42
59
53
71
63
22
53
18
18
37
p99
22
19
45
39
67
58
90
78
112
97
134
116
26
53
20
16
40
32
60
48
80
64
100
80
120
96
20
33
12
11
24
21
35
32
47
42
59
53
71
63
22
53
18
18
37
A-191
-------
Location
St. Louis
St. Louis
St. Louis
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
Scenario1
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
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
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Annual Average NO2 (ppb)3
Mean
18
28
27
38
37
47
46
57
55
18
43
14
11
28
23
42
34
55
45
69
56
83
68
Min
13
20
19
27
26
33
32
40
39
13
30
10
8
20
17
30
25
41
33
51
41
61
50
med
16
25
24
33
32
41
40
49
48
18
43
13
11
27
22
40
33
53
43
67
54
80
65
p98
35
55
53
73
71
92
89
110
106
24
53
18
15
37
30
55
45
73
60
91
74
110
89
p99
35
55
53
73
71
92
89
110
106
24
53
18
15
37
30
55
45
73
60
91
74
110
89
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual
standard, All others - air quality that just meets 1-hour concentration level given percentile form of
alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 Annual means for each monitor were first calculated based on all simulated hourly values in a year.
Then the mean of the annual means was estimated as the sum of all the annual means in a particular
location divided by the number of simulated site-years across the monitoring period. The min, med,
p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual
means.
A-192
-------
1 Table A-125. Estimated annual average NO2 concentrations for monitors < 20 m from a major road using
2 2004-2006 air quality as is and air quality adjusted to Just meet the current and alternative standards.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Scenario1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
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
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
3
3
3
3
3
3
Annual Average NO2 (ppb)3
Mean
24
53
17
15
34
29
51
44
68
58
85
73
102
87
19
34
11
10
22
21
33
31
44
41
55
52
66
62
21
53
16
14
32
29
47
43
63
57
79
71
95
86
28
53
Min
23
53
16
14
32
28
49
41
65
55
81
69
97
83
18
31
10
10
20
19
31
29
41
38
51
48
61
58
18
53
14
13
28
25
42
38
56
50
70
63
84
75
27
53
med
23
53
17
14
34
29
51
43
67
57
84
72
101
86
20
36
11
11
23
21
34
32
45
43
57
53
68
64
22
53
16
15
33
30
49
45
66
60
82
74
99
89
28
53
p98
25
53
18
15
36
31
54
46
72
61
90
77
108
92
20
36
11
11
23
22
34
32
46
43
57
54
69
65
22
53
17
15
34
31
51
46
68
61
85
77
102
92
29
53
p99
25
53
18
15
36
31
54
46
72
61
90
77
108
92
20
36
11
11
23
22
34
32
46
43
57
54
69
65
22
53
17
15
34
31
51
46
68
61
85
77
102
92
29
53
A-193
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Las Vegas
Las Vegas
Las Vegas
Las Vegas
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
Miami
Miami
Miami
Miami
Miami
Scenario1
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
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
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
6
6
6
6
6
6
6
6
6
6
6
6
6
6
2
2
2
2
2
Annual Average NO2 (ppb)3
Mean
18
16
37
33
55
49
74
65
92
82
110
98
19
52
16
15
32
30
48
44
64
59
80
74
96
89
27
46
15
14
30
28
45
41
60
55
75
69
90
83
6
24
5
5
10
Min
18
16
36
32
54
48
72
64
90
79
107
95
19
51
16
14
31
29
47
43
63
58
78
72
94
87
20
36
11
10
22
20
33
30
44
40
55
51
66
61
6
24
5
5
10
med
18
16
36
32
54
48
73
64
91
81
109
97
19
52
16
15
32
30
48
44
64
59
80
74
96
89
29
47
16
15
32
29
47
44
63
58
79
73
95
87
6
24
5
5
10
p98
19
17
38
34
57
51
76
68
96
85
115
102
20
53
16
15
33
30
49
45
65
60
81
75
98
90
31
53
17
16
34
32
52
47
69
63
86
79
103
95
6
24
5
5
11
p99
19
17
38
34
57
51
76
68
96
85
115
102
20
53
16
15
33
30
49
45
65
60
81
75
98
90
31
53
17
16
34
32
52
47
69
63
86
79
103
95
6
24
5
5
11
A-194
-------
Location
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
New York
New York
New York
New York
Phoenix
Phoenix
Phoenix
Phoenix
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
Scenario1
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
Percentile2
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Annual Average NO2 (ppb)3
Mean
10
16
15
21
20
26
24
31
29
28
44
17
15
34
30
51
44
68
59
85
74
102
89
23
40
14
13
28
25
42
38
56
50
70
63
84
75
16
46
13
13
26
25
39
38
Min
10
15
14
20
19
25
24
30
29
27
40
17
15
33
29
50
44
67
58
84
73
100
87
11
19
7
6
13
12
20
18
26
24
33
30
40
36
15
38
12
12
24
23
36
35
med
10
16
15
21
20
26
24
31
29
28
44
17
15
34
30
51
44
68
59
85
74
102
89
31
53
18
16
37
33
55
49
73
66
92
82
110
99
16
46
13
13
26
25
39
38
p98
10
16
15
21
20
26
25
32
30
28
49
17
15
35
30
52
45
69
60
87
75
104
90
32
53
19
17
38
34
57
51
75
68
94
85
113
102
17
53
14
14
28
27
42
41
p99
10
16
15
21
20
26
25
32
30
28
49
17
15
35
30
52
45
69
60
87
75
104
90
32
53
19
17
38
34
57
51
75
68
94
85
113
102
17
53
14
14
28
27
42
41
A-195
-------
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
Scenario1
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Annual Average NO2 (ppb)3
Mean
52
51
65
63
78
76
19
43
14
12
28
23
42
35
57
46
71
58
85
69
Min
48
47
60
58
72
70
14
36
11
9
22
18
33
27
44
36
55
45
66
54
med
52
50
65
63
78
75
18
39
13
11
27
22
40
33
53
44
67
54
80
65
p98
56
55
70
68
85
82
23
50
17
14
35
28
52
42
69
57
87
71
104
85
p99
56
55
70
68
85
82
23
50
17
14
35
28
52
42
69
57
87
71
104
85
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual
standard, All others - air quality that just meets 1-hour concentration level given percentile form of
alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 Annual means for each monitor were first calculated based on all simulated hourly values in a year.
Then the mean of the annual means was estimated as the sum of all the annual means in a particular
location divided by the number of simulated site-years across the monitoring period. The min, med,
p98, p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual
means.
A-196
-------
1 Table A-126. Estimated annual average NO2 concentrations on-roads using 2004-2006 air quality as is, air
2 quality adjusted to Just meet the current and alternative standards, and an on-road adjustment factor.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Denver
Denver
Scenario1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
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
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
300
300
Annual Average NO2 (ppb)3
Mean
20
62
14
13
28
26
43
39
57
52
71
65
85
78
16
35
11
10
23
19
34
29
45
38
56
48
68
58
35
63
20
19
40
38
60
57
80
75
100
94
120
113
36
69
Min
4
13
3
3
6
6
9
8
12
11
15
14
18
17
9
19
7
6
13
11
20
17
26
22
33
28
40
34
20
35
12
11
23
22
35
33
46
43
58
54
69
65
23
42
Med
22
68
16
14
31
29
47
43
63
57
78
71
94
86
15
34
11
9
22
19
33
28
43
37
54
46
65
56
33
59
19
18
38
36
57
54
76
72
95
89
114
107
36
68
p98
40
124
28
26
57
52
85
78
114
104
142
130
170
155
24
54
17
14
34
29
51
43
68
58
85
72
102
87
57
103
33
31
65
61
98
92
130
123
163
153
195
184
51
99
p99
42
128
30
27
59
54
89
81
118
108
148
135
177
162
24
57
18
15
35
30
53
45
70
60
88
75
106
90
60
107
34
32
68
64
102
96
136
128
170
160
204
193
53
103
A-197
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
El Paso
El Paso
El Paso
El Paso
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
Scenario1
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
300
300
300
300
300
600
600
600
600
600
600
600
600
600
600
600
600
600
600
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
200
200
200
200
200
200
Annual Average NO2 (ppb)3
Mean
24
21
48
43
72
64
96
85
120
106
144
128
31
90
28
27
56
53
85
80
113
106
141
133
169
160
25
75
19
17
37
33
56
50
75
67
93
83
112
100
24
96
23
16
47
32
Min
15
13
30
27
46
40
61
54
76
67
91
81
18
54
16
15
33
31
49
46
65
61
81
77
98
92
10
30
8
7
15
14
23
21
31
27
38
34
46
41
17
67
16
11
32
22
Med
24
21
47
42
71
63
95
84
118
105
142
126
30
88
27
26
55
52
82
78
110
103
137
129
164
155
25
75
19
17
37
33
56
50
74
66
93
83
112
100
23
93
23
15
45
31
p98
33
30
67
59
100
89
134
119
167
148
201
178
45
128
41
39
83
78
124
117
166
156
207
196
249
235
42
124
31
28
62
55
93
83
124
111
155
138
186
166
36
143
35
24
70
48
p99
35
31
69
62
104
92
139
123
173
154
208
185
47
141
43
41
86
81
129
122
172
162
215
203
258
243
43
127
32
28
63
57
95
85
127
113
159
142
190
170
37
145
35
24
71
49
A-198
-------
Location
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
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
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Scenario1
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
200
200
200
200
200
200
200
200
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
5400
5400
5400
5400
5400
5400
5400
5400
5400
5400
5400
5400
5400
5400
400
400
400
400
400
400
400
400
400
400
Annual Average NO2 (ppb)3
Mean
70
48
94
64
117
80
141
96
16
43
13
12
26
24
40
37
53
49
66
61
79
73
33
56
18
17
36
33
54
50
73
67
91
83
109
100
14
55
12
11
24
23
36
34
48
45
Min
49
33
65
44
81
55
97
67
2
5
1
1
3
3
4
4
6
5
7
7
9
8
6
10
3
3
6
6
9
9
13
12
16
15
19
17
9
35
8
7
16
15
24
22
32
30
Med
68
46
90
62
113
77
136
93
11
30
9
8
18
17
27
25
36
34
45
42
54
50
32
54
18
16
35
32
53
49
71
65
88
81
106
97
13
53
12
11
23
22
35
33
47
44
p98
105
72
140
96
175
119
209
143
44
118
36
33
72
67
108
100
145
134
181
167
217
200
60
102
33
31
66
61
100
92
133
122
166
153
199
183
19
77
17
16
34
32
51
48
68
64
p99
106
73
142
97
177
121
213
146
46
123
38
35
76
70
113
105
151
140
189
174
227
209
65
109
36
33
72
66
107
99
143
131
179
164
215
197
20
80
17
16
34
32
51
48
69
65
A-199
-------
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
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Scenario1
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
400
400
400
400
2200
2200
2200
2200
2200
2200
2200
2200
2200
2200
2200
2200
2200
2200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
900
900
900
900
900
900
900
900
900
900
900
900
900
900
Annual Average NO2 (ppb)3
Mean
60
57
72
68
35
55
21
18
42
37
64
55
85
74
106
92
127
110
31
70
24
19
47
38
71
57
95
76
118
95
142
114
43
73
26
23
51
46
77
69
103
92
128
116
154
139
Min
40
37
48
45
12
20
7
6
15
13
22
19
30
26
37
32
45
39
18
37
14
11
27
22
41
33
54
44
68
54
81
65
26
45
16
14
31
28
47
42
63
56
78
70
94
85
Med
59
55
70
66
35
55
21
18
42
37
64
55
85
74
106
92
127
110
30
68
23
18
45
36
68
55
91
73
113
91
136
109
42
71
25
23
50
45
75
68
100
90
125
113
150
135
p98
85
80
102
96
58
94
36
31
71
62
107
93
142
123
178
154
214
185
51
112
39
31
78
63
117
94
155
125
194
156
233
188
64
107
38
34
76
68
114
103
152
137
190
171
228
205
p99
86
81
103
97
61
99
37
32
75
65
112
97
150
130
187
162
225
195
59
123
45
36
89
72
134
108
178
143
223
179
267
215
65
109
39
35
77
70
116
105
155
139
194
174
232
209
A-200
-------
Location
Provo
Provo
Provo
Provo
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
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
Other MSA
Other MSA
Other MSA
Other MSA
Scenario1
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
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
300
300
300
300
300
300
300
400
400
400
400
400
400
400
400
400
400
400
400
400
400
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
56500
56500
56500
56500
Annual Average NO2 (ppb)3
Mean
43
94
23
22
45
45
68
67
91
89
114
111
136
134
27
68
22
21
43
42
65
63
87
84
109
105
130
126
28
64
21
17
42
34
63
51
84
68
104
85
125
102
20
47
12
11
Min
26
67
14
14
28
27
41
41
55
54
69
68
83
81
16
38
13
13
26
25
39
38
52
50
65
63
78
75
9
23
7
5
13
11
20
16
26
21
33
27
39
32
1
3
1
1
Med
41
93
22
21
43
42
65
64
87
85
108
106
130
127
26
66
21
20
42
41
63
61
84
81
105
102
126
122
28
66
21
17
42
35
64
52
85
69
106
86
127
104
20
45
11
10
p98
70
129
37
37
75
73
112
110
149
146
186
183
224
219
41
114
34
33
67
65
101
98
134
130
168
163
201
195
51
114
39
31
77
63
116
94
154
126
193
157
231
189
41
97
24
22
p99
71
131
38
37
76
74
114
111
151
148
189
185
227
222
42
119
34
33
68
66
102
99
136
132
171
165
205
198
52
121
40
32
79
65
119
97
158
129
198
161
238
194
45
105
26
24
A-201
-------
Location
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Scenario1
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile2
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
98
99
Site-
Years
56500
56500
56500
56500
56500
56500
56500
56500
56500
56500
11600
11600
11600
11600
11600
11600
11600
11600
11600
11600
11600
11600
11600
11600
Annual Average NO2 (ppb)3
Mean
23
21
35
32
46
43
58
53
70
64
12
39
6
5
13
10
19
15
25
20
32
25
38
30
Min
1
1
2
2
2
2
3
3
4
3
1
3
1
0
1
1
2
1
2
2
3
2
3
3
Med
23
21
34
31
45
42
57
52
68
62
10
34
6
5
11
9
17
14
22
18
28
23
34
27
p98
48
44
72
66
96
88
120
110
144
132
31
100
16
13
33
26
49
39
65
53
82
66
98
79
p99
52
48
77
71
103
95
129
119
155
143
33
109
18
14
35
28
53
43
71
57
88
71
106
85
Notes:
1 Scenario: As is - unadjusted air quality, Current Std - air quality that just meets the current annual
standard, All others - air quality that just meets 1-hour concentration level given percentile form of
alternative standard.
2 Percentile: 98th or 99th percentile of daily maximum 1-hour concentration averaged over three years at
maximum monitor in location.
3 Annual means for each monitor were first calculated based on all simulated hourly values in a year.
Then the mean of the annual means was estimated as the sum of all the annual means in a particular
location divided by the number of simulated site-years across the monitoring period. The min, med, p98,
p99 represent the minimum, median, 98th, and 99th percentiles of the distribution for the annual means.
A-202
-------
1 Table A-127. 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, 2004-2006 air quality.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Standard
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-Years
15
15
15
15
15
15
15
15
15
15
15
15
15
15
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Number of Daily Maximum Exceedances
> 100 ppb
Mean
0
96
0
0
2
1
28
19
81
66
122
105
157
143
0
5
0
0
0
0
2
0
29
9
72
38
105
72
0
23
Min
0
1
0
0
0
0
0
0
0
0
3
1
8
4
0
0
0
0
0
0
0
0
13
2
32
15
56
32
0
7
Med
0
121
0
0
0
0
16
10
90
67
171
133
228
209
0
5
0
0
0
0
2
0
26
8
67
35
102
67
0
14
p98
1
188
0
0
17
7
83
65
168
144
235
205
281
266
0
10
0
0
0
0
7
0
54
20
102
67
138
102
0
69
p99
1
188
0
0
17
7
83
65
168
144
235
205
281
266
0
10
0
0
0
0
7
0
54
20
102
67
138
102
0
69
> 150 ppb
Mean
0
24
0
0
0
0
2
1
15
8
47
32
81
66
0
0
0
0
0
0
0
0
0
0
7
1
29
9
0
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
1
0
13
2
0
0
Med
0
15
0
0
0
0
0
0
6
2
43
19
90
67
0
0
0
0
0
0
0
0
0
0
6
1
26
8
0
0
p98
0
73
0
0
1
1
17
7
58
39
114
89
168
144
0
0
0
0
0
0
0
0
2
0
13
6
54
20
0
3
p99
0
73
0
0
1
1
17
7
58
39
114
89
168
144
0
0
0
0
0
0
0
0
2
0
13
6
54
20
0
3
> 200 ppb
Mean
0
4
0
0
0
0
0
0
2
1
11
5
28
19
0
0
0
0
0
0
0
0
0
0
0
0
2
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
Med
0
0
0
0
0
0
0
0
0
0
3
0
16
10
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
p98
0
20
0
0
0
0
1
1
17
7
49
25
83
65
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
p99
0
20
0
0
0
0
1
1
17
7
49
25
83
65
0
0
0
0
0
0
0
0
0
0
0
0
7
0
0
0
A-203
-------
Location
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Standard
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-Years
8
8
8
8
8
8
8
8
8
8
8
8
3
3
3
3
3
3
3
3
3
3
3
3
3
3
6
6
6
6
6
6
Number of Daily Maximum Exceedances
>100ppb
Mean
0
0
0
0
18
13
84
62
161
138
227
214
1
60
0
0
6
3
76
40
211
159
263
231
289
274
0
142
0
0
8
5
Min
0
0
0
0
5
2
32
22
90
69
149
132
0
41
0
0
4
1
67
30
184
141
232
199
255
240
0
91
0
0
1
0
Med
0
0
0
0
9
5
69
49
149
123
231
217
0
67
0
0
6
1
80
41
215
153
276
247
305
289
0
145
0
0
6
4
p98
0
0
1
0
56
42
158
123
240
215
298
285
3
73
0
0
9
7
81
50
233
183
281
248
306
294
0
174
0
0
18
10
p99
0
0
1
0
56
42
158
123
240
215
298
285
3
73
0
0
9
7
81
50
233
183
281
248
306
294
0
174
0
0
18
10
>150ppb
Mean
0
0
0
0
0
0
9
5
34
25
84
62
0
4
0
0
0
0
6
3
45
15
124
68
211
159
0
13
0
0
0
0
Min
0
0
0
0
0
0
2
0
10
7
32
22
0
2
0
0
0
0
4
1
32
6
111
57
184
141
0
7
0
0
0
0
Med
0
0
0
0
0
0
4
2
20
14
69
49
0
4
0
0
0
0
6
1
49
17
120
73
215
153
0
13
0
0
0
0
p98
0
0
0
0
1
0
32
21
83
69
158
123
0
7
0
0
0
0
9
7
53
22
142
73
233
183
0
21
0
0
1
0
p99
0
0
0
0
1
0
32
21
83
69
158
123
0
7
0
0
0
0
9
7
53
22
142
73
233
183
0
21
0
0
1
0
> 200 ppb
Mean
0
0
0
0
0
0
0
0
4
2
18
13
0
1
0
0
0
0
1
0
6
3
27
11
76
40
0
0
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
5
2
0
0
0
0
0
0
0
0
4
1
18
4
67
30
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
2
1
9
5
0
0
0
0
0
0
0
0
6
1
31
13
80
41
0
0
0
0
0
0
p98
0
0
0
0
0
0
1
0
16
8
56
42
0
2
0
0
0
0
2
0
9
7
33
16
81
50
0
1
0
0
0
0
p99
0
0
0
0
0
0
1
0
16
8
56
42
0
2
0
0
0
0
2
0
9
7
33
16
81
50
0
1
0
0
0
0
A-204
-------
Location
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
El Paso
El Paso
El Paso
El Paso
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
Standard
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-Years
6
6
6
6
6
6
6
6
12
12
12
12
12
12
12
12
12
12
12
12
12
12
2
2
2
2
2
2
2
2
2
2
Number of Daily Maximum Exceedances
>100ppb
Mean
107
88
209
194
273
253
297
290
0
157
0
0
4
2
52
26
159
116
228
188
259
236
2
178
2
1
7
2
78
9
178
53
Min
67
52
172
159
241
220
263
258
0
79
0
0
0
0
12
6
79
45
130
94
163
133
0
161
0
0
5
0
65
6
161
46
Med
100
84
201
188
274
250
302
293
0
172
0
0
4
1
48
23
172
123
238
198
265
245
2
178
2
1
7
2
78
9
178
53
p98
164
137
265
247
320
307
335
329
0
216
0
0
10
4
111
59
227
192
290
251
314
296
3
194
3
2
9
4
90
11
194
60
p99
164
137
265
247
320
307
335
329
0
216
0
0
10
4
111
59
227
192
290
251
314
296
3
194
3
2
9
4
90
11
194
60
>150ppb
Mean
8
5
67
44
149
127
209
194
0
26
0
0
0
0
4
2
26
12
84
46
159
116
1
51
1
0
2
2
7
2
48
4
Min
1
0
40
19
116
90
172
159
0
5
0
0
0
0
0
0
6
2
27
11
79
45
0
42
0
0
0
0
5
0
42
2
Med
6
4
61
42
143
120
201
188
0
24
0
0
0
0
4
1
23
11
85
42
172
123
1
51
1
0
2
2
7
2
48
4
p98
18
10
108
73
208
188
265
247
0
58
0
0
0
0
10
4
59
25
156
100
227
192
2
60
2
0
4
3
9
4
54
6
p99
18
10
108
73
208
188
265
247
0
58
0
0
0
0
10
4
59
25
156
100
227
192
2
60
2
0
4
3
9
4
54
6
> 200 ppb
Mean
1
0
8
5
44
27
107
88
0
4
0
0
0
0
0
0
4
2
18
8
52
26
1
8
0
0
2
1
3
2
7
2
Min
0
0
1
0
19
9
67
52
0
0
0
0
0
0
0
0
0
0
4
1
12
6
0
6
0
0
0
0
0
0
5
0
Med
1
0
6
4
42
26
100
84
0
4
0
0
0
0
0
0
4
1
14
8
48
23
1
8
0
0
2
1
3
2
7
2
p98
1
1
18
10
73
54
164
137
0
11
0
0
0
0
1
0
10
4
37
15
111
59
1
9
0
0
3
2
5
3
9
4
p99
1
1
18
10
73
54
164
137
0
11
0
0
0
0
1
0
10
4
37
15
111
59
1
9
0
0
3
2
5
3
9
4
A-205
-------
Location
Jacksonville
Jacksonville
Jacksonville
Jacksonville
Las Vegas
Las Vegas
Las Vegas
Las Vegas
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
Standard
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-Years
2
2
2
2
11
11
11
11
11
11
11
11
11
11
11
11
11
11
54
54
54
54
54
54
54
54
54
54
54
54
54
54
Number of Daily Maximum Exceedances
>100ppb
Mean
237
117
271
178
0
72
0
0
2
1
55
40
108
97
150
134
186
176
0
19
0
0
1
0
17
10
70
50
139
112
194
176
Min
226
103
262
161
0
0
0
0
0
0
0
0
2
1
7
5
18
16
0
0
0
0
0
0
0
0
2
0
12
10
30
24
Med
237
117
271
178
0
12
0
0
0
0
5
3
43
31
105
81
167
150
0
17
0
0
0
0
15
8
68
46
153
120
209
190
p98
247
131
279
194
0
249
0
0
9
4
209
165
305
286
327
322
339
337
2
55
0
0
5
2
66
45
176
135
279
248
322
306
p99
247
131
279
194
0
249
0
0
9
4
209
165
305
286
327
322
339
337
2
85
0
0
8
2
75
46
186
157
284
257
328
318
>150ppb
Mean
109
15
178
53
0
6
0
0
0
0
2
1
31
17
77
60
108
97
0
1
0
0
0
0
1
0
8
4
31
19
70
50
Min
97
12
161
46
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
2
0
Med
109
15
178
53
0
0
0
0
0
0
0
0
2
0
15
7
43
31
0
0
0
0
0
0
0
0
6
2
30
18
68
46
p98
121
18
194
60
0
26
0
0
0
0
9
4
128
66
257
219
305
286
0
4
0
0
0
0
5
2
29
18
98
68
176
135
p99
121
18
194
60
0
26
0
0
0
0
9
4
128
66
257
219
305
286
0
6
0
0
0
0
8
2
32
21
112
79
186
157
> 200 ppb
Mean
32
3
78
9
0
0
0
0
0
0
0
0
2
1
19
10
55
40
0
0
0
0
0
0
0
0
1
0
6
3
17
10
Min
31
1
65
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
Med
32
3
78
9
0
0
0
0
0
0
0
0
0
0
1
0
5
3
0
0
0
0
0
0
0
0
0
0
4
2
15
8
p98
32
5
90
11
0
1
0
0
0
0
0
0
9
4
81
39
209
165
0
0
0
0
0
0
0
0
5
2
22
14
66
45
p99
32
5
90
11
0
1
0
0
0
0
0
0
9
4
81
39
209
165
0
0
0
0
0
0
1
0
8
2
24
14
75
46
A-206
-------
Location
Miami
Miami
Miami
Miami
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
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Standard
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-Years
4
4
4
4
4
4
4
4
4
4
4
4
4
4
22
22
22
22
22
22
22
22
22
22
22
22
22
22
12
12
12
12
Number of Daily Maximum Exceedances
>100ppb
Mean
0
102
0
0
3
2
33
23
74
64
122
105
157
139
0
11
0
0
1
1
27
12
108
63
185
135
233
196
0
54
0
0
Min
0
57
0
0
0
0
7
2
36
29
82
62
129
106
0
0
0
0
0
0
0
0
8
0
46
17
75
52
0
7
0
0
Med
0
104
0
0
2
1
30
23
72
62
124
108
160
139
0
12
0
0
1
0
31
13
132
79
213
162
257
226
0
38
0
0
p98
0
143
0
0
8
5
64
45
115
104
157
143
181
171
2
36
0
0
4
4
56
28
178
111
266
211
312
275
0
138
0
0
p99
0
143
0
0
8
5
64
45
115
104
157
143
181
171
2
36
0
0
4
4
56
28
178
111
266
211
312
275
0
138
0
0
>150ppb
Mean
0
33
0
0
0
0
3
2
18
12
44
37
74
64
0
0
0
0
0
0
1
1
13
5
52
21
108
63
0
4
0
0
Min
0
7
0
0
0
0
0
0
1
1
12
10
36
29
0
0
0
0
0
0
0
0
0
0
0
0
8
0
0
0
0
0
Med
0
33
0
0
0
0
2
1
18
12
44
35
72
62
0
0
0
0
0
0
1
0
14
5
67
24
132
79
0
2
0
0
p98
0
59
0
0
1
1
8
5
34
25
77
66
115
104
0
2
0
0
1
1
4
4
35
15
96
52
178
111
0
14
0
0
p99
0
59
0
0
1
1
8
5
34
25
77
66
115
104
0
2
0
0
1
1
4
4
35
15
96
52
178
111
0
14
0
0
> 200 ppb
Mean
0
7
0
0
0
0
0
0
3
2
12
9
33
23
0
0
0
0
0
0
0
0
1
1
8
3
27
12
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
1
0
7
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
4
0
0
0
0
0
0
2
1
12
8
30
23
0
0
0
0
0
0
0
0
1
0
9
3
31
13
0
0
0
0
p98
0
18
0
0
0
0
1
1
8
5
25
18
64
45
0
1
0
0
0
0
1
1
4
4
22
9
56
28
0
2
0
0
p99
0
18
0
0
0
0
1
1
8
5
25
18
64
45
0
1
0
0
0
0
1
1
4
4
22
9
56
28
0
2
0
0
A-207
-------
Location
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Provo
Standard
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-Years
12
12
12
12
12
12
12
12
12
12
9
9
9
9
9
9
9
9
9
9
9
9
9
9
3
3
3
3
3
3
3
3
Number of Daily Maximum Exceedances
>100ppb
Mean
3
0
52
12
163
77
231
163
278
220
0
40
0
0
1
0
65
25
224
163
298
265
330
317
14
129
0
0
14
14
19
19
Min
0
0
21
1
103
40
186
103
239
165
0
19
0
0
0
0
37
10
167
126
208
188
249
230
0
56
0
0
0
0
1
1
Med
2
0
46
12
149
67
229
149
281
218
0
29
0
0
1
0
55
23
235
160
307
275
337
326
0
137
0
0
0
0
14
12
p98
10
2
96
33
246
134
293
246
325
290
1
78
0
0
5
2
105
43
245
195
324
292
352
338
43
194
0
0
43
43
43
43
p99
10
2
96
33
246
134
293
246
325
290
1
78
0
0
5
2
105
43
245
195
324
292
352
338
43
194
0
0
43
43
43
43
>150ppb
Mean
0
0
3
0
24
4
85
24
163
77
0
1
0
0
0
0
1
0
21
6
116
65
224
163
7
19
0
0
10
8
14
14
Min
0
0
0
0
4
0
42
4
103
40
0
0
0
0
0
0
0
0
8
0
80
37
167
126
0
3
0
0
0
0
0
0
Med
0
0
2
0
21
3
75
21
149
67
0
0
0
0
0
0
1
0
18
7
100
55
235
160
0
12
0
0
0
0
0
0
p98
1
0
10
2
54
14
146
54
246
134
0
2
0
0
0
0
5
2
36
10
158
105
245
195
20
43
0
0
29
23
43
43
p99
1
0
10
2
54
14
146
54
246
134
0
2
0
0
0
0
5
2
36
10
158
105
245
195
20
43
0
0
29
23
43
43
> 200 ppb
Mean
0
0
0
0
3
0
16
3
52
12
0
0
0
0
0
0
0
0
1
0
12
3
65
25
0
14
0
0
0
0
13
13
Min
0
0
0
0
0
0
2
0
21
1
0
0
0
0
0
0
0
0
0
0
0
0
37
10
0
0
0
0
0
0
0
0
Med
0
0
0
0
2
0
16
2
46
12
0
0
0
0
0
0
0
0
1
0
11
3
55
23
0
1
0
0
0
0
0
0
p98
0
0
1
0
10
2
41
10
96
33
0
0
0
0
0
0
0
0
5
2
21
7
105
43
0
42
0
0
0
0
40
40
p99
0
0
1
0
10
2
41
10
96
33
0
0
0
0
0
0
0
0
5
2
21
7
105
43
0
42
0
0
0
0
40
40
A-208
-------
Location
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
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
Standard
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
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
Site-Years
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
17
17
17
17
17
17
17
17
17
17
17
17
Number of Daily Maximum Exceedances
>100ppb
Mean
84
74
209
196
281
281
0
67
0
0
1
1
58
49
146
134
211
201
255
255
0
52
0
0
3
1
50
16
133
69
190
133
Min
64
50
198
191
242
242
0
15
0
0
0
0
31
23
112
100
160
153
191
191
0
0
0
0
0
0
0
0
12
0
42
12
Med
90
82
213
194
296
296
0
50
0
0
1
1
54
42
143
128
206
197
258
258
0
61
0
0
2
0
52
14
149
69
230
149
p98
98
91
216
202
305
305
0
154
0
0
3
2
94
88
186
179
272
256
313
313
2
149
0
0
12
6
120
46
246
160
299
246
p99
98
91
216
202
305
305
0
154
0
0
3
2
94
88
186
179
272
256
313
313
2
149
0
0
12
6
120
46
246
160
299
246
>150ppb
Mean
16
16
25
23
84
74
0
6
0
0
0
0
1
1
25
16
88
82
146
134
0
4
0
0
0
0
3
1
26
5
77
29
Min
0
0
5
3
64
50
0
0
0
0
0
0
0
0
2
1
57
49
112
100
0
0
0
0
0
0
0
0
0
0
0
0
Med
6
5
22
20
90
82
0
1
0
0
0
0
1
1
22
14
83
78
143
128
0
3
0
0
0
0
2
0
25
3
80
28
p98
43
43
49
47
98
91
0
22
0
0
0
0
3
2
55
36
127
121
186
179
0
14
0
0
2
0
12
6
67
20
171
74
p99
43
43
49
47
98
91
0
22
0
0
0
0
3
2
55
36
127
121
186
179
0
14
0
0
2
0
12
6
67
20
171
74
> 200 ppb
Mean
14
14
15
15
19
19
0
0
0
0
0
0
0
0
1
1
16
12
58
49
0
1
0
0
0
0
1
0
3
1
17
4
Min
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
31
23
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
0
2
2
14
12
0
0
0
0
0
0
0
0
1
1
13
9
54
42
0
0
0
0
0
0
0
0
2
0
14
2
p98
43
43
43
43
43
43
0
1
0
0
0
0
0
0
3
2
35
28
94
88
0
3
0
0
0
0
4
1
12
6
49
14
p99
43
43
43
43
43
43
0
1
0
0
0
0
0
0
3
2
35
28
94
88
0
3
0
0
0
0
4
1
12
6
49
14
A-209
-------
Location
Washington DC
Washington DC
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Other Not MSA
Standard
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-Years
17
17
565
565
565
565
565
565
565
565
565
565
565
565
565
565
116
116
116
116
116
116
116
116
116
116
116
116
116
116
Number of Daily Maximum Exceedances
>100ppb
Mean
237
190
0
28
0
0
0
0
4
2
24
16
65
48
109
86
0
37
0
0
0
0
1
0
7
2
18
7
34
16
Min
90
42
0
0
0
0
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
278
230
0
12
0
0
0
0
1
0
12
7
53
34
104
77
0
11
0
0
0
0
0
0
0
0
2
0
8
1
p98
333
299
1
141
0
0
2
1
29
18
112
85
212
170
267
242
2
192
0
0
3
2
16
7
71
26
116
71
165
112
p99
333
299
1
156
0
0
4
1
37
23
128
110
228
191
300
274
3
195
1
1
5
2
19
9
80
26
147
80
199
144
>150ppb
Mean
133
69
0
2
0
0
0
0
0
0
2
1
8
4
24
16
0
7
0
0
0
0
0
0
1
0
2
1
7
2
Min
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
Med
149
69
0
0
0
0
0
0
0
0
0
0
2
1
12
7
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
246
160
0
23
0
0
0
0
2
1
15
8
52
31
112
85
0
69
0
0
0
0
3
2
12
3
30
12
71
26
p99
246
160
0
27
0
0
1
1
4
1
19
10
63
41
128
110
2
78
0
0
2
2
5
2
14
6
31
15
80
26
> 200 ppb
Mean
50
16
0
0
0
0
0
0
0
0
0
0
1
0
4
2
0
1
0
0
0
0
0
0
0
0
0
0
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
Med
52
14
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
p98
120
46
0
2
0
0
0
0
0
0
2
1
9
5
29
18
0
20
0
0
0
0
1
0
3
2
9
3
16
7
p99
120
46
0
4
0
0
0
0
1
1
4
1
13
7
37
23
1
22
0
0
1
1
2
2
5
2
11
5
19
9
A-210
-------
1 Table a-128. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for monitors
2 >100 m from a major road following adjustment to just meeting the current and alternative standards, 2004-
3 2006 air quality.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Standard
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
15
15
15
15
15
15
15
15
15
15
15
15
15
15
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
0
0
0
0
0
0
0
0
0
2
1
8
4
0
0
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
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
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
2
1
p98
0
4
0
0
0
0
1
0
2
1
17
7
36
22
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
10
5
p99
0
4
0
0
0
0
1
0
2
1
17
7
36
22
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
10
5
> 300 ppb
Mean
0
0
0
0
0
0
0
0
0
0
0
0
2
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
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
p98
0
1
0
0
0
0
0
0
1
1
2
1
17
7
0
0
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
p99
0
1
0
0
0
0
0
0
1
1
2
1
17
7
0
0
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
A-211
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Jacksonville
Standard
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
3
3
3
3
3
3
3
3
3
3
3
3
3
3
6
6
6
6
6
6
6
6
6
6
6
6
6
6
12
12
12
12
12
12
12
12
12
12
12
12
12
12
2
2
2
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
0
0
0
0
0
0
0
2
0
5
3
17
9
0
0
0
0
0
0
0
0
1
1
8
5
32
19
0
1
0
0
0
0
0
0
0
0
4
2
12
6
0
3
0
Min
0
0
0
0
0
0
0
0
0
0
4
1
8
4
0
0
0
0
0
0
0
0
0
0
1
0
14
6
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
1
0
Med
0
0
0
0
0
0
0
0
1
0
5
1
20
11
0
0
0
0
0
0
0
0
1
1
6
4
32
19
0
0
0
0
0
0
0
0
0
0
4
1
11
7
0
3
0
p98
0
0
0
0
0
0
0
0
4
1
7
7
24
11
0
0
0
0
0
0
0
0
1
1
18
10
61
38
0
2
0
0
0
0
0
0
2
1
10
4
25
12
0
5
0
p99
0
0
0
0
0
0
0
0
4
1
7
7
24
11
0
0
0
0
0
0
0
0
1
1
18
10
61
38
0
2
0
0
0
0
0
0
2
1
10
4
25
12
0
5
0
> 300 ppb
Mean
0
0
0
0
0
0
0
0
0
0
2
1
6
3
0
0
0
0
0
0
0
0
0
0
1
1
8
5
0
0
0
0
0
0
0
0
0
0
1
0
4
2
0
2
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
4
1
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
Med
0
0
0
0
0
0
0
0
0
0
1
0
6
1
0
0
0
0
0
0
0
0
0
0
1
1
6
4
0
0
0
0
0
0
0
0
0
0
1
0
4
1
0
2
0
p98
0
0
0
0
0
0
0
0
0
0
5
2
9
7
0
0
0
0
0
0
0
0
1
0
2
1
18
10
0
0
0
0
0
0
0
0
0
0
2
1
10
4
0
4
0
p99
0
0
0
0
0
0
0
0
0
0
5
2
9
7
0
0
0
0
0
0
0
0
1
0
2
1
18
10
0
0
0
0
0
0
0
0
0
0
2
1
10
4
0
4
0
A-212
-------
Location
Jacksonville
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
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
Miami
Miami
Miami
Miami
Miami
Miami
Standard
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
2
2
2
2
2
2
2
2
2
2
2
11
11
11
11
11
11
11
11
11
11
11
11
11
11
54
54
54
54
54
54
54
54
54
54
54
54
54
54
4
4
4
4
4
4
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
1
1
2
1
3
2
7
2
25
3
0
0
0
0
0
0
0
0
0
0
1
1
14
7
0
0
0
0
0
0
0
0
0
0
1
0
4
2
0
2
0
0
0
0
Min
0
0
0
0
0
0
0
5
0
23
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
Med
0
1
1
2
1
3
2
7
2
25
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
0
2
1
0
1
0
0
0
0
p98
0
2
1
4
2
5
4
9
4
27
5
0
0
0
0
0
0
0
0
0
0
7
4
52
30
0
0
0
0
0
0
0
0
1
0
5
2
17
10
0
4
0
0
0
0
p99
0
2
1
4
2
5
4
9
4
27
5
0
0
0
0
0
0
0
0
0
0
7
4
52
30
0
0
0
0
0
0
0
0
2
0
8
2
19
11
0
4
0
0
0
0
> 300 ppb
Mean
0
1
0
2
1
2
2
3
2
7
2
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
Min
0
0
0
0
0
0
0
1
0
5
0
0
0
0
0
0
0
0
0
0
0
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
1
0
2
1
2
2
3
2
7
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
0
0
p98
0
2
0
3
2
4
3
5
4
9
4
0
0
0
0
0
0
0
0
0
0
1
0
9
4
0
0
0
0
0
0
0
0
0
0
1
0
5
2
0
1
0
0
0
0
p99
0
2
0
3
2
4
3
5
4
9
4
0
0
0
0
0
0
0
0
0
0
1
0
9
4
0
0
0
0
0
0
0
0
0
0
2
1
8
2
0
1
0
0
0
0
A-213
-------
Location
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
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Standard
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
99
98
99
98
99
98
99
98
99
98
99
98
Site-
Years
4
4
4
4
4
4
4
4
22
22
22
22
22
22
22
22
22
22
22
22
22
22
12
12
12
12
12
12
12
12
12
12
12
12
12
12
9
9
9
9
9
9
9
9
9
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
0
0
0
3
2
10
6
0
0
0
0
0
0
0
0
0
0
1
1
7
2
0
0
0
0
0
0
0
0
0
0
3
0
12
2
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
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
2
1
10
5
0
0
0
0
0
0
0
0
0
0
1
0
7
1
0
0
0
0
0
0
0
0
0
0
2
0
12
1
0
0
0
0
0
0
0
0
0
p98
0
0
1
1
8
5
21
13
0
1
0
0
0
0
1
0
2
1
4
4
18
8
0
0
0
0
0
0
0
0
2
0
10
2
33
7
0
0
0
0
0
0
0
0
0
p99
0
0
1
1
8
5
21
13
0
1
0
0
0
0
1
0
2
1
4
4
18
8
0
0
0
0
0
0
0
0
2
0
10
2
33
7
0
0
0
0
0
0
0
0
0
> 300 ppb
Mean
0
0
0
0
1
0
3
2
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
3
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
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
2
1
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
2
0
0
0
0
0
0
0
0
0
0
p98
0
0
1
1
1
1
8
5
0
0
0
0
0
0
0
0
1
1
2
1
4
4
0
0
0
0
0
0
0
0
1
0
2
1
10
2
0
0
0
0
0
0
0
0
0
p99
0
0
1
1
1
1
8
5
0
0
0
0
0
0
0
0
1
1
2
1
4
4
0
0
0
0
0
0
0
0
1
0
2
1
10
2
0
0
0
0
0
0
0
0
0
A-214
-------
Location
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Provo
Provo
Provo
Provo
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
St. Louis
St. Louis
St. Louis
St. Louis
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
Standard
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
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
Site-
Years
9
9
9
9
9
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
17
17
17
17
17
17
17
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
1
0
6
2
0
10
0
0
0
0
2
1
13
13
14
14
15
15
0
0
0
0
0
0
0
0
0
0
1
1
12
9
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
0
0
0
Med
0
1
0
7
1
0
0
0
0
0
0
0
0
0
0
0
0
2
2
0
0
0
0
0
0
0
0
0
0
1
1
9
6
0
0
0
0
0
0
0
p98
0
5
2
10
7
0
31
0
0
0
0
7
3
40
40
43
43
43
43
0
0
0
0
0
0
0
0
0
0
3
2
28
25
0
2
0
0
0
0
1
p99
0
5
2
10
7
0
31
0
0
0
0
7
3
40
40
43
43
43
43
0
0
0
0
0
0
0
0
0
0
3
2
28
25
0
2
0
0
0
0
1
> 300 ppb
Mean
0
0
0
1
0
0
0
0
0
0
0
0
0
10
8
14
14
14
14
0
0
0
0
0
0
0
0
0
0
0
0
1
1
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
0
0
0
Med
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
1
1
0
0
0
0
0
0
0
p98
0
0
0
5
2
0
1
0
0
0
0
0
0
29
23
42
41
43
43
0
0
0
0
0
0
0
0
0
0
0
0
3
2
0
1
0
0
0
0
0
p99
0
0
0
5
2
0
1
0
0
0
0
0
0
29
23
42
41
43
43
0
0
0
0
0
0
0
0
0
0
0
0
3
2
0
1
0
0
0
0
0
A-215
-------
Location
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not
Standard
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
17
17
17
17
17
17
17
565
565
565
565
565
565
565
565
565
565
565
565
565
565
116
116
116
116
116
116
116
116
116
116
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
1
0
3
1
13
2
0
0
0
0
0
0
0
0
0
0
0
0
1
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
0
0
0
0
Med
0
0
0
2
0
10
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
p98
0
5
2
12
6
39
10
0
1
0
0
0
0
0
0
1
0
2
1
7
4
0
7
0
0
0
0
0
0
2
0
p99
0
5
2
12
6
39
10
0
1
0
0
0
0
1
0
1
1
4
1
9
7
1
9
0
0
1
0
2
1
2
2
> 300 ppb
Mean
0
0
0
1
0
3
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
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
Med
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
p98
0
2
0
6
2
12
6
0
0
0
0
0
0
0
0
0
0
1
1
2
1
0
3
0
0
0
0
0
0
0
0
p99
0
2
0
6
2
12
6
0
1
0
0
0
0
0
0
1
1
1
1
4
1
0
5
0
0
0
0
1
1
2
2
A-216
-------
1
2
3
Location
MSA
Other Not
MSA
Other Not
MSA
Other Not
MSA
Other Not
MSA
Standard
250
250
300
300
Percentile
98
99
98
99
Site-
Years
116
116
116
116
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
0
0
0
Min
0
0
0
0
Med
0
0
0
0
p98
3
2
7
3
p99
5
2
9
3
> 300 ppb
Mean
0
0
0
0
Min
0
0
0
0
Med
0
0
0
0
p98
2
0
3
2
p99
2
2
5
2
A-217
-------
1 Table A-129. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) for monitors > 20 m and < 100 m from a major
2 road 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
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Standard
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
11
11
11
11
11
11
11
11
11
11
11
11
11
11
6
6
6
6
6
6
6
6
6
6
6
6
6
6
2
2
Number of Daily Maximum Exceedances
> 100 ppb
Mean
0
25
0
0
1
1
19
8
84
42
165
103
214
165
1
94
0
0
4
3
80
64
206
178
277
261
324
320
1
97
Min
0
2
0
0
0
0
1
0
20
7
78
30
115
78
0
39
0
0
0
0
34
17
161
133
248
230
309
302
0
86
Med
0
12
0
0
0
0
11
2
65
32
143
79
208
143
1
90
0
0
3
1
74
62
200
172
273
257
325
319
1
97
p98
1
73
0
0
9
4
67
34
153
102
247
175
291
247
5
152
0
0
15
12
147
118
267
239
314
299
338
338
1
108
p99
1
73
0
0
9
4
67
34
153
102
247
175
291
247
5
152
0
0
15
12
147
118
267
239
314
299
338
338
1
108
> 150 ppb
Mean
0
2
0
0
0
0
1
1
9
4
36
16
84
42
0
6
0
0
0
0
4
3
44
30
122
99
206
178
1
11
Min
0
0
0
0
0
0
0
0
0
0
6
0
20
7
0
0
0
0
0
0
0
0
10
4
78
58
161
133
0
10
Med
0
0
0
0
0
0
0
0
3
1
28
8
65
32
0
5
0
0
0
0
3
1
43
30
112
90
200
172
1
11
p98
0
10
0
0
1
0
9
4
42
18
91
62
153
102
0
15
0
0
1
0
15
12
88
62
190
164
267
239
1
12
p99
0
10
0
0
1
0
9
4
42
18
91
62
153
102
0
15
0
0
1
0
15
12
88
62
190
164
267
239
1
12
> 200 ppb
Mean
0
0
0
0
0
0
0
0
1
1
7
2
19
8
0
0
0
0
0
0
0
0
4
3
27
19
80
64
0
2
Min
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
3
1
34
17
0
1
Med
0
0
0
0
0
0
0
0
0
0
1
0
11
2
0
0
0
0
0
0
0
0
3
1
26
17
74
62
0
2
p98
0
2
0
0
0
0
1
1
9
4
31
13
67
34
0
2
0
0
0
0
2
2
15
12
58
46
147
118
0
2
p99
0
2
0
0
0
0
1
1
9
4
31
13
67
34
0
2
0
0
0
0
2
2
15
12
58
46
147
118
0
2
A-218
-------
Location
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Standard
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
22
22
22
22
22
22
Number of Daily Maximum Exceedances
>100ppb
Mean
1
1
4
2
60
33
153
110
208
188
259
230
0
160
0
0
6
3
57
26
160
118
239
197
276
251
1
50
0
0
3
1
Min
0
0
2
2
48
18
140
95
199
180
258
227
0
137
0
0
4
2
33
15
132
90
220
167
263
234
0
0
0
0
0
0
Med
1
1
4
2
60
33
153
110
208
188
259
230
0
143
0
0
4
3
56
20
147
106
225
181
267
238
1
53
0
0
1
1
p98
1
1
6
2
71
47
166
124
217
195
259
233
0
201
0
0
9
4
83
42
201
157
273
243
297
281
9
116
0
0
17
11
p99
1
1
6
2
71
47
166
124
217
195
259
233
0
201
0
0
9
4
83
42
201
157
273
243
297
281
9
116
0
0
17
11
>150ppb
Mean
0
0
1
1
4
2
33
16
83
60
153
110
0
25
0
0
0
0
6
3
26
14
88
51
160
118
0
3
0
0
0
0
Min
0
0
0
0
2
2
18
7
68
48
140
95
0
16
0
0
0
0
4
2
15
8
59
27
132
90
0
0
0
0
0
0
Med
0
0
1
1
4
2
33
16
83
60
153
110
0
17
0
0
0
0
4
3
20
10
82
51
147
106
0
2
0
0
0
0
p98
0
0
1
1
6
2
47
24
97
71
166
124
0
42
0
0
0
0
9
4
42
24
124
76
201
157
1
12
0
0
2
1
p99
0
0
1
1
6
2
47
24
97
71
166
124
0
42
0
0
0
0
9
4
42
24
124
76
201
157
1
12
0
0
2
1
> 200 ppb
Mean
0
0
1
1
1
1
4
2
20
13
60
33
0
6
0
0
0
0
1
0
6
3
17
10
57
26
0
0
0
0
0
0
Min
0
0
0
0
0
0
2
2
10
6
48
18
0
4
0
0
0
0
0
0
4
2
12
6
33
15
0
0
0
0
0
0
Med
0
0
1
1
1
1
4
2
20
13
60
33
0
4
0
0
0
0
1
0
4
3
12
8
56
20
0
0
0
0
0
0
p98
0
0
1
1
1
1
6
2
30
19
71
47
0
9
0
0
0
0
2
0
9
4
26
15
83
42
0
2
0
0
0
0
p99
0
0
1
1
1
1
6
2
30
19
71
47
0
9
0
0
0
0
2
0
9
4
26
15
83
42
0
2
0
0
0
0
A-219
-------
Location
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
Miami
Miami
Miami
Miami
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Standard
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
22
22
22
22
22
22
22
22
2
2
2
2
2
2
2
2
2
2
2
2
2
2
11
11
11
11
11
11
11
11
11
11
Number of Daily Maximum Exceedances
>100ppb
Mean
46
30
139
107
225
196
272
259
1
173
1
1
8
4
77
57
139
122
203
179
249
225
1
30
0
0
5
2
69
31
198
134
Min
0
0
9
2
41
27
69
64
0
171
0
0
6
2
73
52
132
112
197
175
238
218
0
2
0
0
1
0
14
4
84
36
Med
51
30
143
113
242
202
302
283
1
173
1
1
8
4
77
57
139
122
203
179
249
225
1
32
0
0
6
2
62
29
199
128
p98
101
82
235
190
295
282
330
323
2
175
2
1
9
5
80
62
145
132
209
183
260
232
3
64
0
0
8
4
126
51
285
211
p99
101
82
235
190
295
282
330
323
2
175
2
1
9
5
80
62
145
132
209
183
260
232
3
64
0
0
8
4
126
51
285
211
>150ppb
Mean
3
1
22
12
75
51
139
107
1
76
1
1
1
1
8
4
47
36
93
83
139
122
0
1
0
0
0
0
5
2
35
15
Min
0
0
0
0
0
0
9
2
0
75
0
0
0
0
6
2
42
34
86
76
132
112
0
0
0
0
0
0
1
0
5
2
Med
1
1
23
11
77
55
143
113
1
76
1
1
1
1
8
4
47
36
93
83
139
122
0
0
0
0
0
0
6
2
33
14
p98
17
11
63
43
143
107
235
190
1
76
1
1
2
2
9
5
52
37
100
89
145
132
1
4
0
0
1
1
8
4
60
28
p99
17
11
63
43
143
107
235
190
1
76
1
1
2
2
9
5
52
37
100
89
145
132
1
4
0
0
1
1
8
4
60
28
> 200 ppb
Mean
0
0
3
1
15
7
46
30
1
20
1
1
1
1
1
1
8
4
36
26
77
57
0
0
0
0
0
0
1
0
5
2
Min
0
0
0
0
0
0
0
0
0
17
0
0
0
0
0
0
6
2
34
23
73
52
0
0
0
0
0
0
0
0
1
0
Med
0
0
1
1
15
5
51
30
1
20
1
1
1
1
1
1
8
4
36
26
77
57
0
0
0
0
0
0
0
0
6
2
p98
2
2
17
11
46
36
101
82
1
23
1
1
2
1
2
2
9
5
37
29
80
62
0
2
0
0
0
0
2
1
8
4
p99
2
2
17
11
46
36
101
82
1
23
1
1
2
1
2
2
9
5
37
29
80
62
0
2
0
0
0
0
2
1
8
4
A-220
-------
Location
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Standard
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
11
11
11
11
6
6
6
6
6
6
6
6
6
6
6
6
6
6
3
3
3
3
3
3
3
3
3
3
3
3
3
3
Number of Daily Maximum Exceedances
>100ppb
Mean
281
232
317
290
1
70
0
0
6
2
93
25
216
121
272
216
307
266
0
14
0
0
0
0
29
7
174
120
257
217
304
284
Min
190
121
245
207
0
28
0
0
3
1
62
15
169
76
227
169
274
220
0
13
0
0
0
0
28
4
171
114
239
207
285
263
Med
287
235
331
302
1
56
0
0
6
2
96
24
216
127
272
216
308
265
0
13
0
0
0
0
29
9
174
121
264
221
309
294
p98
339
315
356
345
1
173
0
0
10
4
125
39
267
166
313
267
342
310
0
15
0
0
0
0
30
9
178
126
268
222
317
294
p99
339
315
356
345
1
173
0
0
10
4
125
39
267
166
313
267
342
310
0
15
0
0
0
0
30
9
178
126
268
222
317
294
>150ppb
Mean
115
58
198
134
0
6
0
0
1
0
6
2
45
11
131
45
216
121
0
0
0
0
0
0
0
0
5
1
68
29
174
120
Min
26
9
84
36
0
1
0
0
0
0
3
1
28
6
83
28
169
76
0
0
0
0
0
0
0
0
2
0
55
28
171
114
Med
106
53
199
128
0
3
0
0
1
0
6
2
49
10
138
49
216
127
0
0
0
0
0
0
0
0
6
2
66
29
174
121
p98
190
104
285
211
0
25
0
0
1
0
10
4
60
16
173
60
267
166
0
0
0
0
0
0
0
0
6
2
83
30
178
126
p99
190
104
285
211
0
25
0
0
1
0
10
4
60
16
173
60
267
166
0
0
0
0
0
0
0
0
6
2
83
30
178
126
> 200 ppb
Mean
23
10
69
31
0
2
0
0
0
0
2
0
6
2
32
6
93
25
0
0
0
0
0
0
0
0
0
0
2
1
29
7
Min
3
2
14
4
0
0
0
0
0
0
1
0
3
1
19
3
62
15
0
0
0
0
0
0
0
0
0
0
0
0
28
4
Med
23
10
62
29
0
1
0
0
0
0
1
0
6
2
35
6
96
24
0
0
0
0
0
0
0
0
0
0
3
2
29
9
p98
40
22
126
51
0
6
0
0
0
0
3
1
10
4
48
10
125
39
0
0
0
0
0
0
0
0
0
0
4
2
30
9
p99
40
22
126
51
0
6
0
0
0
0
3
1
10
4
48
10
125
39
0
0
0
0
0
0
0
0
0
0
4
2
30
9
A-221
-------
Location
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
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
Standard
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
8
8
8
8
8
8
8
8
8
8
8
8
8
8
12
12
12
12
12
12
12
12
12
12
Number of Daily Maximum Exceedances
>100ppb
Mean
0
42
0
0
1
1
26
22
83
75
142
131
187
187
0
66
0
0
3
0
59
15
165
85
Min
0
1
0
0
0
0
1
1
23
19
71
62
114
114
0
10
0
0
0
0
16
0
80
29
Med
0
35
0
0
0
0
13
8
65
57
116
105
157
157
0
74
0
0
2
0
54
12
174
82
p98
1
108
1
1
8
4
124
115
242
231
319
304
344
344
1
123
0
0
7
1
115
38
245
154
p99
1
108
1
1
8
4
124
115
242
231
319
304
344
344
1
123
0
0
7
1
115
38
245
154
>150ppb
Mean
0
2
0
0
0
0
1
1
12
10
42
39
83
75
0
3
0
0
0
0
3
0
26
5
Min
0
0
0
0
0
0
0
0
0
0
3
3
23
19
0
0
0
0
0
0
0
0
2
0
Med
0
1
0
0
0
0
0
0
3
2
26
23
65
57
0
3
0
0
0
0
2
0
23
5
p98
0
6
0
0
1
1
8
4
78
66
174
164
242
231
0
7
0
0
1
1
7
1
61
14
p99
0
6
0
0
1
1
8
4
78
66
174
164
242
231
0
7
0
0
1
1
7
1
61
14
> 200 ppb
Mean
0
0
0
0
0
0
0
0
1
1
9
7
26
22
0
0
0
0
0
0
0
0
3
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
0
1
1
13
8
0
0
0
0
0
0
0
0
2
0
p98
0
1
0
0
1
1
1
1
8
4
59
45
124
115
0
1
0
0
0
0
1
1
7
1
p99
0
1
0
0
1
1
1
1
8
4
59
45
124
115
0
1
0
0
0
0
1
1
7
1
A-222
-------
Location
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Standard
250
250
300
300
Percentile
98
99
98
99
Site-
Years
12
12
12
12
Number of Daily Maximum Exceedances
>100ppb
Mean
224
165
266
224
Min
127
80
182
127
Med
241
174
277
241
p98
303
245
338
303
p99
303
245
338
303
>150ppb
Mean
94
30
165
85
Min
35
2
80
29
Med
93
25
174
82
p98
169
66
245
154
p99
169
66
245
154
> 200 ppb
Mean
16
3
59
15
Min
0
0
16
0
Med
14
2
54
12
p98
40
9
115
38
p99
40
9
115
38
A-223
-------
Table A-130. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for
monitors > 20 m and < 100 m from a major road following adjustment to just meeting the current and
alternative standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
El Paso
Standard
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
11
11
11
11
11
11
11
11
11
11
11
11
11
11
6
6
6
6
6
6
6
6
6
6
6
6
6
6
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
0
0
0
0
0
0
0
0
0
1
1
5
2
0
0
0
0
0
0
0
0
0
0
4
2
21
14
0
1
0
0
1
0
1
1
1
1
4
2
16
7
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
1
1
0
0
0
0
0
0
0
0
1
0
2
2
7
4
0
Med
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
3
1
18
11
0
1
0
0
1
0
1
1
1
1
4
2
16
7
0
p98
0
1
0
0
0
0
0
0
2
1
9
4
29
12
0
0
0
0
0
0
0
0
2
2
15
9
53
39
0
1
0
0
1
0
1
1
1
1
6
2
24
10
0
p99
0
1
0
0
0
0
0
0
2
1
9
4
29
12
0
0
0
0
0
0
0
0
2
2
15
9
53
39
0
1
0
0
1
0
1
1
1
1
6
2
24
10
0
> 300 ppb
Mean
0
0
0
0
0
0
0
0
0
0
1
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
4
3
0
1
0
0
0
0
1
1
1
1
1
1
4
2
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
1
0
2
2
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
3
1
0
1
0
0
0
0
1
1
1
1
1
1
4
2
0
p98
0
0
0
0
0
0
0
0
1
0
4
1
9
4
0
0
0
0
0
0
0
0
1
0
3
2
15
12
0
1
0
0
0
0
1
1
1
1
1
1
6
2
0
p99
0
0
0
0
0
0
0
0
1
0
4
1
9
4
0
0
0
0
0
0
0
0
1
0
3
2
15
12
0
1
0
0
0
0
1
1
1
1
1
1
6
2
0
A-224
-------
Location
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
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
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
New York
New York
New York
New York
Standard
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
98
99
98
99
Site-
Years
3
3
3
3
3
3
3
3
3
3
3
3
3
22
22
22
22
22
22
22
22
22
22
22
22
22
22
2
2
2
2
2
2
2
2
2
2
2
2
2
2
11
11
11
11
Number of Daily Maximum Exceedances
> 250 ppb
Mean
2
0
0
0
0
0
0
2
0
6
3
14
7
0
0
0
0
0
0
0
0
0
0
3
1
11
5
1
3
1
1
1
1
1
1
1
1
8
4
28
19
0
0
0
0
Min
1
0
0
0
0
0
0
1
0
4
2
8
4
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
2
25
17
0
0
0
0
Med
2
0
0
0
0
0
0
2
0
4
3
10
5
0
0
0
0
0
0
0
0
0
0
1
1
10
4
1
3
1
1
1
1
1
1
1
1
8
4
28
19
0
0
0
0
p98
3
0
0
0
0
0
0
2
1
9
4
24
13
0
0
0
0
0
0
1
0
3
2
17
9
41
25
1
5
1
1
1
1
2
2
2
2
9
5
31
20
0
1
0
0
p99
3
0
0
0
0
0
0
2
1
9
4
24
13
0
0
0
0
0
0
1
0
3
2
17
9
41
25
1
5
1
1
1
1
2
2
2
2
9
5
31
20
0
1
0
0
> 300 ppb
Mean
0
0
0
0
0
0
0
0
0
2
1
6
3
0
0
0
0
0
0
0
0
0
0
0
0
3
1
1
1
1
1
1
1
1
1
1
1
1
1
8
4
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
2
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
6
2
0
0
0
0
Med
0
0
0
0
0
0
0
0
0
2
1
4
3
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
8
4
0
0
0
0
p98
0
0
0
0
0
0
0
0
0
3
1
9
4
0
0
0
0
0
0
0
0
2
1
5
3
17
11
1
2
1
1
1
1
2
1
2
2
2
2
9
5
0
0
0
0
p99
0
0
0
0
0
0
0
0
0
3
1
9
4
0
0
0
0
0
0
0
0
2
1
5
3
17
11
1
2
1
1
1
1
2
1
2
2
2
2
9
5
0
0
0
0
A-225
-------
Location
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
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Standard
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
99
98
Site-
Years
11
11
11
11
11
11
11
11
11
11
6
6
6
6
6
6
6
6
6
6
6
6
6
6
3
3
3
3
3
3
3
3
3
3
3
3
3
3
8
8
8
8
8
8
8
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
0
0
0
1
0
5
2
19
7
0
1
0
0
0
0
0
0
2
0
6
2
25
4
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
Min
0
0
0
0
0
0
1
0
2
2
0
0
0
0
0
0
0
0
1
0
3
1
15
2
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
1
0
6
2
20
7
0
0
0
0
0
0
0
0
2
0
6
2
24
5
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
p98
0
0
1
0
3
2
8
4
33
14
0
3
0
0
0
0
1
0
4
1
10
4
39
6
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
1
0
0
0
0
1
p99
0
0
1
0
3
2
8
4
33
14
0
3
0
0
0
0
1
0
4
1
10
4
39
6
0
0
0
0
0
0
0
0
0
0
0
0
2
1
0
1
0
0
0
0
1
> 300 ppb
Mean
0
0
0
0
0
0
1
1
5
2
0
0
0
0
0
0
0
0
1
0
2
1
6
2
0
0
0
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
1
0
0
0
0
0
0
0
0
0
0
0
1
0
3
1
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
2
0
6
2
0
0
0
0
0
0
0
0
1
0
2
1
6
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
1
1
3
2
8
4
0
0
0
0
0
0
0
0
1
0
4
1
10
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
p99
0
0
0
0
1
1
3
2
8
4
0
0
0
0
0
0
0
0
1
0
4
1
10
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
A-226
-------
Location
St. Louis
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
Standard
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
Percentile
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
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
0
0
1
1
7
4
0
0
0
0
0
0
0
0
0
0
3
0
13
2
Min
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
1
0
0
0
0
0
0
0
0
0
0
0
2
0
11
1
p98
1
1
1
5
4
45
33
0
1
0
0
0
0
1
0
1
1
7
1
33
6
p99
1
1
1
5
4
45
33
0
1
0
0
0
0
1
0
1
1
7
1
33
6
> 300 ppb
Mean
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
3
0
Min
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
2
0
p98
1
1
1
1
1
8
4
0
1
0
0
0
0
0
0
1
1
1
1
7
1
p99
1
1
1
1
1
8
4
0
1
0
0
0
0
0
0
1
1
1
1
7
1
A-227
-------
Table a-131. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) for monitors < 20 m from a major road following
adjustment to Just meeting the current and alternative standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Standard
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
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
Number of Daily Maximum Exceedances
> 100 ppb
Mean
0
68
0
0
2
0
55
18
197
113
291
227
323
291
0
22
0
0
0
0
15
10
93
69
167
144
235
222
0
Min
0
64
0
0
1
0
33
10
179
86
281
207
314
281
0
5
0
0
0
0
4
1
66
40
138
112
211
197
0
Med
0
66
0
0
3
0
60
21
195
123
288
222
322
288
0
23
0
0
0
0
15
11
104
75
179
155
243
232
0
p98
0
75
0
0
3
0
72
22
216
129
304
253
334
304
0
38
0
0
1
0
26
18
109
91
184
165
250
238
0
p99
0
75
0
0
3
0
72
22
216
129
304
253
334
304
0
38
0
0
1
0
26
18
109
91
184
165
250
238
0
> 150 ppb
Mean
0
3
0
0
0
0
2
0
24
5
103
43
197
113
0
1
0
0
0
0
0
0
7
5
33
23
93
69
0
Min
0
1
0
0
0
0
1
0
14
3
76
29
179
86
0
0
0
0
0
0
0
0
0
0
15
8
66
40
0
Med
0
3
0
0
0
0
3
0
26
6
110
43
195
123
0
1
0
0
0
0
0
0
10
5
32
23
104
75
0
p98
0
5
0
0
0
0
3
0
31
7
123
57
216
129
0
1
0
0
0
0
1
0
12
9
51
38
109
91
0
p99
0
5
0
0
0
0
3
0
31
7
123
57
216
129
0
1
0
0
0
0
1
0
12
9
51
38
109
91
0
> 200 ppb
Mean
0
0
0
0
0
0
0
0
2
0
15
4
55
18
0
0
0
0
0
0
0
0
0
0
5
2
15
10
0
Min
0
0
0
0
0
0
0
0
1
0
7
1
33
10
0
0
0
0
0
0
0
0
0
0
0
0
4
1
0
Med
0
0
0
0
0
0
0
0
3
0
18
4
60
21
0
0
0
0
0
0
0
0
0
0
5
3
15
11
0
p98
0
0
0
0
0
0
0
0
3
0
21
6
72
22
0
0
0
0
0
0
0
0
1
0
9
4
26
18
0
p99
0
0
0
0
0
0
0
0
3
0
21
6
72
22
0
0
0
0
0
0
0
0
1
0
9
4
26
18
0
A-228
-------
Location
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Standard
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
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
2
2
2
2
Number of Daily Maximum Exceedances
>100ppb
Mean
119
0
0
7
3
72
46
185
136
258
228
313
281
2
82
0
0
4
2
108
42
241
195
295
265
324
310
0
202
0
0
Min
96
0
0
1
0
57
29
162
115
244
217
298
260
1
79
0
0
1
1
92
35
221
176
287
256
302
293
0
196
0
0
Med
119
0
0
9
4
76
48
184
138
257
224
320
289
1
80
0
0
4
1
109
44
249
200
295
269
332
318
0
202
0
0
p98
142
0
0
10
6
83
60
210
154
274
244
321
294
3
87
1
0
7
3
122
48
252
210
304
269
337
319
0
208
0
0
p99
142
0
0
10
6
83
60
210
154
274
244
321
294
3
87
1
0
7
3
122
48
252
210
304
269
337
319
0
208
0
0
>150ppb
Mean
16
0
0
0
0
7
3
46
25
113
72
185
136
0
2
0
0
2
1
4
2
52
14
164
95
241
195
0
19
0
0
Min
11
0
0
0
0
1
0
29
15
93
57
162
115
0
1
0
0
1
0
1
1
38
12
142
79
221
176
0
17
0
0
Med
14
0
0
0
0
9
4
48
27
114
76
184
138
0
2
0
0
1
0
4
1
55
14
170
96
249
200
0
19
0
0
p98
22
0
0
0
0
10
6
60
32
131
83
210
154
1
4
0
0
3
2
7
3
63
16
181
109
252
210
0
21
0
0
p99
22
0
0
0
0
10
6
60
32
131
83
210
154
1
4
0
0
3
2
7
3
63
16
181
109
252
210
0
21
0
0
> 200 ppb
Mean
0
0
0
0
0
0
0
7
3
33
17
72
46
0
2
0
0
0
0
2
1
4
2
27
8
108
42
0
0
0
0
Min
0
0
0
0
0
0
0
1
0
21
8
57
29
0
1
0
0
0
0
1
0
1
1
24
6
92
35
0
0
0
0
Med
0
0
0
0
0
0
0
9
4
34
20
76
48
0
1
0
0
0
0
1
1
4
1
25
8
109
44
0
0
0
0
p98
1
0
0
0
0
0
0
10
6
44
23
83
60
0
3
0
0
1
0
3
3
7
3
31
10
122
48
0
0
0
0
p99
1
0
0
0
0
0
0
10
6
44
23
83
60
0
3
0
0
1
0
3
3
7
3
31
10
122
48
0
0
0
0
A-229
-------
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
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Standard
100
100
150
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
2
2
2
2
2
2
2
2
2
2
6
6
6
6
6
6
6
6
6
6
6
6
6
6
2
2
2
2
2
2
2
Number of Daily Maximum Exceedances
>100ppb
Mean
8
3
162
123
282
263
312
306
322
318
3
53
0
0
3
3
50
31
136
107
216
188
270
252
0
87
0
0
3
1
36
Min
6
1
162
117
279
260
311
304
320
316
0
26
0
0
0
0
19
8
59
44
115
91
170
152
0
85
0
0
1
0
33
Med
8
3
162
123
282
263
312
306
322
318
2
48
0
0
2
2
60
39
148
118
224
200
270
256
0
87
0
0
3
1
36
p98
10
5
162
128
285
265
313
307
324
319
8
82
0
0
9
8
65
47
190
150
285
250
335
319
0
89
0
0
4
1
38
p99
10
5
162
128
285
265
313
307
324
319
8
82
0
0
9
8
65
47
190
150
285
250
335
319
0
89
0
0
4
1
38
>150ppb
Mean
0
0
8
3
96
50
216
177
282
263
0
4
0
0
0
0
3
3
23
14
77
54
136
107
0
36
0
0
0
0
3
Min
0
0
6
1
93
43
213
170
279
260
0
0
0
0
0
0
0
0
3
1
29
20
59
44
0
36
0
0
0
0
1
Med
0
0
8
3
96
50
216
177
282
263
0
4
0
0
0
0
2
2
26
14
86
65
148
118
0
36
0
0
0
0
3
p98
0
0
10
5
99
57
218
183
285
265
0
9
0
0
0
0
9
8
33
23
112
73
190
150
0
36
0
0
0
0
4
p99
0
0
10
5
99
57
218
183
285
265
0
9
0
0
0
0
9
8
33
23
112
73
190
150
0
36
0
0
0
0
4
> 200 ppb
Mean
0
0
0
0
8
3
60
35
162
123
0
0
0
0
0
0
0
0
3
3
16
8
50
31
0
7
0
0
0
0
0
Min
0
0
0
0
6
1
59
29
162
117
0
0
0
0
0
0
0
0
0
0
1
1
19
8
0
6
0
0
0
0
0
Med
0
0
0
0
8
3
60
35
162
123
0
0
0
0
0
0
0
0
2
2
17
7
60
39
0
7
0
0
0
0
0
p98
0
0
0
0
10
5
60
40
162
128
0
1
0
0
0
0
1
1
9
8
26
16
65
47
0
8
0
0
0
0
0
p99
0
0
0
0
10
5
60
40
162
128
0
1
0
0
0
0
1
1
9
8
26
16
65
47
0
8
0
0
0
0
0
A-230
-------
Location
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
New York
New York
New York
New York
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Standard
150
200
200
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
5
5
5
5
5
5
5
5
5
5
Number of Daily Maximum Exceedances
>100ppb
Mean
27
75
66
98
88
122
109
1
38
0
0
4
1
71
34
185
134
274
214
312
283
2
61
0
0
5
2
82
48
182
150
Min
25
72
64
89
85
110
99
0
17
0
0
4
0
60
33
175
132
260
201
296
269
0
0
0
0
0
0
0
0
7
2
Med
27
75
66
98
88
122
109
1
38
0
0
4
1
71
34
185
134
274
214
312
283
2
83
0
0
6
2
132
67
285
241
p98
28
77
68
107
91
133
119
1
59
0
0
4
2
81
35
194
135
288
227
327
297
3
126
0
0
11
6
143
97
311
256
p99
28
77
68
107
91
133
119
1
59
0
0
4
2
81
35
194
135
288
227
327
297
3
126
0
0
11
6
143
97
311
256
>150ppb
Mean
1
20
12
48
39
75
66
0
2
0
0
0
0
4
1
37
10
114
59
185
134
0
3
0
0
0
0
5
2
43
21
Min
0
18
9
43
36
72
64
0
0
0
0
0
0
4
0
35
9
112
52
175
132
0
0
0
0
0
0
0
0
0
0
Med
1
20
12
48
39
75
66
0
2
0
0
0
0
4
1
37
10
114
59
185
134
0
2
0
0
0
0
6
2
55
25
p98
1
22
14
52
42
77
68
0
3
0
0
0
0
4
2
38
11
115
66
194
135
0
8
0
0
1
0
11
6
92
44
p99
1
22
14
52
42
77
68
0
3
0
0
0
0
4
2
38
11
115
66
194
135
0
8
0
0
1
0
11
6
92
44
> 200 ppb
Mean
0
3
1
12
9
36
27
0
0
0
0
0
0
0
0
4
1
25
7
71
34
0
0
0
0
0
0
0
0
5
2
Min
0
1
0
9
6
33
25
0
0
0
0
0
0
0
0
4
0
24
4
60
33
0
0
0
0
0
0
0
0
0
0
Med
0
3
1
12
9
36
27
0
0
0
0
0
0
0
0
4
1
25
7
71
34
0
0
0
0
0
0
0
0
6
2
p98
0
4
1
14
12
38
28
0
0
0
0
0
0
0
0
4
2
26
9
81
35
0
1
0
0
0
0
2
1
11
6
p99
0
4
1
14
12
38
28
0
0
0
0
0
0
0
0
4
2
26
9
81
35
0
1
0
0
0
0
2
1
11
6
A-231
-------
Location
Phoenix
Phoenix
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
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
Standard
250
250
300
300
As is
Current Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Number of Daily Maximum Exceedances
>100ppb
Mean
225
205
263
241
0
109
0
0
1
0
49
41
158
145
245
233
298
298
0
60
0
0
4
0
63
Min
41
18
120
73
0
30
0
0
0
0
37
33
135
119
228
218
285
285
0
40
0
0
2
0
30
Med
335
315
349
341
0
119
0
0
1
0
44
38
143
130
239
222
289
289
0
63
0
0
2
0
59
p98
359
340
365
364
0
178
0
0
1
0
65
56
187
178
263
254
319
319
0
76
0
0
13
1
92
p99
359
340
365
364
0
178
0
0
1
0
65
56
187
178
263
254
319
319
0
76
0
0
13
1
92
>150ppb
Mean
121
82
182
150
0
11
0
0
0
0
1
0
20
12
86
74
158
145
0
4
0
0
0
0
4
Min
0
0
7
2
0
0
0
0
0
0
0
0
16
11
66
53
135
119
0
0
0
0
0
0
2
Med
197
132
285
241
0
11
0
0
0
0
1
0
19
11
78
71
143
130
0
2
0
0
0
0
2
p98
203
143
311
256
0
28
0
0
0
0
1
0
24
15
107
98
187
178
0
11
0
0
0
0
13
p99
203
143
311
256
0
28
0
0
0
0
1
0
24
15
107
98
187
178
0
11
0
0
0
0
13
> 200 ppb
Mean
28
12
82
48
0
0
0
0
0
0
0
0
1
0
10
6
49
41
0
0
0
0
0
0
0
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
5
37
33
0
0
0
0
0
0
0
Med
36
16
132
67
0
0
0
0
0
0
0
0
1
0
9
6
44
38
0
0
0
0
0
0
0
p98
61
23
143
97
0
0
0
0
0
0
0
0
1
0
12
7
65
56
0
1
0
0
0
0
1
p99
61
23
143
97
0
0
0
0
0
0
0
0
1
0
12
7
65
56
0
1
0
0
0
0
1
A-232
-------
Location
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Standard
150
200
200
250
250
300
300
Percentile
99
98
99
98
99
98
99
Site-
Years
5
5
5
5
5
5
5
Number of Daily Maximum Exceedances
>100ppb
Mean
17
173
85
247
173
293
247
Min
5
122
43
197
122
256
197
Med
12
155
76
224
155
277
224
p98
43
222
118
307
222
336
307
p99
43
222
118
307
222
336
307
>150ppb
Mean
0
32
6
93
36
173
85
Min
0
12
2
54
13
122
43
Med
0
30
4
80
36
155
76
p98
1
56
17
129
62
222
118
p99
1
56
17
129
62
222
118
> 200 ppb
Mean
0
4
0
20
4
63
17
Min
0
2
0
6
2
30
5
Med
0
2
0
14
2
59
12
p98
0
13
1
47
13
92
43
p99
0
13
1
47
13
92
43
A-233
-------
Table A-132. Estimated number of exceedances of 1-hour concentration levels (250 and 300 ppb) for
monitors < 20 m from a major road following adjustment to just meeting the current and alternative
standards, 2004-2006 air quality.
Location
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Standard
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
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
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
3
3
3
3
3
3
3
3
3
3
3
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
0
0
0
0
0
0
0
0
0
2
0
11
3
0
0
0
0
0
0
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
1
0
7
3
Min
0
0
0
0
0
0
0
0
0
0
1
0
4
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
1
0
Med
0
0
0
0
0
0
0
0
0
0
3
0
11
3
0
0
0
0
0
0
0
0
0
0
0
0
3
2
0
0
0
0
0
0
0
0
1
0
9
4
p98
0
0
0
0
0
0
0
0
0
0
3
0
18
4
0
0
0
0
0
0
0
0
0
0
1
0
9
2
0
0
0
0
0
0
0
0
1
0
10
6
p99
0
0
0
0
0
0
0
0
0
0
3
0
18
4
0
0
0
0
0
0
0
0
0
0
1
0
9
2
0
0
0
0
0
0
0
0
1
0
10
6
> 300 ppb
Mean
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
1
0
Min
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
Med
0
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
0
0
0
0
0
0
1
0
p98
0
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
1
0
0
0
0
0
0
0
0
0
0
0
2
0
p99
0
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
1
0
0
0
0
0
0
0
0
0
0
0
2
0
A-234
-------
Location
Cleveland
Cleveland
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Las Vegas
Las Vegas
Las Vegas
Las Vegas
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
Standard
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
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
Site-
Years
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
6
6
6
6
6
6
6
6
6
6
6
6
Number of Daily Maximum Exceedances
> 250 ppb
Mean
25
11
0
0
0
0
0
0
1
0
2
2
3
2
16
5
0
0
0
0
0
0
0
0
0
0
7
3
43
24
0
0
0
0
0
0
0
0
1
0
3
3
Min
15
6
0
0
0
0
0
0
0
0
1
1
1
1
15
4
0
0
0
0
0
0
0
0
0
0
4
1
37
19
0
0
0
0
0
0
0
0
0
0
0
0
Med
27
14
0
0
0
0
0
0
0
0
1
1
3
1
16
5
0
0
0
0
0
0
0
0
0
0
7
3
43
24
0
0
0
0
0
0
0
0
0
0
2
2
p98
32
14
0
1
0
0
0
0
2
1
3
3
6
3
18
7
0
0
0
0
0
0
0
0
0
0
9
5
49
29
0
0
0
0
0
0
0
0
2
1
9
8
p99
32
14
0
1
0
0
0
0
2
1
3
3
6
3
18
7
0
0
0
0
0
0
0
0
0
0
9
5
49
29
0
0
0
0
0
0
0
0
2
1
9
8
> 300 ppb
Mean
7
3
0
0
0
0
0
0
0
0
2
1
2
2
4
2
0
0
0
0
0
0
0
0
0
0
1
0
8
3
0
0
0
0
0
0
0
0
0
0
1
0
Min
1
0
0
0
0
0
0
0
0
0
1
0
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
6
1
0
0
0
0
0
0
0
0
0
0
0
0
Med
9
4
0
0
0
0
0
0
0
0
1
0
1
1
4
1
0
0
0
0
0
0
0
0
0
0
1
0
8
3
0
0
0
0
0
0
0
0
0
0
0
0
p98
10
6
0
1
0
0
0
0
1
0
3
2
3
3
7
3
0
0
0
0
0
0
0
0
0
0
1
0
10
5
0
0
0
0
0
0
0
0
0
0
2
1
p99
10
6
0
1
0
0
0
0
1
0
3
2
3
3
7
3
0
0
0
0
0
0
0
0
0
0
1
0
10
5
0
0
0
0
0
0
0
0
0
0
2
1
A-235
-------
Location
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
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
New York
New York
New York
New York
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Standard
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
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
Site-
Years
6
6
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
5
5
5
5
5
5
5
5
5
5
5
5
Number of Daily Maximum Exceedances
> 250 ppb
Mean
13
6
0
1
0
0
0
0
0
0
0
0
3
1
10
7
0
0
0
0
0
0
0
0
1
0
4
1
19
5
0
0
0
0
0
0
0
0
1
0
5
2
Min
1
1
0
0
0
0
0
0
0
0
0
0
1
0
7
4
0
0
0
0
0
0
0
0
0
0
4
0
18
4
0
0
0
0
0
0
0
0
0
0
0
0
Med
13
5
0
1
0
0
0
0
0
0
0
0
3
1
10
7
0
0
0
0
0
0
0
0
1
0
4
1
19
5
0
0
0
0
0
0
0
0
0
0
6
2
p98
23
13
0
1
0
0
0
0
0
0
0
0
4
1
12
9
0
0
0
0
0
0
0
0
1
0
4
2
19
5
0
0
0
0
0
0
0
0
2
2
11
5
p99
23
13
0
1
0
0
0
0
0
0
0
0
4
1
12
9
0
0
0
0
0
0
0
0
1
0
4
2
19
5
0
0
0
0
0
0
0
0
2
2
11
5
> 300 ppb
Mean
3
3
0
0
0
0
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
0
0
1
0
4
1
0
0
0
0
0
0
0
0
0
0
2
0
Min
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
4
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
2
2
0
0
0
0
0
0
0
0
0
0
0
0
3
1
0
0
0
0
0
0
0
0
0
0
1
0
4
1
0
0
0
0
0
0
0
0
0
0
2
0
p98
9
8
0
0
0
0
0
0
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
0
0
2
0
4
2
0
0
0
0
0
0
0
0
1
0
3
2
p99
9
8
0
0
0
0
0
0
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
0
0
2
0
4
2
0
0
0
0
0
0
0
0
1
0
3
2
A-236
-------
Location
Phoenix
Phoenix
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
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
Standard
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
300
300
As is
Current
Std
50
50
100
100
150
150
200
200
250
250
300
300
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
Site-
Years
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Number of Daily Maximum Exceedances
> 250 ppb
Mean
21
9
0
0
0
0
0
0
0
0
0
0
0
0
6
4
0
0
0
0
0
0
0
0
0
0
4
0
15
3
Min
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
2
0
4
1
Med
25
13
0
0
0
0
0
0
0
0
0
0
0
0
6
4
0
0
0
0
0
0
0
0
0
0
2
0
10
2
p98
44
20
0
0
0
0
0
0
0
0
0
0
0
0
7
6
0
0
0
0
0
0
0
0
1
0
13
1
38
11
p99
44
20
0
0
0
0
0
0
0
0
0
0
0
0
7
6
0
0
0
0
0
0
0
0
1
0
13
1
38
11
> 300 ppb
Mean
5
2
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
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
0
0
0
0
0
2
0
Med
6
2
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
2
0
p98
11
6
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
13
1
p99
11
6
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
13
1
A-237
-------
Table A-133. Estimated number of exceedances of 1-hour concentration levels (100,150, and 200 ppb) on-roads following adjustment to just meeting
the current and alternative standards, 2004-2006 air quality and an on-road road adjustment factor.
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Standard
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
Site-Years
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
800
Number of Daily Maximum Exceedances
> 100 ppb
Mean
17
193
3
2
58
45
133
116
181
167
2
95
0
0
19
8
84
52
153
114
36
189
2
1
59
47
176
157
259
244
Min
0
4
0
0
0
0
0
0
3
1
0
4
0
0
0
0
4
1
32
14
0
25
0
0
0
0
20
14
94
64
Med
2
259
0
0
39
21
162
131
241
219
0
91
0
0
8
2
78
42
151
111
20
187
0
0
43
31
169
148
261
248
p98
114
337
29
21
206
185
295
276
328
319
18
207
1
0
92
54
189
150
249
215
148
329
24
15
201
181
320
313
352
348
p99
120
341
31
23
218
198
304
288
334
326
21
221
2
1
99
65
207
162
267
238
161
339
30
24
220
193
329
320
355
353
> 150 ppb
Mean
2
126
0
0
13
8
58
45
111
94
0
25
0
0
1
0
19
8
59
34
5
69
0
0
10
8
59
47
138
119
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
0
0
9
7
Med
0
148
0
0
1
0
39
21
123
91
0
15
0
0
0
0
8
2
50
24
0
54
0
0
2
1
43
31
127
108
p98
26
285
4
1
93
64
206
185
266
249
1
104
0
0
12
3
92
54
161
119
41
225
0
0
63
54
201
181
302
291
p99
27
300
5
2
100
75
218
198
281
262
1
109
0
0
13
5
99
65
174
129
53
241
0
0
78
67
220
193
309
296
> 200 ppb
Mean
0
72
0
0
3
2
22
15
58
45
0
6
0
0
0
0
3
1
19
8
1
22
0
0
2
1
18
14
59
47
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
Med
0
57
0
0
0
0
3
1
39
21
0
1
0
0
0
0
0
0
8
2
0
8
0
0
0
0
6
4
43
31
p98
6
225
0
0
29
21
126
103
206
185
0
44
0
0
1
0
26
10
92
54
7
117
0
0
24
15
98
80
201
181
p99
7
237
0
0
31
23
141
110
218
198
0
50
0
0
2
1
33
12
99
65
18
126
0
0
30
24
109
93
220
193
A-238
-------
Location
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
El Paso
Jacksonville
Jacksonville
Standard
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
Site-Years
300
300
300
300
300
300
300
300
300
300
600
600
600
600
600
600
600
600
600
600
1200
1200
1200
1200
1200
1200
1200
1200
1200
1200
200
200
Number of Daily Maximum Exceedances
Ł100 ppb
Mean
63
257
9
5
148
106
263
239
296
286
20
287
13
10
165
146
273
261
313
307
24
281
5
3
108
75
229
199
281
264
11
295
Min
2
135
0
0
20
10
170
114
241
217
0
166
0
0
29
18
163
141
237
234
0
137
0
0
3
2
63
26
137
97
0
229
Med
49
259
2
1
151
100
264
243
308
287
9
286
4
2
166
143
271
260
318
309
12
285
1
0
102
62
235
205
286
271
5
295
p98
190
314
41
23
263
236
315
309
330
323
90
350
57
52
293
275
341
337
355
354
114
349
32
20
254
225
323
309
349
340
48
341
p99
195
320
45
25
269
242
321
313
332
327
103
352
66
53
296
284
346
342
359
357
143
354
37
23
263
234
327
317
353
346
59
342
> 150 ppb
Mean
10
134
1
0
38
23
148
106
239
204
2
189
1
1
50
40
165
146
249
235
3
198
0
0
23
13
108
75
197
162
2
216
Min
0
12
0
0
0
0
20
10
122
66
0
35
0
0
0
0
29
18
117
90
0
22
0
0
0
0
3
2
26
12
0
114
Med
4
133
0
0
23
11
151
100
243
209
0
193
0
0
37
27
166
143
251
237
0
205
0
0
11
5
102
62
205
166
2
223
p98
47
260
5
2
132
87
263
236
309
294
20
300
12
9
165
146
293
275
331
327
20
309
4
2
113
72
254
225
308
289
5
309
p99
52
264
9
3
150
95
269
242
313
299
24
315
18
14
180
163
296
284
337
332
23
320
4
2
137
86
263
234
317
296
6
318
> 200 ppb
Mean
2
52
0
0
9
5
61
38
148
106
0
95
0
0
13
10
77
62
165
146
0
109
0
0
5
3
39
23
108
75
1
134
Min
0
0
0
0
0
0
1
0
20
10
0
3
0
0
0
0
1
0
29
18
0
3
0
0
0
0
0
0
3
2
0
31
Med
0
41
0
0
2
1
48
23
151
100
0
86
0
0
4
2
66
50
166
143
0
102
0
0
1
0
23
12
102
62
1
133
p98
11
164
0
0
41
23
180
132
263
236
3
222
1
0
57
52
203
186
293
275
4
255
0
0
32
20
164
113
254
225
4
260
p99
14
174
0
0
45
25
192
150
269
242
6
258
1
1
66
53
221
204
296
284
4
265
1
0
37
23
178
137
263
234
4
279
A-239
-------
Location
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
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Miami
Miami
Miami
Miami
Standard
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
Site-Years
200
200
200
200
200
200
200
200
1100
1100
1100
1100
1100
1100
1100
1100
1100
1100
5400
5400
5400
5400
5400
5400
5400
5400
5400
5400
400
400
400
400
Number of Daily Maximum Exceedances
Ł100 ppb
Mean
9
2
127
39
241
135
293
218
15
177
6
4
83
70
161
145
210
198
38
160
2
1
54
40
155
132
227
208
6
202
3
2
Min
0
0
30
1
145
33
229
118
0
2
0
0
0
0
1
0
6
3
0
2
0
0
0
0
2
0
13
8
0
100
0
0
Med
5
2
124
27
243
131
294
223
0
171
0
0
39
24
146
118
224
205
25
163
0
0
41
27
155
127
240
217
1
201
0
0
p98
39
5
256
145
320
265
339
309
133
343
77
71
305
288
342
337
350
348
150
314
18
12
188
153
314
296
349
342
46
284
24
18
p99
50
5
269
158
327
279
340
318
148
346
79
73
312
304
343
339
350
349
169
326
24
15
208
176
325
310
352
348
46
286
24
21
> 150 ppb
Mean
2
1
35
6
127
39
211
102
2
99
1
0
22
16
83
70
138
123
6
58
0
0
10
6
54
40
122
100
0
130
0
0
Min
0
0
0
0
30
1
104
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
30
0
0
Med
2
1
23
4
124
27
214
95
0
55
0
0
1
0
39
24
108
83
1
45
0
0
2
1
41
27
115
91
0
139
0
0
p98
5
4
138
25
256
145
302
232
54
317
17
6
174
142
305
288
335
329
43
187
1
1
62
44
188
153
285
258
6
197
1
1
p99
5
4
151
29
269
158
312
253
64
324
19
8
187
151
312
304
336
333
55
215
2
1
75
55
208
176
304
281
6
204
1
1
> 200 ppb
Mean
1
0
9
2
55
10
127
39
0
50
0
0
6
4
36
26
83
70
1
18
0
0
2
1
17
11
54
40
0
77
0
0
Min
0
0
0
0
1
0
30
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
Med
1
0
5
2
42
5
124
27
0
11
0
0
0
0
5
2
39
24
0
8
0
0
0
0
6
3
41
27
0
76
0
0
p98
4
3
39
5
169
47
256
145
6
260
0
0
77
71
219
191
305
288
11
90
0
0
18
12
85
68
188
153
1
160
1
0
p99
4
3
50
5
193
59
269
158
8
275
2
0
79
73
239
206
312
304
14
105
0
0
24
15
104
79
208
176
1
163
1
0
A-240
-------
Location
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
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Standard
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
Site-Years
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
900
900
900
900
900
900
Number of Daily Maximum Exceedances
Ł100 ppb
Mean
56
46
128
117
182
171
35
147
3
2
75
45
192
149
264
232
22
232
5
1
112
53
237
172
295
256
77
284
4
2
146
103
Min
0
0
24
17
75
63
0
0
0
0
0
0
2
0
38
20
0
54
0
0
5
0
76
24
186
105
0
127
0
0
3
0
Med
47
34
135
123
188
178
23
148
0
0
62
32
204
151
284
250
10
242
1
0
102
39
245
178
302
264
53
296
0
0
140
84
p98
144
134
194
187
260
248
149
308
32
17
225
175
326
305
350
339
101
321
37
14
258
187
325
297
339
329
275
357
26
12
320
293
p99
148
140
199
192
262
250
171
321
34
23
248
196
333
312
353
343
130
326
42
14
276
214
329
311
345
332
293
359
35
14
332
312
> 150 ppb
Mean
13
9
56
46
106
94
5
45
0
0
14
7
75
45
157
115
2
110
0
0
24
8
112
53
204
134
10
124
0
0
28
16
Min
0
0
0
0
12
7
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
5
0
51
7
0
2
0
0
0
0
Med
4
2
47
34
111
96
1
31
0
0
6
2
62
32
162
108
0
99
0
0
10
1
102
39
213
130
1
111
0
0
7
2
p98
72
62
144
134
178
170
40
188
3
1
73
49
225
175
310
278
20
269
4
0
109
49
258
187
313
277
57
307
1
0
159
101
p99
77
65
148
140
183
174
45
202
4
1
88
53
248
196
317
294
22
280
4
1
139
53
276
214
319
296
71
325
1
0
171
107
> 200 ppb
Mean
3
2
21
16
56
46
1
13
0
0
3
2
25
13
75
45
0
43
0
0
5
1
39
14
112
53
1
38
0
0
4
2
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
5
0
0
0
0
0
0
0
Med
0
0
11
7
47
34
0
4
0
0
0
0
13
4
62
32
0
27
0
0
1
0
27
4
102
39
0
11
0
0
0
0
p98
24
18
97
81
144
134
13
75
0
0
32
17
120
67
225
175
3
188
0
0
37
14
155
77
258
187
7
186
0
0
26
12
p99
24
21
101
92
148
140
15
89
1
0
34
23
135
80
248
196
3
202
0
0
42
14
189
83
276
214
8
211
0
0
35
14
A-241
-------
Location
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
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Washington DC
Standard
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
Site-Years
900
900
900
900
300
300
300
300
300
300
300
300
300
300
400
400
400
400
400
400
400
400
400
400
1700
1700
1700
1700
1700
1700
1700
1700
Number of Daily Maximum Exceedances
Ł100 ppb
Mean
299
268
338
328
51
306
13
13
63
60
209
201
298
294
15
233
4
3
107
98
226
217
287
282
21
207
5
2
96
50
200
148
Min
151
95
227
204
0
153
0
0
0
0
38
30
217
204
0
82
0
0
3
2
96
82
166
162
0
10
0
0
0
0
2
0
Med
309
280
347
337
44
331
0
0
48
46
214
210
300
290
5
228
0
0
100
90
221
211
291
283
7
238
1
0
81
29
232
157
p98
358
353
363
361
160
360
45
44
187
182
328
328
356
354
76
341
35
31
238
227
331
328
357
355
119
340
37
15
269
209
337
309
p99
360
357
364
363
160
361
45
45
187
183
335
334
359
356
83
347
41
35
249
240
340
340
357
356
143
345
42
16
283
226
345
322
> 150 ppb
Mean
146
103
264
225
17
192
4
4
20
19
63
60
160
151
1
121
0
0
23
20
107
98
194
186
2
102
0
0
22
8
96
50
Min
3
0
92
39
0
44
0
0
0
0
0
0
7
6
0
1
0
0
0
0
3
2
62
49
0
0
0
0
0
0
0
0
Med
140
84
277
236
2
187
0
0
5
3
48
46
157
147
0
113
0
0
12
10
100
90
190
181
0
88
0
0
8
1
81
29
p98
320
293
352
345
68
331
42
42
78
78
187
182
306
299
20
280
9
4
105
96
238
227
308
305
20
270
5
2
126
52
269
209
p99
332
312
356
352
70
340
42
42
79
79
187
183
314
306
24
300
10
9
108
101
249
240
320
317
22
289
6
2
150
66
283
226
> 200 ppb
Mean
49
30
146
103
12
87
1
1
13
13
26
24
63
60
0
50
0
0
4
3
39
33
107
98
0
41
0
0
5
2
36
14
Min
0
0
3
0
0
1
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
Med
19
7
140
84
0
61
0
0
0
0
17
15
48
46
0
33
0
0
0
0
28
22
100
90
0
21
0
0
1
0
17
3
p98
222
165
320
293
44
260
20
15
45
44
95
91
187
182
2
198
0
0
35
31
135
124
238
227
4
176
1
0
37
15
173
85
p99
245
184
332
312
44
278
20
15
45
45
95
91
187
183
5
221
0
0
41
35
142
133
249
240
6
202
1
0
42
16
192
110
A-242
-------
Location
Washington DC
Washington DC
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not MSA
Standard
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
Site-Years
1700
1700
56500
56500
56500
56500
56500
56500
56500
56500
56500
56500
11600
11600
11600
11600
11600
11600
11600
11600
11600
11600
Number of Daily Maximum Exceedances
Ł100 ppb
Mean
260
220
10
143
0
0
20
14
80
63
143
125
4
124
0
0
6
2
28
13
60
34
Min
42
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
299
256
1
143
0
0
5
2
62
43
144
119
0
100
0
0
0
0
4
1
22
6
p98
358
346
79
322
5
2
123
96
264
237
322
311
43
331
2
2
61
23
173
113
249
193
p99
362
352
100
336
8
5
152
121
288
263
337
328
65
339
6
3
86
35
198
134
269
216
> 150 ppb
Mean
171
117
1
59
0
0
2
1
20
14
57
44
1
62
0
0
1
0
6
2
19
8
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
191
108
0
36
0
0
0
0
5
2
36
23
0
24
0
0
0
0
0
0
2
0
p98
323
288
12
233
0
0
26
17
123
96
228
201
7
257
1
0
10
3
61
23
141
80
p99
335
298
18
257
1
0
37
25
152
121
255
228
13
272
2
2
17
7
86
35
164
104
> 200 ppb
Mean
96
50
0
22
0
0
0
0
5
3
20
14
0
29
0
0
0
0
1
0
6
2
Min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
81
29
0
5
0
0
0
0
0
0
5
2
0
4
0
0
0
0
0
0
0
0
p98
269
209
2
138
0
0
5
2
44
30
123
96
2
180
0
0
2
2
18
6
61
23
p99
283
226
3
163
0
0
8
5
60
43
152
121
5
201
1
1
6
3
26
11
86
35
A-243
-------
Table A-134. Estimated number of exceedances of 1-
following adjustment to just meeting the current and
road road adjustment factor.
hour concentration levels (250 and 300 ppb) on-roads
alternative standards, 2004-2006 air quality and an on-
Location
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
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
Standard
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
Site-Years
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
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
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
38
0
0
1
0
8
5
28
20
0
1
0
0
0
0
1
0
6
2
0
8
0
0
0
0
6
4
25
19
0
21
0
0
3
1
25
13
75
49
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
4
1
Med
0
15
0
0
0
0
0
0
6
3
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
11
6
0
10
0
0
0
0
11
4
64
34
p98
1
169
0
0
13
8
61
44
145
119
0
11
0
0
0
0
5
1
39
15
1
54
0
0
5
2
48
39
121
99
3
79
0
0
16
7
93
59
210
158
p99
1
179
0
0
15
8
74
49
160
131
0
12
0
0
0
0
7
2
51
19
2
63
0
0
11
6
55
45
132
111
3
91
0
0
18
10
103
64
216
174
> 300 ppb
Mean
0
19
0
0
0
0
3
2
13
8
0
0
0
0
0
0
0
0
1
0
0
3
0
0
0
0
2
1
10
8
0
8
0
0
1
0
9
5
38
23
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
Med
0
3
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
1
0
1
0
0
0
0
2
1
23
11
p98
0
117
0
0
4
1
29
21
93
64
0
2
0
0
0
0
1
0
12
3
0
33
0
0
0
0
24
15
63
54
0
38
0
0
5
2
41
23
132
87
p99
0
127
0
0
5
2
31
23
100
75
0
2
0
0
0
0
2
1
13
5
0
38
0
0
0
0
30
24
78
67
0
43
0
0
9
3
45
25
150
95
A-244
-------
Location
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
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
Los Angeles
Los Angeles
Standard
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
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
600
600
600
600
600
600
600
600
600
600
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
5400
5400
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
42
0
0
4
3
32
24
96
79
0
52
0
0
1
0
13
7
50
32
1
72
1
0
3
1
22
3
68
15
0
21
0
0
2
1
14
10
46
34
0
6
Min
0
0
0
0
0
0
0
0
4
1
0
0
0
0
0
0
0
0
0
0
0
6
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
Med
0
28
0
0
0
0
20
12
84
66
0
35
0
0
0
0
6
2
32
18
0
61
0
0
4
1
9
4
58
5
0
1
0
0
0
0
0
0
10
5
0
1
p98
0
147
0
0
33
25
130
101
231
206
1
191
0
0
11
5
72
47
189
141
3
202
3
2
9
4
96
12
196
67
0
170
0
0
52
31
123
89
247
219
2
45
p99
0
185
0
0
40
30
145
113
246
226
1
203
0
0
14
8
92
52
201
163
3
227
3
2
10
4
112
13
221
81
1
181
0
0
59
49
138
100
265
235
3
55
> 300 ppb
Mean
0
18
0
0
1
1
13
10
50
40
0
24
0
0
0
0
5
3
23
13
1
38
0
0
2
1
9
2
35
6
0
9
0
0
1
0
6
4
22
16
0
2
Min
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
0
Med
0
7
0
0
0
0
4
2
37
27
0
11
0
0
0
0
1
0
11
5
0
26
0
0
2
1
5
2
23
4
0
0
0
0
0
0
0
0
1
0
0
0
p98
0
73
0
0
12
9
57
52
165
146
0
119
0
0
4
2
32
20
113
72
3
141
2
1
5
4
39
5
138
25
0
86
0
0
17
6
77
71
174
142
1
21
p99
0
103
0
0
18
14
66
53
180
163
0
133
0
0
4
2
37
23
137
86
3
158
2
1
5
4
50
5
151
29
0
96
0
0
19
8
79
73
187
151
1
28
A-245
-------
Location
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
Phoenix
Phoenix
Phoenix
Standard
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
Site-Years
5400
5400
5400
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
900
900
900
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
0
0
0
6
3
22
15
0
42
0
0
1
0
7
5
27
21
0
4
0
0
1
0
9
4
33
17
0
16
0
0
1
0
14
4
52
20
0
11
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
Med
0
0
0
0
1
0
11
5
0
32
0
0
0
0
2
1
15
11
0
0
0
0
0
0
2
0
20
8
0
5
0
0
0
0
4
0
38
8
0
1
0
p98
0
0
4
2
42
31
102
80
0
126
0
0
7
5
59
44
108
97
3
41
0
0
12
5
54
36
140
88
0
101
0
0
11
2
73
32
187
94
1
74
0
p99
0
0
6
3
54
38
126
96
0
128
0
0
8
6
59
46
110
101
4
45
0
0
14
8
61
40
158
99
0
115
0
0
14
2
83
36
213
119
1
87
0
> 300 ppb
Mean
0
0
0
0
2
1
10
6
0
22
0
0
0
0
3
2
13
9
0
2
0
0
0
0
3
2
14
7
0
6
0
0
0
0
5
1
24
8
0
3
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
Med
0
0
0
0
0
0
2
1
0
11
0
0
0
0
0
0
4
2
0
0
0
0
0
0
0
0
6
2
0
1
0
0
0
0
1
0
10
1
0
0
0
p98
0
0
1
1
18
12
62
44
0
98
0
0
1
1
24
18
72
62
1
20
0
0
3
1
32
17
73
49
0
53
0
0
4
0
37
14
109
49
0
21
0
p99
0
0
2
1
24
15
75
55
0
100
0
0
1
1
24
21
77
65
1
26
0
0
4
1
34
23
88
53
0
58
0
0
4
1
42
14
139
53
0
22
0
A-246
-------
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
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
DC
Washington
Standard
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
900
900
900
900
900
900
900
300
300
300
300
300
300
300
300
300
300
400
400
400
400
400
400
400
400
400
400
1700
1700
1700
1700
1700
1700
1700
1700
1700
1700
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
1
0
16
8
65
41
7
40
0
0
9
8
16
16
31
29
0
19
0
0
1
1
13
11
51
45
0
15
0
0
1
0
13
5
47
20
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
2
1
35
13
0
43
0
0
0
0
1
1
23
22
0
8
0
0
0
0
4
3
41
35
0
3
0
0
0
0
3
1
26
7
p98
0
5
2
101
44
256
196
43
166
0
0
43
43
61
57
105
101
0
124
0
0
17
17
67
61
159
147
1
93
0
0
14
4
81
34
202
115
p99
0
7
2
107
62
274
227
43
167
0
0
43
43
64
62
105
101
0
140
0
0
22
20
75
69
167
162
2
110
0
0
15
6
102
36
216
141
> 300 ppb
Mean
0
0
0
4
2
28
16
3
24
0
0
4
4
13
13
20
19
0
8
0
0
0
0
4
3
23
20
0
6
0
0
0
0
5
2
22
8
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
7
2
0
17
0
0
0
0
0
0
5
3
0
0
0
0
0
0
0
0
12
10
0
1
0
0
0
0
1
0
8
1
p98
0
1
0
26
12
159
101
40
69
0
0
42
42
45
44
78
78
0
80
0
0
9
4
35
31
105
96
0
43
0
0
5
2
37
15
126
52
p99
0
1
0
35
14
171
107
40
69
0
0
42
42
45
45
79
79
0
87
0
0
10
9
41
35
108
101
0
56
0
0
6
2
42
16
150
66
A-247
-------
Location
DC
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other MSA
Other 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
Other Not
MSA
Standard
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
Site-Years
56500
56500
56500
56500
56500
56500
56500
56500
56500
56500
11600
11600
11600
11600
11600
11600
11600
11600
11600
11600
Number of Daily Maximum Exceedances
> 250 ppb
Mean
0
8
0
0
0
0
1
1
7
4
0
14
0
0
0
0
0
0
2
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
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
p98
0
70
0
0
1
1
15
9
58
41
2
119
0
0
2
0
6
2
23
8
p99
1
90
0
0
2
1
23
14
76
56
2
137
1
0
3
2
11
5
34
15
> 300 ppb
Mean
0
3
0
0
0
0
0
0
2
1
0
6
0
0
0
0
0
0
1
0
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
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
32
0
0
0
0
5
2
26
17
0
66
0
0
1
0
2
2
10
3
p99
0
45
0
0
1
0
8
5
37
25
2
86
0
0
2
2
6
3
17
7
A-248
-------
A-249
-------
A-9.4 Comparison of Historical and Recent Ambient Air Quality
(As Is)
This section presents the preliminary results using the ambient monitoring data obtained from
AQS that were separated into two six-year groups, one representing historical data (1995-2000)
and the other representing more recent data (2001-2006). This initial analysis performed in the
1st draft REA used the total number of exceedances of the potential benchmark levels of 150,
200, 250, and 300 ppb, for monitors sited >100 m and <100 m from a major road. It differs from
the analyses performed in Chapter 7 of the final REA where the number of times the daily
maximum exceeded the potential benchmark levels was recorded (including a benchmark level of
100 ppb) for different monitor road categories (>100 m, 20 m< x <100 m, and <20 m from a
major road) and for two three-year groups (2001-2003 and 2004-2006). It is presented here
mainly for comparison of the two six-year groups of data, because the historical data set was not
re-analyzed using the added benchmark level, was not separated into three monitor-to-major road
categories, and did not calculate the number of daily maximum exceedances in a year.
A summary of the descriptive statistics for the annual average ambient NC>2 concentrations at
each selected location is provided in Tables A-l 11 and A-l 12 for monitors sited >100 m and
<100 m from a major road, respectively. None of the locations contained a measured
exceedance of the current annual average standard of 0.053 ppm at any monitor. The highest
observed annual average NC>2 concentrations were measured in Los Angeles and Phoenix during
the historical monitoring period and considering the monitors >100 m from a major road. There
were a fewer number of locations with monitors sited <100 m of a major road, however in most
of the locations where comparative monitoring data were available, the annual average NC>2
concentrations were greater at the monitors <100 m of a major road (in 23 of 27 possible
location/year-group combinations). Four locations (Denver, Los Angeles, Phoenix, St. Louis)
contained higher concentrations at the more distant monitors for one year-group when compared
with the monitors <100 m from a major road. Where concentrations were greater at the near
road monitors, the concentrations were on average about 20-25% higher when compared with the
more distant monitors in each corresponding location, regardless of year-group. A comparison
of the year-group of data within each monitor site-group indicates that the more recent
monitoring concentrations were lower, on average by about 13-15%. These average trends in
concentration across year-group and monitor site group were generally observed across all
percentiles of the distribution.
Table A-135. Monitoring site-years and annual average NO2 concentrations for two monitoring periods,
historical and recent air quality data (as is) using monitors sited >100 m of a major road.
Location
Atlanta
Boston
Chicago
Cleveland
Colorado Springs
Denver
1995-2000
Site-
Years
24
18
28
5
25
7
Annual Mean (ppb) 1
mean min med p95 p98 p99
14
18
20
19
16
22
5
5
9
17
7
15
15
18
22
20
17
23
25
25
27
21
24
26
27
25
28
21
35
26
27
25
28
21
35
26
2001-2006
Site-
Years
29
14
17
3
-
5
Annual Mean (ppb) 1
mean min med p95 p98 p99
12
9
21
18
-
21
3
5
16
17
-
18
14
9
19
17
-
21
19
12
28
19
-
26
23
12
28
19
-
26
23
12
28
19
-
26
A-250
-------
Location
Detroit
El Paso
Jacksonville
Las Vegas
Los Angeles
Miami
New York
Philadelphia
Phoenix
Provo
St. Louis
Washington DC
Other MSA
Other Not MSA
1995-2000
Site-
Years
12
8
6
8
92
9
47
35
14
6
18
33
1135
200
Annual Mean (ppb) 1
mean min med p95 p98 p99
19
19
15
10
27
9
24
21
30
24
17
18
14
8
12
14
14
3
6
9
11
15
26
23
5
9
1
0
19
18
15
6
28
9
26
20
29
24
19
19
14
7
26
23
16
24
40
10
35
33
34
24
21
25
24
16
26
23
16
24
46
10
36
33
34
24
21
26
26
19
26
23
16
24
46
10
36
33
34
24
21
26
28
19
2001-2006
Site-
Years
12
24
4
27
105
10
48
26
14
6
13
35
1177
243
Annual Mean (ppb) 1
mean min med p95 p98 p99
19
15
14
10
20
8
20
19
25
24
16
17
12
7
14
8
13
1
5
7
10
14
21
21
12
7
1
1
19
16
14
7
20
8
19
18
24
23
16
18
12
6
23
18
15
22
33
10
28
28
29
29
21
24
20
14
23
18
15
22
34
10
31
28
29
29
21
25
22
16
23
18
15
22
36
10
31
28
29
29
21
25
24
16
1 Annual means for each monitor were first calculated based on all hourly values in a year. Then the mean of the
annual means was estimated as the sum of all the annual means in a particular location divided by the number of
site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95th, 98th,
and 99th percentiles of the distribution for the annual mean.
2 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001 ,
therefore there are no 2001-2006 data.
Table A-136. Monitoring site-years and annual average NO2 concentrations for two monitoring periods,
historical and recent air quality data (as is) using monitors sited <100 m of a major road.
Location
Boston
Chicago
Cleveland
Colorado Springs
Denver
El Paso
Las Vegas
Los Angeles
Miami
New York
Philadelphia
Phoenix
St. Louis
Washington DC
1995-2000
Site-
Years
40
19
6
1
19
6
8
101
15
46
11
8
38
36
Annual Mean (ppb) 1
mean min med p95 p98 p99
18
29
26
18
14
29
19
25
11
31
30
31
18
23
6
22
23
18
6
23
7
4
6
22
26
24
9
13
20
31
27
18
9
29
25
23
9
29
29
30
19
23
31
34
28
18
35
35
27
45
17
42
34
40
26
27
31
34
28
18
35
35
27
46
17
42
34
40
27
27
31
34
28
18
35
35
27
46
17
42
34
40
27
27
2001-2006
Site-
Years
33
19
8
5
6
8
72
10
33
13
13
30
31
Annual Mean (ppb) 1
mean min med p95 p98 p99
18
27
20
31
18
15
25
10
29
23
25
15
20
7
18
14
27
13
3
4
6
18
18
11
8
13
18
28
22
29
19
19
27
10
28
24
24
15
20
25
32
24
37
22
23
37
16
40
30
37
23
26
30
32
24
37
22
23
40
16
40
30
37
25
26
30
32
24
37
22
23
41
16
40
30
37
25
26
1 Annual means for each monitor were first calculated based on all hourly values in a year. Then the mean of the
annual means was estimated as the sum of all the annual means in a particular location divided by the number of
site-years across the monitoring period. The min, med, p95, p98, p99 represent the minimum, median, 95th, 98th,
and 99th percentiles of the distribution for the annual mean.
2 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001 ,
therefore there are no 2001-2006 data.
A-251
-------
The estimated total number of exceedances of four potential health effect benchmark levels
(150, 200, 250, and 300 ppb NO2 for 1-hr) is shown in Tables A-l 13 and A-l 14 for the historical
and recent ambient monitoring data, respectively, and where the monitors were sited >100 m
from a major road. The number of exceedances of each benchmark were summed for the year at
each monitor; a single monitor value of 10 could represent ten 1-hr exceedances that occurred in
one day, 10 exceedances in 10 days, or some combination of multiple hours or days that totaled
10 exceedances for the year. In general, the number of benchmark exceedances was low across
all locations and considering both year-groups of the as is air quality. The average number of
exceedances of the lowest 1-hour concentration level of 150 ppb across each location was
typically none or one. Considering that there are 8,760 hours in a year, this number of
exceedances amounts to a small fraction of the year (0.01%) containing an exceedance of the
potential health effect benchmark level. For locations with greater than 1 yearly average
exceedance, the numbers were primarily driven by a single site-year of data. For example, the
Colorado Springs mean is 3 exceedances per year for the years 1995-2000; however, this mean
was driven by a single site-year that contained 69 exceedances of 200 ppb. That particular
monitor (ID 0804160181) does not appear to have any unusual attributes (e.g., the closest major
road is beyond a distance of 160 meters and the closest stationary source emitting >5 tons per
year (tpy) is at a distance >4 km) except that a power generating utility (NAICS code 221112)
located 7.2 km from the monitor has estimated emissions of 4,205 tpy. It is not known at this
time whether this particular facility is influencing the observed concentration exceedances at this
specific monitoring site. Similarly, Detroit contained the largest number of excedances of 200
ppb (a maximum of 12) for as is air quality data from years 2001-2006 (Table A-l 12). Again,
all of those exceedances occurred at one monitor (ID 2616300192) during one year (2002). The
number of exceedances of higher potential benchmark concentration levels at each of the
locations was less than that observed at the 200 ppb level. Most locations had no exceedances of
250 or 300 ppb, with higher numbers confined to the same aforementioned cities where
exceedances of 200 ppb were observed.
When considering the historical data and monitors sited <100 m of a major road (Table A-
115), a few locations contained exceedances of the potential health effect benchmark levels,
driven mainly by observations from one or two monitors. For example, in Phoenix a single year
from one monitor (ID 0401330031) was responsible for all observed exceedances of 200 ppb.
This monitor is located 78 m from a major road along with 10 stationary sources located within
10 km of this monitor, 9 of which contained estimated emissions of less than 60 tpy (one source
emitted 272 tpy, see Appendix A, section 4). It is not known if observed exceedances of 200 ppb
at this monitor are a result of proximity of major roads or the stationary sources. There were
fewer locations with observed exceedances of the benchmark levels at the monitors sited within
100 m of a major road considering the more recent as is air quality. Eleven of thirteen total
locations contained an average of zero exceedances of the 150 ppb benchmark level (Table A-
116).
A-252
-------
2 Table A-137. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 historical NO2 air
3 quality (as is) using monitors sited >100 m of a major road.
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
Other MSA
Other Not
MSA
Exceedances of 150 ppb 1
mean
0
0
0
0
8
1
1
0
0
0
3
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
1
1
0
0
47
4
10
0
0
0
22
0
0
0
2
0
12
1
0
0
P98
1
1
0
0
143
4
10
0
0
0
42
0
3
10
2
0
12
2
0
2
p99
1
1
0
0
143
4
10
0
0
0
44
0
3
10
2
0
12
2
1
4
Exceedances of 200 ppb 1
mean
0
0
0
0
3
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
3
2
3
0
0
0
2
0
0
0
0
0
8
1
0
0
p98
1
0
0
0
69
2
3
0
0
0
2
0
0
3
0
0
8
2
0
0
p99
1
0
0
0
69
2
3
0
0
0
4
0
0
3
0
0
8
2
0
1
Exceedances of 250 ppb 1
mean
0
0
0
0
1
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
4
1
0
0
p98
0
0
0
0
23
0
1
0
0
0
1
0
0
0
0
0
4
1
0
0
p99
0
0
0
0
23
0
1
0
0
0
2
0
0
0
0
0
4
1
0
0
Exceedances of 300 ppb 1
mean
0
0
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
4
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
0
0
0
0
4
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
Notes:
1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular 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
the number of exceedances in any one year within the monitoring period.
August 2008 - Draft
253
-------
2
3
Table A-138. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 2001-2006 recent NO2 air quality
(as is) using monitors sited >100 m of a major road.
Location
Atlanta
Boston
Chicago
Cleveland
Denver
Detroit
El Paso
Jacksonville
Las Vegas
Los Angeles
Miami
New York
Philadelphia
Phoenix
Provo
St. Louis
Washington
DC
Other MSA
Other Not
MSA
Exceedances of 150 ppb 1
mean
0
0
0
0
0
2
0
2
0
0
0
0
0
0
7
0
0
0
0
min
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
1
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
16
0
6
0
0
0
0
0
0
39
0
0
0
0
P98
1
0
0
0
0
16
1
6
0
1
0
0
1
0
39
0
0
0
1
p99
1
0
0
0
0
16
1
6
0
1
0
0
1
0
39
0
0
0
2
Exceedances of 200 ppb 1
mean
0
0
0
0
0
1
0
1
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
med
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
12
0
2
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
0
12
0
2
0
0
0
0
1
0
0
0
0
0
0
p99
0
0
0
0
0
12
0
2
0
0
0
0
1
0
0
0
0
0
1
Exceedances of 250 ppb 1
mean
0
0
0
0
0
1
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
8
0
1
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
0
8
0
1
0
0
0
0
1
0
0
0
0
0
0
p99
0
0
0
0
0
8
0
1
0
0
0
0
1
0
0
0
0
0
1
Exceedances of 300 ppb 1
mean
0
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
p98
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
p99
0
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
Notes:
1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular 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.
2 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001 , therefore there are no 2001 -2006 data.
4
5
6
August 2008 - Draft
254
-------
2 Table A-139. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 historical NO2 air
3 quality (as is) using monitors sited <100 m of a major road.
Location
Boston
Chicago
Cleveland
Colorado
Springs
Denver
El Paso
Las Vegas
Los Angeles
Miami
New York
Philadelphia
Phoenix
St. Louis
Washington
DC
Exceedances of 150 ppb 1
mean
0
0
2
0
0
2
1
2
0
0
0
27
0
0
min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
med
0
0
0
0
0
1
0
0
0
0
0
1
0
0
p95
0
0
9
0
6
7
11
11
3
2
1
147
0
0
P98
1
0
9
0
6
7
11
18
3
3
1
147
0
1
p99
1
0
9
0
6
7
11
33
3
3
1
147
0
1
Exceedances of 200 ppb 1
mean
0
0
0
0
0
0
1
0
0
0
0
5
0
0
min
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
p95
0
0
1
0
1
2
11
1
1
0
0
37
0
0
p98
1
0
1
0
1
2
11
2
1
3
0
37
0
0
p99
1
0
1
0
1
2
11
2
1
3
0
37
0
0
Exceedances of 250 ppb 1
mean
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
1
0
1
0
3
0
0
0
0
3
0
0
p98
0
0
1
0
1
0
3
0
0
0
0
3
0
0
p99
0
0
1
0
1
0
3
0
0
0
0
3
0
0
Exceedances of 300 ppb 1
mean
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
0
3
0
0
0
0
0
0
0
p98
0
0
0
0
0
0
3
0
0
0
0
0
0
0
p99
0
0
0
0
0
0
3
0
0
0
0
0
0
0
Notes:
1 The mean number of exceedances represents the number of exceedances occurring at all monitors in a particular 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.
August 2008 - Draft
255
-------
1
2
3
Table A-140. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 2001-2006 recent NO2 air quality
(as is) using monitors sited <100 m of a major road.
Location
Boston
Chicago
Cleveland
Denver
El Paso
Las Vegas
Los Angeles
Miami
New York
Philadelphia
Phoenix
St. Louis
Washington
DC
Exceedances of 150 ppb 1
mean
0
0
0
1
0
0
0
1
0
0
0
0
0
min
0
0
0
0
0
0
0
0
0
0
0
0
0
med
0
0
0
1
0
0
0
0
0
0
0
0
0
p95
0
0
1
1
0
0
2
5
1
0
0
0
0
P98
0
0
1
1
0
0
2
5
1
0
0
0
0
p99
0
0
1
1
0
0
6
5
1
0
0
0
0
Exceedances of 200 ppb 1
mean
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
0
0
3
0
0
0
0
0
p98
0
0
0
0
0
0
1
3
0
0
0
0
0
p99
0
0
0
0
0
0
1
3
0
0
0
0
0
Exceedances of 250 ppb 1
mean
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
0
0
3
0
0
0
0
0
p98
0
0
0
0
0
0
1
3
0
0
0
0
0
p99
0
0
0
0
0
0
1
3
0
0
0
0
0
Exceedances of 300 ppb 1
mean
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
0
0
0
0
0
0
0
3
0
0
0
0
0
p98
0
0
0
0
0
0
0
3
0
0
0
0
0
p99
0
0
0
0
0
0
0
3
0
0
0
0
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-
wears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95h, 98th, and 99th percentiles of the distribution for
[the number of exceedances in any one year within the monitoring period.
August 2008 - Draft
256
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i A-9.5 Comparison of On-Road Concentrations Derived From
2 Historical and Recent Ambient Air Quality (As Is)
3 This section presents the preliminary results using the ambient monitoring data obtained from
4 AQS that were separated into two six-year groups, one representing historical data (1995-2000)
5 and the other representing more recent data (2001-2006). These estimated on-road
6 concentrations were generated by applying the simulation procedure described above in section
7 A-8 to ambient monitors >100 m of a major road and represent the second scenario evaluated.
8 This initial analysis used the total number of exceedances of the potential benchmark levels of
9 150, 200, 250, and 300 ppb. It differs from the analyses performed in Chapter 7 of the final REA
10 where the number of times the daily maximum exceeded the potential benchmark levels was
11 recorded (also including a benchmark level of 100 ppb). It is presented here mainly for
12 comparison of the two six-year groups, because the historical data set was not re-analyzed using
13 the added benchmark level and did have the number of daily maximum exceedances in a year
14 calculated.
15
16 Descriptive statistics for estimated on-road NO2 concentrations are presented in Table A-l 17.
17 For the 18 named locations, the calculation only used monitors sited at a distance >100 m of a
18 major road. The two grouped locations (i.e., "Other CMSA" and "Not MSA") did not have
19 estimated monitor distances to major roads therefore all monitoring data available were used to
20 estimate the distribution of on-road NC>2 concentrations.
21
22 The simulated on-road annual average NO2 concentrations are, on average, a factor of 1.8
23 higher than their respective ambient levels. This falls within the range of ratios reported in the
24 ISA (about 2-fold higher concentrations on roads) (ISA, section 2.5.4). Los Angeles, New York,
25 and Phoenix were predicted to have the highest on-road NC>2 levels. This is a direct result of
26 these locations already containing some of the highest "as-is " NC>2 concentrations prior to the
27 on-road simulation (see Table A-l 11).
28
29 The median of the simulated concentration estimates for Los Angeles were compared with
30 NC>2 measurements provided by Westerdahl et al. (2005) for arterial roads and freeways in the
31 same general location during spring 2003. Although the averaging time is not exactly the same,
32 comparison of the medians is judged to be appropriate.8 The estimated median on-road
33 concentration for 2001-2006 is 36 ppb which falls within the range of 31 ppb to 55 ppb identified
34 by Westerdahl et al. (2005).
35
36 On average, most locations are predicted to have fewer than 10 exceedances per year for the
37 200 ppb potential health effect benchmark while the median frequency of exceedances in most
38 locations is estimated to be 1 or less per year (Tables A-l 18 and A-l 19). When considering the
39 lower 1-hour benchmark of 150 ppb, most locations (17 out of 20) were estimated to have less
40 than 50/year, on average. There are generally fewer predicted mean exceedances of the potential
41 health effect benchmark levels when considering recent air quality compared with the historical
8 Table A-l 18 considers annual average of hourly measurements while Westerdahl et al. (2005) reported between 2
to 4 hour average concentrations. Over time, the mean of 2-4 hour averages will be similar to the mean of hourly
concentrations, with the main difference being in the variability (and hence the various percentiles of the
distribution outside the central tendency).
August 2008 - Draft 257
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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
22
23
24
25
26
27
28
29
air quality. Areas with a relatively high number of estimated exceedances (e.g., Provo) are likely
influenced by the presence of a small number of monitors and one or a few exceptional site-years
where there were unusually high concentrations at the upper percentiles of the concentration
distribution.
The upper percentiles for estimated number of exceedances of the 150 ppb, 1-hr average
level in most locations using the historical ambient monitoring data was between 100 and 300
per year, while a few locations were estimated to contain up to a several hundred exceedances
(e.g., Los Angeles, New York, and Phoenix). There were lower numbers of estimated
exceedances considering the 2001-2006 air quality compared with the historical data, with most
locations containing under 200 estimated exceedances of 150 ppb per year at the 98th and 99th
percentiles. As expected, the frequency of benchmark exceedances at all locations was lower
when considering any of the higher benchmark levels (i.e., 200, 250, 300 ppb, 1-hr average)
compared with 150 ppb.
The number of predicted benchmark exceedances across large urban areas may be used to
broadly represent particular locations within those types of areas. For example, Chicago, New
York, and Los Angeles are large CMSAs, have several monitoring sites, and have a large number
of roadways. Each of these locations was estimated to have, on average, about 10 exceedances
of 200 ppb per year on-roads. Assuming that the on-road exceedances distribution generated
from the existing monitoring is proportionally representing the distribution of roadways within
each location, about one-half of the roads in these areas would not have any on-road
concentrations in excess of 200 ppb. This is because the median value for exceedances of 200
ppb in most locations was estimated as zero. However, Tables A-l 18 and A-l 19 indicate that
there is also a possibility of tens to just over a hundred exceedances of 200 ppb in a year as an
upper bound estimate on certain roads/sites in a particular year.
Table A-141. Estimated annual average on-road NO2 concentrations for two monitoring periods, historical
and recent air quality data (as is).
Location
Atlanta
Boston
Chicago
Cleveland
Colorado
Springs2
Denver
Detroit
El Paso
Jacksonville
Las Vegas
Los Angeles
Miami
New York
Philadelphia
1995-2000
Site-
Years
2400
1800
2800
500
2500
700
1200
800
600
800
9200
900
4700
3500
Annual Mean (ppb) 1
mean min med p95 p98 p99
26
32
37
35
30
39
35
34
28
17
50
17
43
39
6
7
11
22
9
19
15
17
18
4
8
11
14
19
25
32
39
34
30
38
34
33
27
11
49
17
42
36
49
51
59
47
52
55
52
49
37
45
83
23
73
63
57
55
63
49
64
58
57
54
39
50
91
25
78
73
60
57
66
53
73
62
59
57
41
55
97
26
83
77
2001-2006
Site-
Years
2900
1400
1700
300
500
1200
2400
400
2700
10500
1000
4800
2600
Annual Mean (ppb) 1
mean min med p95 p98 p99
21
16
37
32
39
34
26
25
18
37
15
35
34
4
7
20
22
23
18
10
17
2
6
9
12
18
23
16
35
32
38
34
26
25
13
36
14
34
32
40
25
57
42
54
47
39
34
43
63
21
56
52
43
28
64
43
61
52
43
36
46
72
24
62
60
47
29
66
45
62
54
43
37
50
77
24
66
64
August 2008 - Draft
258
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Location
Phoenix
Provo
St. Louis
Washington DC
Other MSA
Other Not MSA
1995-2000
Site-
Years
1400
600
1800
3300
113500
20000
Annual Mean (ppb) 1
mean min med p95 p98 p99
54
43
31
33
26
14
33
29
7
12
1
0
52
42
33
33
25
12
75
58
47
53
47
31
78
62
50
58
53
35
80
64
52
61
57
39
2001-2006
Site-
Years
1400
600
1300
3500
117700
24300
Annual Mean (ppb) 1
mean min med p95 p98 p99
45
43
30
31
21
12
26
26
16
9
1
1
43
41
29
31
21
11
63
61
41
51
39
27
70
69
46
56
45
31
72
70
49
59
48
33
1 Annual means for each monitor were first calculated based on all simulated hourly values in a year. Then the
mean of the annual means was estimated as the sum of all the annual means in a particular location divided by
the number of simulated site-years across the monitoring period. The min, med, p95, p98, p99 represent the
minimum, median, 95th, 98th, and 99th percentiles of the distribution for the annual mean.
2 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001 ,
therefore there are no 2001-2006 data.
1
2
August 2008 - Draft
259
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1
2
3
Table A-142. Estimated total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year on-roads, 1995-2000
historical NO2 air quality (as is).
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
Other MSA
Other Not
MSA
Exceedances of 150 ppb 1
mean
24
11
39
15
45
48
39
21
3
14
166
3
63
25
104
21
14
21
10
2
min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
med
1
1
2
1
0
17
19
8
0
0
54
0
8
2
31
0
0
1
0
0
p95
160
79
212
108
267
185
158
96
13
95
738
13
397
124
447
112
74
128
55
11
P98
271
106
338
130
447
230
207
141
30
294
1023
27
560
311
630
195
121
208
109
31
p99
357
125
385
146
626
288
270
149
36
306
1268
27
685
369
670
245
132
240
168
55
Exceedances of 200 ppb 1
mean
4
1
7
2
21
8
10
4
0
2
43
0
13
4
14
2
2
3
1
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
0
4
2
0
0
0
6
0
0
0
2
0
0
0
0
0
p95
31
9
41
19
171
36
48
20
1
5
213
2
92
20
65
9
15
20
6
2
p98
57
20
97
27
264
46
72
31
2
34
348
4
155
45
89
33
25
39
18
7
p99
87
24
118
31
325
53
86
39
4
36
508
5
212
63
102
34
28
56
32
14
Exceedances of 250 ppb 1
mean
1
0
1
0
12
2
4
1
0
0
12
0
3
1
2
0
1
1
0
0
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
0
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
3
1
6
1
111
10
21
5
0
0
63
0
21
4
13
0
10
2
1
1
p98
11
4
23
5
183
12
34
7
1
6
118
0
44
11
21
1
13
8
3
2
p99
21
7
30
5
219
15
35
8
1
6
188
1
55
15
27
4
14
11
6
4
Exceedances of 300 ppb 1
mean
0
0
0
0
7
1
2
0
0
0
4
0
1
0
1
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
med
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
1
0
0
1
55
4
14
0
0
0
17
0
4
0
3
0
7
1
0
0
p98
1
1
3
1
121
6
21
2
0
0
39
0
10
5
6
0
11
2
1
1
p99
2
1
7
1
160
7
26
2
0
0
68
0
14
7
11
0
13
3
2
2
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
the number of exceedances in any one year within the monitoring period.
August 2008 - Draft
260
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1
2
3
Table A-143. Estimated total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year on-roads, 2001-2006
recent NO2 air quality (as is).
Location2
Atlanta
Boston
Chicago
Cleveland
Denver
Detroit
El Paso
Jacksonville
Las Vegas
Los Angeles
Miami
New York
Philadelphia
Phoenix
Provo
St. Louis
Washington
DC
Other MSA
Other Not
MSA
Exceedances of 150 ppb 1
mean
8
0
24
14
41
20
6
7
9
42
1
21
12
37
117
7
11
4
1
min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
med
0
0
1
3
6
3
0
2
0
4
0
1
1
2
1
0
0
0
0
p95
52
1
160
79
171
116
34
29
39
227
4
129
62
184
658
48
81
17
4
P98
101
2
211
89
270
149
45
53
169
405
9
210
110
302
702
84
130
44
14
p99
121
10
337
89
379
171
54
53
205
546
16
280
211
350
703
102
141
76
27
Exceedances of 200 ppb 1
mean
1
0
4
2
4
5
1
3
1
7
0
3
1
3
70
1
1
0
0
min
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
1
0
0
0
0
0
0
0
0
0
0
0
p95
8
0
17
16
25
29
4
15
3
37
0
22
5
14
547
3
7
1
1
p98
16
0
44
23
40
44
8
23
14
87
1
45
12
28
662
10
14
5
4
p99
25
1
69
23
53
45
9
24
15
129
2
72
30
44
662
14
21
10
8
Exceedances of 250 ppb 1
mean
0
0
0
0
0
2
0
2
0
1
0
1
0
0
33
0
0
0
0
min
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
p95
1
0
1
4
3
16
1
8
0
7
0
3
1
1
234
0
0
0
0
p98
3
0
5
5
6
22
1
15
0
20
0
10
1
3
606
2
1
1
2
p99
6
0
10
6
7
28
1
15
2
28
0
16
7
4
612
2
2
1
3
Exceedances of 300 ppb 1
mean
0
0
0
0
0
1
0
1
0
0
0
0
0
0
13
0
0
0
0
min
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
p95
0
0
0
2
0
13
0
5
0
1
0
0
0
0
3
0
0
0
0
p98
1
0
1
3
1
14
0
8
0
3
0
1
1
0
423
0
0
0
1
p99
2
0
1
3
1
21
0
8
0
10
0
2
1
0
435
1
0
0
2
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.
2 Colorado Springs monitoring data were collected as part of short-term study completed in September 2001 , therefore there are no 2001 -2006 data.
August 2008 - Draft
261
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2 A-9.6 Results Tables of Historical NO2 Ambient Monitoring Data
3 (1995-2000) Adjusted to Just Meeting the Current Standard
4
5 This section presents the preliminary results using the historical ambient monitoring data
6 (1995-2000) adjusted to just meet the current annual average standard only. This initial analysis
7 calculated the total number of exceedances of the potential benchmark levels of 150, 200, 250,
8 and 300 ppb, for ambient monitors sited >100 m and <100 m from a major road. These results
9 are presented in Tables A-120 and A-121, respectively. In addition on-road concentrations were
10 also estimated using the adjusted air quality concentrations, using the same procedure described
11 in section A-8. The total estimated number of exceedances of the potential health effect
12 benchmark levels on-roads given just meeting the current standard is provided in Table A-122.
13 Each of the result tables presented in this section differs from the analyses performed in Chapter
14 7 of the final REA where the number of times the daily maximum exceeded the potential
15 benchmark levels was recorded (including a benchmark level of 100 ppb) for different monitor
16 road categories (>100 m, 20 m< x <100 m, and <20 m from a major road), and for not just the
17 current standard but all of the potential alternative standards. It is presented here mainly as a
18 companion to the as is air quality results presented in sections A-9.1 and A-9.2.
August 2008 - Draft 262
-------
1
2
3
Table A-144. Total number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 historical NO2 air
quality adjusted to just meeting the current annual average standard (0.053 ppm) using monitors sited >100 m of a major road.
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
Other MSA
Other Not
MSA
Exceedances of 150 ppb 1
mean
42
1
1
2
50
141
75
16
122
3
9
72
1
2
8
16
4
9
2
20
min
0
0
0
0
0
1
2
1
82
0
0
4
0
0
0
2
0
0
0
0
med
2
0
1
1
3
12
65
9
137
1
2
91
0
0
5
4
1
3
0
0
p95
197
7
5
7
283
648
162
69
147
11
56
133
4
10
26
71
16
34
13
116
P98
233
7
7
7
318
648
162
69
147
11
83
133
7
18
26
71
16
38
28
241
p99
233
7
7
7
318
648
162
69
147
11
96
133
7
18
26
71
16
38
40
336
Exceedances of 200 ppb 1
mean
4
0
0
0
32
24
13
2
12
0
1
10
0
0
0
1
1
1
0
4
min
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
med
0
0
0
0
0
2
13
1
15
0
0
10
0
0
0
0
0
0
0
0
p95
19
1
1
0
180
141
25
14
20
1
4
27
0
0
4
5
15
3
1
18
p98
21
1
1
0
241
141
25
14
20
1
6
27
2
12
4
5
15
4
3
53
p99
21
1
1
0
241
141
25
14
20
1
8
27
2
12
4
5
15
4
6
87
Exceedances of 250 ppb 1
mean
0
0
0
0
16
5
4
0
2
0
0
1
0
0
0
0
1
0
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
0
1
2
0
1
0
0
0
0
0
0
0
0
0
0
0
p95
2
1
0
0
123
28
15
2
7
0
1
6
0
0
0
0
14
2
0
4
p98
3
1
0
0
135
28
15
2
7
0
2
6
0
9
0
0
14
3
1
15
p99
3
1
0
0
135
28
15
2
7
0
2
6
0
9
0
0
14
3
1
42
Exceedances of 300 ppb 1
mean
0
0
0
0
8
2
2
0
0
0
0
0
0
0
0
0
1
0
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
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
p95
1
0
0
0
72
9
10
0
1
0
0
2
0
0
0
0
13
1
0
1
p98
1
0
0
0
83
9
10
0
1
0
1
2
0
5
0
0
13
2
0
8
p99
1
0
0
0
83
9
10
0
1
0
2
2
0
5
0
0
13
2
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, 95 , 98th, and 99th percentiles of the distribution for
the number of exceedances in any one year within the monitoring period.
August 2008 - Draft
263
-------
1
2
3
Table 145. Total estimated number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year, 1995-2000 historical NO2
air quality adjusted to just meeting the current annual average standard (0.053 ppm) using monitors sited <100 m of a major road.
!ocation
oston
hicago
leveland
olorado
prings
enver
1 Paso
as Vegas
DS
ngeles
liami
ew York
Philadelphia
Phoenix
St. Louis
Washington
DC
Exceedances of 150 ppb 1
mean
2
4
35
7
12
23
47
8
70
1
5
77
2
12
min
0
0
9
7
0
5
0
0
2
0
0
0
0
0
med
0
2
16
7
0
24
25
0
56
0
3
9
1
9
p95
11
16
110
7
77
36
226
42
161
6
26
339
11
47
P98
22
16
110
7
77
36
226
56
161
10
26
339
13
61
p99
22
16
110
7
77
36
226
79
161
10
26
339
13
61
Exceedances of 200 ppb 1
mean
0
0
5
2
1
6
6
1
9
0
0
32
0
1
min
0
0
0
2
0
0
0
0
0
0
0
0
0
0
med
0
0
1
2
0
7
1
0
7
0
0
1
0
0
p95
1
0
24
2
10
13
28
6
34
1
3
198
1
9
p98
2
0
24
2
10
13
28
8
34
3
3
198
1
17
p99
2
0
24
2
10
13
28
9
34
3
3
198
1
17
Exceedances of 250 ppb 1
mean
0
0
2
1
0
2
3
0
2
0
0
12
0
0
min
0
0
0
1
0
0
0
0
0
0
0
0
0
0
med
0
0
0
1
0
1
0
0
0
0
0
0
0
0
p95
0
0
10
1
5
6
13
0
15
0
1
92
0
0
p98
1
0
10
1
5
6
13
1
15
3
1
92
0
3
p99
1
0
10
1
5
6
13
2
15
3
1
92
0
3
Exceedances of 300 ppb 1
mean
0
0
1
1
0
0
1
0
1
0
0
4
0
0
min
0
0
0
1
0
0
0
0
0
0
0
0
0
0
med
0
0
0
1
0
0
0
0
0
0
0
0
0
0
p95
0
0
3
1
2
2
11
0
8
0
1
31
0
0
p98
1
0
3
1
2
2
11
0
8
1
1
31
0
2
p99
1
0
3
1
2
2
11
0
8
1
1
31
0
2
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-
wears across the monitoring period. The min, med, p95, p98, and p99 represent the minimum, median, 95 , 98th, and 99th percentiles of the distribution for
[the number of exceedances in any one year within the monitoring period.
August 2008 - Draft
264
-------
1
2
3
Table A-146. Total estimated number of exceedances of short-term (1-hour) potential health effect benchmark levels in a year on-roads, 1995-2000
historical NO2 air quality adjusted to just meeting the current annual average standard (0.053 ppm).
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
Other MSA
Other Not
MSA
Exceedances of 150 ppb 1
mean
597
231
386
526
866
980
982
488
1381
348
323
802
199
362
811
1434
486
562
199
247
min
0
0
0
42
0
15
5
19
365
0
0
33
0
0
15
84
0
0
0
0
med
215
108
242
407
565
585
860
317
1328
47
154
788
64
174
605
1363
368
358
65
45
p95
2122
930
1288
1305
2666
2765
2413
1443
2485
1618
1219
1637
950
1352
2493
3215
1402
1843
858
1234
P98
2566
1282
1609
1568
3106
3021
2771
2106
2677
2108
1555
1885
1251
1967
2818
3526
1630
2409
1262
1771
p99
2778
1394
1802
1762
3332
3149
2882
2391
3110
2908
1935
2043
1384
2536
2922
3729
1843
2563
1572
2130
Exceedances of 200 ppb 1
mean
251
53
111
157
308
497
405
152
610
106
97
359
50
86
229
443
144
176
52
95
min
0
0
0
1
0
0
2
0
40
0
0
2
0
0
0
1
0
0
0
0
med
42
11
32
83
80
111
284
67
549
6
24
289
5
21
88
230
51
64
6
7
p95
1094
299
498
457
1348
2097
1227
545
1426
663
427
985
313
400
954
1643
523
721
268
549
p98
1472
369
615
586
1792
2304
1439
997
1515
894
671
1201
475
689
1293
1871
693
949
444
928
p99
1640
390
707
700
1902
2451
1589
1126
1801
1248
865
1353
602
865
1375
2058
728
1073
592
1203
Exceedances of 250 ppb 1
mean
106
14
36
51
123
254
175
54
263
38
32
159
14
24
63
135
46
60
15
39
min
0
0
0
0
0
0
2
0
2
0
0
0
0
0
0
0
0
0
0
0
med
7
1
2
13
11
26
97
16
195
1
4
95
0
2
12
32
9
9
0
1
p95
535
95
195
215
574
1467
576
186
773
318
158
550
103
125
304
543
232
316
84
221
p98
843
132
289
269
803
1695
776
440
839
526
264
683
175
245
436
697
289
411
156
438
p99
947
161
364
306
934
1930
872
485
1002
596
366
797
230
341
544
817
323
478
231
635
Exceedances of 300 ppb 1
mean
45
4
13
18
61
126
80
21
114
15
11
72
4
7
17
43
16
23
5
17
min
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
med
1
0
0
1
1
12
40
6
66
0
0
26
0
0
2
2
0
1
0
0
p95
277
28
86
102
299
866
317
83
407
98
54
297
35
38
78
208
92
133
25
91
p98
435
52
153
131
373
1182
424
190
443
297
105
364
64
76
132
303
133
217
57
198
p99
514
65
196
149
421
1286
482
251
470
355
172
451
81
138
181
339
163
247
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 , 98th, and 99th percentiles of the distribution for
he number of exceedances in any one year within the monitoring period.
August 2008 - Draft
265
-------
i A-9.7 Results Tables of Recent NO2 Ambient Monitoring Data
2 (2001-2006) As Is and Just Meeting the Current and Alternative
3 Standards
4
August 2008 - Draft 266
-------
2 A-10 References
3 Bell S and Ashenden TW. (1997). Spatial and temporal variation in nitrogen dioxide pollution
4 adj acent to rural roads. Water Air Soil Pollut. 95:87-98.
5 Beckerman B, Jerrett M, Brook JR, Verma DK, Arain MA, Finkelstein MM. (2008).
6 Correlation of nitrogen dioxide with other traffic pollutants near a major expressway. Atmos
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 Rizzo (2008). Investigation of how distributions of hourly nitrogen dioxide concentrations have
38 changed over time in six cities. Nitrogen Dioxide NAAQS Review Docket (EPA-HQ-
39 OAR-2006-0922).
40 Rodes C, Sheldon L, Whitaker D, Clayton A, Fitzgerald K, Flanagan J, DiGenova F, Hering S,
41 Frazier C. (1998). Measuring Concentrations of Selected Air Pollutants Inside California
42 Vehicles. California Environmental Protection Agency, Air Resources Board. Final Report,
43 December 1998.
44 Rodes CE and Holland DM. (1981). Variations of NO, NO2 and Os concentrations downwind
45 of a Los Angeles freeway. Atmos Environ. 15:243-250.
August 2008 - Draft 267
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1 Roorda-Knape MC, Janssen NAH, De Hartog JJ, Van Vliet PHN, Harssema H, Brunekreef B.
2 (1998). Air Pollution from traffic in city districts near major roadways. Atmos Environ.
3 32(11)1921-1930.
4 Shorter JH, Herndon S, Zahniser MS, Nelson DD, Wormhoudt J, Demerjian KL, Kolb CE.
5 (2005). Real-Time measurements of nitrogen oxide emissions from in-use New York City
6 transit buses using a chase vehicle. Environ Sci Technol. 39:7991-8000.
7 Singer BC, Hodgson AT, Hotchi T, Kim JJ (2004). Passive measurement of nitrogen oxides to
8 assess traffic-related pollutant exposure for the East Bay Children's Respiratory Health
9 Study. Atmos Environ. 38:393-403.
10 US EPA. (2007a). US EPA Air Quality System (AQS). Download Detailed AQS Data.
11 Available at: http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm.
12 US EPA. (2007b). Field Guide to Air Quality Data (vl.0.0). February 28, 2007. Available at:
13 http://www.epa.gov/ttn/airs/aqsdatamart/documentation/index.htm.
14 US EPA. (2007c). Nitrogen Dioxide Health Assessment Plan: Scope and Methods for Exposure
15 and Risk Assessment. September 2007. Office of Air Quality Planning and Standards.
16 Available at: http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html.
17 US EPA. (2007d). ALLNEICAP Annual 11302007 file posted at:
18 http://www.epa.gov/ttn/chief/net/2002inventory.htmlffinventorydata.
19 US EPA. (2007e). Air Trends. Nitrogen Dioxide, http://www.epa.gov/airtrends/nitrogen.html.
20 Westerdahl D, Fruin S, Sax T, Fine PM, Sioutas C. (2005). Mobile platform measurements of
21 ultrafine particles and associated pollutant concentrations on freeways and residential streets
22 in Los Angeles. Atmos Environ. 39:3597-3610.
23 US EPA. (2007f). Integrated Science Assessment for Oxides of Nitrogen-Health Criteria
24 (First External Review Draft) and Annexes (August 2007). Research Triangle Park, NC:
25 National Center for Environmental Assessment. Available at:
26 http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=l 81712.
27
28
29
30
August 2008 - Draft 268
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Appendix B. Supplement to the NO2 Exposure Assessment
-------
Table of Contents
Appendix B Supplement to the NC>2 Exposure Assessment
i
B-l Overview 1
B-2 Human Exposure Modeling using APEX 3
B-2.1 History 3
B-2.2 APEX Model Overview 4
B-2.2.1 Study Area Characterization 5
B-2.2.2 Simulated Individuals 7
B-2.2.3 Activity Pattern Sequences 10
B-2.2.4 Calculating Microenvironmental Concentrations 14
B-2.2.5 Exposure Calculations 19
B-2.2.6 Exposure Model Output 19
B-3 Philadelphia Exposure Assessment Case-Study
21
B-3.1 Study Area Selection and Description 21
B-3.2 Exposure Period of Analysis 22
B-3.3 Populations Analyzed 22
B-3.4 Simulated Individuals 22
B-3.4.1 Asthma Prevalence Rates 23
B-3.5 Air Quality Data Generated by AERMOD 24
B-3.5.1 Meteorological Inputs 24
B-3.5.2 Surface Characteristics and Land Use Analysis 27
B-3.5.3 Meteorological Data Analysis 30
B-3.5.4 On-Road Emissions Preparation 31
B-3.5.5 Stationary Sources Emissions Preparation 38
B-3.5.6 Fugitive and Airport Emissions Preparation 43
B-3.5.7 Receptor Locations 47
B-3.5.8 Other AERMOD Specifications 48
B-3.5.9 Air Quality Concentration Adjustment 49
B-3.5.10 Meteorological Data Used By APEX 50
B-3.5.11 Mi croenvironment Descriptions 50
B-3.5.12 Adjustment for Just Meeting the Current Standard 56
B-3.6 Philadelphia Exposure Modeling Results 58
B-3.6.1 Overview 58
B-3.6.2 Evaluation of Modeled NO2 Air Quality Concentrations (as is) 58
B-3.6.3 Comparison of estimated on-roadNO2 concentrations 61
B-3.6.4 Annual Average Exposure Concentrations (as is) 64
B-3.6.5 One-Hour Exposures (as is) 65
B-3.6.6 One-Hour Exposures Associated with Just Meeting the Current Standard 75
B-3.6.7 Additional Exposure Results 77
B-4 Atlanta Exposure Assessment Case-Study 90
B-4.1 Supplemental AERMOD Modeling Inputs and Discussion 91
B-4.1.1 Major Link On-Road Emission Estimates 91
B-4.1.2 Stationary Sources Emissions Preparation 93
-------
B-4.1.3 Airport Emissions Preparation 94
B-4.1.4 Receptor Locations 97
B-4.1.5 Data used to generate dispersion model-to-monitor comparison figures in REA.
98
B-4.1.6 Comparison of estimated on-roadNC>2 concentrations 101
B-4.2 Supplemental APEX Modeling Inputs and Discussion 105
B-4.2.1 Simulated Individuals 105
B-4.2.2 Asthma Prevalence Rates 105
B-4.2.3 Meteorological Data Used by APEX 106
B-4.2.4 Method Used for Indoor Source Contributions 106
B-4.2.5 Method Used for Cooking Probabilities 106
B-4.2.6 In-vehicle and Near-Road PROX factors 107
B-4.2.7 Supplemental Exposure Results 108
B-4.2.6 Supplemental Exposure Results 108
B-5 References 125
Attachments
Attachment 1: Technical Memorandum on Meteorological Data Preparation for AERMOD for
NO2 REA for Atlanta, GA 2001-2003 130
Attachment 2: Technical Memorandum on Longitudinal Diary Construction Approach 154
Attachment 2: Technical Memorandum on Longitudinal Diary Construction Approach 154
Attachment 3: Technical Memorandum on the Evaluation Cluster-Markov Algorithm 161
Attachment 4. Technical Memorandum on the Analysis of NHIS Asthma Prevalence Data... 175
Attachment 5: Technical Memorandum on Analysis of Air Exchange Rate Data 199
Attachment 6: Technical Memorandum on HAPEM Near Road Population Data Base
Development (from Task 2. Near roadway concentrations (revised)) 219
Attachment 7: Technical Memorandum on HAPEM Near Road Population Data Base
Development (Estimating near roadway populations and areas for HAPEM6) 237
Attachment 8. Technical Memorandum on the Uncertainty Analysis Of Residential Air
Exchange Rate Distributions 244
Attachment 9. Technical Memorandum on the Distributions of Air Exchange Rate Averages
Over Multiple Days 275
in
-------
List of Tables
Table B-l. Examples of profile variables in APEX 7
Table B-2. Summary of activity pattern studies used in CHAD 12
Table B-3. Mass balance model parameters 14
Table B-4. Factors model parameters 15
Table B-5. List of microenvironments and calculation methods used 16
Table B-6. Mapping of CHAD activity locations to APEX microenvironments 17
Table B-7. Example of APEX output files 20
Table B-8. Asthma prevalence rates by age and gender used for Philadelphia 23
Table B-9. Number of AERMET raw hourly surface meteorology observations, percent
acceptance rate, 2001-2003 24
Table B-10. Number of calms reported by AERMET by year for Philadelphia 25
Table B-l 1. Number and AERMET acceptance rate of upper-air observations 2001-2003 25
Table B-12. Seasonal definitions and specifications for Philadelphia 27
Table B-13. Monthly precipitation compared to NCDC 30-year climatic normal for
Philadelphia, 2001-2003 31
Table B-14. Hourly scaling factors (in percents) applied to Philadelphia County AADT
volumes 33
Table B-15. Seasonal scaling factors applied to Philadelphia County AADT volumes 34
Table B-l6. Signals per mile, by link type, applied to Philadelphia County AADT volumes.... 34
Table B-17. Statistical summary of AADT volumes (one direction) for Philadelphia County
AERMOD simulations 34
Table B-l8. Average calculated speed by link type 36
Table B-19. On-road area source sizes 37
Table B-20. Combined stacks parameters for stationary NOx emission sources in Philadelphia
County 40
Table B-21. Matched stacks between the CAMD and NEI database 41
Table B-22. Emission parameters for the three Philadelphia County fugitive NOx area emission
sources 43
Table B-23. Philadelphia International airport (PHL)NOX emissions 46
Table B-24. Philadelphia County NOx monitors 47
Table B-25. Comparison of ambient monitoring and AERMOD predicted NO2 concentrations in
Philadelphia 49
Table B-26. Air conditioning prevalence estimates with 95% confidence intervals 50
Table B-27. Geometric means (GM) and standard deviations (GSD) for air exchange rates by
city, A/C type, and temperature range 51
Table B-28. Probability of gas stove cooking by hour of the day 53
Table B-29. Adjustment factors and potential health effect benchmark levels used by APEX to
simulate just meeting the current standard 56
Table B-30. Summary statistics of on-road hourly NO2 concentrations (ppb) and the numbers of
potential health effect benchmark levels using AERMOD and the on-road ambient
monitor simulation approaches in Philadelphia 63
Table B-31. Estimated number of asthmatics in Philadelphia County exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using modeled air
quality (as is) and with just meeting the current standard (std), and with and without
indoor sources 78
IV
-------
Table B-32. Estimated percent of asthmatics in Philadelphia County exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using modeled air
quality (as is) and with just meeting the current standard (std), and with and without
indoor sources 79
Table B-33. Estimated number of asthmatic children in Philadelphia County exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using modeled air
quality (as is) and with just meeting the current standard (std), and with and without
indoor sources 84
Table B-34. Estimated percent of asthmatic children in Philadelphia County exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using modeled air
quality (as is) and with just meeting the current standard (std), and with and without
indoor sources 85
Table B-35. 30 year annual average temperature and precipitation summary for Atlanta, GA.. 90
Table B-36. The major-facility combined stacks within 10 km of the Atlanta modeling domain.
95
Table B-37. Data used to generate cumulative density functions plotted in Figure 8-6 of REA. 98
Table B-38. Data used to generate diurnal variation plotted in Figure 8-7 of REA 99
Table B-39. On-road/non-road NC>2 concentration ratios using AERMOD roadway link
concentration prediction and nearest corresponding receptor concentration > 100 m of
a major road 101
Table B-40. Estimated on-road/non-road NC>2 concentration ratios using m ratio derived from
data reported in published NO2 measurement studies 103
Table B-41. Mean asthma prevalence rates, along with lower and upper 95% confidence limits,
by age and gender used for Atlanta 105
Table B-42. In-vehicle and near-road PROX factors used in APEX 107
Table B-43. Estimated number of asthmatics in the Atlanta modeling domain exposed at or
above potential health effect benchmark levels (1 to 6 times per year), using 2001
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, without indoor sources 109
Table B-44. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or
above potential health effect benchmark levels (1 to 6 times per year), using 2001
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, without indoor sources 110
Table B-45. Estimated number of asthmatic children in the Atlanta modeling domain exposed at
or above potential health effect benchmark levels (1 to 6 times per year), using 2001
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, without indoor sources Ill
Table B-46. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at
or above potential health effect benchmark levels (1 to 6 times per year), using 2001
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, without indoor sources 112
Table 47. Estimated number of asthmatics in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled
air quality (as is), with just meeting the current standard (cs), and potential alternative
standards, without indoor sources 113
-------
Table B-48. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled
air quality (as is), with just meeting the current standard (cs), and potential alternative
standards, without indoor sources 114
Table B-49. Estimated number of asthmatic children in the Atlanta modeling domain exposed at
or above potential health effect benchmark levels (1 to 6 times per year), using 2002
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, without indoor sources 115
Table B-50. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at
or above potential health effect benchmark levels (1 to 6 times per year), using 2002
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, without indoor sources 116
Table B-51. Estimated number of asthmatic in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2003 modeled
air quality (as is), with just meeting the current standard (cs), and potential alternative
standards, without indoor sources 117
Table B-52. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or
above potential health effect benchmark levels (1 to 6 times per year), using 2003
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, without indoor sources 118
Table B-53. Estimated number of asthmatic children in the Atlanta modeling domain exposed at
or above potential health effect benchmark levels (1 to 6 times per year), using 2003
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, without indoor sources 119
Table B-54. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at
or above potential health effect benchmark levels (1 to 6 times per year), using 2003
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, without indoor sources 120
Table B-55. Estimated number of asthmatics in the Atlanta modeling domain exposed at or
above potential health effect benchmark levels (1 to 6 times per year), using 2002
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, with indoor sources 121
Table B-56. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or
above potential health effect benchmark levels (1 to 6 times per year), using 2002
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, with indoor sources 122
Table B-57. Estimated number of asthmatic children in the Atlanta modeling domain exposed at
or above potential health effect benchmark levels (1 to 6 times per year), using 2002
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, with indoor sources 123
Table B-58. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at
or above potential health effect benchmark levels (1 to 6 times per year), using 2002
modeled air quality (as is), with just meeting the current standard (cs), and potential
alternative standards, with indoor sources 124
VI
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List of Figures
Figure B-l. Example of a profile function file for A/C prevalence 10
Figure B-2. Land-use and sectors around the Philadelphia-area surface meteorological station
(KPHL). Sector borders are 80, 184, 262, and 312 degrees from geographic North.
Philadelphia city center is labeled 29
Figure B-3. Estimated ZQ values for the Philadelphia case-study analysis using visual and
AERSURFACE land-use estimations 30
Figure B-4. Example of Light- and heavy-duty vehicle NOx emissions grams/mile (g/mi) for
arterial and freeway functional classes, 2001 36
Figure B-5. Differences in facility-wide annual NOx emission totals between NEI and CAMD
data bases for Philadelphia County 2002 43
Figure B-6. Locations of the four ancillary area sources. Also shown are centroid receptor
locations 45
Figure B-7. Centroid locations within fixed distances to major point and mobile sources in
Philadelphia county 47
Figure B-8. Frequency distribution of distance between each Census receptor and its nearest
road-centered receptor in Philadelphia County 48
Figure B-9. Example input file from APEX for Indoors-residence microenvironment 52
Figure B-10. Example input file from APEX for all Indoors microenvironments (non-residence).
54
Figure B-11. Example input file from APEX for outdoor near road microenvironment 55
Figure B-12 . Distribution of AERMOD estimated annual average NC>2 concentrations at each of
the 16,857 receptors in Philadelphia County for years 2001-2003 59
Figure B-13. Measured and modeled diurnal pattern of NO2 concentrations at three ambient
monitor sites 60
Figure B-14. Comparison of on-road/non-road ratios developed from AERMOD concentration
estimates and those derived from published NO2 measurement studies 62
Figure B-15. Estimated annual average total NO2 exposure concentrations for all simulated
persons in Philadelphia County, using modeled 2001-2003 air quality (as is), with
modeled indoor sources 64
Figure B-16. Comparison of AERMOD predicted and ambient monitoring annual average NO2
concentrations (as is) and APEX exposure concentrations (with and without modeled
indoor sources) in Philadelphia County for year 2002 65
Figure B-17. Estimated maximum NO2 exposure concentration for all simulated persons in
Philadelphia County, using modeled 2001-2003 air quality (as is), with and without
modeled indoor sources. Values above the 99th percentile are not shown 67
Figure B-l 8. Estimated number of all simulated asthmatics in Philadelphia County with at least
one NO2 exposure at or above the potential health effect benchmark levels, using
modeled 2001-2003 air quality (as is), with modeled indoor sources 67
Figure B-19. Estimated number of simulated asthmatic children in Philadelphia County with at
least one NO2 exposure at or above the potential health effect benchmark levels, using
modeled 2001-2003 air quality (as is), with modeled indoor sources 68
Figure B-20. Comparison of the estimated number of all simulated asthmatics in Philadelphia
County with at least one NO2 exposure at or above potential health effect benchmark
VII
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levels, using modeled 2002 air quality (as is) , with and without modeled indoor
sources 68
Figure B-21. Fraction of time all simulated persons in Philadelphia County spend in the twelve
microenvironments associated with the potential NO2 health effect benchmark levels,
a) > 200 ppb, b) > 250 ppb, and c) > 300 ppb, year 2002 simulation with indoor
sources 71
Figure B-22. Fraction of time all simulated persons in Philadelphia County spend in the twelve
microenvironments associated with the potential NO2 health effect benchmark levels,
a) > 200 ppb, b) > 250 ppb, and c) > 300 ppb, year 2002 simulation without indoor
sources 72
Figure B-23. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures above potential health effect benchmark levels, using 2003 modeled air
quality (as is), with modeled indoor sources 74
Figure B-24. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures above potential health effect benchmark levels, using modeled 2002 air
quality (as is), with and without indoor sources 74
Figure B-25. Estimated percent of all asthmatics in Philadelphia with at least one exposure at or
above the potential health effect benchmark level, using modeled 2001-2003 air
quality just meeting the current standard, with modeled indoor sources 76
Figure B-26. Estimated number of all asthmatics in Philadelphia with at least one exposure at or
above the potential health effect benchmark level, using modeled 2002 air quality just
meeting the current standard, with and without modeled indoor sources 76
Figure B-27. Estimated percent of asthmatics in Philadelphia County with repeated exposures
above health effect benchmark levels, using modeled 2002 air quality just meeting the
current standard, with and without modeled indoor sources 77
Figure B-28. Estimated percent of all asthmatics in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003
modeled air quality (as is), with modeled indoor sources 80
Figure B-29. Estimated percent of all asthmatics in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003
modeled air quality (as is), with no indoor sources 80
Figure B-30. Estimated percent of all asthmatics in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003
modeled air quality just meeting the current standard (std), with modeled indoor
sources 81
Figure B-31. Estimated percent of all asthmatics in Philadelphia County with at least one NO2
exposure at or above potential health effect benchmark level, using 2001-2003
modeled air quality just meeting the current standard (std), with no indoor sources.. 81
Figure B-32. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with
modeled indoor sources 82
Figure B-33. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is),
without indoor sources 82
VIM
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Figure B-34. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hour, using 2001-2003 modeled air quality just
meeting the current standard (std), with modeled indoor sources 83
Figure B-35. Estimated percent of all asthmatics in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hour, using 2001-2003 modeled air quality just
meeting the current standard (std), with no indoor sources 83
Figure B-36. Estimated percent of asthmatic children in Philadelphia County with at least one
NO2 exposure at or above potential health effect benchmark level, using 2001-2003
modeled air quality (as is), with modeled indoor sources 86
Figure B-37. Estimated percent of asthmatic children in Philadelphia County with at least one
NO2 exposure at or above potential health effect benchmark level, using 2001-2003
modeled air quality (as is), with no indoor sources 86
Figure B-38. Estimated percent of asthmatic children in Philadelphia County with at least one
NO2 exposure at or above potential health effect benchmark level, using 2001-2003
modeled air quality just meeting the current standard (std), with modeled indoor
sources 87
Figure B-39. Estimated percent of asthmatic children in Philadelphia County with at least one
NO2 exposure at or above potential health effect benchmark level, using 2001-2003
modeled air quality just meeting the current standard (std), with no indoor sources.. 87
Figure B-40. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with
modeled indoor sources 88
Figure B-41. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality (as is), with
no indoor sources 88
Figure B-42. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality meeting the
current standard (std), with modeled indoor sources 89
Figure B-43. Estimated percent of asthmatic children in Philadelphia County with repeated NO2
exposures at or above 200 ppb 1-hr, using 2001-2003 modeled air quality meeting the
current standard (std), with no indoor sources 89
Figure B-44. Example of Light- and heavy-duty vehicle NOx emissions grams/mile (g/mi) for
arterial and freeway functional classes, 2001 93
Figure B-45. Polygon representing the Atlanta-Hartsfield International Airport area source 94
Figure B-46. Frequency distribution of distance between each Census block receptor and its
nearest major-roadway-link-centered receptor 97
IX
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B-1 Overview
This appendix contains supplemental descriptions of the methods and data used in the NC>2
exposure assessment, as well as detailed results from the exposure analyses performed. First, a
broad description of the exposure modeling approach is described, applicable to the two
exposure modeling case-studies conducted to date: Philadelphia and Atlanta. This is followed
with details regarding the required inputs for the model and the assumptions made for both of the
case-study assessments. The primary output for each exposure assessment was the numbers of
exceedances of short-term (1-hour) potential health effect benchmark levels experienced by the
asthmatic population residing within each location.
The first simulation location included Philadelphia County and was summarized in the 1st
draft Risk and Exposure Assessment (REA). The results from this assessment are presented here
as they existed in that document and the draft Technical Support Document draft (TSD) and no
adjustments were made to modeling approach used to generate the exposure results. However,
additional comparative analyses are presented here to clarify certain issues raised in the review
of this case-study by CASAC in May, 2008. These include additional comparisons of the
AERMOD modeled air quality with the available ambient monitor data (section 3.6.2) as well as
a comparison of the two on-road concentration estimation approaches used (section 3.6.3).
It should be noted that due to the differences in the approach used in the Philadelphia
analysis, the results are not directly comparable to the Atlanta case-study. In the dispersion
modeling approach used for Philadelphia, minor roadway link emissions were not estimated.
This lack of accounting for a potentially large emission source could have been responsible for
the underestimations in modeled ambient concentrations when compared with available ambient
monitoring data. It followed that the modeled air quality was then adjusted to account for the
difference in concentration using the monitored data. This was another difference in the
approach used for Philadelphia that was not used in Atlanta. The results for the Philadelphia
analysis are still included here since they still estimate exposures for the population within the
County, only with different uncertainties in the results when compared with the Atlanta data due
to the differing approach used. Most of the uncertainties in the results described in the Atlanta
REA can be similarly applied to the Philadelphia assessment (e.g., uncertainty in the CHAD data
base, population data bases, asthma prevalence rate, etc.), however, this Appendix does not
include a full characterization of uncertainty in the Philadelphia results since it was not used in
the final REA.
As mentioned above second case-study was conducted in portions of the Atlanta
Metropolitan Statistical Area (MSA) that included four counties. This is the exposure
assessment case study included in the final REA. Supplemental data and discussion not included
in the final REA regarding the dispersion modeling and exposure modeling approaches for the
Atlanta exposure case-study are provided here.
The discussion that follows includes three main sections. First is a broad overview of the
APEX model that was used in this NC>2 National Ambient Air Quality Standard (NAAQS)
review to estimate human exposures. This is followed with a description of the Philadelphia
County approach, data inputs, and results. And third, additional data and discussion regarding
the Atlanta exposure assessment are described. This is then followed with a series of
B-1
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Attachments, further documenting some of the data sources and modeling approaches used, as
well as previously conducted uncertainty analyses on selected input parameters.
B-2
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B-2 Human Exposure Modeling using APEX
The Air Pollutants Exposure model (APEX) is a personal computer (PC)-based program
designed to estimate human exposure to criteria and air toxic pollutants at the local, urban, and
consolidated metropolitan levels. APEX, also known as TREVI.Expo, is the human inhalation
exposure module of EPA's Total Risk Integrated Methodology (TRIM) model framework (US
EPA, 1999), a modeling system with multimedia capabilities for assessing human health and
ecological risks from hazardous and criteria air pollutants. It is being developed to support
evaluations with a scientifically sound, flexible, and user-friendly methodology. Additional
information on the TRIM modeling system, as well as downloads of the APEX Model, user's
guide, and other supporting documentation, can be found on EPA's Technology Transfer
Network (TTN) at http://www.epa.gov/ttn/fera.
B-2.1 History
APEX was derived from the National Ambient Air Quality Standards (NAAQS) Exposure
Model (NEM) series of models, developed to estimate exposure to the criteria pollutants (e.g.,
carbon monoxide (CO), ozone 63). In 1979, EPA began by assembling a database of human
activity patterns that could be used to estimate exposures to indoor and outdoor pollutants
(Roddin et al., 1979). These data were then combined with measured outdoor concentrations in
NEM to estimate exposures to CO (Biller et al., 1981; Johnson and Paul, 1983). In 1988,
OAQPS began to incorporate probabilistic elements into the NEM methodology and use activity
pattern data based on various human activity diary studies to create an early version of
probabilistic NEM for Os (i.e., pNEM/Os). In 1991, a probabilistic version of NEM was
extended to CO (pNEM/CO) that included a one-compartment mass-balance model to estimate
CO concentrations in indoor microenvironments. The application of this model to Denver,
Colorado has been documented in Johnson et al. (1992). Additional enhancements to pNEM/Os
in the early- to mid-1990's allowed for probabilistic exposure assessments in nine urban areas for
the general population, outdoor children, and outdoor workers (Johnson et al., 1996a; 1996b;
1996c). Between 1999 and 2001, updated versions of pNEM/CO (versions 2.0 and 2.1) were
developed that relied on activity diary data from EPA's Consolidated Human Activities Database
(CHAD) and enhanced algorithms for simulating gas stove usage, estimating alveolar ventilation
rate (a measure of human respiration), and modeling home-to-work commuting patterns.
The first version of APEX was essentially identical to pNEM/CO (version 2.0) except that it
was capable of running on a PC instead of a mainframe. The next version, APEX2, was
substantially different, particularly in the use of a personal profile approach (i.e., simulation of
individuals) rather than a cohort simulation (i.e., groups of similar persons). APEX3 introduced
a number of new features including automatic site selection from national databases, a series of
new output tables providing summary exposure and dose statistics, and a thoroughly reorganized
method of describing microenvironments and their parameters. Most of the spatial and temporal
constraints of pNEM and APEX1 were removed or relaxed by version 3.
The version of APEX used in this exposure assessment is APEX4, described in the APEX
User's Guide and the APEX Technical Support Document (US EPA, 2006a; 2006b) and referred
to here as the APEX User's Guide and TSD.
B-3
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B-2.2 APEX Model Overview
APEX estimates human exposure to criteria and toxic air . . . .. ..
„....,, , , ,-, . , , ,-. A microenvironment is a three-
pollutants at the local, urban, or consolidated metropolitan ,. . . ,. , .
F , , 4 , 4. . . 4 , , dimensional space in which human
area levels using a stochastic, microenvironmental approach. . . ... K . . .
„, j , j , i 4 j 4 r i r-u Au .- i contact with an environmental
The model randomly selects data for a sample of hypothetical „ . .. . . . .. .
• •••,, r- . , , .• , . , / • i 4. pollutant takes place and which can
individuals from an actual population database and simulates r . . . „ , . • _,
, , ,,,.,.,..,,/ , ,, ... , be treated as a well-characterized,
each hypothetical individual s movements through time and . .. . , . ..
f iU . i.- , ^^. 4- 4 4u • . relatively homogeneous location
space (e.g., at home, in vehicles) to estimate their exposure to ... ' .. " „ . .
11 \ / AT.T-V • T . .- j .u with respect to pollutant
a pollutant. APEX simulates commuting, and thus exposures ,r .. ^ .,. ...
,,F, ,, , , , .. f • j- -j i u concentrations for a specified time
that occur at home and work locations, tor individuals who . ,
work in different areas than they live.
APEX can be conceptualized as a simulated field study that would involve selecting an actual
sample of specific individuals who live in (or work and live in) a geographic area and then
continuously monitoring their activities and subsequent inhalation exposure to a specific air
pollutant during a specific period of time.
The main differences between APEX and an actual field study are that in APEX:
• The sample of individuals is a virtual sample, not actual persons. However, the
population of individuals appropriately balanced according to various demographic
variables and census data using their relative frequencies, in order to obtain a
representative sample (to the extent possible) of the actual people in the study area
• The activity patterns of the sampled individuals (e.g., the specification of indoor and
other microenvironments visited and the time spent in each) are assumed by the model to
be comparable to individuals with similar demographic characteristics, according to
activity data such as diaries compiled in EPA's Consolidated Human Activity Database
(or CHAD; US EPA, 2002; McCurdy et al., 2000)
• The pollutant exposure concentrations are estimated by the model using a set of user-
input ambient outdoor concentrations (either modeled or measured) and information on
the behavior of the pollutant in various microenvironments;
• Variation in ambient air quality levels can be simulated by either adjusting air quality
concentrations to just meet alternative ambient standards, or by reducing source
emissions and obtaining resulting air quality modeling outputs that reflect these potential
emission reductions, and
• The model accounts for the most significant factors contributing to inhalation exposure -
the temporal and spatial distribution of people and pollutant concentrations throughout
the study area and among microenvironments - while also allowing the flexibility to
adjust some of these factors for alternative scenarios and sensitivity analyses.
APEX is designed to simulate human population exposure to criteria and air toxic pollutants
at local, urban, and regional scales. The user specifies the geographic area to be modeled and the
number of individuals to be simulated to represent this population. APEX then generates a
personal profile for each simulated person that specifies various parameter values required by the
model. The model next uses diary-derived time/activity data matched to each personal profile to
generate an exposure event sequence (also referred to as activity pattern or diary) for the
modeled individual that spans a specified time period, such as one year. Each event in the
B-4
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sequence specifies a start time, exposure duration, geographic location, microenvironment, and
activity performed. Probabilistic algorithms are used to estimate the pollutant concentration
associated with each exposure event. The estimated pollutant concentrations account for the
effects of ambient (outdoor) pollutant concentration, penetration factors, air exchange rates,
decay/deposition rates, and proximity to emission sources, depending on the microenvironment,
available data, and estimation method selected by the user. Because the modeled individuals
represent a random sample of the population of interest, the distribution of modeled individual
exposures can be extrapolated to the larger population. The model simulation can be broadly
described in five steps that follow:
1. Characterize the study area. APEX selects census tracts within a study area - and thus
identifies the potentially exposed population - based on user-defined criteria and
availability of air quality and meteorological data for the area.
2. Generate simulated individuals. APEX stochastically generates a sample of
hypothetical individuals based on the census data for the study area and human profile
distribution data (such as age-specific employment probabilities).
3. Construct a sequence of activity events. APEX constructs an exposure event sequence
spanning the period of the simulation for each of the simulated individuals and based on
the activity pattern data.
4. Calculate hourly concentrations in microenvironments. APEX users define
microenvironments that people in the study area would visit by assigning location codes
in the activity pattern to the user-specified microenvironments. The model then
calculates hourly concentrations of a pollutant in each of these microenvironments for the
period of simulation, based on the user-provided microenvironment descriptions and
hourly air quality data. Microenvironmental concentrations are calculated for each of the
simulated individuals.
5. Estimate exposures.
APEX estimates a concentration for each exposure event based on the microenvironment
occupied during the event. These values can be averaged by clock hour to produce a sequence of
hourly average exposures spanning the specified exposure period. These hourly values may be
further aggregated to produce daily, monthly, and annual average exposure values.
B-2.2.1 Study Area Characterization
The APEX study area has traditionally been on the scale of a city or slightly larger
metropolitan area, although it is now possible to model larger areas such as combined statistical
areas (CSAs). In the exposure analyses performed as part of this NAAQS review, the study area
is defined by either a single or a few counties. The demographic data used by the model to
create personal profiles is provided at the census block level. For each block the model requires
demographic information representing the distribution of age, gender, race, and work status
within the study population. Each block has a location specified by latitude and longitude for
some representative point (e.g., geographic center). The current release of APEX includes input
files that already contain this demographic and location data for all census tracts, block groups,
and blocks in the 50 United States, based on the 2000 Census. In this assessment, exposures
were evaluated at the block level.
B-5
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B-2.2.1.1 Air Quality Data
Air quality data can be input to the model as measured data from an ambient monitor or that
generated by air quality modeling. This exposure analysis used modeled air quality data, whereas
the principal emission sources included both mobile and stationary sources as well as fugitive
emissions. Air quality data used for input to APEX were generated using AERMOD, a steady-
state, Gaussian plume model (EPA, 2004). The following steps were performed using
AERMOD.
1 Collect and analyze general input parameters. Meteorological data, processing
methodologies used to derive input meteorological fields (e.g., temperature, wind
speed, precipitation), and information on surface characteristics and land use are
needed to help determine pollutant dispersion characteristics, atmospheric
stability and mixing heights.
2. Estimate emissions. The emission sources modeled included, major stationary
emission sources, on-road emissions that occur on major roadways, and fugitive
emissions.
3. Define receptor locations. Three sets of receptors were identified for the
dispersion modeling, including ambient monitoring locations, census block
centroids, and links along major roadways.
4. Estimate concentrations at receptors. Hourly concentrations were estimated for
each year of the simulation (years 2001 through 2003) by combining
concentration contributions from each of the emission sources and accounting for
sources not modeled.
In APEX, the ambient air quality data are assigned to geographic areas called districts. The
districts are used to assign pollutant concentrations to the blocks/tracts and microenvironments
being modeled. The ambient air quality data are provided by the user as hourly time series for
each district. As with blocks/tracts, each district has a representative location (latitude and
longitude). APEX calculates the distance from each block/tract to each district center, and
assigns the block/tract to the nearest district, provided the block/tract representative location
point (e.g., geographic center) is in the district. Each block/tract can be assigned to only one
district. In this assessment the district was synonymous with the receptor modeled in the
dispersion modeling.
B-2.2.1.2 Meteorological Data
Ambient temperatures are input to APEX for different sites (locations). As with districts,
APEX calculates the distance from each block to each temperature site and assigns each block to
the nearest site. Hourly temperature data are from the National Climatic Data Center Surface
Airways Hourly TD-3280 dataset (NCDC Surface Weather Observations). Daily average and 1-
hour maxima are computed from these hourly data.
There are two files that are used to provide meteorological data to APEX. One file, the
meteorological station location file, contains the locations of meteorological data recordings
expressed in latitude and longitude coordinates. This file also contains start and end dates for the
data recording periods. The temperature data file contains the data from the locations in the
B-6
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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
APEX stochastically generates a user-specified number of simulated persons to represent the
population in the study area. Each simulated person is represented by a personal profile, a
summary of personal attributes that define the individual. APEX generates the simulated person
or profile by probabilistically selecting values for a set of profile variables (Table B-l). The
profile variables could include:
• Demographic variables, generated based on the census data;
• Physical variables, generated based on sets of distribution data;
• Other daily varying variables, generated based on literature-derived distribution data that
change daily during the simulation period.
APEX first selects demographic and physical attributes for each specified individual, and
then follows the individual over time and calculates his or her time series of exposure.
Table B-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
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-7
-------
census information (US Census Bureau, 2007). The employment statistics are broken down by
gender and age group, so that each gender/age group combination is given an employment
probability fraction (ranging from 0 to 1) within each census tract. The age groupings used are:
16-19, 20-21, 22-24, 25-29, 30-34, 35-44, 45-54, 55-59, 60-61, 62-64, 65-69, 70-74, and >75.
Children under 16 years of age were assumed to be not employed.
Since this analysis was conducted at the census block level, block level employment
probabilities were required. It was assumed that the employment probabilities for a census tract
apply uniformly to the constituent census blocks.
B-2.2.2.2 Commuting
In addition to using estimates of employment by tract, APEX also incorporates home-to-
work commuting data. Commuting data were originally derived from the 2000 Census and were
collected as part of the Census Transportation Planning Package (CTPP) (US DOT, 2007). The
data used contain counts of individuals commuting from home to work locations at a number of
geographic scales. These data were processed to calculate fractions for each tract-to-tract flow to
create the national commuting data distributed with APEX. This database contains commuting
data for each of the 50 states and Washington, D.C.
Commuting within the Home Tract
The APEX data set does not differentiate people that work at home from those that
commute within their home tract.
Commuting Distance Cutoff
A preliminary data analysis of the home-work counts showed that a graph of log(flows)
versus log(distance) had a near-constant slope out to a distance of around 120 kilometers.
Beyond that distance, the relationship also had a fairly constant slope but it was flatter, meaning
that flows were not as sensitive to distance. A simple interpretation of this result is that up to
120 km, the majority of the flow was due to persons traveling back and forth daily, and the
numbers of such persons decrease fairly rapidly with increasing distance. Beyond 120 km, the
majority of the flow is made up of persons who stay at the workplace for extended times, in
which case the separation distance is not as crucial in determining the flow.
To apply the home-work data to commuting patterns in APEX, a simple rule was chosen. It
was assumed that all persons in home-work flows up to 120 km are daily commuters, and no
persons in more widely separated flows commute daily. This meant that the list of destinations
for each home tract was restricted to only those work tracts that are within 120 km of the home
tract. When the same cutoff was performed on the 1990 census data, it resulted in 4.75% of the
home-work pairs in the nationwide database being eliminated, representing 1.3% of the workers.
The assumption is that this 1.3% of workers do not commute from home to work on a daily
basis. It is expected that the cutoff reduced the 2000 data by similar amounts.
Eliminated Records
B-8
-------
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 Concentmt ion = LeaverMult x avg(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 these files for APEX on the basis of the tract-level
commuting data and finely-resolved land use data. The software calculates commuting flows
between census blocks for the employed population according equation (2).
=Flow tractxFpopxFland
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.
FP0p = 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-9
-------
Thus, it is assumed that the frequency of commuting to a workplace block within a tract is
proportional to the amount of commercial and industrial land in the block.
B-2.2.2.3 Profile Functions
A Profile Functions file contains settings used to generate results for variables related to
simulated individuals. While certain settings for individuals are generated automatically by
APEX based on other input files, including demographic characteristics, others can be specified
using this file. For example, the file may contain settings for determining whether the profiled
individual's residence has an air conditioner, a gas stove, etc. As an example, the Profile
Functions file contains fractions indicating the prevalence of air conditioning in the cities
modeled in this assessment (Figure B-l). APEX uses these fractions to stochastically generate
air conditioning status for each individual. The derivation of particular data used in specific
microenvironments is provided below.
AC_Home
! Has air conditioning at home
TABLE
INPUT 1 PROBABILITY 2 "A/C probabilities"
0.850.15
RESULT INTEGER 2 "Yes/No"
12
#
Figure B-l. Example of a profile function file for A/C prevalence.
B-2.2.3 Activity Pattern Sequences
Exposure models use human activity pattern data to predict and estimate exposure to
pollutants. Different human activities, such as spending time outdoors, indoors, or driving, will
have varying pollutant exposure concentrations. To accurately model individuals and their
exposure to pollutants, it is critical to understand their daily activities.
The Consolidated Human Activity Database (CHAD) provides data for where people spend
time and the activities performed. CHAD was designed to provide a basis for conducting multi-
route, multi-media exposure assessments (McCurdy et al., 2000). The data contained within
CHAD come from multiple activity pattern surveys with varied structures (Table B-2), however
the surveys have commonality in containing daily diaries of human activities and personal
attributes (e.g., age and gender).
There are four CHAD-related input files used in APEX. Two of these files can be
downloaded directly from the CHADNet (http://www.epa.gov/chadnetl), and adjusted to fit into
the APEX framework. These are the human activity diaries file and the personal data file, and
are discussed below. A third input file contains metabolic information for different activities
listed in the diary file, these are not used in this exposure analysis. The fourth input file maps
five-digit location codes used in the diary file to APEX microenvironments; this file is discussed
in the section describing microenvironmental calculations (Section B-2.2.4.4).
B-2.2.3.1 Personal Information file
B-10
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Personal attribute data are contained in the CHAD questionnaire file that is distributed with
APEX. This file also has information for each day individuals have diaries. The different
variables in this file are:
• The study, person, and diary day identifiers
• Day of week
• Gender
• Employment status
• Age in years
• Maximum temperature in degrees Celsius for this diary day
• Mean temperature in degrees Celsius for this diary day
• Occupation code
• Time, in minutes, during this diary day for which no data are included in the database
B-2.2.3.2 Diary Events file
The human activity diary data are contained in the events file that is distributed with APEX.
This file contains the activities for the nearly 23,000 people with intervals ranging from one
minute to one hour. An individuals' diary varies in length from one to 15 days. This file
contains the following variables:
• The study, person, and diary day identifiers
• Start time of this activity
• Number of minutes for this activity
• Activity code (a record of what the individual was doing)
• Location code (a record of where the individual was)
B-11
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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);
Akland etal. (1985)
Spier etal. (1992)
Spier etal. (1992)
Klepeis etal. (1996);
Tsang and Klepeis
(1996)
Klepeis etal. (1996);
Tsang and Klepeis
(1996)
Hartwell etal. (1984);
Akland etal. (1985)
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-12
-------
A common approach for constructing long-term activity patterns from short-term records is
to re-select a daily activity pattern from the pool of data for each day, with the implicit
assumption that there is no correlation between activities from day to day for the simulated
individual. This approach tends to result in long-term activity patterns that are very similar
across the simulated population. Thus, the resulting exposure estimates are likely to
underestimate the variability across the population, and therefore, underestimate the high-end
exposure concentrations or the frequency of exceedances.
A contrasting approach is to select a single activity pattern (or a single pattern for each
season and/or weekday-weekend) to represent a simulated individual's activities over the
duration of the exposure assessment. This approach has the implicit assumption that an
individual's day-to-day activities are perfectly correlated. This approach tends to result in long-
term activity patterns that are very different across the simulated population, and therefore may
over-estimate the variability across the population.
Cluster-Markov Algorithm
A new algorithm has been developed and incorporated into APEX to represent the day-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.
1. For each demographic group (age, gender, employment status), temperature range, and
day-of-week combination, the associated time-activity records are partitioned into 3
groups using cluster analysis. The clustering criterion is a vector of 5 values: the time
spent in each of 5 microenvironment categories (indoors - residence; indoors - other
building; outdoors - near road; outdoors - away from road; in vehicle).
2. For each simulated individual, a single time-activity record is randomly selected from
each cluster.
3. A Markov process determines the probability of a given time-activity pattern occurring
on a given day based on the time-activity pattern of the previous day and cluster-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.
Details regarding the Cluster-Markov algorithm and supporting evaluations are provided in
Attachment 1.
B-13
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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.
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
The mass balance equation for a pollutant in a microenvironment is described by:
B-14
-------
dt
-=AC!n-ACoa(-AC_;+AC,
equation (3)
where:
dCME(t)
A Cin
A COM
A Cremovai
A Csource
Change in concentration in a microenvironment at time t (ppb),
Rate of change in microenvironmental concentration due to influx
of air (ppb/hour),
Rate of change in microenvironmental concentration due to outflux
of air (ppb/hour),
Rate of change in microenvironmental concentration due to
removal processes (ppb/hour), and
Rate of change in microenvironmental concentration due to an
emission source inside the microenvironment (ppb/hour).
Within the time period of an hour each of the rates of change, A.Cin, ACOMf, ACremova/, and
rce, is assumed to be constant. At each hour time step of the simulation period, APEX
estimates the hourly equilibrium, hourly ending, and hourly mean concentrations using a series
of equations that account for concentration changes expected to occur due to these physical
processes. Details regarding these equations are provided in the APEX User's Guide. APEX
reports hourly mean concentration as hourly concentration for a specific hour. The calculation
then continues to the next hour by using the end concentration for the previous hour as the initial
microenvironmental concentration. A description of the input parameters estimates used for
microenvironments using the mass balance approach is provided below.
B-2.2.4.2 Factors Model
The factors method is simpler than the mass balance method. It does not calculate
concentration in a microenvironment from the concentration in the previous hour and it has
fewer parameters. Table B-4 lists the parameters required by the factors method to calculate
concentrations in a microenvironment without emissions sources.
Table B-4. Factors model parameters.
Variable
' proximity
' penetration
Definition
Proximity factor
Penetration factor
Units
unitless
unitless
Value Range
' proximity — "
0 ^ f penetration - 1
The factors method uses the following equation to calculate hourly mean concentration in a
microenvironment from the user-provided hourly air quality data:
s^hourlymean
-ambient
r
J
proximity
r
J pene
penetration
equation (4)
where:
/proximity
/penetration
Hourly concentration in a microenvironment (ppb)
Hourly concentration in ambient environment (ppb)
Proximity factor (unitless)
Penetration factor (unitless)
B-15
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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
AERandDE
AERandDE
AER and DE
AERandDE
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.
B-2.2.4.4
Mapping of APEX Microenvironments to CHAD Diaries
B-16
-------
The Microenvironment Mapping file matches the APEX Microenvironments to CHAD
Location codes. Table B-6 gives the mapping used for the APEX simulations.
Table B-6. Mapping of CHAD activity locations to APEX microenvironments.
CHAD 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-17
-------
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-18
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B-2.2.5 Exposure Calculations
APEX calculates exposure as a time series of exposure concentrations that a simulated
individual experiences during the simulation period. APEX determines the exposure using
hourly ambient air concentrations, calculated concentrations in each microenvironment based on
these ambient air concentrations (and indoor sources if present), and the minutes spent in a
sequence of microenvironments visited according to the composite diary. The hourly exposure
concentration at any clock hour during the simulation period is determined using the following
equation:
_ 7=1
i - ~ equation (5)
where:
Ci = Hourly exposure concentration at clock hour / of the simulation period
N = Number of events (i.e., microenvironments visited) in clock hour / of
the simulation period.
C^j™ea" = Hourly mean concentration in microenvironment y (ppm)
t(j) = Time spent in microenvironment y' (minutes)
T =60 minutes
From the hourly exposures, APEX calculates time series of 1-hour average exposure
concentrations that a simulated individual would experience during the simulation period.
APEX then statistically summarizes and tabulates the hourly (or daily, annual average)
exposures. In this analysis, the exposure indicator is 1-hr exposures above selected health effect
benchmark levels. From this, APEX can calculate two general types of exposure estimates:
counts of the estimated number of people exposed to a specified NC>2 concentration level and the
number of times per year that they are so exposed; the latter metric is in terms of person-
occurrences or person-days. The former highlights the number of individuals exposed at least
one or more times per modeling period to the health effect benchmark level of interest. APEX
can also report counts of individuals with multiple exposures. This person-occurrences measure
estimates the number of times per season that individuals are exposed to the exposure indicator
of interest and then accumulates these estimates for the entire population residing in an area.
APEX tabulates and displays the two measures for exposures above levels ranging from 200
to 300 ppb by 50 ppb increments for 1-hour average exposures. These results are tabulated for
the population and subpopulations of interest.
B-2.2.6 Exposure Model Output
All of the output files written by APEX are ASCII text files. Table B-7 lists each of the
output data files written for these simulations and provides descriptions of their content.
Additional output files that can produced by APEX are given in Table 5-1 of the APEX User's
B-19
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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.
B-20
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B-3 Philadelphia Exposure Assessment Case-Study
This section documents detailed methodology and input data used in the Philadelphia
inhalation exposure assessment for NO2 conducted in support of the current review of the NO2
primary NAAQS. As mentioned in the Overview (section B-l), the Philadelphia analyses were
not updated since the 1st draft REA and not used in the final REA. One major difference in the
Philadelphia County assessment compared with that performed for Atlanta was the lack of
accounting for minor road emissions
Two important components of the analysis include the approach for estimating temporally
and spatially variable NO2 concentrations and simulating contact of humans with these pollutant
concentrations. A combined air quality and exposure modeling approach has been used here to
generate estimates of 1-hour NO2 exposures within Philadelphia. Details on the approaches used
are provided below and include the following:
• Description of the area assessed and populations considered
• Summary of the air quality modeling methodology and associated input data
• Description of the inhalation exposure model and associated input data
• Evaluation of estimated NO2 exposures using modeling methodology
B-3.1 Study Area Selection and Description
The selection of areas to include in the exposure analysis takes into consideration the location
of field and epidemiology studies, the availability of ambient monitoring and other input data,
the desire to represent a range of geographic areas, population demographics, general
climatology, and results of the ambient air quality characterization.
Philadelphia was selected as a location of interest through a similar statistical analysis of the
ambient NO2 air quality data described in Appendix A for each monitoring site within a location.
Criteria were established for selecting sites with high annual means and/or high numbers of
exceedances of potential health effect benchmark concentrations. The analysis considered all
data combined, as well as the more recent air quality data (2001-2006) separately.
The 90th percentile served as the point of reference for the annual means, and across all
complete site-years for 2001-2006, this value was 23.5 ppb. Seventeen locations contained one
or more site-years with an annual average concentration at or above the 90th percentile. When
combined with the number of 1-hour NO2 concentrations at or above 200 ppb, only two locations
fit these criteria, Philadelphia and Los Angeles. In comparing the size of the potential modeling
domains and the anticipated complexity in modeling influence of roadway exposures,
Philadelphia was determined to be a more manageable case-study.
Philadelphia County is comprised of 17,315 blocks containing a population of 1,517,550
persons. For this analysis the population studied was limited those residents of Philadelphia
County residing in census blocks that were either within 400 meters of a major roadway or
B-21
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within 10 km of a major emission source (see section B-3.5 for definition). This was done to
maintain balance between the representation of the study area/objectives and the computational
load regarding file size and processing time. There were 16,857 such blocks containing a
population of 1,475,651.
B-3.2 Exposure Period of Analysis
The exposure periods modeled were 2001 through 2003 to envelop the most recent year of
travel demand modeling (TDM) data available for the respective study locations (i.e., 2002) and
to include a 3 years of meteorological data to achieve a degree of stability in the dispersion and
exposure model estimates.
B-3.3 Populations Analyzed
A detailed consideration of the population residing in each modeled area was included where
the exposure modeling was performed. The assessment includes the general population (All
Persons) residing in each modeled area and considered susceptible and vulnerable populations as
identified in the ISA. These include population subgroups defined from either an exposure or
health perspective. The population subgroups identified by the ISA (US EPA, 2007a) that were
included and that can be modeled in the exposure assessment include:
• Children (ages 5-18)
• Asthmatic children (ages 5-18)
• All persons (all ages)
• All Asthmatics (all ages)
In addition to these population subgroups, individuals anticipated to be exposed more
frequently to NO2 were considered, including those commuting on roadways and persons
residing near major roadways. To date, this document provides a summary of the subpopulations
of interest (all asthmatics and asthmatic children), supplemented with additional exposure and
risk results for the total population where appropriate.
B-3.4 Simulated Individuals
Due to the large size of the air quality input files, the modeled area was separated into three
sections. The number of simulated persons in each model run (3 sections per 3 years) was set to
50,000, yielding a total of 150,000 persons simulated for each year. The parameters controlling
the location and size of the simulated area were set to include the county(s) in the selected study
area. The settings that allow for replacement of CHAD data that are missing gender,
employment or age values were all set to preclude replacing missing data. The width of the age
window was set to 20 percent to increase the pool of diaries available for selection. The variable
that controls the use of additional ages outside the target age window was set to 0.1 to further
enhance variability in diary selection. See the APEX User's Guide for further explanation of
these parameters. The total population simulated for Philadelphia County was approximately
1.48 million persons, of which there a total simulated population of 163,000 asthmatics. The
model simulated approximately 281,000 children, of which there were about 48,000 asthmatics.
B-22
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Due to random sampling, the actual number of specific subpopulations modeled varied slightly
by year.
B-3.4.1
Asthma Prevalence Rates
One of the important population subgroups for the exposure assessment is asthmatic children.
Evaluation of the exposure of this group with APEX requires the estimation of children's asthma
prevalence rates. The proportion of the population of children characterized as being asthmatic
was estimated by statistics on asthma prevalence rates recently used in the NAAQS review for
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
B-23
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B-3.5 Air Quality Data Generated by AERMOD
Air quality data input to the model were generated by air quality modeling using AERMOD.
Principal emission sources included both mobile and stationary sources as well as fugitive
emissions. The methodology is described below.
B-3.5.1 Meteorological Inputs
All meteorological data used for the AERMOD dispersion model simulations were processed
with the AERMET meteorological preprocessor, version 06341. This section describes the input
data and processing methodologies used to derive input meteorological fields for each of the five
regions of interest.
B-3.5.1.1 Data Selection
Raw surface meteorological data for the 2001 to 2003 period were obtained from the
Integrated Surface Hourly (ISH) Database,1 maintained by the National Climatic Data Center
(NCDC). The ISH data used for this study consists of typical hourly surface parameters
(including air and dew point temperature, atmospheric pressure, wind speed and direction,
precipitation amount, and cloud cover) from hourly Automated Surface Observing System
(ASOS) stations. No on-site observations were used.
The surface meteorological station used for this analysis is located at Philadelphia
International (KPHL) airport. The selection of surface meteorological stations minimized the
distance from the station to city center, minimized missing data, and maximized land-use
representativeness of the station site compared to the city center.
The total number of surface observations and the percentage of those observations accepted
by AERMET (i.e., those observations that were both not missing and within the expected ranges
of values), are shown by Table B-9. Note that instances of calm winds are not rejected by the
AERMET processor, but are later treated as calms in the dispersion analysis. There were 1,772
hours in Philadelphia (7%) with calm winds (see Table B-10).
Table B-9. Number of AERMET raw hourly surface meteorology observations, percent acceptance rate,
2001-2003.
Surface Variable
Precipitation
Station Pressure
Cloud Height
Sky Cover
Horizontal Visibility
Temperature
Dew Point
Temperature
Relative Humidity
Philadelphia (KPHL)
n=26,268
% Accepted a
100
99
99
95
99
99*
99
99
http://wwwl .ncdc.noaa.gov/pub/data/techrpts/tr200101/tr2001-01 .pdf
B-24
-------
Wind Direction
Wind Speed
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.
Table B-10. Number of calms reported by AERMET by year for Philadelphia.
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
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
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
% Accepted
100
100
100
100
100
85*
100
100
100
99
63
62
100
100
100
99*
73
73
100
100
100
97*
2 http://raob.fsl.noaa.gov/
B-25
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Height
Level
1500-
2000m
2000-
2500m
2500-
3000m
3000-
3500m
3500-
4000m
>4000
m
Variable
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
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Pressure
Height
Temperature
DewPointTemperature
WindDirection
WindSpeed
Philadelphia (KIAD)
n
4204
4204
3354
3354
3354
3354
3354
3354
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
71
71
100
100
100
95*
50
50
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-26
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B-3.5.2
Surface Characteristics and Land Use Analysis
In addition to the standard meteorological observations of wind, temperature, and cloud
cover, AERMET analyzes three principal variables to help determine atmospheric stability and
mixing heights: the Bowen ratio3, surface albedo4 as a function of the solar angle, and surface
roughness.5
The January 2008 version of AERSURFACE was used to estimate land-use patterns and
calculate the Bowen ratio, surface albedo, and surface roughness as part of the AERMET
processing. AERSURFACE uses the US Geological Survey (USGS) National Land Cover Data
1992 archives (NLCD92). 6 Three to four land-use sectors were manually identified around the
surface meteorological station using this land-use data. These land-use sectors are used to
identify the Bowen ratio and surface albedo, which are assumed to represent an area around the
station of radius 10 km, and to calculate surface roughness by wind direction.
A monthly temporal resolution was used for the Bowen ratio, albedo, and surface roughness
at the meteorological site. Because the site was located at an airport, a lower surface roughness
was calculated for the 'Commercial/Industrial/Transportation' land-use type to reflect the
dominance of transportation land cover rather than commercial buildings. Philadelphia has at
least one winter month of continuous snow cover, which tends to increase albedo, decrease
Bowen ratio, and decrease surface roughness for most land-use types during the winter months
compared to a snow-free area. Seasons were assigned based on 1971-2000 NCDC 30-year
climatic normals and on input from the state climatologist (Table B-12).
Table B-12. Seasonal definitions and specifications for Philadelphia.
Location
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
Figure B-2 illustrates show the manually created land-use sectors around the application site;
a 1.9 mile (3 km) radius circle was used. Data are from the NLCD92 database. Prior to the
3 For any moist surface, the Bowen Ratio is the ratio of heat energy used for sensible heating (conduction and
convection) to the heat energy used for latent heating (evaporation of water or sublimation of snow). The Bowen
ratio ranges from about 0.1 for the ocean surface to more than 2.0 for deserts. Bowen ratio values tend to decrease
with increasing surface moisture for most land-use types.
4 The ratio of the amount of electromagnetic radiation reflected by the earth's surface to the amount incident upon it.
Value varies with surface composition. For example, snow and ice vary from 80% to 85% and bare ground from
10% to 20%.
5 The presence of buildings, trees, and other irregular land topography that is associated with its efficiency as a
momentum sink for turbulent air flow, due to the generation of drag forces and increased vertical wind shear.
6 http://seamless.usgs.gov/
B-27
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release of AERSURFACE, the user was required to manually pull values of Bowen ratio (Po),
albedo (a), and surface roughness (ZQ) 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. AERSURF ACE
resolves these issues by providing a uniform methodology for calculation of surface effects on
dispersion; it also only varies surface roughness by wind direction.
Before AERSURF ACE, without an automated algorithm to determine land-use patterns, it
was simplest for the user to visually estimate land usage by sector. With AERSURF ACE, the
land-use is automatically determined. The proximity of the meteorological site to an airport and
whether the site was located in an arid region were previously not explicitly accounted for as
they now are in AERSURF ACE. Snow cover, too, is critical for determination of a, but was
largely left to user's discretion regarding its presence. With AERSURF ACE, the lookup tables
have separate columns for winter without much snow and for winter with abundant snow. The
user determines if winter at a particular location contains at least one month of continuous snow
cover, and AERSURF ACE will pull values of the surface characteristics from the appropriate
winter column.
We conducted a sensitivity test to evaluate the impacts of using this new tool on the present
analysis. Figure B-3 shows a sample comparison of surface roughness values at the Philadelphia
site with and without the use of AERSURF ACE. In the Figure, estimated surface roughness
values using visual land-use estimations and look-up table values are shown in muted shades and
AERSURF ACE values in dark shades. Monthly season definitions are the same in both cases.
However, in the AERSURF ACE case, winter was specified as having a one-month period of
snow cover. Also, in the AERSURF ACE case the site was specified as being at an airport.
In this case, ZQ values are much lower with AERSURF ACE than with a visual estimation of
land-use. In the AERSURF ACE tool, Philadelphia was noted as being at an airport, tending to
represent the lower building heights in the region and the inverse distance weighting
implemented in the tool. Thus, lower z0 values were obtained over most developed-area sectors
in this scenario. The indication that at least one month of continuous snow cover is present also
tends to lower wintertime z0 values. In addition to these systematic differences, the automated
AERSURF ACE land-use analysis for Philadelphia tended to identify less urban coverage and
more water coverage, lowering roughness values, but it also tended to identify more forest cover
and less cultivated land cover than our visual analysis, increasing some ZQ values.
Po and a also varied significantly between the scenarios. However, this was largely due to
two practical matters: First, the independence of these variables of wind direction in the
AERSURF ACE case and secondly the use of monthly-varying moisture conditions in one test
case and not another. Thus we have not presented those results
B-28
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Open Water
Low in ten atyR esi dentiai
HignintensityResaental
Commercial/InrJustrial/Transportation
BareRock/SandClay
Quames/StrlpMines/GravelPlts
Trans itonal
DeciduousForest
Eve ngreerf crest
MixedForest
Shrubland
GrasslancVHerbaceous
Paslure/Hay
Crops
UrBan/RecreatnnaiGrasses
woodywenands
EmergenlHertiaceousweoanas
75100VI
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-29
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0.7
0.6
c
01
-------
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%
pitation 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
B-3.5.4 On-Road Emissions Preparation
Information on traffic data in the Philadelphia area was obtained from the Delaware Valley
Regional Planning Council (DVRPC7) via their most recent, baseline travel demand modeling
(TDM) simulation - that is, the most recent simulation calibrated to match observed traffic data.
DVRPC provided the following files.
• Shapefiles of TDM outputs for the 2002 baseline year for all links in their network.
• Input files for the MOBILE6.2 emissions model that characterize local inputs that differ
from national defaults, including fleet registration distribution information.
• Postprocessing codes they employ for analysis of TDM outputs into emission inventory
data, to ensure as much consistency as possible between the methodology used for this
study and that of DVRPC. These include DVRPC's versions of the local SVMT.DEF,
HVMT.DEF, and FVMT.DEF MOBILE6.2 input files describing the vehicle miles
traveled (VMT) by speed, hour, and facility, respectively, by county in the Delaware
Valley area.
• A lookup table used to translate average annual daily traffic (AADT) generated by the
TDM into hourly values.
Although considerable effort was expended to maintain consistency between the DVRPC
approach to analysis of TDM data and that employed in this analysis, including several personal
communications with agency staff on data interpretation, complete consistency was not possible
due to the differing analysis objectives. The DVRPC creates countywide emission inventories.
This study created spatially and temporally resolved emission strengths for dispersion modeling.
B-3.5.4.1
Emission Sources and Locations
7 http://www.dvrpc.org/
B-31
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The TDM simulation's shapefile outputs include annual average daily traffic (AADT)
volumes and a description of the loaded highway network. The description of the network
consists of a series of nodes joining individual model links (i.e., roadway segments) to which the
traffic volumes are assigned, and the characteristics of those links, such as endpoint location,
number of lanes, link distance, and TDM-defined link daily capacity.8
To reduce the scope of the analysis, the full set of links in the DVRPC network was first
filtered to include only those roadway types considered major (i.e., freeway, parkway, major
arterial, ramp), and that had AADT values greater than 15,000 vehicles per day (one direction).
However, the locations of links in the model do not necessarily agree well with the roads
they are attempting to represent. While the exact locations of the links may not be mandatory for
DVRPC's travel demand modeling, the impacts of on-road emissions on fixed receptors is
crucially linked to the distance between the roadways and receptors. Hence, it was necessary to
modify the link locations from the TDM to the best known locations of the actual roadways. The
correction of link locations was done based on the locations of the nodes that define the end
points of links with a GIS analysis, as follows.
A procedure was developed to relocate TDM nodes to more realistic locations. The
nodes in the TDM represent the endpoints of links in the transportation planning network and are
specified in model coordinates. The model coordinate system is a Transverse Mercator
projection of the TranPlan Coordinate System with a false easting of 31068.5, false northing of-
200000.0, central meridian: -75.00000000, origin latitude of 0.0, scale factor of 99.96, and in
units of miles. The procedure moved the node locations to the true road locations and translated
to dispersion model coordinates. The Pennsylvania Department of Transportation (PA DOT)
road network database9 was used as the specification of the true road locations. The nodes were
moved to coincide with the nearest major road of the corresponding roadway type using a built-
in function of ArcGIS. Once the nodes had been placed in the corrected locations, a line was
drawn connecting each node pair to represent a link of the adjusted planning network.
To determine hourly traffic on each link, the AADT volumes were converted to hourly
values by applying DVRPC's seasonal and hourly scaling factors. To determine hourly traffic
on each link, the AADT volumes were converted to hourly values by applying DVRPC's
seasonal and hourly scaling factors. The heavy-duty vehicle fraction - which is assumed by
DVRPC to be about 6% in all locations and times - was also applied.10 Another important
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-32
-------
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-33
-------
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
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-34
-------
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-35
-------
X
O
z
01
O)
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-36
-------
Ramp
Arterial
Freeway
N/A
34
51
35
31
62
35
44
66
35
32
62
N/A
N/A
N/A
The resulting emission factors were then coupled with the TDM-based activity estimates to
calculate emissions from each of the 1,268 major roadway links. However, many of the links
were two sides of the same roadway segment. To speed model execution time, those links that
could be combined into a single emission source were merged together. This was done only for
the 628 links (314 pairs) where opposing links were paired in space and exhibited similar activity
levels within 20% of each other.
B-3.5.4.3 Other Emission Parameters
Each roadway link is characterized as a rectangular area source with the width given by the
number of lanes and an assumed universal lane width of 12 ft (3.66 m). The length and
orientation of each link is determined as the distance and angle between end nodes from the
adjusted TDM locations. In cases where the distance is such that the aspect ratio is greater than
100:1, the links were disaggregated into sequential links, each with a ratio less than that
threshold. There were 27 links that exceeded this ratio and were converted to 55 segmented
sources. Thus, the total number of area sources included in the dispersion simulations is 982.
Table B-19 shows the distribution of on-road area source sizes. Note that there are some road
segments whose length was zero after GIS adjustment of node location. This is assumed to be
compensated by adjacent links whose length will have been expanded by a corresponding
amount.
Table B-19. On-road area source sizes.
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 Particulate Matter Exposure Assessment Study
for the Ports of Los Angeles and Long Beach. State of California Air Resources Board, Final Report, April 2006.
B-37
-------
elevated or complex terrain was included in the modeling. That is, all sources are assumed to lie
in a flat plane.
B-3.5.5 Stationary Sources Emissions Preparation
Data for the parameterization of major point sources in Philadelphia comes primarily from
two sources: the 2002 National Emissions Inventory (NEI; US EPA, 2007b) and Clean Air
Markets Division (CAMD) Unit Level Emissions Database (US EPA, 2007c). These two
databases have complimentary information.
The NEI database contains stack locations, emissions release parameters (i.e., height,
diameter, exit temperature, exit velocity), and annual emissions for 707 NOx-emitting stacks
(206 of which are considered fugitive release points) in Philadelphia County. The CAMD
database, on the other hand, has information on hourly NOX emission rates for all the units in the
US, where the units are the boilers or equivalent, each of which can have multiple stacks. The
alignment of facilities between the two databases is not exact, however. Some facilities listed in
the NEI, are not included in the CAMD database. Of those facilities that do match, in many cases
there is no clear pairing between the individual stacks assigned within the databases.
B-3.5.5.1 Data Source Alignment
To align the information between the two databases and extract the useful portion of each for
dispersion modeling, the following methodology was used.
1. Attention was limited stacks within the NEI data base that (a) lie within Philadelphia
County and (b) were part of a facility with total emissions from all stacks exceeding
lOOtpyNOx.
2. Individual stacks that had identical stack physical parameters and were co-located
within about 10m were combined to be simulated as a single stack with their
emissions summed.
3. All fugitive releases were removed from the list, to be analyzed as a separate source
group.
The resulting 19 distinct, combined stacks from the NEI are shown in Table B-20.
The CAMD database was then queried for facilities that matched the facilities identified from
the NEI database. Facility matching was done on the facility name, Office of Regulatory
Information Systems (ORIS) identification code (when provided) and facility total emissions to
ensure a best match between the facilities. Once facilities were paired, individual units and
stacks in the data bases were paired, based on annual emission totals. Table B-21 shows the
matching scheme for the seven major facilities in Philadelphia County.15
15 Note that Jefferson Smurfit does not exist in the CAMD database. The matching here was based on facility types
as follows. Smurfit in PA was taken as a packaging/recycling facility, and the stack assumed to be a Cogen facility,
based on information in the NEEDS database (http://www.epa.gov/interstateairquality/pdfs/NEEDS-NODA.xls).
The best matched cogen plant in Philadelphia County in both the NEEDS and CAMD database is the Gray's Ferry
Cogen Partnership (ORIS 54785), which was a reasonable match for Smurfit's total emissions. It was assumed that
the hourly emission profile also matches well.
B-38
-------
In Table B-21, there are sometimes multiple CAMD units that pair with a single NEI
combined stack. In these cases the hourly emission rates from the matching CAMD units are
summed for each hour. For example, in the case of stack 859 for "Sunoco, Inc - Philadelphia"
five CAMD hourly records are summed into a single hourly record. Then each resulting hourly
value is scaled by a factor of 1032.8 / 938.9 = 1.10, so that the annual total matches the NEI
annual total.
Similarly, there are sometimes multiple combined stacks that pair with single units. In this
case the CAMD values are disaggregated according to NEI-defmed stack contributions. For
example, "Sunoco, Inc - Philadelphia" stack 855's profile is determined by taking the hourly
profile from CAMD unit number 52106-150101, and scaling each value by a factor of 26.2 tpy /
48.2 tpy total = 0.54. Then each resulting hourly value is scaled by a factor of 48.2/162.1 = 0.3
so that the sum of the annual totals for the 4 stacks corresponding to unit number 52106-150101
matches the NEI total. For consistency, in each case the 2001 and 2003 hourly emission profiles
were determined using the same scaling factors, but applied to the respective CAMD emission
profile.
It is clear from Table B-21 that most facilities agree well in total annual NOX emissions
between the two databases. However, in the case of the "Sunoco Chemicals (Former Allied
Signal)" facility, nearly half of the NEI emissions (without fugitives) do not appear in the
CAMD database. The reason for this is unknown and no information was readily available on
the relative accuracy of the two databases.
Figure B-5 illustrates the discrepancy versus fraction of hours with positive emissions,
according to the CAMD data base. The figure suggests that the discrepancies are not primarily
the result of facilities with episodic emissions (i.e., "peak load" facilities). Although there is
good agreement on facility-wide emissions between the two data bases, there are larger
discrepancies between CAMD unit emissions and NEI stack emissions. This is to be expected
given the discrepancy in resolution between the two data bases.
B-39
-------
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-40
-------
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-41
-------
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-42
-------
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 -
^T 90
c
o
** n -
t u
o
-E
AC] -
880007
50607 88i°°6
• J54785(Grays Ferry)
3160 «7QB
04780(Jefferson Sir
52
urfitl
106
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-43
-------
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-44
-------
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).
noco (ReleaseHght = 3m)
\ ^ Sunoco (ReleaseHght = 23+ m)
KPHL Airport Baggage Handling Area
12
• Kilometers
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-45
-------
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
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-46
-------
B-3.5.7 Receptor Locations
Three sets of receptors were chosen to represent the locations of interest. First, all NOx
monitor locations, shown by Table B-24, within the Philadelphia county were included as
receptor locations. Although all receptors are assumed to be on a flat plane, they are placed at
the standard breathing height of 5.9 ft (1.8 m).
Table B-24. Philadelphia County NOx monitors.
Site ID
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.
8195
Figure B-7. Centroid locations within fixed distances to major point and mobile sources in Philadelphia
county.
B-47
-------
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.
IHlL|lL,HL>L,LhLLiLLL,L,LL.| LLLLLLLLI
~ 1 UU /O
- 90%
80%
- 70%
- 60%
- 50%
- 40%
- 30%
- 20%
- 10%
- no/.
~
u_
Q
O
LO O
IT) O
CM O
CM -^ CD CO
CM CM CM CM CM
O
O
LO
CD
CM
IT) O
IT) h-
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).
B-3.5.8
Other AERMOD Specifications
Since each of the case-study locations were MSA/CMS As, all emission sources were
characterized as urban. The AERMOD toxics enhancements were also employed to speed
calculations from area sources. NOX chemistry was applied to all sources to determine NO2
concentrations. For the each of the roadway, fugitive, and airport emission sources, the ozone
limiting method (OLM) was used, with plumes considered ungrouped. Because an initial NO2
fraction of NOX is anticipated to be about 10% or less (Finlayson-Pitts and Pitts, 2000; Yao et al.,
2005), a conservative value of 10% for all sources was selected. For all point source simulations
B-48
-------
the Plume Volume Molar Ratio Method (PVMRM) was used to estimate the conversion of NOX
to NC>2, with the following settings:
1. 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.
2. The equilibrium value for the NO2:NOX ratio was taken as 75%, the national average
ambient ratio.19
3. 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).
B-3.5.9 Air Quality Concentration Adjustment
The hourly concentrations estimated from each of the three source categories were combined
at each receptor. Then a local concentration, reflecting the concentration contribution from
emission sources not included in the simulation, was added to the sum of the concentration
contributions from each of these sources at each receptor. The local concentration was estimated
from the difference between the model predictions at the local NC>2 monitors and the observed
values. It should be noted that this local concentration may also include any model error present
in estimating concentration at the local monitoring sites. Table B-25 presents a summary of the
estimated local concentration added to the AERMOD hourly concentration data.
Table B-25. Comparison of ambient monitoring and AERMOD predicted NO2 concentrations in
Philadelphia.
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/scramOOl/guidance/guide/appw_03.pdf.
B-49
-------
mean | | 6 |
1 the difference represents concentrations attributed to sources
not modeled by AERMOD and model error.
2 the mean difference between measured and modeled was
added uniformly at each receptor hourly concentration to
generate the AERMOD final concentrations.
* monitor did not meet completeness criteria used in the air
quality characterization.
B-3.5.10 Meteorological Data Used By APEX
APEX used the same meteorological data that was used for the AERMOD modeling, the
station located at Philadelphia International (KPHL) airport.
B-3.5.11 Microenvironment Descriptions
B-3.5.11.1 Microenvironment 1: Indoor-Residence
The Indoors-Residence microenvironment uses several variables that affect 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) =
3S50P(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
B-50
-------
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
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 C7, 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-51
-------
conditioner and three for those without; the last few distributions for each air conditioning setting
were the same due to the available data to populate the field. The parameter estimates for the
removal factor (DE) is also shown following the AER data.
Micro number
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
Figure B-9. Example input file from APEX for Indoors-residence microenvironment.
Indoor source contributions
A number of studies, as described in the NOX ISA, have noted the importance of gas cooking
appliances as sources of NC>2 emissions. An indoor emission source term was included in the
APEX simulations to estimate exposure to indoor sources of NC>2. Three types of data were used
to implement this factor:
• The fraction of households in the Philadelphia MSA that use gas for cooking fuel
• The range of contributions to indoor NO2 concentrations that occur from cooking
with gas
• The diurnal pattern of cooking in households.
The fraction of households in Philadelphia County that use gas cooking fuel (i.e., 55%) was
taken from the US Census Bureau's American Housing Survey for the Philadelphia Metropolitan
Area: 2003.
B-52
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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
NO2 indoors and outdoors, among several other parameters, and conducted under several
different cooking procedures and stove operating conditions. A uniform distribution of
concentration contributions for input to APEX was estimated as follows.
• The concurrent outdoor NO2 concentration measurement was subtracted from each
indoor concentration measurement, to yield net indoor concentrations
• Net indoor concentrations for duplicate cooking tests (same food cooked the same
way) were averaged for each indoor room, to yield average net indoor concentrations
• The minimum and maximum average net indoor concentrations for any test in any
room were used as the lower and upper bounds of a uniform distribution
This resulted in a minimum average net indoor concentration of 4 ppb and a maximum net
average indoor concentration of 188 ppb.
An analysis by Johnson et al (1999) of survey data on gas stove usage collected by Koontz et
al (1992) showed an average number of meals prepared each day with a gas stove of 1.4. The
diurnal allocation of these cooking events was estimated as follows.
• Food preparation time obtained from CHAD diaries was stratified by hour of the day,
and summed for each hour, and summed for total preparation time.
• The fraction of food preparation occurring in each hour of the day was calculated as
the total number of minutes for that hour divided by the overall total preparation time.
The result was a measure of the probability of food preparation taking place during
any hour, given one food preparation event per day.
• Each hourly fraction was multiplied by 1.4, to normalize the expected value of daily
food preparation events to 1.4.
The estimated probabilities of cooking by hour of the day are presented in Table B-28. For
this analysis it was assumed that the probability that food preparation would include stove usage
was the same for each hour of the day, so that the diurnal allocation of food preparation events
would be the same as the diurnal allocation of gas stove usage. It was also assumed that each
cooking event lasts for exactly 1 hour, implying that the average total daily gas stove usage is 1.4
hours.
Table B-28. Probability of gas stove cooking by hour of the day.
Hour of Day
0
1
2
3
4
5
6
7
8
9
Probability of
Cooking
0
0
0
0
0
5
10
10
10
5
B-53
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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-54
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Microenvironments 8 and 9: Outdoor microenvironments
Two outdoor microenvironments, the Near Road and Public Garage/Parking Lot, used the
factors method to calculate pollutant exposure. Penetration factors are not applicable to outdoor
environments (effectively, PEN=1). Proximity factors were developed from the AERMOD
concentration predictions, i.e., the block-centroid-to-nearest-roadway concentration ratios. Based
on the resulting sets of ratio values, the ratio distributions were stratified by hour of the day into
3 groups as indicated by the "hours-block" specification in the example file in Figure B-l 1. The
lower and upper bounds for sampling were specified as the 5th and 95th percentile values,
respectively, of each distribution.
Micro number =8 ! Outdoor near road PROXIMITY FACTOR
Pollutant = 1
Parameter Type = PR
Hours - Block = 111111222222222222233311
ResampHours = YES
ResampDays = YES
ResampWork = YES
Block DType Season Area C1 C2 C3 Shape Par1 Par2 Par3 Par4 LTrunc UTrunc ResampOut
111 1111 LogNormal 1.251 1.478 0. . 0.86 2.92 Y
211 1111 LogNormal 1.555 1.739 0. . 0.83 4.50 Y
311 1111 LogNormal 1.397 1.716 0. . 0.73 4.17 Y
Figure B-ll. Example input file from APEX for outdoor near road microenvironment.
B-3.5.11.3 Microenvironment 10: Outdoors-General.
The general outdoor environment concentrations are well represented by the modeled
concentrations. Therefore, both the penetration factor and proximity factor for this
microenvironment were set to 1.
B-3.5.11.4 Microenvironments 11 and 12: In Vehicle- Cars and Trucks, and Mass Transit
Penetration factors were developed from data provided in Chan and Chung (2003). Inside-
vehicle and outdoor NC>2 concentrations were measured with for three ventilation conditions, air-
recirculation, fresh air intake, and with windows opened. Since major roads were the focus of
this assessment, reported indoor/outdoor ratios for highway and urban streets were used here.
Mean values range from about 0.6 to just over 1.0, with higher values associated with increased
ventilation (i.e., window open). A uniform distribution was selected for the penetration factor
for Inside-Cars/Trucks (ranging from 0.6 to 1.0) due to the limited data available to describe a
more formal distribution and the lack of data available to reasonably assign potentially
influential characteristics such as use of vehicle ventilation systems for each location. Mass
transit systems, due to the frequent opening and closing of doors, was assigned a uniform
distribution ranging from 0.8 to 1.0 based on the reported mean values for fresh air intake and
open windows. Proximity factors were developed as described above for Microenvironments 8
and 9.
B-55
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B-3.5.12 Adjustment for Just Meeting the Current Standard
To simulate just meeting the current standard, dispersion modeled concentration were not
rolled-up as was done for the monitor concentrations used in the air quality characterization. A
proportional approach was used as done in the Air Quality Characterization, but to reduce
computer processing time, the health effect benchmark levels were proportionally reduced by the
similar factors described for each specific location and simulated year. Since it is a proportional
adjustment, the end effect of adjusting concentrations upwards versus adjusting benchmark
levels downward within the model is the same. The difference in the exposure and risk modeling
was that the modeled air quality concentrations were used to generate the adjustment factors.
Table B-29 provides the adjustment factors used and the adjusted potential health effect
benchmark concentrations to simulate just meeting the current standard. When modeling indoor
sources, the indoor concentration contributions needed to be scaled downward by the same
proportions.
Table B-29. Adjustment factors and potential health effect benchmark levels used by APEX to simulate just
meeting the current standard.
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-56
-------
B = proportion of exposure concentration from indoor
= indoor source concentration contribution
= an exposure concentration of interest
It follows that if we are interested in adjusting the ambient concentrations upwards by
some proportional factor F, this can be described with the following:
F x A x Can*** + Bx C mdoor > C ' threshold equation (7)
This is equivalent to
A x Cambient +Bx (Cmdoor I F) > (Cthreshold I F) equation (8)
Therefore, if the potential health effect benchmark level and the indoor concentrations are
both proportionally scaled downward by the same adjustment factor, the contribution of both
sources of exposure (i.e., ambient and indoor) are maintained and the same number of estimated
exceedances would be obtained as if the ambient concentration were proportionally adjusted
upwards by factor F.
B-57
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B-3.6 Philadelphia Exposure Modeling Results
B-3.6.1 Overview
The results of the exposure and risk characterization are presented here for Philadelphia
County. These results are not to be directly compared with the results in the final REA, due to
differences in the model approach used here. The main difference that does not allow for
comparison with the Atlanta exposure assessment is that the minor road emissions were not
modeled in the Philadelphia County assessment.
Several scenarios were considered for the exposure assessment, including two averaging time
for NC>2 concentrations (annual and 1-hour), inclusion of indoor sources, and for evaluating just
meeting the current standard. To date, year 2002 served as the base year for all scenarios, years
2001 and 2003 were only evaluated for a limited number of scenarios. Exposures were
simulated for four groups; children and all persons, and the asthmatic population within each of
these.
The exposure results summarized below focus on the population group where exposure
estimations are of greatest interest, namely asthmatic individuals. The complete results for each
of these two population subgroups are provided in section B-3.6.7. However, due to certain
limitations in the data summaries output from the current version of APEX, some exposure data
could only be output for the entire population modeled (i.e., all persons - includes asthmatics and
healthy persons of all ages). The summary data for the entire population (e.g., annual average
exposure concentrations, time spent in microenvironments at or above a potential health effect
benchmark level) can be representative of the asthmatic population since the asthmatic
population does not have its microenvironmental concentrations and activities estimated any
differently from those of the total population.
B-3.6.2 Evaluation of Modeled NO2 Air Quality Concentrations (as is)
Since the current NC>2 standard is 0.053 ppm annual average, the predicted air quality
concentrations were first summarized by calculating annual average concentration. The
distribution for the AERMOD predicted NC>2 concentrations at each of the 16,857 receptors for
years 2001 through 2003 are illustrated in Figure B-12. Variable concentrations were estimated
by the dispersion model over the three year period (2001-2003). The NC>2 concentration
distribution was similar for years 2001 and 2002, with mean annual average concentrations of
about 21 ppb and a COV of just over 30%. On average, NC>2 annual average concentrations
were lowest during simulated year 2003 (mean annual average concentration was about 16 ppb),
largely a result of the comparably lower local concentration added (Table B-28). While the
mean annual average concentrations were lower than those estimated for 2001 and 2002, a
greater number of annual average concentrations were estimated above 53 ppb for year 2003. In
addition, year 2003 also contained greater variability in annual average concentrations as
indicated by a COV of 53%.
B-58
-------
200
"o. 180
c
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 NCh 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 peakNC>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 NO2 concentrations at the other two monitors when compared with the measured data
(middle and bottom graphs of Figure B-13), although the patterns are still similar. The greatest
difference in 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-59
-------
35
•S- 30 -
25 -
S 20 4
o
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
--H--+-+..+--"
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
Figure B-13. Measured and modeled diurnal pattern of NO2 concentrations at three ambient monitor sites.
B-60
-------
B-3.6.3 Comparison of estimated on-road NO2 concentrations
The two independent approaches used to estimate on-road NO2 concentrations, one using
ambient monitor data combined with an on-road adjustment factor (section A-8) and the other
using the AERMOD dispersion model (section B-3.5), were compared to one another. There are
no on-road NO2 concentration measurements in Philadelphia for the modeled data to be
compared with, although it should be noted that the data used to estimate the adjustment factors
and applied to the monitor data are measurement based.
First a comparison can be made between the factor used for estimating on-road
concentrations in the air quality analysis and similar factors calculated using AERMOD
estimated concentrations. As described in section A-8, an empirical distribution of on-road
adjustment factors was derived from on-road and near-road NO2 concentration measurements
published in the extant literature. The derived empirical distribution was separated into two
components, one for application to summertime ambient concentrations, and the second for all
other seasons. The two empirical distributions are presented in Figure B-14, and represent the
on-road adjustment factors multiplied by the ambient monitor concentration (> 100 m from a
major road) and used to estimate the on-road concentration in the air quality characterization
(chapter 7 of the REA). The one-hour NO2 concentrations estimated at every AERMOD
receptor in Philadelphia were compared with the concentrations estimated at their closest on-road
receptor to generate a similar ratio (i.e., on-road/non-road concentrations). These ratios were
also stratified into two seasonal categories, one containing the summer ratios (June, July, and
August) and the other for all other times of the year. The AERMOD on-road factor distributions
in semi-empirical form are also presented in Figure B-14. There are similarities in comparing
each of the AERMOD with the measurement study derived distributions, most importantly at the
upper percentiles. Intersection of the two approaches occurs at about the 70th percentile and
continues through the 90th percentile. While the two seasonal distributions for AERMOD are
very similar to one another, they diverge at the upper percentiles, with the summer ratios
containing greater values at the same percentiles. This is similar to what was observed in the
measurement derived distribution, although the summer ratio distribution consistently contained
greater values at all percentiles compared with the non-summer distribution.
There are differences that exist when comparing the two approaches at the mid to lower
percentiles, with the AERMOD ratios consistently lower than the empirically derived factors.
This is likely due to the differences in the population of samples used to generate each type of
distribution. The measurement study derived distribution used data from on-road concentration
measurements and from monitoring sites located at a distance from the road, sites that by design
of the algorithm and the factor selection criteria are likely not under the influence of non-road
NO2 emission sources. Thus, the measurement study derived ratios never fall below a value of
one, there are no on-road concentrations less than any corresponding non-road influenced
concentrations. This was, by design, a reasonable assumption for estimating the on-road
concentrations for the air quality characterization. The AERMOD receptors however, include all
types of emission sources such that there are possibilities for concentrations at non-road
receptors that are greater than on-road, a more realistic depiction of the actual relationship
between on-road and non-road receptors. Furthermore, the AERMOD distribution extends
B-61
-------
beyond the range of values offered by the measurement study derived ratios at the very upper
percentiles. One issue with this comparison is that the AERMOD developed ratios are from a 1-
hour averaging time, while the study derived ratios were from averaging times of mostly 7-14
days. This could be why the AERMOD ratios have much greater variability than with the study
derived ratios.
1.0 -
0.9 -
0.8 -
I1 0.7 -
I 0.6
o
0.5 -
1 0.4
O 0.3 -
0.2 -
0.1 -
0.0
- - -Summer-AERMOD
Not Summer - AERMOD
+ Summer-Studies
o Not Summer-Studies
0.1
Figure B-14.
those derived
1.0 10.0
On-Road/Non-Road Ratio
100.0
Comparison of on-road/non-road ratios developed from AERMOD concentration estimates and
from published NO2 measurement studies.
Briefly for the second comparison, hourly on-road NO2 concentrations were estimated using
AERMOD for 979 on-road receptors in Philadelphia for the year 2002. The 24 hourly values
modeled for each day at each receptor were rounded to the nearest 1 ppb and then adjusted for
sources not modeled using the ambient monitor data (Table B-25). The second set of estimated
on-road NO2 concentrations was generated as part of the Air Quality Characterization by
applying randomly selected on-road adjustment factors to the ambient monitor concentrations in
the Philadelphia CMSA.
Table B-30 compares the summary statistics of the hourly concentrations and the number of
exceedances of the potential health effect benchmark levels. The AERMOD predicted and
ambient monitor simulated concentration distributions have very similar means and percentiles.
However the variance of the modeled values is about 60 % higher than the variance of the
simulated on-road monitor concentrations. This variance difference is largely a function of
differences in the extreme upper tails of the distributions and most notable when comparing the
numbers of exceedances of the potential health effect benchmark levels. The AERMOD on-road
receptors consistently have a greater number of exceedances of potential health effect benchmark
levels than that estimated using the on-road monitor simulation. For example, the AERMOD
receptors had an average of 35 exceedances of 200 ppb per site-year while the simulated on-road
B-62
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monitors had an average of 2 exceedances per year. The maximum number of exceedances per
site-year was 530 for the AERMOD modeled data and 59 for the simulated on-road monitor data.
The apparent contradiction between the similarity of the hourly concentration distributions
and the large differences in the exceedance distributions can be explained by the fact that 200
ppb is the 99.605th percentile of the AERMOD hourly concentrations and is the 99.974th
percentile of the simulated on-road monitor concentrations. Thus on average, 0.395 % of hourly
AERMOD values exceed 200 ppb per year and 0.026 % of hourly simulated on-road monitored
values exceed 200 ppb per year. These differences could be due to the greater number of
receptors modeled by AERMOD (n=979) compared with the on-road monitor simulation (n=5).
Again, the AERMOD generated data could include locations greatly influenced by roadway
emissions that are not captured by the simplified approach conducted in the Air Quality
Characterization.
Table B-30. Summary statistics of on-road hourly NO2 concentrations (ppb) and the numbers of potential
health effect benchmark levels using AERMOD and the on-road ambient monitor simulation approaches in
Philadelphia.
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-hourNO2
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
B-63
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B-3.6.4 Annual Average Exposure Concentrations (as is)
The hourly NO2 concentrations output from AERMOD were input into the exposure model,
providing a range of estimated exposures output by APEX. Figure B-15 illustrates the annual
average exposure concentrations for the entire simulated population (both asthmatics and healthy
individual of all ages), for each of the years analyzed and where indoor sources were modeled.
While years 2001 and 2002 contained very similar population exposure concentration
distributions, the modeled year 2003 contained about 20% lower annual average concentrations.
The lower exposure concentrations for year 2003 are similar to what was observed for the
predicted air quality (Figure B-12), however, all persons were estimated to contain exposures
below an annual average concentration of 53 ppb, even considering indoor source concentration
contributions. Again, while Figure B-15 summarizes the entire population, the data are
representative of what would be observed for the population of asthmatics or asthmatic children.
100
2001 with indoor sources
2002 with indoor sources
2003 with indoor sources
10
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-16). As a point of
reference, the annual average concentration for 2002 ambient monitors ranged from 24 ppb to 29
ppb. Many of the AERMOD predicted annual average concentrations were below that of the
lowest ambient monitoring concentration of 24 ppb, although a few of the receptors contained
concentrations above the highest measured annual average concentration. Estimated exposure
concentrations were below that of both the modeled and measured air quality. For example,
exposure concentrations were about 5 ppb less than the modeled air quality when the exposure
B-64
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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
01
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
10 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-65
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information relevant to the asthmatic population (such as time spent in particular
microenvironments).
B-3.6.5.1 Maximum Estimated Exposure Concentrations
A greater variability was observed in maximum exposure concentrations for the 2003 year
simulation compared with years 2001 and 2002 (Figure B-17). While annual average exposure
concentrations for the total population were the lowest of the 3-year simulation, year 2003
contained a greater number of individual maximum exposures at and above the lowest potential
health effect benchmark level. When indoor sources are not modeled however, over 90% of the
simulated persons do not have an occurrence of a 1-hour exposure above 200 ppb in a year.
B-3.6.5.2 Number of Estimated Exposures above Selected Levels
When considering the total asthmatic population simulated in Philadelphia County and using
current air quality of 2001-2003, nearly 50,000 persons were estimated to be exposed at least one
time to a one-hour concentration of 200 ppb in a year (Figure B-18). These exposures include
both the NC>2 of ambient origin and that contributed by indoor sources. The number of
asthmatics exposed to greater concentrations (e.g., 250 or 300 ppb) drops dramatically and is
estimated to be somewhere between 1,000 - 15,000 depending on the 1-hour concentration level
and the year of air quality data used. Exposures simulated for year 2003 contained the greatest
number of asthmatics exposed in a year consistently for all potential health effect benchmark
levels, while year 2002 contained the lowest number of asthmatics. Similar trends across the
benchmark levels and the simulation years were observed for asthmatic children, albeit with
lower numbers of asthmatic children with exposures at or above the potential health effect
benchmark levels.
B-66
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100
90 -
80 -
70 -
c
HI
o
-------
1.4E+4
O.OE+0
200
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
Figure B-19. Estimated number of simulated asthmatic children in Philadelphia County with at least one
NO2 exposure at or above the potential health effect benchmark levels, using modeled 2001-2003 air quality
(as is), with modeled indoor sources.
200
Potential Health Effect Benchmark Level (ppb)
2002 AQ (as is) - with indoor sources
2002 AQ (as is) - no indoor sources
Simulated Year - Scenario
Figure B-20. Comparison of the estimated number of all simulated asthmatics in Philadelphia County with at
least one NO2 exposure at or above potential health effect benchmark levels, using modeled 2002 air quality
(as is), with and without modeled indoor sources.
B-68
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For example, nearly 12,000 were estimated to be exposed to at least a one-hour NC>2
concentration of 200 ppb in a year (Figure B-19). Additional exposure estimates were generated
using the modeled 2002 air quality (as is) and where the contribution from indoor sources was
not included in the exposure concentrations. APEX allows for the same persons to be simulated,
i.e., demographics of the population were conserved, as well as using the same individual time-
location-activity profiles generated for each person. Figure B-20 compares the estimated number
of asthmatics experiencing exposures above the potential health effect benchmarks, both with
indoor sources and without indoor sources included in the model runs. The number of
asthmatics at or above the selected concentrations is reduced by between 50-80%, depending on
benchmark level, when not including indoor source (i.e., gas cooking) concentration
contributions.
An evaluation of the time spent in the 12 microenvironments was performed to estimate
where simulated individuals are exposed to concentrations above the potential health effect
benchmark levels. Currently, the output generated by APEX is limited to compiling the
microenvironmental time for the total population (includes both asthmatic individuals and
healthy persons) and is summarized to the total time spent above the selected potential health
effect benchmark levels. As mentioned above, the data still provide a reasonable approximation
for each of the population subgroups (e.g., asthmatics or asthmatic children) since their
microenvironmental concentrations and activities are not estimated any differently from those of
the total population by APEX.
As an example, Figure B-21 (a, b, c) summarizes the percent of total time spent in each
microenvironment for simulation year 2002 that was associated with estimated exposure
concentrations at or above 200, 250, and 300 ppb (results for years 2001 and 2003 were similar).
Estimated exposures included the contribution from one major category of indoor sources (i.e.,
gas cooking). The time spent in the indoor residence and bars/restaurants were the most
important for concentrations >200 ppb, contributing to approximately 75% of the time persons
were exposed (Figure B-21 a). This is likely a result of the indoor source concentration
contribution to each individual's exposure concentrations. The importance of the particular
microenvironment however changes with differing potential health effect benchmark levels.
This is evident when considering the in-vehicle and outdoor near-road microenvironments,
progressing from about 19% of the time exposures were at the lowest potential health effect
benchmark level (200 ppb) to a high of 64% of the time exposures were at the highest
benchmark level (300 ppb, Figure B-21c).
The microenvironments where higher exposure concentrations occur were also evaluated for
the exposure estimates generated without indoor source contributions. Figure B-22 illustrates
that the time spent in the indoor microenvironments contributes little to the estimated exposures
above the selected benchmark levels. The contribution of these microenvironments varied only
slightly with increasing benchmark concentration, ranging from about 2-5%. Most of the time
associated with high exposures was associated with the transportation microenvironments (In-
Vehicle or In-Public Transport) or outdoors (Out-Near Road, Out-Parking Lot, Out-Other). The
importance of time spent outdoors near roadways exhibited the greatest change in contribution
with increased health benchmark level, increasing from around 30 to 44% of time associated
with concentrations of 200 and 300 ppb, respectively. While more persons are likely to spend
B-69
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time inside a vehicle than outdoors near roads, there is attenuation of the on-road concentration
that penetrates the in-vehicle microenvironment, leading to lowered concentrations, occurring
less frequently above 300 ppb than outdoors near roads.
B-70
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In-Public Trans
\Other
Out-Other
Out-Parking Lot
Out-Near Road
In-Other
In-Shopping—7
In-Office-/
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
In-Bar & Restaurant
b) > 250 ppb
In-Bar & Restaurant
In-Public Trans |n.Residence J Hn-School
Other J / |r In-Day Care
iHn-Office
In-Shopping
— In-Other
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-71
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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-Vehicle
Out-Near Road
Out-Other
Out-Parking Lot
b) > 250 ppb
In-Bar& Restaurant,-In-School
In-Residence^k //~tn-°al9.are
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-72
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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.
Draft-Do Not Quote or Cite B-73
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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.
Estimated Number of
Repeated Exposures
in a Year
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 quality (as is), with and without indoor
sources.
Draft-Do Not Quote or Cite
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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.
Draft-Do Not Quote or Cite B-75
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90
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-25. Estimated percent of all asthmatics in Philadelphia with at least one exposure at or above the
potential health effect benchmark level, using modeled 2001-2003 air quality just meeting the current
standard, with modeled indoor sources.
In evaluating the influence of indoor source contribution for the scenario just meeting the
current standard, the numbers of individuals exposed at selected levels are reduced without
indoor sources, ranging from about 26 percent lower for the 200 ppb level to around 11 percent
for the 300 ppb level when compared with exposure estimates that accounted for indoor sources
(Figure B-26).
200
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.
Draft-Do Not Quote or Cite
B-76
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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
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).
Draft-Do Not Quote or Cite
B-77
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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
Draft-Do Not Quote or Cite
B-78
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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
Draft-Do Not Quote or Cite
B-79
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T3 Cfl
01 —
I/) Cfl
o ro
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 air quality (as is), with modeled
indoor sources.
300
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
Potential Health Effect Benchmark Level (ppb)
Figure B-29. Estimated percent of all asthmatics in Philadelphia County with at least one NO2 exposure at or
above potential health effect benchmark level, using 2001-2003 modeled air quality (as is), with no indoor
sources.
Draft-Do Not Quote or Cite
B-80
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100
5 5
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.
IS|
Ł 0) O
C/) >- Q)
< C Ł
I 1
250
Potential Health Effect Benchmark Level (ppb)
2003 AQ (std) - no indoor soucrces
2002 AQ (std) - no indoor soucrces
2001 AQ (std) - no indoor soucrces
Simulated Year - Scenario
Figure B-31. Estimated percent of all asthmatics in Philadelphia County with at least one NO2 exposure at or
above potential health effect benchmark level, using 2001-2003 modeled air quality just meeting the current
standard (std), with no indoor sources.
Draft-Do Not Quote or Cite
B-81
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35
2003 (as is)
2002 (as is)
Simulated Year-Scenario
Estimated Number of
Repeated Exposures to
200 ppb 1-hour in a Year
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.
Draft-Do Not Quote or Cite
B-82
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2003 (std)
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.
Draft-Do Not Quote or Cite
B-83
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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
Draft-Do Not Quote or Cite
B-84
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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
Draft-Do Not Quote or Cite
B-85
-------
30-r-^"^ M
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200
250
Potential Health Effect Benchmark Level (ppb)
2003 f\Q (as is) - with indoor soucrces
2002 I\Q (as is) - with indoor soucrces
2001 f\Q (as is) - with indoor soucrces
Sim ulated Year - Scenario
Figure B-36. Estimated percent of asthmatic children in Philadelphia County with at least one NO2 exposure
at or above potential health effect benchmark level, using 2001-2003 modeled air quality (as is), with modeled
indoor sources.
300
2003 AQ (as is) - no indoor soucrces
2002 I\Q (as is) - no indoor soucrces
2001 f\Q (as is) - no indoor soucrces
Sim ulated Year - Scenario
Potential Health Effect Benchmark Level (ppb)
Figure B-37. Estimated percent of asthmatic children in Philadelphia County with at least one NO2 exposure
at or above potential health effect benchmark level, using 2001-2003 modeled air quality (as is), with no
indoor sources.
Draft-Do Not Quote or Cite
B-86
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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.
2*S
I ft o
: >- ot
: .E Ł
i — D)
: S =
250
Potential Health Effect Benchmark Level (ppb)
2003 AQ (std) - no indoor soucrces
2002 AQ (std) - no indoor soucrces
2001 AQ (std) - no indoor soucrces
Simulated Year - Scenario
Figure B-39. Estimated percent of asthmatic children in Philadelphia County with at least one NO2 exposure
at or above potential health effect benchmark level, using 2001-2003 modeled air quality just meeting the
current standard (std), with no indoor sources.
Draft-Do Not Quote or Cite
B-87
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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.
Draft-Do Not Quote or Cite
B-88
-------
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.
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 quality meeting the current standard (std), with no
indoor sources.
Draft-Do Not Quote or Cite
B-89
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B-4 Atlanta Exposure Assessment Case-Study
This section provides supplemental discussion on methodology and additional detailed
input data used in the Atlanta inhalation exposure assessment for NC>2 conducted in support of
the current review of the NO2 primary NAAQS. The general exposure modeling approach has
been broadly defined in Appendix section B-2.
In defining the years modeled, 3-years (2001-2003) were selected to allow for reasonable
representation of variability in meteorology. Table B-35 summarizes the temperature and
precipitation in Atlanta over the last 30 years, with years 2001-2003 showing a range of values
across each variable considered.
Table B-35. 30 year annual average temperature and precipitation summary for Atlanta, GA.
Year
1990
2007
1994
1991
1986
2006
1993
1998
1980
1995
1999
2002
1987
1989
2004
1996
2001
2005
1985
1988
2000
1992
1982
1984
2003
1981
1979
1997
1978
1983
average
Annual
Temperature
(°F)
65
64.4
64.2
63.7
63.7
63.4
63.4
63.4
63.2
63.1
63.1
62.8
62.8
62.7
62.7
62.7
62.5
62.3
62.3
62.3
62.1
61.9
61.9
61.9
61.7
61.7
61.6
61.1
60.9
60.1
62.6
Year
1989
1992
1994
1990
2005
1982
1991
1984
1979
2004
2003
1995
1997
1983
1985
2006
1993
2002
1980
1987
1998
1988
1996
1981
1978
1986
1999
2001
2000
2007
average
Annual
Precipitation
(inches)
63.3
60.1
60.0
57.6
56.4
56.2
55.9
55.4
54.7
53.6
52.9
52.8
51.7
51.6
49.8
48.5
48.1
47.8
46.9
46.2
46.2
45.9
44.6
41.9
41.4
40.5
38.9
38.4
35.6
31.9
49.2
Notes:
Both temperature and precipitation are ordered by
Draft-Do Not Quote or Cite
B-90
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maximum to minimum values.
B-4.1 Supplemental AERMOD Modeling Inputs and Discussion
Air quality data input to the APEX exposure model were generated by air quality
modeling using AERMOD. Principal emission sources included both mobile and stationary
sources as well as emissions from Atlanta Hartsfield International Airport.20 The supplemental
data used for estimating the emission sources, in addition to other AERMOD parameters used for
the Atlanta exposure analysis are described below.
B-4.1.1 Major Link On-Road Emission Estimates
Information on traffic data in the Atlanta area was obtained from the Atlanta Regional
Commission (ARC) - the regional planning and intergovernmental coordination agency for the
10-county metropolitan area.- via their most recent, baseline travel demand modeling (TDM)
simulation - that is, the most recent simulation calibrated to match observed traffic data. ARC
provided the following files.
• Excel ™ files of loaded network TDM outputs for the 2005 ARC baseline year for all
links in the 13 county network domain.
• Excel ™ data file of node end point locations.
• Arterial and freeway MOBILE6.2 emissions model input files for the 2008 summer
ozone season, characterizing local inputs that differ from national defaults, and 2002
registration distribution.
Although considerable effort was expended to maintain consistency between the ARC
approach to analysis of TDM data and that employed in this analysis, complete consistency was
not possible due to the differing analysis objectives. The ARC creates countywide emission
inventories. This study created spatially and temporally resolved emission strengths for
dispersion modeling. Information about expected differences in traffic between the 2005 data
year and 2001-2003 modeled years was not provided, nor was information about seasonal
differences in MOBILE6.2 inputs. These are discussed further below.
B-4.1.1.1 Emission Sources and Locations
The TDM simulation's data file outputs include a description of the fixed information for
the highway network links and traffic descriptors for four time periods: morning, afternoon,
evening, and nighttime. Each period's data includes freeflow speed, total vehicle count, total
heavy duty truck count, total single occupancy vehicle count, and TDM-calculated congested
speeds for the period. The description of the network consists of a series of nodes joining
individual model links (i.e., roadway segments) to which the traffic volumes are assigned, and
the characteristics of those links, such as endpoint location, number of lanes, link distance, and
TDM-defined link daily capacity.21
20 Fugitive emissions from major point sources in the Atlanta area were not included as was done in the Philadelphia
County case study, since the NEI shows all emissions to be accounted by stack totals.
21 The TDM capacity specifications are not the same as those defined by the Highway Capacity Manual (HCM).
Following previous analyses, the HCM definition of capacity was used in later calculations, as discussed below.
Draft-Do Not Quote or Cite B-91
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The full set of links in the 13 county regional network was filtered to include only those
roadway links that are considered major as determined by TDM- based vehicle counts and within
the four part of a fifth county (Clayton), which contains a small portion of the beltway. That is,
all links with AADT values greater than 15,000 vehicles per day (one direction) in Cobb,
DeKalb, Fulton, and Gwinnett were included, and those with greater than 15,000 AADT in
Clayton County that lie north of 3,717,036 m N in the UTM Zone 16, WGS84 datum were also
included. The treatment of non-major links is discussed below.
Link locations from the TDM were modified to represent the best known locations of the
actual roadways, since there was not always a direct correlation between the two. The correction
of link locations was done based on the locations of the nodes that define the end points of links
with a GIS analysis, as follows.
A procedure was developed to relocate TDM nodes to more realistic locations. The
nodes in the TDM represent the endpoints of links in the transportation planning network and are
specified by node indices, cross-referenced to locations in the Georgia West Stateplane. The
procedure moved the node locations to the true road locations and translated to dispersion model
coordinates. The ESRI StreetMap™ Pro road network database, an enhanced version of the Tele
Atlas North America, Inc database was used as the specification of the true road locations. The
nodes were moved to coincide with the nearest major road of the corresponding roadway type
using a built in function of ArcGIS. Once the nodes had been placed in the corrected locations, a
line was drawn connecting each node pair to represent a link of the adjusted planning network.
B-4.1.1.2 Emission Source Strength
On-road mobile emission factors were derived from the MOBILE6.2 emissions. The
simulations were executed to calculate average running NOX emission factors in grams per mile
for a specific functional class (Freeway, Arterial, Local, or Ramp) and speed. Iterative
MOBILE6.2 simulations were conducted to create tables of average Atlanta region emission
factors resolved by speed (2.5 to 65 mph), functional class, season, and year (2001, 2002, or
2003) for each of the eight combined MOBILE vehicle classes.22 The resulting tables were then
consolidated into speed, functional class, and seasonal values for combined light- and heavy-duty
vehicles. To create seasonal-hourly resolved emissions, spring and fall values were taken as the
average of corresponding summer and winter values. Figure B-44 shows an example of the
calculated emission factors for Summer, 2001.
The resulting emission factors were then coupled with the TDM-based activity estimates
to calculate emissions from each of the 4,899 major roadway links. However, many of the links
were two sides of the same roadway segment. To speed model execution time, those links that
could be combined into a single emission source were merged together. This was done only for
the 734 links (367 pairs) where opposing links were paired in space and exhibited similar activity
levels within 20% of each other.
22 HDDV - Heavy-Duty Diesel Vehicle, HDGV - Heavy-Duty Gasoline Vehicle, LDDT - Light-Duty Diesel Truck,
LDDV - Light-Duty Diesel Vehicle, LDGT12 - Light-Duty Gasoline Track with gross vehicle weight rating < 6,000
Ibs and a loaded vehicle weight of < 5,750 Ibs, LDGT 34 - Light-Duty Gasoline Track 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.
Draft-Do Not Quote or Cite B-92
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oc
—v 9n -
c
o 15
w I0
UJ
O -in
Z 10 -
v " * - .
^^^_ _^_
.^^
D 10 20 30 40
Average Speed (mph
.-•X
- - - - Summer Freeway LDV
- - - - Summer Freeway
HDV
50 60 70
)
Figure B-44. Example of Light- and heavy-duty vehicle NOx emissions grams/mile (g/mi) for arterial and
freeway functional classes, 2001.
B-4.1.2
Stationary Sources Emissions Preparation
Data for the parameterization of major point sources in Atlanta comes primarily from
three sources: the 2002 National Emissions Inventory (NEI; US EPA, 2007b), Clean Air Markets
Division (CAMD) Unit Level Emissions Database (US EPA, 2007c), and temporal emission
01
profile information contained in the EMS-HAP (version 3.0) emissions model. The NEI
database contains stack locations, emissions release parameters (i.e., height, diameter, exit
temperature, exit velocity), and annual emissions for NOx-emitting facilities. The CAMD
database, on the other hand, has information on hourly NOX emission rates for units in the US,
where the units are the boilers or equivalent, each of which can have multiple stacks. The
alignment of facilities between the two databases is not exact, however. Some facilities listed in
the NEI, are not included in the CAMD database. Of those facilities that do match, in many cases
there is no clear pairing between the individual stacks assigned within the databases.
Major stationary sources for this analysis were selected from the NEI according to the
following criteria:
(1) Stacks within facilities whose total NOx emissions are at least 100 tpy, and
(2) Stacks within facilities located either within the 4-county modeling domain or within
10 km of the modeling domain.
There are 7 NOx-emitting facilities in the NEI that meet these criteria. Stacks within the
facilities that were listed separately in the NEI were combined for modeling purposes if they had
identical stack physical parameters and were co-located within about 10m. This process
resulted in 28 combined stacks, listed in Table B-36. These 28 major-facility combined stacks
1 http ://www. epa. gov/ttn/chief/emch/proj ection/emshapS 0 .html
Draft-Do Not Quote or Cite
B-93
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account for 16% of the of NOX point sources and 51% of the total NOX point source emissions in
this buffered four county Atlanta area.
The CAMD database was then queried for facilities that matched the facilities identified
from the NEI database. Facility matching was done on the facility name, Office of Regulatory
Information Systems (ORIS) identification code (when provided) and facility total emissions to
ensure a best match between the facilities. However, because Georgia was not part of many of
the market-based reduction programs that constitute the CAMD emissions database, only one of
the 7 major facilities in the four-county focus area was found in the CAMD data base:the
Georgia Power Company McDonough Steam-Generating Plant. The CAMD hourly emissions
profiles for these two units are summed together and then, after appropriate scaling, used to
represent 2 major-facility combined stacks.
For the remaining 26 major-facility combined stacks, hourly NOX emissions profiles were
created based on the hourly profile typical of that stack's SCC, the season, and the day of week.
These SCC-based temporal profiles are year-independent, and were developed for the EPA's
24
25
EMS-HAP model, described in the EMS-HAP model Version 2 User's Guide, Section D-7
As with CAMD hourly emissions, these SCC-based emission profiles are scaled such that the
annual total emissions are equal to those of NEI 2002.
B-4.1.3 Airport Emissions Preparation
The Atlanta-Hartsfield International Airport emissions were assigned to a polygon that
defined an area source for simulation. The perimeter dimensions of the Atlanta-Hartsfield
International Airport were determined by GIS analysis of aerial photographs, and the polygon
representing the airport is estimated to have an area of 3 km2 (see Figure B-45). As with some
point source emissions, the annual NOX emission totals were extracted from the NEI and the
temporal profiles from the EPA's EMS-HAP model. These seasonal, SCC-based emissions were
scaled such that the annual total emissions are equal to those of NEI 2002: 5,761 tpy, with about
90% coming from commercial aircraft.
Fulton
College Parit
scnorwoMi
5D.Oai-75.IOD
I -1WOW
Figure B-45. Polygon representing the Atlanta-Hartsfield International Airport area source.
24 http://www.epa.gov/scramOO l/dispersion_related.htm#ems-hap
25 http://www.epa.gov/scramOOl/userg/other/emshapv2ug.pdf
Draft-Do Not Quote or Cite
B-94
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Table B-36. The major-facility combined stacks within 10 km of the Atlanta modeling domain.
County
Clayton
Clayton
Clayton
Clayton
Cobb
Cobb
Cobb
Cobb
Fulton
Fulton
Fulton
Fulton
Fulton
Fulton
Fulton
NEI Site ID
NEI2GA300105
NEI2GA300105
NEI2GA300105
NEI2GA300105
NEI12840
NEI12840
NEI2GA700022
NEI2GA700022
NEIGA1210021
NEIGA1210021
NEIGA1210021
NEIGA1210021
NEIGA1210021
NEIGA1210021
NEIGA1210401
Facility Name
Delta Air Lines Inc TOC
Delta Air Lines Inc TOC
Delta Air Lines Inc TOC
Delta Air Lines Inc TOC
Georgia Power Company McDonough Steam-
Electric Generating Plant
Georgia Power Company McDonough Steam-
Electric Generating Plant
Caraustar Mill Group Inc
Caraustar Mill Group Inc
Owens Corning - Fairburn Plant
Owens Corning - Fairburn Plant
Owens Corning - Fairburn Plant
Owens Corning - Fairburn Plant
Owens Corning - Fairburn Plant
Owens Corning - Fairburn Plant
Lafarge Building Materials
sec1
20200102,
20200401
20400110
20400110
10200502,
10200602,
10200603
20100101,
20100201
10100212
30790001 ,
30790003
10200202,
10200501,
10200601
30501 299
30501 204,
30501 205,
30501 299
30501 204,
30501 205,
30501 299
30501 204,
30501 205,
30501 299
30501 203
30501 203
30500606
Lat.
33.6425
33.64417
33.64361
33.64194
33.82472
33.82472
33.81778
33.81778
33.53861
33.53861
33.53861
33.53861
33.53861
33.53861
33.8225
Lon.
-84.41556
-84.41805
-84.41805
-84.41278
-84.475
-84.475
-84.64889
-84.64889
-84.61694
-84.61694
-84.61694
-84.61694
-84.61694
-84.61694
-84.47
Stack-
Total NOx
Emiss.
(TRY)
1.23
0.04
67.51
32.82
11.91
4883.4
1.81
362.3
2.14
12
13.29
5.63
327
242
943
Facility-
Total
Emiss.
(TRY)
101.6
101.6
101.6
101.6
4895.3
4895.3
364.1
364.1
602.1
602.1
602.1
602.1
602.1
602.1
1252.9
Stack
Hght.2
(m)
8
9
14
18
17
255
13
38
16
19
19
19
21
204
20
Exit
Gas
Temp.
2(K)
527
977
444
590
663
405
367
450
352
347
347
391
316
322
586
Stack
Diam.
2(m)
0.4
6.9
11.3
0.8
3.5
7.9
0.8
1.8
0.7
3
3.2
2.4
1.2
1.2
2
Exit Gas
Vel.2
(mis)
18
10
2
18
19
20
10
25
13
13
8
7
8
8
13
Draft-Do Not Quote or Cite
B-95
-------
County
Fulton
Fulton
Fulton
Fulton
Fulton
Henry
Henry
Henry
Henry
Henry
Henry
Henry
Henry
1 Combine
2 The phys
rounded to
NEI Site ID
NEIGA1210401
NEIGA1210020
NEIGA1210020
NEIGA1210020
NEIGA1210020
NEIGA1315100
NEIGA1315100
NEIGA1315100
NEIGA1315100
NEIGA1315100
NEIGA1315100
NEIGA1315100
NEIGA1315100
d stacks may have m
cal stack parameters
one decimal place.
Facility Name
Lafarge Building Materials
Owens-Brockway Glass Container Inc - Atlanta
GA plant
Owens-Brockway Glass Container Inc - Atlanta
GA plant
Owens-Brockway Glass Container Inc - Atlanta
GA plant
Owens-Brockway Glass Container Inc - Atlanta
GA plant
Transcontinental Gas Pipe Line - Station 120
Transcontinental Gas Pipe Line - Station 120
Transcontinental Gas Pipe Line - Station 120
Transcontinental Gas Pipe Line - Station 120
Transcontinental Gas Pipe Line - Station 120
Transcontinental Gas Pipe Line - Station 120
Transcontinental Gas Pipe Line - Station 120
Transcontinental Gas Pipe Line - Station 120
ultiple Source Classification Codes (SCCs)
are converted from English units into metric units. The st
sec1
30500606,
3050061 3
10200602
10200602
10200602
10200602
20200202
20200252
20200252
20200252
20200202
20200252
20200252
20200201
ack height, exi
Lat.
33.8225
33.66972
33.67083
33.67083
33.67083
33.56944
33.56944
33.56944
33.56944
33.56944
33.56944
33.56944
33.56944
gas tempera!
Lon.
-84.47
-84.41861
-84.42083
-84.42083
-84.42083
-84.255
-84.255
-84.255
-84.255
-84.255
-84.255
-84.255
-84.255
jre, and exit g
Stack-
Total NOx
Emiss.
(TRY)
309.89
10.06
208.49
402.49
89.42
7.88
642.88
184.17
945.58
36.6
280.57
218.68
31.08
as velocity are
Facility-
Total
Emiss.
(TRY)
1252.9
710.5
710.5
710.5
710.5
2347.4
2347.4
2347.4
2347.4
2347.4
2347.4
2347.4
2347.4
rounded to
Stack
Hght.2
(m)
24
18
27
27
27
5
8
8
8
8
8
9
10
integers
Exit
Gas
Temp.
2(K)
336
497
589
589
644
744
625
625
637
669
670
625
743
and the
Stack
Diam.
2(m)
0.9
1
1.2
1.4
0.9
0.2
0.6
0.7
0.7
0.4
0.6
0.6
1
stack d\t
Exit Gas
Vel.2
(m/s)
12
8
24
19
25
22
38
31
28
17
41
38
42
meter is
Draft-Do Not Quote or Cite
B-96
-------
B-4.1.4
Receptor Locations
The distance relationship between the major roadway link and block centroids receptors
can be estimated by looking at the distance between the road-centered and the block centroid
receptors. Figure B-46 presents the histogram of the shortest distance between each centroid
receptor and its nearest major-roadway-link-centered receptor. Approximately 1% of the blocks
are within 50 m of a major roadway link and the geometric mean of the distribution is between
750 m and 800 m. Approximately 26% of the blocks are within 400 m of a major roadway link
center. However, these values represent the distances of the block centroids to road centers
instead of road edges, so that they overestimate the actual distances to the zone most influenced
by roadway by an average of 10 m and a range of 4 m to 29 m (based on the distribution of the
on-road area source widths).
450
T 100%
00000000000000000000000000
ooooooooooooooooooooooooo
0
Distance (m)
Figure B-46. Frequency distribution of distance between each Census block receptor and its nearest major-
roadway-link-centered receptor.
B-97
-------
B-4.1.5
Data used to generate dispersion model-to-monitor comparison figures in REA.
Table B-37. Data used to generate cumulative density functions plotted in Figure 8-6 of REA.
Monitor
130890002
130893001
131210048
Receptor(s)
AERMOD P2.5
AERMOD P50
AERMOD P97.5
AMBIENT MONITOR
AERMOD MONITOR
AERMOD P2.5
AERMOD P50
AERMOD P97.5
AMBIENT MONITOR
AERMOD MONITOR
AERMOD P2.5
AERMOD P50
AERMOD P97.5
AMBIENT MONITOR
AERMOD MONITOR
Percentile Concentration (ppb)
100
108
137
169
90
160
94
106
145
66
103
111
122
157
136
137
99
63
70
85
53
79
56
60
82
49
58
61
70
96
63
72
95
49
51
68
39
60
47
49
65
38
48
48
52
71
47
52
90
41
46
59
32
51
40
45
57
32
45
43
47
61
39
47
80
30
34
50
24
39
28
34
49
24
32
33
39
53
30
40
70
21
28
44
19
29
20
26
43
19
24
26
31
47
24
31
60
14
22
38
15
22
14
19
36
16
17
21
25
40
19
26
50
9
17
33
12
16
10
14
29
13
12
17
20
33
16
22
25
2
8
19
5
3
4
6
16
7
5
9
12
21
9
14
0
0
0
0
1
0
0
0
0
1
0
0
0
0
1
0
B-98
-------
Table B-38. Data used to generate diurnal variation plotted in Figure 8-7 of REA.
Monitor ID
130890002
130893001
Hour
of Day
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Annual Average NO2 Concentration at Given Receptor
AERMOD
P2.5
15
16
15
16
20
23
23
19
16
13
11
10
10
10
9
9
9
15
19
27
22
15
16
15
15
16
15
15
20
23
23
18
16
12
11
10
10
9
9
9
10
15
20
29
23
AERMOD
P50
22
22
21
21
28
33
29
23
18
15
13
13
12
12
12
12
12
19
26
39
32
22
23
22
20
21
19
20
25
28
27
21
17
14
13
13
12
12
11
12
13
18
24
34
28
AERMOD
P97.5
31
31
30
30
35
38
35
32
29
31
34
36
38
40
40
41
42
45
44
47
40
32
32
30
30
30
29
30
35
38
35
28
23
21
23
25
27
29
29
27
28
34
39
47
40
AMBIENT
MONITOR
20
18
17
16
16
16
16
17
18
15
11
9
8
7
7
8
8
10
14
18
20
21
22
21
18
16
16
15
15
17
19
19
18
14
12
10
9
9
9
10
11
13
17
20
22
AERMOD
MONITOR
21
23
21
23
27
31
29
24
19
16
15
15
15
16
15
15
15
21
26
34
27
21
22
22
19
19
18
18
24
27
25
20
16
13
12
11
11
10
10
10
11
17
23
33
27
B-99
-------
Monitor ID
131210048
Hour
of Day
21
22
23
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Annual Average NO2 Concentration at Given Receptor
AERMOD
P2.5
15
16
16
22
22
21
21
28
32
28
22
18
15
13
12
11
11
10
11
12
18
26
39
33
23
23
22
AERMOD
P50
20
21
20
26
26
25
25
32
34
31
24
20
18
17
16
16
15
15
16
17
23
31
43
37
28
28
26
AERMOD
P97.5
31
31
29
35
35
33
34
40
45
40
31
27
26
27
30
32
33
33
33
35
41
45
54
46
37
38
35
AMBIENT
MONITOR
22
21
19
24
23
21
20
20
21
23
24
23
20
16
13
12
11
11
11
12
14
18
23
26
26
26
25
AERMOD
MONITOR
19
21
19
27
27
26
27
33
36
31
24
21
18
17
16
16
15
15
15
17
23
31
44
38
29
30
28
B-100
-------
B-4.1.6
Comparison of estimated on-road NO2 concentrations
Table B-39 provides the semi-empirical distribution derived from the relationship of the on-road
concentrations estimated by AERMOD and the concentrations at receptors located at least 100
meters form a major road. The data were separated in to two season categories, summer (June,
July and August) and not summer (all other months). Table B-40 contains the values for each of
the same distribution types, however were derived from measurement data reported in published
literature sources (see Appendix A-8 for details). Each of the distributions were illustrated in
Figure 8-8 of the final REA.
Table B-39. On-road/non-road NO2 concentration ratios using AERMOD roadway link concentration
prediction and nearest corresponding receptor concentration > 100 m of a major road.
Probability
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.2
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.3
AERMOD Predicted
On-road/Non-road
Not Summer
0.46
1.05
1.12
1.17
1.20
1.23
1.25
1.28
1.30
1.31
1.33
1.35
1.36
1.38
1.39
1.40
1.42
1.43
1.45
1.46
1.47
1.49
1.50
1.51
1.53
1.54
1.55
1.57
1.58
1.59
1.61
Summer
0.30
1.10
1.22
1.29
1.35
1.40
1.44
1.47
1.50
1.53
1.55
1.58
1.60
1.62
1.64
1.66
1.68
1.70
1.72
1.73
1.75
1.77
1.79
1.81
1.82
1.84
1.86
1.87
1.89
1.91
1.93
B-101
-------
Probability
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
0.39
0.4
0.41
0.42
0.43
0.44
0.45
0.46
0.47
0.48
0.49
0.5
0.51
0.52
0.53
0.54
0.55
0.56
0.57
0.58
0.59
0.6
0.61
0.62
0.63
0.64
0.65
0.66
0.67
0.68
0.69
0.7
0.71
0.72
0.73
0.74
0.75
0.76
AERMOD Predicted
On-road/Non-road
Not Summer
1.62
1.63
1.65
1.66
1.67
1.69
1.70
1.72
1.73
1.75
1.76
1.77
1.79
1.81
1.82
1.84
1.85
1.87
1.89
1.90
1.92
1.94
1.96
1.97
1.99
2.01
2.03
2.05
2.07
2.09
2.11
2.14
2.16
2.18
2.21
2.23
2.26
2.28
2.31
2.34
2.37
2.40
2.43
2.46
2.50
2.54
Summer
1.94
1.96
1.98
2.00
2.02
2.03
2.05
2.07
2.09
2.11
2.13
2.15
2.17
2.19
2.21
2.23
2.25
2.27
2.29
2.31
2.34
2.36
2.38
2.40
2.43
2.45
2.48
2.51
2.53
2.56
2.59
2.62
2.65
2.68
2.71
2.74
2.78
2.82
2.85
2.89
2.93
2.97
3.01
3.05
3.10
3.14
B-102
-------
Probability
0.77
0.78
0.79
0.8
0.81
0.82
0.83
0.84
0.85
0.86
0.87
0.88
0.89
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1
AERMOD Predicted
On-road/Non-road
Not Summer
2.57
2.61
2.66
2.70
2.75
2.80
2.86
2.92
2.98
3.05
3.13
3.22
3.31
3.43
3.56
3.72
3.90
4.12
4.41
4.81
5.41
6.44
9.32
122
Summer
3.19
3.25
3.30
3.36
3.43
3.50
3.57
3.65
3.73
3.83
3.94
4.05
4.19
4.36
4.55
4.77
5.06
5.45
5.90
6.51
7.53
8.89
12.8
215
Notes:
This ratio was calculated from 7-day averaged
concentrations for the on-road and non-road
receptors to allow for a better comparison
with the study -derived ratios (Table B-40) that
were based on 7-14 day averages.
Table B-40. Estimated on-road/non-road NO2 concentration ratios using m ratio derived from data reported
in published NO2 measurement studies.
Season
Not
Summer
Measurement Derived
Probability1
0.03
0.08
0.14
0.19
0.24
0.29
0.34
0.40
0.45
0.50
On-Road/Non-Road2
1.22
1.25
1.36
1.36
1.42
1.47
1.58
1.59
1.64
1.75
B-103
-------
Season
Summer
Measurement Derived
Probability1
0.55
0.60
0.66
0.71
0.76
0.81
0.86
0.92
0.97
0.03
0.07
0.12
0.16
0.21
0.25
0.30
0.34
0.39
0.43
0.48
0.52
0.57
0.61
0.66
0.70
0.75
0.79
0.84
0.88
0.93
0.97
On-Road/Non-Road2
1.78
1.79
1.79
1.82
1.86
2.08
2.14
2.50
2.54
1.49
1.51
1.52
1.67
1.70
1.74
1.75
1.78
1.78
1.79
1.90
1.92
1.93
1.94
2.13
2.19
2.21
2.32
2.95
3.43
3.45
3.70
Notes:
1 In the figure presentation, the n ratios for each
season are plotted as the ith value against (i-
3/8)/(n+1/4), which is the Blom normal score. The
lowest value has i=1 . It was only used for the
plotting.
This value is obtained by (1 +m) and is what was
used to estimate on-road concentrations from
ambient monitor concentrations, effectively
representing the ratio of on-Road/non-road
concentrations. See Appendix A, Section 8.
B-104
-------
B-4.2 Supplemental APEX Modeling Inputs and Discussion
B-4.2.1
Simulated Individuals
The number of simulated persons in each model run was set to 50,000 persons simulated
for each year. The parameters controlling the location and size of the simulated area were set to
include the counties in the selected study area. The settings that allow for replacement of CHAD
data that are missing gender, employment or age values were all set to preclude replacing
missing data. The width of the age window was set to 20 percent to increase the pool of diaries
available for selection. The variable that controls the use of additional ages outside the target
age window was set to 0.1 to further enhance variability in diary selection. See the APEX User's
Guide for further explanation of these parameters.
B-4.2.2
Asthma Prevalence Rates
One of the important population subgroups for the exposure assessment is asthmatic
children. Evaluation of the exposure of this group with APEX requires the estimation of
children's asthma prevalence rates. The proportion of the population of children characterized as
being asthmatic was estimated by statistics on asthma prevalence rates recently used in the
NAAQS review for 63 (EPA, 2007d; 2007e). Specifically, the analysis generated age and
gender specific asthma prevalence rates for children ages 0-17 using data provided in the
National Health Interview Survey (NHIS) for 2003 (CDC, 2007). These asthma rates were
characterized by geographic regions, namely Midwest, Northeast, South, and West. Adult
asthma prevalence rates for Atlanta were derived from the Behavioral Risk Factor Surveillance
System (BRFSS) survey information for year 2004-2005 (Blackwell and Kanny, 2007; Georgia
Department of Human Resources, 2007). Average rates for adult males and females in Atlanta
were derived from reported county prevalence rates for both genders. First each of the four
county prevalence rates was weighted by their population, then averaged, and finally stratified by
gender using the statewide reported gender prevalence. The adult prevalence rates were assumed
to apply to all individuals uniformly. Table B-38 provides a summary of the prevalence rates
used in the exposure analysis by age and gender.
Table B-41. Mean asthma prevalence rates, along with lower and upper 95% confidence limits, by age and
Region
(Study Area)
Atlanta
(South)
Age
0
1
2
3
4
5
6
7
8
9
10
11
Females
Prevalence1 se L95 U95
0.034
0.052
0.071
0.088
0.099
0.119
0.122
0.112
0.093
0.091
0.108
0.132
0.013
0.012
0.014
0.017
0.019
0.022
0.023
0.022
0.019
0.018
0.020
0.023
0.015
0.031
0.046
0.056
0.064
0.079
0.079
0.072
0.059
0.059
0.071
0.090
0.077
0.085
0.109
0.134
0.150
0.175
0.182
0.170
0.144
0.139
0.162
0.191
Males
Prevalence1 se L95 U95
0.041
0.070
0.102
0.129
0.144
0.165
0.164
0.133
0.138
0.168
0.178
0.162
0.019
0.016
0.017
0.021
0.024
0.024
0.025
0.023
0.023
0.025
0.025
0.022
0.015
0.041
0.070
0.088
0.099
0.118
0.116
0.090
0.095
0.121
0.130
0.119
0.110
0.116
0.146
0.184
0.205
0.224
0.226
0.194
0.197
0.230
0.240
0.218
B-105
-------
Region
(Study Area)
Age
12
13
14
15
16
17
17+
Females
Prevalence1 se L95 U95
0.123
0.097
0.095
0.100
0.115
0.145
0.083
0.020
0.017
0.016
0.016
0.016
0.029
0.085
0.065
0.064
0.070
0.084
0.091
0.175
0.142
0.137
0.141
0.156
0.223
Males
Prevalence1 se L95 U95
0.145
0.143
0.153
0.151
0.140
0.122
0.050
0.020
0.019
0.019
0.017
0.018
0.026
0.106
0.105
0.116
0.116
0.105
0.075
0.195
0.192
0.200
0.194
0.185
0.193
Notes:
1 prevalence is given in fraction of the population. Multiply by 100 to obtain the percent.
se - Standard error
L95 - Lower limit on 95th confidence interval
U95 - Upper limit on 95th confidence interval
B-4.2.3 Meteorological Data Used by APEX
APEX used meteorological data from the station located at Atlanta Hartsfield
International (KATL) airport. This was one of the stations used for the AERMOD simulations.
B-4.2.4
Method Used for Indoor Source Contributions
Data used for estimating the contribution to indoor NCh 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
NCh 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 NCh concentration measurement was subtracted from each in
door concentration measurement, to yield net indoor concentrations
• Net indoor concentrations for duplicate cooking tests (same food cooked the same
way) were averaged for each indoor room, to yield average net indoor concentrations
• The minimum and maximum average net indoor concentrations for any test in any
room were used as the lower and upper bounds of a uniform distribution.
This resulted in a minimum average net indoor concentration of 4 ppb and a maximum
net average indoor concentration of 188 ppb.
B-4.2.5 Method Used for Cooking Probabilities
An analysis by Johnson et al (1999) of survey data on gas stove usage collected by
Koontz et al (1992) showed an average number of meals prepared each day with a gas stove of
1.4. The diurnal allocation of these cooking events was estimated as follows.
• Food preparation time obtained from CHAD diaries was stratified by hour of the day,
and summed for each hour, and summed for total preparation time.
B-106
-------
• The fraction of food preparation occurring in each hour of the day was calculated as
the total number of minutes for that hour divided by the overall total preparation time.
The result was a measure of the probability of food preparation taking place during
any hour, given one food preparation event per day.
• Each hourly fraction was multiplied by 1.4, to normalize the expected value of daily
food preparation events to 1.4.
This resulted in estimated probabilities of cooking by hour of the day. For this analysis it
was assumed that the probability that food preparation would include stove usage was the same
for each hour of the day, so that the diurnal allocation of food preparation events would be the
same as the diurnal allocation of gas stove usage. It was also assumed that each cooking event
lasts for exactly 1 hour, implying that the average total daily gas stove usage is 1.4 hours.
B-4.2.6 In-vehicle and Near-Road PROX factors
These data were used for the in-vehicle and near-road PROX factors used by APEX. They were
developed from the 1-hour on-road to non-road receptor concentrations predicted by AERMOD.
The data were stratified by two seasons (summer: June, July, and August) and by hour of the day
(Table B-42).
Table B-42. In-vehicle and near-road PROX factors used in APEX.
Season
Not
Summer
Summer
Hour of Day
1 1 PM-6AM
6AM-7PM
7PM-11PM
1 1 PM-6AM
6AM-7PM
7PM-11PM
Parameter Estimates1
GM
1.942
2.989
1.879
1.992
4.619
1.965
GSD
2.093
2.549
2.085
2.149
2.820
2.177
Lower
Bound
1.000
1.021
1.000
1.000
1.067
1.000
Upper
Bound
9.4
18.8
9.0
10.2
30.0
10.4
Notes:
1 A lognormal distribution was selected to fit the data, represented by the
geometric mean (GM), geometric standard deviation (GSD). Lower and
upper bounds were approximated by the 5th and 95th percentiles of the
fitted distribution.
B-107
-------
B-4.2.7 Supplemental Exposure Results
B-4.2.6 Supplemental Exposure Results
This section provides complete exposure and risk characterization results for the two
subpopulations, all asthmatics and asthmatic children. The data are presented in series of
summary tables across each of the scenarios investigated (i.e. with modeled air quality as is and
simulating just meeting the current and alternative standards), with and without modeled indoor
sources (i.e., gas stoves), for each of the potential health effect benchmark levels (i.e., 100, 150,
200, 250, 300 ppb 1-hour), and across three years of modeled air quality (i.e., 2001 to 2003).
Due to limits on the number of benchmarks allowed by APEX per simulation, only the
benchmarks of 100, 200, and 300 ppb were evaluated for the potential alternative standards.
When evaluating the indoor source contributions, the 99th percentile form was used for each the
50, 100, and 150 ppb 1-hour standard levels, the 98th percentile form was evaluated only at a 100
ppb 1-hour standard level for comparison with the 99th form.
B-108
-------
B-4.2.6.1 All Asthmatics, Year 2001, No Indoor sources
Table B-43. Estimated number of asthmatics in the Atlanta modeling domain exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), with just meeting
the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs01
cs01
cs01
cs01
cs01
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs01
cs01
cs01
cs01
cs01
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Persons With Number of Repeated Exposures
1
212426
207070
162453
212426
203267
145688
212426
212319
207070
212426
211944
203267
212426
212426
211837
212426
212426
211301
207070
97322
23621
203267
77290
15640
212426
212426
209426
191912
158650
118104
212426
212426
212426
212212
211355
2
212426
197375
1 1 8639
212426
187734
96733
212426
211783
197375
212426
211462
187734
212426
212426
210980
212426
212426
209159
197375
49063
5035
187734
34654
2678
212426
212051
203963
167166
112587
66738
212426
212426
212426
211730
209266
3
212426
185109
87359
212265
170380
66202
212426
211033
185109
212426
210123
170380
212426
212426
208998
212426
212265
205409
185109
25710
1553
170380
16551
911
212426
211837
195018
141135
81200
39636
212426
212426
212319
210926
205731
4
212372
170648
63524
212051
150883
44510
212426
209908
170648
212426
208087
150883
212426
212372
205784
212426
212051
200053
170648
14890
750
150883
8195
536
212426
211408
185217
117997
58757
24960
212426
212426
212158
209801
200696
5
212212
1 55436
47402
211997
133154
31869
212426
208623
1 55436
212426
204927
133154
212426
212212
201981
212426
211997
193197
1 55436
8838
428
133154
5142
268
212426
210658
174343
100053
43171
15801
212426
212426
211997
208087
1 94643
6
211997
140760
35511
211515
118586
22657
212426
205945
140760
212426
200910
118586
212426
211997
197107
212426
211515
1 86609
140760
5410
268
118586
2892
161
212426
209801
162078
83449
31816
10337
212426
212372
211730
205731
187734
1 value is the 1-hour concentration that air quality is adjusted consider ng particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-109
-------
Table B-44. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), with just meeting
the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs01
cs01
cs01
cs01
cs01
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs01
cs01
cs01
cs01
cs01
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Percent of Persons With Number of Repeated Exposures
1
100%
97%
76%
100%
96%
69%
100%
100%
97%
100%
100%
96%
100%
100%
100%
100%
100%
99%
97%
46%
11%
96%
36%
7%
100%
100%
99%
90%
75%
56%
100%
100%
100%
100%
99%
2
100%
93%
56%
100%
88%
46%
100%
100%
93%
100%
100%
88%
100%
100%
99%
100%
100%
98%
93%
23%
2%
88%
16%
1%
100%
100%
96%
79%
53%
31%
100%
100%
100%
100%
99%
3
100%
87%
41%
100%
80%
31%
100%
99%
87%
100%
99%
80%
100%
100%
98%
100%
100%
97%
87%
12%
1%
80%
8%
0%
1 00%
1 00%
92%
66%
38%
19%
100%
100%
100%
99%
97%
4
100%
80%
30%
100%
71%
21%
100%
99%
80%
100%
98%
71%
100%
100%
97%
100%
100%
94%
80%
7%
0%
71%
4%
0%
100%
100%
87%
56%
28%
12%
100%
100%
100%
99%
94%
5
1 00%
73%
22%
1 00%
63%
15%
1 00%
98%
73%
1 00%
96%
63%
1 00%
1 00%
95%
1 00%
1 00%
91%
73%
4%
0%
63%
2%
0%
100%
99%
82%
47%
20%
7%
1 00%
1 00%
1 00%
98%
92%
6
100%
66%
17%
100%
56%
11%
100%
97%
66%
100%
95%
56%
100%
100%
93%
100%
100%
88%
66%
3%
0%
56%
1%
0%
100%
99%
76%
39%
15%
5%
100%
100%
100%
97%
88%
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-110
-------
B-4.2.6.2 Asthmatic Children, Year 2001, No Indoor Sources
Table B-45. Estimated number of asthmatic children in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), with
just meeting the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs01
cs01
cs01
cs01
cs01
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs01
cs01
cs01
cs01
cs01
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Persons With Number of Repeated Exposures
1
64113
62881
51794
64113
61917
47456
64113
64113
62881
64113
64060
61917
64113
64113
64060
64113
64113
64006
62881
32030
7177
61917
25656
4499
64113
64113
63685
59025
51044
37868
64113
64113
64113
64113
64006
2
64113
60953
39957
64113
58596
33476
64113
64060
60953
64113
64006
58596
64113
64113
63953
64113
64113
63685
60953
17086
1660
58596
12587
857
64113
64113
62560
54044
38564
22924
64113
64113
64113
64006
63685
3
64113
58275
31226
64113
54847
23567
64113
64006
58275
64113
63899
54847
64113
64113
63738
64113
64113
62721
58275
9373
321
54847
6535
107
64113
64113
60525
46866
29191
13444
64113
64113
64113
63953
62881
4
64113
54847
23514
64113
50241
16015
64113
63738
54847
64113
63578
50241
64113
64113
63042
64113
64113
61435
54847
5892
107
50241
3053
54
64113
64060
58168
41350
22067
9320
64113
64113
64113
63738
61542
5
64113
51366
17622
64113
45635
12159
64113
63578
51366
64113
63042
45635
64113
64113
62346
64113
64113
60150
51366
3321
54
45635
1821
0
64113
64060
56722
36476
15908
6374
64113
64113
64113
63524
60364
6
64113
48313
14194
64060
41617
9213
64113
63042
48313
64113
62185
41617
64113
64113
61435
64113
64060
58864
48313
2089
0
41617
857
0
64113
63899
53883
31119
12748
4338
64113
64113
64060
63042
59079
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-111
-------
Table B-46. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2001 modeled air quality (as is), with
just meeting the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs01
cs01
cs01
cs01
cs01
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs01
cs01
cs01
cs01
cs01
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Percent of Persons With Number of Repeated Exposures
1
100%
98%
81%
100%
97%
74%
100%
100%
98%
100%
100%
97%
100%
100%
100%
100%
100%
100%
98%
50%
11%
97%
40%
7%
100%
100%
99%
92%
80%
59%
100%
100%
100%
100%
100%
2
100%
95%
62%
100%
91%
52%
100%
100%
95%
100%
100%
91%
100%
100%
100%
100%
100%
99%
95%
27%
3%
91%
20%
1%
100%
100%
98%
84%
60%
36%
100%
100%
100%
100%
99%
3
100%
91%
49%
100%
86%
37%
100%
100%
91%
100%
100%
86%
100%
100%
99%
100%
100%
98%
91%
15%
1%
86%
10%
0%
1 00%
1 00%
94%
73%
46%
21%
100%
100%
100%
100%
98%
4
100%
86%
37%
100%
78%
25%
100%
99%
86%
100%
99%
78%
100%
100%
98%
100%
100%
96%
86%
9%
0%
78%
5%
0%
100%
100%
91%
64%
34%
15%
100%
100%
100%
99%
96%
5
1 00%
80%
27%
1 00%
71%
19%
1 00%
99%
80%
1 00%
98%
71%
1 00%
1 00%
97%
1 00%
1 00%
94%
80%
5%
0%
71%
3%
0%
100%
100%
88%
57%
25%
10%
1 00%
1 00%
1 00%
99%
94%
6
100%
75%
22%
100%
65%
14%
100%
98%
75%
100%
97%
65%
100%
100%
96%
100%
100%
92%
75%
3%
0%
65%
1%
0%
100%
100%
84%
49%
20%
7%
100%
100%
100%
98%
92%
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-112
-------
B-4.2.6.3 All Asthmatics, Year 2002, No Indoor sources
Table B-47. Estimated number of asthmatics in the Atlanta modeling domain exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with just meeting
the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Persons With Number of Repeated Exposures
1
212426
207820
165345
212426
204070
150776
212426
212372
207820
212426
212212
204070
212426
212426
211997
212426
212426
211301
207820
103535
29620
204070
83824
21264
212426
212426
209855
195768
161864
124531
212426
212426
212426
212426
212372
2
212426
199089
123674
212372
190037
100268
212426
212051
199089
212426
211408
190037
212426
212426
210658
212426
212372
209319
199089
49920
7392
190037
36904
4285
212426
212265
204713
170862
117997
68988
212426
212426
212426
212372
212051
3
212426
187252
89555
212319
172469
68184
212426
211301
187252
212426
210123
172469
212426
212426
209319
212426
212319
206213
187252
29352
2785
172469
19496
1500
212426
211997
197429
146063
84199
41350
212426
212426
212426
212319
211140
4
212265
172576
64756
212051
153883
45045
212426
210016
172576
212426
208248
153883
212426
212265
206588
212426
212051
201124
172576
17300
1178
153883
11141
803
212426
211301
187359
122281
59293
25870
212426
212426
212426
211997
209962
5
212051
157954
48045
211944
136797
32191
212426
208891
157954
212426
205677
136797
212426
212051
203481
212426
211944
1 94804
157954
10391
696
136797
6160
268
212426
210819
176540
102196
43171
17782
212426
212426
212372
211890
208516
6
211944
143813
35351
211676
120246
23192
212426
206909
143813
212426
202356
120246
212426
211944
199143
212426
211676
188430
143813
6963
321
120246
4178
107
212426
209748
164756
85431
32405
11944
212426
212426
212319
211301
206695
1 value is the 1-hour concentration that air quality is adjusted consider ng particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-113
-------
Table B-48. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with just meeting
the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Percent of Persons With Number of Repeated Exposures
1
100%
98%
78%
100%
96%
71%
100%
100%
98%
100%
100%
96%
100%
100%
100%
100%
100%
99%
98%
49%
14%
96%
39%
10%
100%
100%
99%
92%
76%
59%
100%
100%
100%
100%
100%
2
100%
94%
58%
100%
89%
47%
100%
100%
94%
100%
100%
89%
100%
100%
99%
100%
100%
99%
94%
23%
3%
89%
17%
2%
100%
100%
96%
80%
56%
32%
100%
100%
100%
100%
100%
3
100%
88%
42%
100%
81%
32%
100%
99%
88%
100%
99%
81%
100%
100%
99%
100%
100%
97%
88%
14%
1%
81%
9%
1%
100%
100%
93%
69%
40%
19%
100%
100%
100%
100%
99%
4
100%
81%
30%
100%
72%
21%
100%
99%
81%
100%
98%
72%
100%
100%
97%
100%
100%
95%
81%
8%
1%
72%
5%
0%
100%
99%
88%
58%
28%
12%
100%
100%
100%
100%
99%
5
100%
74%
23%
100%
64%
15%
100%
98%
74%
100%
97%
64%
100%
100%
96%
100%
100%
92%
74%
5%
0%
64%
3%
0%
100%
99%
83%
48%
20%
8%
100%
100%
100%
100%
98%
6
100%
68%
17%
100%
57%
11%
100%
97%
68%
100%
95%
57%
100%
100%
94%
100%
100%
89%
68%
3%
0%
57%
2%
0%
100%
99%
78%
40%
15%
6%
100%
100%
100%
99%
97%
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-114
-------
B-4.2.6.4 Asthmatic Children, Year 2002, No Indoor Sources
Table B-49. Estimated number of asthmatic children in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with
just meeting the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Persons With Number of Repeated Exposures
1
64113
63149
53347
64113
62667
49330
64113
64113
63149
64113
64060
62667
64113
64113
64060
64113
64113
64006
63149
34387
8784
62667
27263
6052
64113
64113
63524
60632
52598
40975
64113
64113
64113
64113
64113
2
64113
61221
42099
64113
59400
34976
64113
64113
61221
64113
64006
59400
64113
64113
63846
64113
64113
63471
61221
16604
1768
59400
12051
911
64113
64113
62506
54740
40493
23996
64113
64113
64113
64113
64113
3
64113
58918
31816
64113
55704
24049
64113
63846
58918
64113
63738
55704
64113
64113
63578
64113
64113
62614
58918
9480
428
55704
5999
107
64113
64006
60900
48688
30262
13819
64113
64113
64113
64113
63846
4
64113
55758
22603
64060
51259
15051
64113
63685
55758
64113
63310
51259
64113
64113
62881
64113
64060
61757
55758
5249
161
51259
3321
107
64113
63846
58971
43171
20568
8034
64113
64113
64113
64060
63685
5
64006
52973
16711
64006
47349
10873
64113
63417
52973
64113
62828
47349
64113
64006
62399
64113
64006
60632
52973
3267
107
47349
1928
0
64113
63792
56775
37172
14890
5731
64113
64113
64113
64006
63363
6
63953
49437
12855
63899
43224
7552
64113
63256
49437
64113
61971
43224
64113
63953
61435
64113
63899
59025
49437
2035
54
43224
964
0
64113
63578
54097
31869
11516
3428
64113
64113
64113
63792
63256
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-115
-------
Table B-50. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with
just meeting the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Percent of Persons With Number of Repeated Exposures
1
100%
98%
83%
100%
98%
77%
100%
100%
98%
100%
100%
98%
100%
100%
100%
100%
100%
100%
98%
54%
14%
98%
43%
9%
100%
100%
99%
95%
82%
64%
100%
100%
100%
100%
100%
2
100%
95%
66%
100%
93%
55%
100%
100%
95%
100%
100%
93%
100%
100%
100%
100%
100%
99%
95%
26%
3%
93%
19%
1%
100%
100%
97%
85%
63%
37%
100%
100%
100%
100%
100%
3
100%
92%
50%
100%
87%
38%
100%
100%
92%
100%
99%
87%
100%
100%
99%
100%
100%
98%
92%
15%
1%
87%
9%
0%
1 00%
1 00%
95%
76%
47%
22%
100%
100%
100%
100%
100%
4
100%
87%
35%
100%
80%
23%
100%
99%
87%
100%
99%
80%
100%
100%
98%
100%
100%
96%
87%
8%
0%
80%
5%
0%
100%
100%
92%
67%
32%
13%
100%
100%
100%
100%
99%
5
1 00%
83%
26%
1 00%
74%
17%
1 00%
99%
83%
1 00%
98%
74%
1 00%
1 00%
97%
1 00%
1 00%
95%
83%
5%
0%
74%
3%
0%
100%
99%
89%
58%
23%
9%
1 00%
1 00%
1 00%
1 00%
99%
6
100%
77%
20%
100%
67%
12%
100%
99%
77%
100%
97%
67%
100%
100%
96%
100%
100%
92%
77%
3%
0%
67%
2%
0%
100%
99%
84%
50%
18%
5%
100%
100%
100%
99%
99%
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-116
-------
B-4.2.6.5 All Asthmatics, Year 2003, No Indoor sources
Table B-51. Estimated number of asthmatic in the Atlanta modeling domain exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), with just meeting
the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs03
cs03
cs03
cs03
cs03
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs03
cs03
cs03
cs03
cs03
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Persons With Number of Repeated Exposures
1
212426
206534
162721
212426
202731
143653
212426
212212
206534
212426
211944
202731
212426
212426
211676
212426
212426
211087
206534
98072
25924
202731
79057
17836
212426
212426
209105
192447
158114
117461
212426
212426
212426
212426
212426
2
212426
197429
117514
212426
187520
94911
212426
211837
197429
212426
211248
187520
212426
212426
210337
212426
212426
208837
197429
48366
5892
187520
33958
3749
212426
212158
203963
165452
111730
66470
212426
212426
212426
212426
212372
3
212372
184360
84520
212319
169148
63203
212426
210980
184360
212426
209373
169148
212426
212372
208516
212426
212319
205838
184360
26406
2571
169148
16926
1446
212426
211837
194804
139582
78843
39261
212426
212426
212426
212426
212212
4
212319
168827
61596
212104
149973
44349
212426
209587
168827
212426
207284
149973
212426
212319
205249
212426
212104
199625
168827
15265
857
149973
8570
428
212426
211194
183824
117568
57204
25228
212426
212426
212426
212319
211997
5
212158
1 54526
45099
211622
131923
31762
212426
207927
1 54526
212426
204338
131923
212426
212158
201017
212426
211622
193037
1 54526
8784
268
131923
5035
107
212426
210016
172522
97429
41296
15158
212426
212426
212426
212265
211408
6
212051
139261
33958
210980
115908
22228
212426
205998
139261
212426
199250
115908
212426
212051
195072
212426
210980
184413
139261
5570
54
115908
2946
0
212426
209051
160257
80450
30744
9695
212426
212426
212426
212265
210712
1 value is the 1-hour concentration that air quality is adjusted consider ng particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-117
-------
Table B-52. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), with just meeting
the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs03
cs03
cs03
cs03
cs03
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs03
cs03
cs03
cs03
cs03
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Percent of Persons With Number of Repeated Exposures
1
100%
97%
77%
100%
95%
68%
100%
100%
97%
100%
100%
95%
100%
100%
100%
100%
100%
99%
97%
46%
12%
95%
37%
8%
100%
100%
98%
91%
74%
55%
100%
100%
100%
100%
100%
2
100%
93%
55%
100%
88%
45%
100%
100%
93%
100%
99%
88%
100%
100%
99%
100%
100%
98%
93%
23%
3%
88%
16%
2%
100%
100%
96%
78%
53%
31%
100%
100%
100%
100%
100%
3
100%
87%
40%
100%
80%
30%
100%
99%
87%
100%
99%
80%
100%
100%
98%
100%
100%
97%
87%
12%
1%
80%
8%
1%
1 00%
1 00%
92%
66%
37%
18%
100%
100%
100%
100%
100%
4
100%
79%
29%
100%
71%
21%
100%
99%
79%
100%
98%
71%
100%
100%
97%
100%
100%
94%
79%
7%
0%
71%
4%
0%
100%
99%
87%
55%
27%
12%
100%
100%
100%
100%
100%
5
1 00%
73%
21%
1 00%
62%
15%
1 00%
98%
73%
1 00%
96%
62%
1 00%
1 00%
95%
1 00%
1 00%
91%
73%
4%
0%
62%
2%
0%
100%
99%
81%
46%
19%
7%
1 00%
1 00%
1 00%
1 00%
1 00%
6
100%
66%
16%
99%
55%
10%
100%
97%
66%
100%
94%
55%
100%
100%
92%
100%
99%
87%
66%
3%
0%
55%
1%
0%
100%
98%
75%
38%
14%
5%
100%
100%
100%
100%
99%
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-118
-------
B-4.2.6.6 Asthmatic Children, Year 2003, No Indoor Sources
Table B-53. Estimated number of asthmatic children in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), with
just meeting the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs03
cs03
cs03
cs03
cs03
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs03
cs03
cs03
cs03
cs03
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Persons With Number of Repeated Exposures
1
64113
62935
52008
64113
61864
46492
64113
64006
62935
64113
63953
61864
64113
64113
63953
64113
64113
63899
62935
31334
7981
61864
25335
5142
64113
64113
63578
59239
50830
37547
64113
64113
64113
64113
64113
2
64113
60846
39582
64113
58864
32405
64113
63953
60846
64113
63846
58864
64113
64113
63685
64113
64113
63417
60846
16818
1821
58864
11569
1071
64113
64006
62292
52758
37600
23192
64113
64113
64113
64113
64060
3
64113
58061
28977
64060
54526
22603
64113
63738
58061
64113
63471
54526
64113
64113
63363
64113
64060
62774
58061
9373
643
54526
5678
321
64113
63899
59936
45956
27316
14676
64113
64113
64113
64113
64006
4
64060
54579
21907
64006
49812
15747
64113
63471
54579
64113
63256
49812
64113
64060
62560
64113
64006
61435
54579
5463
161
49812
3107
0
64113
63738
57900
39957
20193
9373
64113
64113
64113
64060
63953
5
63953
51312
16818
63846
45045
11355
64113
63363
51312
64113
62560
45045
64113
63953
62024
64113
63846
59989
51312
2999
0
45045
1928
0
64113
63524
55543
34922
15158
5249
64113
64113
64113
64006
63846
6
63899
47723
13444
63846
40921
8570
64113
63149
47723
64113
61596
40921
64113
63899
60632
64113
63846
58275
47723
2035
0
40921
857
0
64113
63417
53133
30102
12051
3214
64113
64113
64113
64006
63738
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-119
-------
Table B-54. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2003 modeled air quality (as is), with
just meeting the current standard (cs), and potential alternative standards, without indoor sources.
Air Quality
Adjustment
Level1
(ppb)
100
100
100
100
100
100
150
150
150
150
150
150
200
200
200
200
200
200
50
50
50
50
50
50
asis
asis
asis
asis
asis
asis
cs03
cs03
cs03
cs03
cs03
Form2
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
98
98
98
99
99
99
asis
asis
asis
asis
asis
asis
cs03
cs03
cs03
cs03
cs03
1-hour
Benchmark
(ppb)
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
100
200
300
000
100
150
200
250
300
100
150
200
250
300
Percent of Persons With Number of Repeated Exposures
1
100%
98%
81%
100%
96%
73%
100%
100%
98%
100%
100%
96%
100%
100%
100%
100%
100%
100%
98%
49%
12%
96%
40%
8%
100%
100%
99%
92%
79%
59%
100%
100%
100%
100%
100%
2
100%
95%
62%
100%
92%
51%
100%
100%
95%
100%
100%
92%
100%
100%
99%
100%
100%
99%
95%
26%
3%
92%
18%
2%
100%
100%
97%
82%
59%
36%
100%
100%
100%
100%
100%
3
100%
91%
45%
100%
85%
35%
100%
99%
91%
100%
99%
85%
100%
100%
99%
100%
100%
98%
91%
15%
1%
85%
9%
1%
1 00%
1 00%
93%
72%
43%
23%
100%
100%
100%
100%
100%
4
100%
85%
34%
100%
78%
25%
100%
99%
85%
100%
99%
78%
100%
100%
98%
100%
100%
96%
85%
9%
0%
78%
5%
0%
100%
99%
90%
62%
31%
15%
100%
100%
100%
100%
100%
5
1 00%
80%
26%
1 00%
70%
18%
1 00%
99%
80%
1 00%
98%
70%
1 00%
1 00%
97%
1 00%
1 00%
94%
80%
5%
0%
70%
3%
0%
100%
99%
87%
54%
24%
8%
1 00%
1 00%
1 00%
1 00%
1 00%
6
100%
74%
21%
100%
64%
13%
100%
98%
74%
100%
96%
64%
100%
100%
95%
100%
100%
91%
74%
3%
0%
64%
1%
0%
100%
99%
83%
47%
19%
5%
100%
100%
100%
100%
99%
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 1-hour concentration averaged across three years; cs - current annual
average standard.
B-120
-------
B-4.2.6.7 All Asthmatics, Year 2002, With Indoor Sources
Table B-55. Estimated number of asthmatics in the Atlanta modeling domain exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with just meeting
the current standard (cs), and potential alternative standards, with indoor sources.
Air Quality
Adjustment
Level1
(ppb)
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
50
50
50
100
100
100
100
100
100
150
150
150
Form2
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
99
99
99
99
99
99
98
98
98
99
99
99
1-hour
Benchmark
(ppb)
0
100
150
200
250
300
100
150
200
250
300
100
200
300
100
200
300
100
200
300
100
200
300
Percent of Persons With Number of Repeated Exposures
1
212426
212426
211890
197268
166952
127156
212426
212426
212426
212426
212372
211890
86556
20514
212426
205731
154204
212426
208677
170273
212426
212158
204284
2
212426
212426
210873
175843
121960
72630
212426
212426
212426
212426
211944
210980
37707
3856
212426
193786
104070
212426
201017
126191
212426
211569
191001
3
212426
212319
208516
152383
87520
44242
212426
212426
212426
212372
211515
209801
18532
1071
212426
179110
70594
212426
190948
92394
212426
210980
175147
4
212426
212319
205570
129191
62989
26995
212426
212426
212426
212319
210712
207766
10070
375
212426
160792
48313
212426
177718
68077
212426
209641
157097
5
212426
212265
201231
109694
46438
17943
212426
212426
212426
211890
209373
205463
6535
107
212426
144938
33637
212426
164649
50134
212426
207605
140278
6
212426
212212
1 96679
92930
33905
11409
212426
212426
212426
211462
207605
202838
3910
0
212319
127370
24049
212426
151205
37386
212426
205356
123674
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 h 1-hour concentration averaged across three years; cs - current annual
average standard.
B-121
-------
Table B-56. Estimated percent of asthmatics in the Atlanta modeling domain exposed at or above potential
health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with just meeting
the current standard (cs), and potential alternative standards, with indoor sources.
Air Quality
Adjustment
Level1
(ppb)
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
50
50
50
100
100
100
100
100
100
150
150
150
Form2
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
99
99
99
99
99
99
98
98
98
99
99
99
1-hour
Benchmark
(ppb)
0
100
150
200
250
300
100
150
200
250
300
100
200
300
100
200
300
100
200
300
100
200
300
Percent of Persons With Number of Repeated Exposures
1
100%
100%
100%
93%
79%
60%
100%
100%
100%
100%
100%
100%
41%
10%
100%
97%
73%
100%
98%
80%
100%
100%
96%
2
100%
100%
99%
83%
57%
34%
100%
100%
100%
100%
100%
99%
18%
2%
100%
91%
49%
100%
95%
59%
100%
100%
90%
3
100%
100%
98%
72%
41%
21%
100%
100%
100%
100%
100%
99%
9%
1%
100%
84%
33%
100%
90%
43%
100%
99%
82%
4
100%
100%
97%
61%
30%
13%
100%
100%
100%
100%
99%
98%
5%
0%
100%
76%
23%
100%
84%
32%
100%
99%
74%
5
1 00%
1 00%
95%
52%
22%
8%
1 00%
1 00%
1 00%
1 00%
99%
97%
3%
0%
1 00%
68%
16%
1 00%
78%
24%
1 00%
98%
66%
6
1 00%
100%
93%
44%
16%
5%
100%
100%
100%
100%
98%
95%
2%
0%
100%
60%
11%
100%
71%
18%
100%
97%
58%
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 h 1-hour concentration averaged across three years; cs - current annual
average standard.
B-122
-------
B-4.2.6.8 Asthmatic Children, Year 2002, With Indoor Sources
Table B-57. Estimated number of asthmatic children in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with
just meeting the current standard (cs), and potential alternative standards, with indoor sources.
Air Quality
Adjustment
Level1
(ppb)
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
50
50
50
100
100
100
100
100
100
150
150
150
Form2
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
99
99
99
99
99
99
98
98
98
99
99
99
1-hour
Benchmark
(ppb)
0
100
150
200
250
300
100
150
200
250
300
100
200
300
100
200
300
100
200
300
100
200
300
Percent of Persons With Number of Repeated Exposures
1
64113
64113
64006
60471
52812
41028
64113
64113
64113
64113
64113
63792
27852
5517
64113
62560
49170
64113
63363
53508
64113
64060
62292
2
64113
64113
63738
55651
40653
24638
64113
64113
64113
64113
64006
63363
12694
1018
64113
59882
35297
64113
61757
41725
64113
63899
59239
3
64113
64060
63203
50348
31012
15212
64113
64113
64113
64113
63953
62774
6106
214
64113
57150
25067
64113
59882
32351
64113
63792
56400
4
64113
64060
62292
44563
23085
9534
64113
64113
64113
64060
63738
61971
2946
107
64113
52544
17729
64113
56775
24960
64113
63524
51848
5
64113
64006
61221
39261
16979
5785
64113
64113
64113
64006
63524
60739
1553
54
64113
48848
12105
64113
53722
18532
64113
63363
47777
6
64113
64006
59507
34065
12694
3696
64113
64113
64113
63953
63524
59561
696
0
64006
44403
8570
64113
50723
13819
64113
62989
43974
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 h 1-hour concentration averaged across three years; cs - current annual
average standard.
B-123
-------
Table B-58. Estimated percent of asthmatic children in the Atlanta modeling domain exposed at or above
potential health effect benchmark levels (1 to 6 times per year), using 2002 modeled air quality (as is), with
just meeting the current standard (cs), and potential alternative standards, with indoor sources.
Air Quality
Adjustment
Level1
(ppb)
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
50
50
50
100
100
100
100
100
100
150
150
150
Form2
asis
asis
asis
asis
asis
asis
cs02
cs02
cs02
cs02
cs02
99
99
99
99
99
99
98
98
98
99
99
99
1-hour
Benchmark
(ppb)
0
100
150
200
250
300
100
150
200
250
300
100
200
300
100
200
300
100
200
300
100
200
300
Percent of Persons With Number of Repeated Exposures
1
100%
100%
100%
94%
82%
64%
100%
100%
100%
100%
100%
99%
43%
9%
100%
98%
77%
100%
99%
83%
100%
100%
97%
2
100%
100%
99%
87%
63%
38%
100%
100%
100%
100%
100%
99%
20%
2%
100%
93%
55%
100%
96%
65%
100%
100%
92%
3
100%
100%
99%
79%
48%
24%
100%
100%
100%
100%
100%
98%
10%
0%
100%
89%
39%
100%
93%
50%
100%
99%
88%
4
100%
100%
97%
70%
36%
15%
100%
100%
100%
100%
99%
97%
5%
0%
100%
82%
28%
100%
89%
39%
100%
99%
81%
5
1 00%
1 00%
95%
61%
26%
9%
1 00%
1 00%
1 00%
1 00%
99%
95%
2%
0%
1 00%
76%
19%
1 00%
84%
29%
1 00%
99%
75%
6
1 00%
100%
93%
53%
20%
6%
100%
100%
100%
100%
99%
93%
1%
0%
100%
69%
13%
100%
79%
22%
100%
98%
69%
1 value is the 1-hour concentration that air quality is adjusted considering particular form; cs is the
current annual average value of 0.053 ppm.
2 asis -current air quality, not adjusted; 98 -98th percentile 1 -hour concentration averaged across
three years; 99 - 99 h 1-hour concentration averaged across three years; cs - current annual
average standard.
B-124
-------
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Attachment 1: Technical Memorandum on Meteorological Data
Preparation for AERMOD for NO2 REA for Atlanta, GA 2001-2003
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Meteorological data preparation for AERMOD for NO2 REA for Atlanta, GA
2001-2003
James Thurman and Roger Erode
U.S. EPA, OAQPS, AQAD
Air Quality Modeling Group
1. Introduction
While National Weather Service (NWS) surface observational data are often used as the source
of meteorological inputs for AERMOD, sometimes the data are not truly representative of the
modeling domain, especially for urban applications. Often the meteorological data is from an
airport, which has different surface characteristics than the sources being modeled. The airport
meteorological tower is often located in open spaces while the sources are located in urban areas
with trees, buildings, and other obstacles. For the Atlanta study, the airport, Atlanta Hartsfield
Airport was initially chosen as the representative meteorological location. The sources used in
the study are located in urban areas. Therefore, the airport data, due to lower surface roughness
at the airport, may not adequately represent conditions at the sources.
To address the concern regarding representativeness of the Atlanta NWS data for this study,
meteorological data from the Southeast Aerosol Research and Characterization study (SEARCH)
site in Atlanta were used as the primary source of meteorology for the AERMOD runs for the
years 2001 through 2003. Figure la shows the locations of the SEARCH site, located at
Jefferson St, and hereafter referenced as 1ST, and Hartsfield International Airport, hereafter
referenced as ATL. The 1ST site is located in an urban area, while the airport is on the outskirts
of the city. Figure Ib provides a closer look at the 1ST site and it can be clearly seen that the site
is in an urban setting.
The methodologies used to prepare meteorological data for AERMOD are described below,
including the analysis of surface characteristics data, and AERMET processing for the 1ST site
and ATL. Also discussed is the methodology used to process upper air data from Peachtree
City, GA and Birmingham, AL.
Another potential concern related to the use of NWS meteorological data for dispersion
modeling is the often high incidence of calms and variable wind conditions. The AERMOD
model currently cannot simulate dispersion under these conditions. To reduce the number of
calms and missing winds in the ATL data, archived one-minute winds for the ASOS station at
ATL were used to calculate hourly average wind speed and directions, which were used to
supplement the standard archive of winds reported for ATL in the Integrated Surface Hourly
(ISH) database. Details regarding this procedure are described below.
Section 2 describes preparation of the 1ST data, Section 3 describes the preparation of data and
calculation of hourly winds from one-minute ASOS data for ATL, Section 4 describes
preparation of upper air data from Peachtree City and Birmingham, Section 5 describes
AERSURFACE processing for surface characteristics, and Section 6 describes the AERMET
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processing. Section 7 describes an additional adjustment that was made to the processed
meteorological data to address an issue regarding AERMOD formulation for the urban option
that contributed to anomalous modeled concentrations from a preliminary analysis. Section 8
provides a brief analysis of the AERMET output for 1ST and ATL. References are listed in
Section 9.
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Figure 1. a) location of 1ST (red dot) relative to ATL (red airplane) and b) zoomed in view of
1ST (white dot).
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2. SEARCH data preparation
SEARCH data for the Jefferson St. monitor (1ST) in Atlanta was downloaded from the public
archive section of the SEARCH website, http://www. atmospheric-
research.com/public/index.html, for 2001 through 2003. Trace gas and met data were chosen.
The data in the SEARCH spreadsheets were reported on a 0 to 23 hour basis, with the reported
time represented the beginning of the observational hour. The convention for meteorological
data input to dispersion models is that the reported time represents the end of the averaging
period. The AERMOD model also requires meteorological inputs on a 1 to 23 hour basis. After
adjusting the 1ST data to conform to the AERMOD model conventions, missing values for wind
speed, wind direction, and temperature were reset to the missing values of those variables as
described in AERMET Appendix B, Table B-3. (U.S. EPA, 2004). The anemometer height for
the 1ST data was set to 10 m.
Since data quality is an important consideration for meteorological inputs to dispersion models,
the 1ST data were reviewed for completeness and reasonableness. Specifically, hourly wind
speeds, wind directions, and temperatures for 1ST were compared to the values for Atlanta
Hartsfield Airport (ATL) for the three years of 2001 through 2003. Analysis of the wind
directions showed generally good agreement between 1ST and ATL data throughout most of the
period. However, this comparison identified somewhat anomalous directions for the period of
May 2 through May 8, 2001 (Figure 2). The original wind directions for the 1ST data (red lines),
revealed an approximate 180 degree shift in wind direction when compared to the ATL wind
directions (blue lines). This shift followed a significant period of missing data for 1ST from late
April to early May 2001. After May 8, the wind directions appeared to be in better agreement
with airport wind directions. A similar problem had been encountered for a SEARCH site in
Birmingham as part of another study, and was later confirmed to be a 120-degree offset. Based
on this review and prior experience with a similar problem, it was decided to shift the 1ST wind
direction by 180 degrees for the period beginning with 1700 LST May 2 and ending at 1500 LST
May 8. Figure 3 shows the resulting directions (green line), which are more in line with the
airport directions. After correcting for the wind directions, the hourly winds and temperatures
were written to text files for input in to AERMET. Figure 4 shows the wind roses for each year
for 1ST. Winds were predominantly from the northwest with a secondary maximum from the
east.
The number of calms and missing hours (winds or temperature) for 1ST were compiled for each
year to determine if data substitution from the airport was necessary in AERMET processing.
Table 1 lists the number of calms and missing winds and temperatures for the 1ST site for 2001
through 2003. Note that a wind speed threshold of 0.28 m/s was used in processing the 1ST data
through AERMET. As a result, any wind speed reported less than 0.28 m/s was treated as a calm
hour. Unlike NWS surface observations, which treat any wind speed below 3 knots as a calm,
the 1ST data are based on a sonic anemometer, which has virtually no threshold since the
observations are not dependent on mechanical parts. Several manufacturers of sonic
anemometers report starting thresholds of 0.01 m/s. While such low winds speeds may be a
reasonable starting threshold for an instantaneous wind speed sample from a sonic anemometer,
it may not be appropriate as a threshold for defining a valid hourly average wind speed to be
used in a steady-state plume model such as AERMOD, with a single hourly average wind
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direction. Under conditions that would result in an hourly average wind speed on the order of
0.01 m/s, there would be no well-defined transport direction. The AERMOD model formulation
includes adjustments to the minimum wind speed to account for turbulence effects under very
light wind conditions, with the minimum effective wind speed that will be used for dilution in
AERMOD of about 0.2828 m/s. Based on these considerations, a threshold of 0.28 m/s was
selected as the most appropriate value to be applied for the 1ST data, with any hourly average
wind speeds below that threshold being classified as calm. Note that the current meteorological
monitoring guidance for dispersion modeling applications (EPA, 2000) specifies a maximum
acceptable starting threshold of 0.5 m/s for site-specific meteorological monitoring programs.
Table 1. Number of calms, missing winds, and missing temperatures for each year for 2001
through 2003 for the 1ST site.
Variable
Calms*
Missing winds*
Missing temperature
Year
2001
427
165
187
2002
287
497
205
2003
19
792
379
# anything less than 0.28 m/s was considered calm
* missing wind speed and/or wind direction.
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October 2, 2008
Wind direction (dag) tor ATI. (blue) and JST (red) for May 2001
A li
7 8 9 10
12 13 1* 15 16 17 t8 19 3D 21 23 23 24 25 26 27 28 23 3D 31
12345
Wind direction (des) rMerences for ATL - JST tor Mac 2001
8 9 10 11 13 1* 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Figure 2. May 2001 a) wind directions for the SEARCH monitor (red line) and Hartsfield International Airport (blue line) and b),
wind direction differences (airport - SEARCH).
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8
Figure 3. Hourly wind directions for original SEARCH (red), airport (blue) and shifted SEARCH (green) for May 2 through May 8,
2001.
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October 2, 2008
VWIDSPEH)
Wlrts)
SOUTH.•••'
WNDSPEH)
(K/KH4)
Cllmi 3.47U
Figure 4. Annual wind roses for 1ST for a) 2001, b) 2002, and c) 2003.
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3. Surface airport data
Surface data from an NWS site was needed to supplement the data from the SEARCH site. For
AERMOD, the most representative data for an NWS site should be used, most often the nearest
location. For Atlanta, Atlanta Hartsfield Airport (ATL) was chosen as the site. Integrated
Surface Hourly (ISH) data was downloaded from the National Climatic Data Center (NCDC) for
2001,2002, and 2003.
Surface data from NWS locations often contain a large number of calms and variable winds.
This is due to the METAR reporting method used for NWS observations. Currently, the wind
speed and direction used to represent the hour in AERMOD is a single two-minute average,
usually reported about 10 minutes before the hour. The METAR system reports winds of less
than three knots as calm, and winds up to six knots will be reported as variable when the
variation in the 2-minute wind direction is more than 60 degrees. This variable wind is reported
as a non-zero wind speed with a missing wind direction. The number of calms and variable
winds can influence concentration calculations in AERMOD because concentrations are not
calculated for calms or variable wind hours. For daily or annual averages, this can result in
underestimated concentrations. This is especially of concern for applications involving low-level
releases since the worst-case dispersion conditions for such sources are associated with low wind
speeds, and the hours being discarded as calm or variable are biased toward this condition.
Recently, NCDC began archiving the two-minute average wind speeds for each minute of the
hour for most ASOS stations. These values have not been subjected to the METAR coding for
calm and variable winds. Recent work in AQMG has focused on utilizing these 1 minute winds
to calculate hourly average winds to reduce the number of calms and variable winds for a given
station and year. For data input into AERMOD, one minute winds for ATL were used to
calculate hourly average winds for 2001 through 2003. These winds would be input to
AERMET and replace the winds reported for the hour from the ISH dataset. Following is the
methodology used to calculate the hourly average winds:
One minute data files are monthly, so each month for 2001 through 2003 was downloaded. The
program used to calculate hourly average winds is executed for each year.
1. Each line of the data file was read and QA performed on the format of the line to check if
the line is valid data line. Currently, the one minute data files loosely follow a fixed
format, but there are numerous exceptions. The program performed several checks on
the line to ensure that wind direction and wind speed were in the correct general location.
If a minute was listed twice, the second line for that minute was assumed to be the correct
line. In the files, wind directions were recorded at the nearest whole degree and wind
speed to the nearest whole knot.
2. If the reported wind speed was less than 2 knots, the wind speed was reset to 1 knot. This
was done because anything less than 2 knots was considered below the instrument
threshold (if the anemometer is not a sonic anemometer, which was the case for ATL
prior to April 2007). So a reported wind speed of 0 knots may not necessarily be a calm
wind. This also conforms to the meteorological monitoring guidance recommendation of
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applying a wind speed of one half the threshold value to each wind sample below
threshold when processing samples to obtain hourly averages. At the same time, the x-
and y-components of the wind direction were calculated using equations 1 and 2 below,
which are the functions inside the summation of equations 6.2.17 and 6.2.18 of the
meteorological guidance document (U.S. EPA, 2000). The components were only
calculated for minutes that did not require resetting.
vx=-sin0 (1)
vy=-cos0 (2)
where vx and vy are the x- and y-components of the one minute wind direction 9.
3. For all minutes that passed the QA check in step 1, the wind speeds were converted from
knots to m/s.
4. Before calculating hourly averages, the number of valid minutes (those with wind
directions) was checked for each hour. An hourly average would be calculated if the
there were at least two valid minutes for the hour. This could be even minutes, odd
minutes, or a mixture of non-overlapping even and odd minutes. Even minutes were
given priority over odd. If at least two valid minutes were found, then all available
minutes would be used to calculate hourly averages. The most observations that could be
used were 30 2-minute values (30 even or 30 odd).
5. For wind speed averages, all available non-overlapping minutes' speeds were used, even
those subject to resets as described in step 2. The hourly wind speed was an arithmetic
average of the wind speeds used.
6. For wind directions, the x- and y-components were summed according to equations
6.2.17 and 6.2.18 of the meteorological monitoring guidance (U.S. EPA, 2000),
summarized in equations 3 and 4 below with vxi and vyi calculated in equations 1 and 2.
The hourly wind direction was calculated using equation 6.2.19 of the meteorological
monitoring guidance (U.S. EPA, 2000), summarized in equation 5. The one minute
average wind directions do not use the flow correction as shown in equation 6.2.19, since
the calculated direction is the direction from which the wind was blowing, not the
direction in which it is blowing, as shown by the flow correction in 6.2.19. Instead, the
one minute program corrected for the direction from which the wind was blowing.
0 = Arc i&xs I + CORR (5)
V
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Where Vx and Vy are the hourly averaged x- and y-components of the wind, 6 is the
hourly averaged wind direction, N is the number of observations used for the hour, and
= 180 for Vx > 0 and Vy > 0 or Vx < 0 and Vy > 0
CORK = 0 for Vx < 0 and Vy < 0
= 360forFx>OandF^<0
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4. Upper air data
For AERMET processing, an upper air station must be paired with the surface station. For both
1ST and ATL, the Peachtree City upper air station, FFC, was chosen as the most representative
upper air site. Upper air data in the Forecast System Laboratory (FSL) format was downloaded
from the FSL, (now Global Systems Division) website, http://www.fsl.noaa.gov/. The data
period chosen was January 1, 2001 through December 31, 2003 for all times and all levels. The
selected wind speed units were chosen as tenths of a meter per second. The data was
downloaded as one file for all three years.
Analysis of the data revealed 31 occurrences of missing 1200 UTC soundings for the three years,
mostly in 2001. The AERMOD processor requires a 1200 UTC sounding in order to calculate
the convective mixing height for the day. As a result, if the 1200 UTC sounding is missing, all
of the daytime convective hours for that day will be considered as missing by the AERMOD
model. In order to minimize missing data as much as possible, these gaps in the data were filled
with data from the Birmingham, AL upper air station, BMX or from the FFC data itself. Table 2
lists the missing dates and method of data substitution. These substitutions should have very
limited impact on the Atlanta NO2 modeling since BMX is reasonably representative of Atlanta,
and modeling results for low-level releases, such as mobile sources, are not very sensitive to the
convective mixing heights in AERMOD.
Table 2. Missing 1200 UTC sounding dates in upper air data with substitution
method. Unless specified otherwise, substitution times are the same as the missing
date/time.
Date/time
03/11/01
03/12/01
03/13/01
05/06/01
06/13/01
06/14/01
06/15/01
08/11/01
11/21/01
11/22/01
11/23/01
01/11/02
02/19/02
03/23/02
03/24/02
03/25/02
Substitution
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
Date/time
04/17/02
04/18/02
04/19/02
04/20/02
04/21/02
04/22/02
04/26/02
04/27/02
06/14/02
06/23/02
07/21/02
09/08/02
09/09/02
01/22/03
03/09/03
06/26/03
Substitution
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
BMX
FFC 07/20/02
BMX
BMX
BMX
BMX
BMX
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5. AERSURFACE
The AERSURFACE tool (U.S. EPA, 2008a) was used to determine surface characteristics
(albedo, Bowen ratio, and surface roughness) for input to AERMET. Surface characteristics
were calculated for the 1ST meteorological tower site (33.77753° N, 84.41666° W) and for the
ATL meteorological tower (33.63° N 84.44167° W). As noted in the AERSURFACE User's
Guide (U.S. EPA, 2008), AERSURFACE should be run for the location of the actual
meteorological tower to ensure accurate representation of the conditions around the site.
A draft version of AERSURFACE (08256) that utilizes 2001 NLCD was used to determine the
surface characteristics for this application since the 2001 land cover data will be more
representative of this modeling period than the 1992 NLCD data supported by the current version
of AERSURFACE available on EPA's SCRAM website. Both meteorological data sites were
run according to the methodology in Section 3.2.2 of the 1st draft NC>2 risk and exposure
assessment technical support document (U.S. EPA, 2008b): both sites were run as non-arid
regions, ATL was considered "at an airport" for the low, medium, and high intensity developed
categories, default seasonal assignments to each month, and no continuous snow cover.
Moisture conditions for Bowen ratio (average, dry, or wet) were assigned to each month based
on the analysis shown in Table 30 of the technical support document (U.S. EPA, 2008b).
Months with at least twice the normal precipitation level were denoted as wet, those with less
than one-half the normal precipitation level were assigned dry and all others were average. This
resulted in three AERSURFACE runs for each site with average, dry, or wet conditions because
AERSURFACE can not assign moisture conditions to individual months within one
AERSURFACE run. Table 3 shows the assignment to each month for each year. Figures 5 and
6 show the sectors used for surface roughness for 1ST and ATL.
After running AERSURFACE, a year specific set of surface characteristics was generated for
each year by merging results for the appropriate moisture condition for each month for the year,
i.e. for 2001, the average moisture surface characteristics for January through June were
concatenated with the dry July and August surface characteristics, average September surface
characteristics, dry October and November surface characteristics, and average December
surface characteristics. These merged AERSURFACE results were used in Stage 3 of
AERMET.
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Legend
| water
| | developed open space
| | developed low intensity
| developed medium intensity
^^^| developed high intensity
| | barren land (rock/sand/clay)
| deciduous forest
| evergreen forest
| | mixed forest
| | shrub/scrub
| | grassland/herbaceous
| | pasture/hay
f cultivated crops
| | wood wetlands
Z| emergent herbaceous wetlands
Figure 5. 2001 NLCD for 1ST with surface roughness 1 km radius and sectors (denoted by
numbers 1 through 4). Numbers outside 1 km radius are the starting directions of each sector.
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Legend
| water
| | developed open space
| | developed low intensity
| developed medium intensity
^^^| developed high intensity
| | barren land (rock/sand/clay)
| deciduous forest
| evergreen forest
| | mixed forest
| | shrub/scrub
| | grassland/herbaceous
| | pasture/hay
f cultivated crops
| | wood wetlands
^\ emergent herbaceous wetlands
Figure 6. 2001 NLCD for ATL with surface roughness 1 km radius and sectors (denoted by
numbers 1 through 5). Numbers outside 1 km radius are the starting directions of each sector.
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Table 3. Assignment of average, dry, or wet conditions for
each month for ATL and 1ST for 2001, 2002, and 2003.
Month
January
February
March
April
May
June
July
August
September
October
November
December
Year
2001
Average
Average
Average
Average
Average
Average
Dry
Dry
Average
Dry
Dry
Average
2002
Average
Average
Average
Average
Average
Average
Average
Dry
Average
Average
Average
Average
2003
Dry
Average
Average
Average
Wet
Average
Average
Average
Average
Dry
Average
Average
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6. AERMET
The meteorological data files (upper air, ATL ISH data, 1ST surface data, and ATL one minute
data) were processed in AERMET, which includes three "Stages" for processing of
meteorological data. Stage 1 was used to read in all the data files and perform initial QA. The
upper air data was processed via the UPPERAIR pathway. The ATL ISH data was processed via
the SURFACE pathway, and the 1ST surface data and ATL one minute hourly average winds
were processed via the ONSITE pathway. Winds and temperatures were read into AERMET for
the 1ST data and hourly averaged winds were read into AERMET for the ATL one minute hourly
average winds. For 1ST, the THRESHOLD keyword was set to 0.28 m/s as described in Section
2. For the hourly averaged one minute ATL winds, the threshold was set to 0.01 m/s.
For each year, there were two separate runs of Stage 2 of AERMET, the merging of surface data
and upper air data; one for ATL and one for 1ST. For ATL, the Stage 1 upper air output, ATL
ISH output, and ATL one minute output were merged together via the MERGE pathway. For
1ST, the upper air output, ATL ISH output, and 1ST output were merged together.
As with Stage 2, there were two separate Stage 3 runs for each year. First, for ATL, the output
from Stage 2 was processed. For each year, the year specific surface characteristics created by
concatenating the appropriate surface characteristics for each month were used. The ATL one
minute hourly averaged winds would be the primary source of wind data. All other variables
would come from the ATL ISH data. ATL ISH winds would be used only when the ATL one
minute hourly averaged winds were missing. The substitution was done via the SUBNWS
keyword in the Stage 3 input file. The anemometer height was set to 10 m (keyword
NWS_HGT).
The second run was for 1ST. The 1ST winds and temperature would be the primary source of
data. Other variables would come from the ATL ISH data and the ATL winds or temperature
would be used only when the values were missing for 1ST for a particular hour. Surface
characteristics were the year specific surface characteristics for 1ST. For later post-processing,
the NWS_HGT keyword was set to 9.9 m. This would allow for identification of hours where
the ATL winds were used. For hours with valid data at the 1ST site, the 10m height read into
AERMET from the 1ST met file in stage 1 would be used. Note that even for hours using ATL
data, surface characteristics for 1ST were used.
After AERMET processing for each year for 1ST and ATL, a FORTRAN program was used to
substitute the records from the ATL *.SFC and *.PFL files into the 1ST *.SFC and *.PFL files
when ATL data was substituted for missing values in the 1ST data (anemometer heights of 9.9
m). This substitution was done so that the ATL hours that were substituted into the 1ST data
would have data based on the ATL surface characteristics. The entire record, including
anemometer heights, was substituted. The resulting files were a hybrid of 1ST data and ATL
hybrid data. The number of hours substituted with ATL data were 165, 497, and 792 for 2001,
2002, and 2003 respectively.
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October 2, 2008
7. Adjustment of mechanical mixing heights
Preliminary model-to-monitor comparisons using the processed meteorological data for 1ST
should generally good agreement between modeled and observed concentrations. However,
several spuriously high 1-hour modeled concentrations were also noted. Examination of the
meteorological conditions associated with these high modeled concentrations indicated a
consistent pattern of occurring on the first convective hour of the day. This was indicative of an
issue with the AERMOD model formulation for the urban option that has been identified, but has
not been addressed yet. The urban option in AERMOD currently applies only to nighttime stable
hours when the urban heat island effect is expected to increase turbulence relative to the
surrounding rural areas. The issue that contributes to these high modeled concentrations for
Atlanta is that the urban-enhanced turbulence disappears once the atmosphere becomes
convective, with no transitional period to account for residual enhanced turbulence that is likely
to occur during the transition from night to day. As a result, low-level releases may be subjected
to very limited mixing conditions for the first convective hour of the day, which may lead to
unrealistically high concentrations. Every outlier examined was consistent with this pattern, and
no such anomalies occurred at other hours of the day. In one case, the 1-hour concentration for
the last stable hour was about an order of magnitude lower then the concentration for the first
convective hour, with very similar wind speeds and directions.
In order to minimize the impact that these anomalously high 1-hour concentrations may have on
the exposure assessment for Atlanta, an adjustment was made to the mechanical mixing heights
in the processed meteorological data files for the first convective hour of each day. Morning
mechanical mixing heights for both 1ST and ATL were adjusted for the first convective hour of
each day to apply a minimum value of 240 meters. If the mechanical mixing height calculated
by AERMET was less than 240 meters, it was reset to 240 meters, and if it was larger than 240
meters then no change was made. This adjustment was intended to account for some limited
residual mixing from the urban nighttime boundary layer for the first convective hour. The value
of 240 meters is about one half of the urban nighttime boundary layer for a city with the
population of Atlanta. Modifying only the mechanical mixing height is considered a reasonable
approach to account for residual turbulence since the convective mixing height is driven directly
by the daytime solar heating. This adjustment may underestimate the amount of residual mixing
that could occur, but is considered to be a reasonable compromise for this application, and
subsequent modeling comparisons indicated much better agreement between modeled and
monitored concentrations.
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October 2, 2008
8. Analysis of processed meteorology
Table 4 lists the number of hours that were based on one-minute hourly averaged winds for ATL.
Table 4 also lists the number of calms and missing winds for the hybrid ATL data and ISH data
for ATL. For each year, over 90% of the winds were hourly averaged winds from the one-
minute data and the number of calms and missing winds were dramatically reduced.
Table 4. Number of hours using hourly averaged one minute winds and number of calms and
missing winds for ATL hybrid data and ATL ISH data.
Year
2001
2002
2003
One minute hours
8028 (92%)
7959 (91%)
8171 (93%)
One minute
calms
118
85
123
Missing
48
43
19
ISH
calms
917
856
765
missing
645
492
277
Wind roses and histograms of wind speed for 1ST and ATL inputs into AERMOD are shown in
Figures 7 through 9 for 2001, 2002, and 2003. Both sites exhibit similar wind roses, with
predominant wind directions from the northwest and secondary peaks generally from the east or
southwest.
Both the wind roses and histograms show a larger number of lower wind speeds for the 1ST site
than for the ATL site, even with the one minute hourly averaged winds included in the ATL data.
This is consistent with expected influence on wind speeds of the higher surface roughness
surrounding the 1ST site as compared to the ATL site.
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October 2, 2008
Vtind Class Frequency OistrtturtHm
d
VMnd Class Frequency OigmtHiHoo
Figure 7. 2001 wind roses and wind speed histograms for a) ATL hybrid, b) 1ST hybrid, c) ATL
hybrid and d) 1ST hybrid.
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October 2, 2008
Wind Class Frequency OistrtturtHm
d
VMnd Class Frequency Oi
Figure 8. 2002 wind roses and wind speed histograms for a) ATL hybrid, b) 1ST hybrid, c) ATL
hybrid and d) 1ST hybrid.
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October 2, 2008
Vtind Class Frequency Dha
d
VMnd Class Frequency DiantmHon
Figure 9. 2002 wind roses and wind speed histograms for a) ATL hybrid, b) 1ST hybrid, c) ATL
hybrid and d) 1ST hybrid.
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9. References
U.S. EPA, 2000: Meteorological Monitoring Guidance for Regulatory Modeling
Applications. EPA-454/R-99-005. U.S. Environmental Protection Agency,
Research Triangle Park, NC 27711.
U.S. EPA, 2004: User's Guide for the AERMOD Meteorological Preprocessor
(AERMET). EPA-454/B-03-002. U.S. Environmental Protection Agency,
Research Triangle Park, NC 27711.
U.S. EPA, 2008a: AERSURFACE User's Guide. EPA-454/B-08-001. U.S.
Environmental Protection Agency, Research Triangle Park, NC 27711.
U.S. EPA, 2008b: Risk and Exposure Assessment to Support the Review of the NO2
Primary National Ambient Air Quality Standard: Technical Support Document.
EPA-452/P-08-002. U.S. Environmental Protection Agency, Research Triangle
Park, NC 27711.
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Attachment 2: Technical Memorandum on Longitudinal Diary
Construction Approach
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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
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underestimate the variability across the population, and therefore, underestimate the high-end
concentrations.
A contrasting approach is to select a single activity pattern (or a single pattern for each
season and/or weekday-weekend) to represent a simulated individual's activities over the
modeling period. This approach has the implicit assumption that an individual's day to day
activities are perfectly correlated. This approach tends to result in long-term activity patterns
that are very different across the simulated population, and therefore may over-estimate the
variability across the population.
The Cluster-Markov Algorithm
Recently, a new algorithm has been developed and incorporated into APEX that attempts
to more realistically represent the day-to-day correlation of activities for individuals. The
algorithms first use cluster analysis to divide the daily activity pattern records into groups that
are similar, and then select a single daily record from each group. This limited number of daily
patterns is then used to construct a long-term sequence for a simulated individual, based on
empirically-derived transition probabilities. This approach is intermediate between the
assumption of no day-to-day correlation (i.e., re-selection for each time period) and perfect
correlation (i.e., selection of a single daily record to represent all days).
The steps in the algorithm are as follows.
• For each demographic group (age, gender, employment status), temperature range,
and day-of-week combination, the associated time-activity records are partitioned into
3 groups using cluster analysis. The clustering criterion is a vector of 5 values: the
time spent in each of 5 microenvironment categories (indoors - residence; indoors -
other building; outdoors - near road; outdoors - away from road; in vehicle).
• For each simulated individual, a single time-activity record is randomly selected from
each cluster.
• Next the Markov process determines the probability of a given time-activity pattern
occurring on a given day based on the time-activity pattern of the previous day and
cluster-to-cluster transition probabilities. The cluster-to-cluster transition
probabilities are estimated from the available multi-day time-activity records. (If
insufficient multi-day time-activity records are available for a demographic group,
season, day-of-week combination, then the cluster-to-cluster transition probabilities
are estimated from the frequency of time-activity records in each cluster in the CHAD
data base.).
Figure 1 illustrates the Cluster-Markov algorithm in flow chart format.
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CHAD Data Base
Demographic
Group 1
Weekday
Season 1
Demographic
Group 1
Weekday
Season 2
Demographic
Group 1
Weekend
TRANSITION
PROBABILITIES
Annual Time-Activity Sequence
Figure 1. Flow chart of Cluster-Markov algorithm used for constructing longitudinal time-activity diaries.
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Evaluation of modeled diary profiles versus observed diary profiles
The Cluster-Markov algorithm is also incorporated into the Hazardous Air
Pollutant Exposure Model (HAPEM). Rosebaum and Cohen (2004) incorporated the
algorithm in HAPEM and tested modeled longitudinal profiles with multi-day diary data
sets collected as part of the Harvard Southern California Chronic Ozone Exposure Study
(Xue et al. 2005, Geyh et al. 2000). In this study, 224 children in ages between 7 and 12
yr were followed for 1 year from June 1995 to May 1996, for 6 consecutive days each
month. The subjects resided in two separate areas of San Bernardino County: urban
Upland CA, and the small mountain towns of Lake Arrowhead, Crestline, and Running
Springs, CA.
For purposes of clustering the activity pattern records were characterized
according to time spent in each of 5 aggregate microenvironments: indoors-home,
indoors-school, indoors-other, outdoors, and in-transit. For purposes of defining diary
pools and for clustering and calculating transition probabilities the activity pattern
records were divided by day type (i.e., weekday, weekend), season (i.e., summer or ozone
season, non-summer or non-ozone season), age (7-10 and 11-12), and gender.
Week-long sequences (Wednesday through Tuesday) for each of 100 people in
each age/gender group for each season were simulated. To evaluate the algorithm the
following statistics were calculated for the predicted multi-day activity patterns and
compared them with the actual multi-day diary data.
• For each age/gender group for each season, the average time in each
microenvironment
• For each simulated person-week and microenvironment, the average of the
within-person variance across all simulated persons. (The within-person
variance was defined as the variance of the total time per day spent in the
microenvironment across the week.)
• For each simulated person-week the variance across persons of the mean time
spent in each microenvironment.
In each case the predicted statistic for the stratum was compared to the statistic for
the corresponding stratum in the actual diary data. The mean normalized bias for the
statistic, which is a common performance measure used in dispersion model performance
and was also calculated as follows.
(predicted - observed)
j
N i 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 3.
Comparison of Cluster-Markov approach with other algorithms
As part of the application of APEX in support of US EPA's recent review of the
ozone NAAQS several sensitivity analyses were conducted (US EPA, 2007). One of
these was to make parallel simulations using each of the three algorithms for constructing
multi-day time-activity sequences that are incorporated into APEX.
Table 1 presents the results for the number of persons in Atlanta population
groups with moderate exertion exposed to 8-hour average concentrations exceeding 0.07
ppm. The results show that the predictions made with alternative algorithm 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 3: Technical Memorandum on the Evaluation
Cluster-Markov Algorithm
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INTERNATIONAL
TECHNICAL MEMORANDUM
TO: Ted Palma, US EPA
FROM: Arlene Rosenbaum and Jonathan Cohen, ICF Consulting
DATE: November 4, 2004
SUBJECT: Evaluation of a multi-day activity pattern algorithm for creating longitudinal
activity patterns.
BACKGROUND
In previous work ICF reviewed the HAPEM4 modeling approach for developing
annual average activity patterns from the CHAD database and recommended an approach to
improve the model's pattern selection process to better represent the variability among
individuals. This section summarizes the recommended approach. (For details see
Attachment 2)
Using cluster analysis, first the CHAD daily activity patterns are grouped into either
two or three categories of similar patterns for each of the 30 combinations of day type
(summer weekday, non-summer weekday, and weekend) and demographic group (males or
females; age groups: 0-4, 5-11, 12-17, 18-64, 65+). Next, for each combination of day type
and demographic group, category-to-category transition probabilities are defined by the
relative frequencies of each second-day category associated with each given first-day
category, where the same individual was observed for two consecutive days. (Consecutive
day activity pattern records for a single individual constitute a small subset of the CHAD
data.)
To implement the proposed algorithm, for each day type and demographic group, one
daily activity pattern per category is randomly selected from the corresponding CHAD data
to represent that category. That is, if there are 3 cluster categories for each of 3 day types, 9
unique activity patterns are selected to be averaged together to create an annual average
activity pattern to represent an individual in a given demographic group and census tract.
The weighting for each of the 9 activity patterns used in the averaging process is
determined by the product of two factors. The first is the relative frequency of its day type,
i.e., 0.18 for summer weekdays, 0.54 for non-summer weekdays, and 0.28 for weekends.
The second factor in the weighting for the selected activity pattern is determined by
simulating a sequence of category-types as a one-stage Markov chain process using the
transition probabilities. The category for the first day is selected according to the relative
frequencies of each category. The category for the second day is selected according to the
category-to-category transition probabilities for the category selected for the first day. The
category for the third day is selected according to the transition probabilities for the category
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selected for the second day. This is repeated for all days in the day type (65 for summer
weekdays, 195 for non-summer weekdays, 104 for weekends), producing a sequence of daily
categories. The relative frequency of the category-type in the sequence associated with the
selected activity pattern is the second factor in the weighting.
PROPOSED ALGORITHM STEPS
The proposed algorithm is summarized in Figure 1. Each step is explained in this
section.
Data Preparation
Step 1: Each daily activity pattern in the CHAD data base is summarized by the total
minutes in each of five micro-environments: indoors - residence; indoors - other
building; outdoors - near road; outdoors - away from road; in vehicle. These five
numbers are assumed to represent the most important features of the activity pattern
for their exposure impact.
Step 2: All CHAD activity patterns for a given day-type and demographic group are
subjected to cluster analysis, resulting in 2 or 3 cluster categories. Each daily activity
pattern is tagged with a cluster category.
Step 3: For each day-type and demographic group, the relative frequency of each 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:
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Step 6: A cluster category is selected for the first day of the day-type sequence,
according to the relative frequency of the cluster category days in the CHAD data set.
Step 7: A cluster category is selected for each subsequent day in the day-type
sequence day by day using the category-to-category transition probabilities.
Step 8: The relative frequency of each cluster category in the day-type sequence is
determined.
Step 9: The activity patterns selected for each cluster category (Step 5) are averaged
together using the cluster category frequencies (Step 8) as weights, to create a 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, age
group, and home sector from a given set of distributions consistent with the
population of the study area.
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Step 2: A specific age within the age group is selected from a uniform distribution.
Step 3: The employment status is simulated as a function of the age.
Step 4: For each simulated day, the user defines an initial pool of possible diary days
based on a user-specified function of the day type (e.g., weekday/weekend) and
temperature.
Step 5: The pool is further restricted to match the target gender and employment
status exactly and the age within 2A years for some parameter A. The diary days
within the pool are assigned a weight of 1 if the age is within A years of the target age
and a weight of w (user-defined parameter) if the age difference is between A and 2A
years. For each simulated day, the probability of selecting a given diary day is equal
to the age weight divided by the total of the age weights for all diary days in the pool
for that day.
Approach to Incorporation of Day-to-Day Dependence into APEX Algorithm
If we were going to incorporate day-to-day dependence of activity patterns into the
APEX model, we would propose preparing the data with cluster analysis and transition
probabilities as described in Steps 1-4 for the proposed HAPEM 5 algorithm, with the
following modifications.
• For Step 2 the activity patterns would be divided into groups based on day-type
(weekday, weekend), temperature, gender, employment status, and age, with
cluster analysis applied to each group. However, because the day-to-day
transitions in the APEX activity selection algorithm can cross temperature bins,
we would propose to use broad temperature bins for the clustering and transition
probability calculations so that the cluster definitions would be fairly uniform
across temperature bins. Thus we would probably define the bins according to
season (e.g., summer, non-summer).
• In contrast to HAPEM, the sequence of activity patterns may be important in
APEX. Therefore, for Step 4 transition probabilities would be specified for
transitions between days with the same day-type and season, as in HAPEM, and
also between days with different day-types and/or seasons. For example,
transition probabilities would be specified for transitions between summer
weekdays of each category and summer weekends of each category.
Another issue for dividing the CHAD activity records for the purposes of clustering
and calculating transition probabilities is that the diary pools specified for the APEX activity
selection algorithm use varying and overlapping age ranges. One way to address this
problem would be to simply not include consideration of age in the clustering process, under
the assumption that cluster categories are similar across age groups, even if the frequency of
each cluster category varies by age group. This assumption could be tested by examination
of the cluster categories stratified by age group that were developed for HAPEM5. If the
assumption is found to be valid, then the cluster categories could be pre-determined for input
to APEX, while the transition probabilities could be calculated within APEX during the
simulation for each age range specified for dairy pools.
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If the assumption is found to be invalid, then an alternative approach could be
implemented that would create overlapping age groups for purposes of clustering as follows.
APEX age group ranges and age window percentages would be constrained to some
maximum values. Then a set of overlapping age ranges that would be at least as large as the
largest possible dairy pool age ranges would be defined for the purposes of cluster analysis
and transition probability calculation. The resulting sets of cluster categories and transition
probabilities would be pre-determined for input into APEX and the appropriate set used by
APEX for each diary pool used during the simulation.
The actual activity pattern sequence selection would be implemented as follows. The
activity pattern for first day in the year would be selected exactly as is currently done in
APEX, as described above. For the selecting the second day's activity pattern, each age
weight would be multiplied by the transition probability PAB where A is the cluster for the
first day's activity pattern and B is the cluster for a given activity pattern in the available pool
of diary days for day 2. (Note that day 2 may be a different day-type and/or season than day
1). The probability of selecting a given diary day on day 2 is equal to the age weight times
PAB divided by the total of the products of age weight and PAB for all diary days in the pool
for day 2. Similarly, for the transitions from day 2 to day 3, day 3 to day 4, etc.
Testing the Approach with the Multi-day Data set
We tested this approach using the available multi-day data set. For purposes of
clustering we characterized the activity pattern records according to time spent in each of 5
microenvironments: indoors-home, indoors-school, indoors-other, outdoors (aggregate of the
3 outdoor microenvironments), and in-transit.
For purposes of defining diary pools and for clustering and calculating transition
probabilities we divided the activity pattern records by day type (i.e., weekday, weekend),
season (i.e., summer or ozone season, non-summer or non-ozone season), age (6-10 and 11-
12), and gender. Since all the subjects are 6-12 years of age and all are presumably
unemployed, we need not account for differences in employment status. For each day type,
season, age, and gender, we found that the activity patterns appeared to group in three
clusters.
In this case, we simulated week-long sequences (Wednesday through Tuesday) for
each of 100 people in each age/gender group for each season, using the transition
probabilities. To evaluate the algorithm we calculated the following statistics for the
predicted multi-day activity patterns for comparison with the actual multi-day diary data.
For each age/gender group for each season, the average time in each
microenvironment
For each age/gender group, season, and microenvironment, the average of the
within-person variance across all simulated persons (We defined the 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
B-166
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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.26
We also calculated the mean normalized bias for the statistic, which is a common
performance measure used in dispersion model performance and which is calculated as
follows.
* rr, T , 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
26 For the diary data, because the number of days per person varies, the average of the within-person variances
was calculated as a weighted average, where the weight is the degrees of freedom, i.e., one less than the number
of days simulated. Similarly, the variance across persons of the mean time was appropriately adjusted for the
different degrees of freedom using analysis of variance.
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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%
B-169
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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%
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Consolidated Human Activity Database - CHAD (CHAD)
Winter Weekday
Pattern Group
Summer Weekday
Pattern Group
*
...V
Cluster Analysis
ransition
nalysis
Weekend
Pattern Group
*•
€1
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-172
-------
20
o
€, 10
•c
1 5
Ł
a.
• Indoor, home
• Indoor, school
indoor, other
x Outdoor
x In chicle
5 10 15
Observed (hours/day)
20
Figure 2. Comparison of predicted and observed average time in each of 5 microenvironments
for age/gender groups and seasons.
•g 4
= ^
o °
•c o
1
"O -|
i
n -
^^
^^
&^
01234
Observed (hours/day)
• Indoor, school
indoor, other
Outdoor
x In chicle
5
Figure 3. Comparison of predicted and observed average time in each of 4 microenvironments
for age/gender groups and seasons.
B-173
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80
70
Rn
•o 'so
o ou
S 40
3 ^U
•- ^0
w JU
on
10
0
C
/
-2
/
-r * .
/'.
/*
+Ł•
S"
f
• girls, 6-10, summer
• girls, 6-10, winter
boys , 6-10, summer
x boys, 6-10, winter
X girls, 11-12, summer
• girls, 11-12, winter
+ boys, 11-12, summer
- boys, 11-12, winter
1 1 1 1
) 20 40 60 80
Observed
Figure 4. Comparison of predicted and observed variance across persons for time spent in each
of 5 microenvironments for age/gender groups and seasons.
30 | •
OR J
TO -IR
D ID
E
(/) in
/
X • S
\/
f
f.
f
• 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
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-174
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Attachment 4. Technical Memorandum on the Analysis of NHIS
Asthma Prevalence Data
B-175
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INTERNATIONAL
DRAFT MEMORANDUM
To: John Langstaff
From: Jonathan Cohen, Arlene Rosenbaum
Date: September 30, 2005
EPA 68D01052, Work Assignment 3-08. Analysis of NHIS Asthma Prevalence
Ke: Data
This memorandum describes our analysis of children's asthma prevalence data from the National
Health Interview Survey (NHIS) for 2003. Asthma prevalence rates for children aged 0 to 17
years were calculated for each age, gender, and region. The regions defined by NHIS are
"Midwest," "Northeast," "South," and "West." For this project, asthma prevalence was defined
as the probability of a Yes response to the question C ASHMEV: "Ever been told that... had
asthma?" among those that responded Yes or No to this question. The responses were weighted
to take into account the complex survey design of the NHIS survey. Standard errors and
confidence intervals for the prevalence were calculated using a logistic model, taking into
account the survey design. Prevalence curves showing the variation of asthma prevalence
against age for a given gender and region were plotted. A scatterplot smoothing technique using
the LOESS smoother was applied to smooth the prevalence curves and compute the standard
errors and confidence intervals for the smoothed prevalence estimates. Logistic analysis of the
prevalence curves shows statistically significant differences in prevalence by gender and by
region. Therefore we did not combine the prevalence rates for different genders or regions.
Logistic Models
NHIS survey data for 2003 were provided by EPA. One obvious approach to calculate
prevalence rates and their uncertainties for a given gender, region, and age is to calculate the
proportion of Yes responses among the Yes and No responses for that demographic group,
weighting each response by the survey weight. Although that approach was initially used, two
problems are that the distributions of the estimated prevalence rates are not well approximated by
normal distributions, and that the estimated confidence intervals based on the normal
approximation often extend outside the [0, 1] interval. A better approach is to use a logistic
transformation and fit a model of the form:
Prob (asthma) = exp(beta) / (1 + exp(beta)),
where beta may depend on the explanatory variables for age, gender, or region. This is
equivalent to the model:
B-176
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Beta = logit (prob (asthma)} = log { prob (asthma) / [1 - prob (asthma)] }.
The distribution of the estimated values of beta is more closely approximated by a normal
distribution than the distribution of the corresponding estimates of prob (asthma). By applying a
logit transformation to the confidence intervals for beta, the corresponding confidence intervals
for prob (asthma) will always be inside [0, 1]. Another advantage of the logistic modeling is that
it can be used to compare alternative statistical models, such as models where the prevalence
probability depends upon age, region, and gender, or on age and region but not gender.
A variety of logistic models for asthma prevalence were fit and compared, where the transformed
probability variable beta is a given function of age, gender, and region. SAS's
SURVEYLOGISTIC procedure was used to fit the logistic models, taking into account the NHIS
survey weights and survey design (stratification and clustering).
The following Table G-l lists the models fitted and their log-likelihood goodness-of-fit
measures. 16 models were fitted. The Strata column lists the four possible stratifications: no
stratification, by gender, by region, by region and gender. For example, "4. region, gender"
means that separate prevalence estimates were made for each combination of region and gender.
As another example, "2. gender" means that separate prevalence estimates were made for each
gender, so that for each gender, the prevalence is assumed to be the same for each region. The
prevalence estimates are independently calculated for each stratum.
Table G-l. Alternative logistic models for asthma prevalence.
Model
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Description
1 . logit(prob) = linear in age
1 . logit(prob) = linear in age
1 . logit(prob) = linear in age
1 . logit(prob) = linear in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
2. logit(prob) = quadratic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
3. logit(prob) = cubic in age
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
4. logit(prob) = f(age)
Strata
1. none
2. gender
3. region
4. region, gender
1. none
2. gender
3. region
4. region, gender
1. none
2. gender
3. region
4. region, gender
1. none
2. gender
3. region
4. region, gender
- 2 Log Likelihood
54168194.62
53974657.17
54048602.57
53837594.97
53958021.20
53758240.99
53818198.13
53593569.84
53849072.76
53639181.24
53694710.66
53441122.98
53610093.48
53226610.02
53099749.33
52380000.19
DF
2
4
8
16
3
6
12
24
4
8
16
32
18
36
72
144
B-177
-------
The Description column describes how beta depends upon the age:
Linear in age: Beta = a + |3 * age, where a and |3 vary with the strata.
Quadratic in age: Beta = a + |3 x age + y x age2 where a |3 and y vary with the
strata.
Cubic in age: Beta = a + |3 x age + y x age2 + 5 x age3 where a |3, y, and 5 vary
with the strata.
f(age) Beta = arbitrary function of age, with different functions for
different strata
The category f(age) is equivalent to making age one of the stratification variables, and is also
equivalent to making beta a polynomial of degree 16 in age (since the maximum age for children
is 17), with coefficients that may vary with the strata.
The fitted models are listed in order of complexity, where the simplest model (1) is an
unstratified linear model in age and the most complex model (16) has a prevalence that is an
arbitrary function of age, gender, and region. Model 16 is equivalent to calculating independent
prevalence estimates for each of the 144 combinations of age, gender, and region.
Table G-l also includes the -2 Log Likelihood, a goodness-of-fit measure, and the degrees of
freedom, DF, which is the total number of estimated parameters. Two models can be compared
using their -2 Log Likelihood values; lower values are preferred. If the first model is a special
case of the second model, then the approximate statistical significance of the first model is
estimated by comparing the difference in the -2 Log Likelihood values with a chi-squared
random variable with r degrees of freedom, where r is the difference in the DF. This is a
likelihood ratio test. For all pairs of models from Table G-l, all the differences are at least
70,000 and the likelihood ratios are all extremely statistically significant at levels well below 5
percent. Therefore the model 16 is clearly preferred and was used to model the prevalences.
The SURVEYLOGISTIC model predictions are tabulated in Table G-2 below and plotted in
Figures 1 and 3 below. Also shown in Table G-2 and in Figures 2 and 4 are results for smoothed
curves calculated using a LOESS scatterplot smoother, as discussed below.
The SURVEYLOGISTIC procedure produces estimates of the beta values and their 95 %
confidence intervals for each combination of age, region, and gender. Applying the inverse logit
transformation,
Prob (asthma) = exp( beta) / (1 + exp(beta)),
converted the beta values and 95 % confidence intervals into predictions and 95 % confidence
intervals for the prevalence, as shown in Table G-2 and Figures 1 and 3. The standard error for
the prevalence was estimated as
Std Error {Prob (asthma)} = Std Error (beta) x exp(- beta) / (1 + exp(beta) )2,
which follows from the delta method (a first order Taylor series approximation).
Loess Smoother
B-178
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The estimated prevalence curves shows that the prevalence is not a smooth function of age. The
linear, quadratic, and cubic functions of age modeled by SURVEYLOGISTIC were one strategy
for smoothing the curves, but they did not provide a good fit to the data. One reason for this
might be due to the attempt to fit a global regression curve to all the age groups, which means
that the predictions for age A are affected by data for very different ages. We instead chose to
use a local regression approach that separately fits a regression curve to each age A and its
neighboring ages, giving a regression weight of 1 to the age A, and lower weights to the
neighboring ages using a tri-weight function:
Weight = {1 - [ |age - A / q ]3}, where | age - A| <= q.
The parameter q defines the number of points in the neighborhood of the age a. Instead of calling
q the smoothing parameter, SAS defines the smoothing parameter as the proportion of points in
each neighborhood. We fitted a quadratic function of age to each age neighborhood, separately
for each gender and region combination. We fitted these local regression curves to the beta
values, the logits of the asthma prevalence estimates, and then converted them back to estimated
prevalence rates by applying the inverse logit function exp(beta) / (1 + exp(beta)). In addition to
the tri-weight variable, each beta value was assigned a weight of
1 / [std error (beta)]2, to account for their uncertainties.
The SAS LOESS procedure was applied to estimate smoothed curves for beta, the logit of the
prevalence, as a function of age, separately for each region and gender. We fitted curves using
the choices 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 for the smoothing parameter in an effort to
T7 OS
determine the optimum choice based on various regression diagnostics. '
Quantities predicted in these smoothing parameter tests were the predicted value, standard error,
confidence interval lower bound and confidence interval upper bound for the betas, and the
corresponding values for the prevalence rates.
The polygonal curves joining values for different ages show the predicted values with vertical
lines indicating the confidence intervals in Figures 3 and 4 for smoothing parameters 0 (i.e., no
smoothing) and 0.5, respectively. Note that the confidence intervals are not symmetric about the
predicted values because of the inverse logit transformation.
Two outlier cases were adjusted to avoid wild variations in the "smoothed" curves: For the West region, males,
age 0, there were 97 children surveyed that all gave No answers to the asthma question, leading to an estimated
value of -15.2029 for beta with a standard error of 0.14. For the Northeast region, females, age 0, there were 29
children surveyed that all gave No answers to the asthma question, leading to an estimated value of -15.2029 for
beta with a standard error of 0.19. In both cases the raw probability of asthma equals zero, so the corresponding
estimated beta would be negative infinity, but SAS's software gives -15.2029 instead. To reduce the impact of these
outlier cases, we replaced their estimated standard errors by 4, which is approximately four times the maximum
standard error for all other region, gender, and age combinations.
28
With only 18 points, a smoothing parameter of 0.2 cannot be used because the weight function assigns zero
weights to all ages except age A, and a quadratic model cannot be uniquely fitted to a single value. A smoothing
parameter of 0.3 also cannot be used because that choice assigns a neighborhood of 5 points only (0.3 x 18 = 5,
rounded down), of which the two outside ages have assigned weight zero, making the local quadratic model fit
exactly at every point except for the end points (ages 0, 1, 16 and 17). Usually one uses a smoothing parameter
below one so that not all the data are used for the local regression at a given x value.
B-179
-------
Note that in our application of LOESS, we used weights of 1 / [std error (beta)]2, so that a2 = 1
for this application. The LOESS procedure estimates a2 from the weighted sum of squares. Since
in our application we assume a2 = 1, we multiplied the estimated standard errors by 1 /
estimated a, and adjusted the widths of the confidence intervals by the same factor.
Additionally, because the true value of a equals 1, the best choices of smoothing parameter
should give residual standard errors close to one. Using this criterion the best choice varies with
gender and region between smoothing parameters 0.4 (3 cases), 0.5 (2 cases), 0.6 (1 case), and
0.7 (1 case).
As a further regression diagnostic the residual errors from the LOESS model were divided by
std error (beta) to make their variances approximately constant. These approximately studentized
residuals, 'student,' should be approximately normally distributed with a mean of zero and a
variance of a2 = 1. To test this assumption, normal probability plots of the residuals were
created for each smoothing parameter, combining all the studentized residuals across genders,
regions, and ages. The plots for smoothing parameters seem to be equally straight for each
smoothing parameter.
The final regression diagnostic is a plot of the studentized residuals against the smoothed beta
values. Ideally there should be no obvious pattern and an average studentized residual close to
zero. The plots indeed showed no unusual patterns, and the results for smoothing parameters 0.5
and 0.6 seem to showed a fitted LOESS close to the studentized residual equals zero line.
The regression diagnostics suggested the choice of smoothing parameter as 0.4 or 0.5. Normal
probability plots did not suggest any preferred choices. The plots of residuals against smoothed
predictions suggest the choices of 0.5 or 0.6. We therefore chose the final value of 0.5. These
predictions, standard errors, and confidence intervals are presented in tabular form below as
Table G-2.
B-180
-------
Figure 1. Raw asthma prevalence rates by age and gender tor each region
region = Mi dwes t
gender
Male
Figure 1. Raw asthma prevalence rates by age and gender tor each region
region=Northeast
Male
B-181
-------
Figure 1. Raw asthma prevalence rates by age and gender tor each region
regi on = South
gender
Male
Figure 1. Raw asthma prevalence rates by age and gender tor each region
regi on=We st
Male
B-182
-------
Figure 2. Smoothed asthma prevalence rates by age for each region and gender
region = Mi dwes t
10 11 12 13 14 15 16 17
gender
Fema1e
Male
Figure 2. Smoothed asthma prevalence rates by age for each region and gender
region=Northeast
1 I ' ' ' I ' ' ' I ' ' ' I ' ' ' I ' ' ' I '
10 11 12 13 14 15
Male
B-183
-------
Figure 2. Smoothed asthma prevalence rates by age for each region and gender
regi on = South
gender
Fema1e
Male
Figure 2. Smoothed asthma prevalence rates by age for each region and gender
regi on=We st
gender
Male
B-184
-------
Figure 3. Raw asthma prevalence rates and confidence intervals
gender
age
Fema1e
Figure 3. Raw asthma prevalence rates and confidence intervals
region=Northeast
1 I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I
10 11 12 13 14 15 16 17
Male
B-185
-------
Figure 3. Raw asthma prevalence rates and confidence intervals
regi on = South
gender
age
Fema1e
Figure 3. Raw asthma prevalence rates and confidence intervals
regi on=We st
Male
B-186
-------
Rgure 4. Smoothed asthma prevalence rates and confidence intervals
region = Mi dwes t
gender
age
Fema1e
Rgure 4. Smoothed asthma prevalence rates and confidence intervals
region=Northeast
1 I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I
10 11 12 13 14 15 16 17
Male
B-187
-------
Rgure 4. Smoothed asthma prevalence rates and confidence intervals
regi on = South
gender
age
Fema1e
Male
Rgure 4. Smoothed asthma prevalence rates and confidence intervals
regi on=We st
1 I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I ' ' ' ' I
10 11 12 13 14 15 16 17
Male
B-188
-------
Table G-2. Raw and smoothed prevalence rates, with confidence intervals, by region,
gender, and age.
Obs
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
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Age
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
Smoothed
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Prevalence
0.04161
0.06956
0.10790
0.07078
0.05469
0.07324
0.06094
0.07542
0.09049
0.08100
0.08463
0.09540
0.14869
0.09210
0.04757
0.09032
0.10444
0.08612
0.09836
0.11040
0.10916
0.16190
0.27341
0.19597
0.10055
0.21214
0.22388
0.16966
Std
Error
0.02965
0.03574
0.04254
0.01995
0.02578
0.01778
0.03474
0.01944
0.03407
0.02163
0.03917
0.02613
0.08250
0.02854
0.02927
0.02563
0.03638
0.02181
0.04283
0.02709
0.04859
0.03486
0.06817
0.03920
0.04780
0.03957
0.05905
0.03371
95 % Conf
Interval -
Lower
Bound
0.01001
0.02143
0.04840
0.03736
0.02131
0.04228
0.01936
0.04205
0.04233
0.04417
0.03317
0.05106
0.04643
0.04534
0.01389
0.04728
0.05160
0.04842
0.04062
0.06298
0.04400
0.09838
0.16112
0.12296
0.03816
0.13724
0.12907
0.10716
95 % Conf
Interval -
Upper
Bound
0.15717
0.20330
0.22336
0.13008
0.13325
0.12395
0.17579
0.13163
0.18298
0.14393
0.19942
0.17131
0.38520
0.17808
0.15051
0.16571
0.19997
0.14857
0.21943
0.18643
0.24600
0.25484
0.42437
0.29763
0.23952
0.31309
0.35959
0.25807
B-189
-------
Obs
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Age
14
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
Smoothed
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.10511
0.14020
0.12026
0.13341
0.13299
0.14040
0.17497
0.16478
0.06419
0.03134
0.02824
0.06250
0.05102
0.10780
0.18650
0.15821
0.24649
0.21572
0.11609
0.17822
0.14158
0.12788
0.09726
0.12145
0.16718
0.12757
0.13406
0.14718
0.13986
0.17728
0.23907
Std
Error
0.04233
0.02603
0.03805
0.02266
0.03933
0.02235
0.04786
0.04037
0.03612
0.01537
0.01694
0.01751
0.02343
0.02078
0.04864
0.02705
0.05823
0.03661
0.04818
0.03525
0.05280
0.02799
0.03614
0.02642
0.05814
0.02700
0.04783
0.02976
0.04422
0.02996
0.05031
95 % Conf
Interval -
Lower
Bound
0.04637
0.09164
0.06327
0.09056
0.07288
0.09764
0.09970
0.09320
0.02068
0.01042
0.00859
0.03321
0.02040
0.06960
0.10898
0.10696
0.15035
0.14543
0.04973
0.11280
0.06576
0.07751
0.04588
0.07391
0.08134
0.07864
0.06458
0.09254
0.07331
0.12020
0.15449
95 % Conf
Interval -
Upper
Bound
0.22104
0.20857
0.21670
0.19226
0.23037
0.19777
0.28884
0.27468
0.18227
0.09046
0.08879
0.11457
0.12189
0.16328
0.30057
0.22775
0.37686
0.30774
0.24793
0.27003
0.27873
0.20375
0.19448
0.19317
0.31276
0.20031
0.25769
0.22603
0.25050
0.25366
0.35075
B-190
-------
Obs
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
Region
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Midwest
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Age
11
12
12
13
13
14
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Prevalence
0.18961
0.13660
0.19487
0.18501
0.16939
0.16673
0.16795
0.14583
0.17953
0.24965
0.20116
0.21152
0.23741
0.00000
0.06807
0.12262
0.07219
0.07217
0.07522
0.08550
0.07709
0.08704
0.08171
0.07597
0.11603
0.19149
0.16106
0.22034
0.18503
0.11002
0.17054
Std
Error
0.03044
0.04784
0.03078
0.04498
0.02841
0.05094
0.02631
0.04241
0.02561
0.06037
0.03048
0.06481
0.05816
0.00000
0.06565
0.07443
0.03765
0.03707
0.02212
0.03991
0.02021
0.03804
0.02252
0.03754
0.03012
0.06960
0.03737
0.07076
0.04087
0.05128
0.04039
95 % Conf
Interval -
Lower
Bound
0.13100
0.06668
0.13541
0.11230
0.11528
0.08886
0.11734
0.08054
0.12951
0.15033
0.14187
0.11131
0.13243
0.00000
0.00670
0.03476
0.02088
0.02561
0.03764
0.03324
0.04162
0.03596
0.04269
0.02801
0.06258
0.08937
0.09219
0.11195
0.10844
0.04241
0.09628
95 % Conf
Interval -
Upper
Bound
0.26639
0.25946
0.27221
0.28945
0.24195
0.29104
0.23459
0.24967
0.24347
0.38489
0.27721
0.36490
0.38835
0.00000
0.44174
0.35164
0.22109
0.18713
0.14468
0.20269
0.13840
0.19592
0.15080
0.18998
0.20515
0.36372
0.26629
0.38783
0.29764
0.25654
0.28407
B-191
-------
Obs
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
Region
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Age
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
Smoothed
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.17541
0.14457
0.12980
0.13487
0.15128
0.14072
0.11890
0.16615
0.22638
0.17374
0.15807
0.15137
0.07460
0.14564
0.13603
0.14601
0.19074
0.15662
0.03904
0.04768
0.05533
0.04564
0.05525
0.05161
0.03842
0.06766
0.07436
0.09964
0.17601
0.14854
0.23271
Std
Error
0.07488
0.03538
0.04964
0.03098
0.05287
0.03068
0.04426
0.03375
0.06285
0.03402
0.05513
0.02946
0.03409
0.02761
0.05328
0.03095
0.07382
0.05374
0.03829
0.03299
0.03425
0.01831
0.03119
0.01505
0.02923
0.01784
0.02906
0.02330
0.04519
0.02948
0.09319
95 % Conf
Interval -
Lower
Bound
0.07159
0.08042
0.05930
0.07799
0.07366
0.08367
0.05568
0.10211
0.12650
0.10861
0.07694
0.09519
0.02971
0.09279
0.06081
0.08805
0.08451
0.06784
0.00547
0.00991
0.01596
0.01850
0.01781
0.02680
0.00840
0.03734
0.03393
0.05859
0.10393
0.09428
0.09832
95 % Conf
Interval -
Upper
Bound
0.36981
0.24618
0.26087
0.22319
0.28547
0.22704
0.23597
0.25877
0.37158
0.26626
0.29719
0.23220
0.17506
0.22127
0.27686
0.23241
0.37568
0.32151
0.23095
0.20023
0.17461
0.10821
0.15872
0.09709
0.15853
0.11955
0.15522
0.16441
0.28234
0.22623
0.45756
B-192
-------
Obs
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
Region
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
Northeast
South
South
South
South
South
South
South
South
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Age
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Prevalence
0.20731
0.13074
0.22820
0.33970
0.22240
0.13761
0.21238
0.21785
0.17652
0.11448
0.16617
0.17736
0.18279
0.19837
0.17078
0.16201
0.17033
0.11894
0.18246
0.24306
0.20406
0.22559
0.24185
0.02459
0.03407
0.08869
0.05182
0.05097
0.07110
0.08717
0.08759
Std
Error
0.04235
0.05195
0.04524
0.08456
0.04298
0.05024
0.04071
0.06659
0.03731
0.05849
0.03516
0.05489
0.03589
0.05450
0.03078
0.04973
0.02889
0.04584
0.02858
0.05798
0.03216
0.06980
0.06066
0.01116
0.01282
0.03373
0.01167
0.02373
0.01386
0.03240
0.01718
95 % Conf
Interval -
Lower
Bound
0.12875
0.05785
0.14338
0.19726
0.14157
0.06507
0.13589
0.11464
0.10824
0.04005
0.10200
0.09349
0.11611
0.11222
0.11288
0.08618
0.11547
0.05417
0.12740
0.14759
0.14187
0.11748
0.13291
0.01002
0.01465
0.04118
0.03127
0.02012
0.04584
0.04122
0.05624
95 % Conf
Interval -
Upper
Bound
0.31640
0.26922
0.34311
0.51855
0.33157
0.26785
0.31617
0.37465
0.27460
0.28601
0.25907
0.31067
0.27581
0.32635
0.25000
0.28386
0.24408
0.24139
0.25438
0.37326
0.28447
0.38930
0.39898
0.05906
0.07723
0.18067
0.08472
0.12319
0.10869
0.17500
0.13394
B-193
-------
Obs
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
Gender
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Age
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
17
17
0
0
1
Smoothed
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.11010
0.09897
0.09409
0.11870
0.15318
0.12150
0.09608
0.11192
0.09955
0.09287
0.07477
0.09117
0.10602
0.10821
0.14411
0.13237
0.12646
0.12346
0.11376
0.09653
0.02915
0.09469
0.11985
0.09988
0.14183
0.11501
0.13141
0.14466
0.01164
0.04132
0.10465
Std
Error
0.03209
0.01914
0.02943
0.02157
0.04317
0.02282
0.03538
0.02171
0.03288
0.01897
0.02719
0.01786
0.03214
0.02026
0.04267
0.02251
0.02981
0.02004
0.03270
0.01717
0.01339
0.01619
0.03357
0.01586
0.03685
0.01620
0.03007
0.02946
0.00852
0.01867
0.03216
95 % Conf
Interval -
Lower
Bound
0.06113
0.06387
0.05015
0.07855
0.08611
0.07925
0.04565
0.07204
0.05111
0.05850
0.03606
0.05855
0.05750
0.07077
0.07875
0.08989
0.07860
0.08543
0.06365
0.06458
0.01174
0.06436
0.06801
0.06978
0.08366
0.08365
0.08280
0.09067
0.00275
0.01487
0.05629
95 % Conf
Interval -
Upper
Bound
0.19035
0.15025
0.16968
0.17548
0.25777
0.18182
0.19105
0.16985
0.18493
0.14436
0.14864
0.13929
0.18732
0.16201
0.24907
0.19071
0.19723
0.17519
0.19510
0.14190
0.07054
0.13721
0.20259
0.14099
0.23028
0.15612
0.20226
0.22291
0.04790
0.10956
0.18635
B-194
-------
Obs
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
Region
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
South
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Age
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
14
15
15
16
16
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Prevalence
0.06981
0.11644
0.10189
0.10794
0.12852
0.08480
0.14393
0.22243
0.16450
0.13908
0.16386
0.10695
0.13329
0.13660
0.13818
0.15978
0.16839
0.21482
0.17848
0.15078
0.16247
0.13727
0.14480
0.14136
0.14318
0.16110
0.15339
0.16172
0.15088
0.15836
0.14038
Std
Error
0.01623
0.03486
0.01672
0.03253
0.02139
0.02973
0.02379
0.04227
0.02373
0.03392
0.02460
0.04272
0.02322
0.03841
0.02276
0.03742
0.02450
0.04702
0.02453
0.03440
0.02224
0.03260
0.01976
0.03119
0.01928
0.03444
0.01875
0.03519
0.01746
0.03879
0.01773
95 % Conf
Interval -
Lower
Bound
0.04125
0.06353
0.07024
0.05874
0.08793
0.04190
0.09861
0.15052
0.11821
0.08485
0.11613
0.04747
0.08951
0.07712
0.09484
0.09920
0.12062
0.13676
0.13021
0.09492
0.11881
0.08489
0.10610
0.09049
0.10537
0.10438
0.11612
0.10394
0.11598
0.09614
0.10533
95 % Conf
Interval -
Upper
Bound
0.11576
0.20382
0.14557
0.19005
0.18405
0.16410
0.20534
0.31592
0.22430
0.21964
0.22617
0.22347
0.19392
0.23049
0.19702
0.24720
0.23012
0.32086
0.23972
0.23112
0.21820
0.21438
0.19453
0.21409
0.19165
0.24037
0.19992
0.24291
0.19398
0.24974
0.18467
B-195
-------
Obs
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
Region
South
South
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Male
Male
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Female
Age
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
12
12
13
13
14
Smoothed
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Prevalence
0.11156
0.12247
0.00983
0.01318
0.02367
0.03105
0.08097
0.05440
0.07528
0.07444
0.09263
0.07696
0.01976
0.07737
0.15792
0.07298
0.06955
0.08146
0.07753
0.09062
0.13440
0.10215
0.06573
0.12152
0.15354
0.12719
0.10120
0.13054
0.14759
0.11968
0.08748
Std
Error
0.02737
0.02596
0.00990
0.00987
0.01862
0.01312
0.03759
0.01482
0.03851
0.01842
0.03196
0.02064
0.01347
0.02123
0.07301
0.01985
0.02567
0.01987
0.02825
0.01994
0.04481
0.02347
0.03719
0.02660
0.04584
0.02688
0.03594
0.02498
0.04125
0.02369
0.03284
95 % Conf
Interval -
Lower
Bound
0.06810
0.07537
0.00135
0.00248
0.00497
0.01204
0.03170
0.02948
0.02679
0.04257
0.04621
0.04194
0.00513
0.04157
0.06009
0.03947
0.03321
0.04691
0.03731
0.05507
0.06802
0.06061
0.02102
0.07376
0.08329
0.07852
0.04934
0.08440
0.08346
0.07629
0.04105
95 % Conf
Interval -
Upper
Bound
0.17746
0.19286
0.06802
0.06700
0.10522
0.07769
0.19166
0.09825
0.19404
0.12701
0.17703
0.13701
0.07302
0.13949
0.35487
0.13107
0.13989
0.13776
0.15417
0.14558
0.24832
0.16709
0.18736
0.19374
0.26584
0.19950
0.19631
0.19650
0.24769
0.18284
0.17675
B-196
-------
Obs
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
Region
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Female
Female
Female
Female
Female
Female
Female
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Age
14
15
15
16
16
17
17
0
0
1
1
2
2
3
3
4
4
5
5
6
6
7
7
8
8
9
9
10
10
11
11
Smoothed
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Prevalence
0.11063
0.10099
0.11236
0.12538
0.12224
0.14672
0.14371
0.00000
0.03075
0.05457
0.04584
0.07833
0.06254
0.05897
0.07844
0.07267
0.09122
0.19732
0. 11262
0.13335
0.12119
0.08881
0.12691
0.15183
0.13161
0.17199
0.15079
0.12897
0.16356
0.19469
0.16965
Std
Error
0.02132
0.03841
0.02051
0.04343
0.02210
0.04582
0.03992
0.00000
0.02534
0.02662
0.01889
0.02789
0.01442
0.02530
0.01913
0.03354
0.02482
0.10033
0.02937
0.04859
0.02916
0.03493
0.02806
0.05484
0.02705
0.05164
0.02837
0.03747
0.02584
0.04002
0.02623
95 % Conf
Interval -
Lower
Bound
0.07145
0.04674
0.07428
0.06188
0.08108
0.07743
0.07558
0.00000
0.00437
0.02056
0.01729
0.03833
0.03627
0.02500
0.04398
0.02870
0.04765
0.06632
0.06021
0.06322
0.06799
0.04015
0.07464
0.07210
0.08037
0.09260
0.09590
0.07151
0.11192
0.12785
0.11699
95 % Conf
Interval -
Upper
Bound
0.16744
0.20471
0.16645
0.23755
0.18021
0.26052
0.25621
0.00000
0.18642
0.13695
0.11595
0.15342
0.10573
0.13281
0.13607
0.17208
0.16763
0.45969
0.20092
0.25970
0.20680
0.18508
0.20758
0.29200
0.20811
0.29715
0.22915
0.22159
0.23279
0.28505
0.23956
B-197
-------
Obs
277
278
279
280
281
282
283
284
285
286
287
288
Region
West
West
West
West
West
West
West
West
West
West
West
West
Gender
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Male
Age
12
12
13
13
14
14
15
15
16
16
17
17
Smoothed
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Prevalence
0.13214
0.17494
0.19947
0.16217
0.10759
0.16487
0.18459
0.17018
0.19757
0.17888
0.18078
0.19218
Std
Error
0.04542
0.02738
0.04814
0.02773
0.03838
0.02644
0.05348
0.02480
0.04862
0.02540
0.04735
0.04291
95 % Conf
Interval -
Lower
Bound
0.06547
0.12002
0.12127
0.10747
0.05220
0.11214
0.10138
0.11996
0.11892
0.12718
0.10548
0.11118
95 % Conf
Interval -
Upper
Bound
0.24865
0.24792
0.31029
0.23732
0.20880
0.23582
0.31235
0.23578
0.30993
0.24569
0.29227
0.31153
B-198
-------
Attachment 5: Technical Memorandum on Analysis of Air Exchange
Rate Data
B-199
-------
INTERNATIONAL
DRAFT MEMORANDUM
To: John Langstaff
From: Jonathan Cohen, Hemant Mallya, Arlene Rosenbaum
Date: September 30, 2005
Re: EPA 68D01052, Work Assignment 3-08. Analysis of Air Exchange Rate Data
EPA is planning to use the APEX exposure model to estimate ozone exposure in 12 cities /
metropolitan areas: Atlanta, GA; Boston, MA; Chicago, IL; Cleveland, OH; Detroit, Ml; Houston,
TX; Los Angeles, CA; New York, NY; Philadelphia, PA; Sacramento, CA; St. Louis, MO-IL;
Washington, DC. As part of this effort, ICF Consulting has developed distributions of residential
and non-residential air exchange rates (AER) for use as APEX inputs for the cities to be
modeled. This memorandum describes the analysis of the AER data and the proposed APEX
input distributions. Also included in this memorandum are proposed APEX inputs for penetration
and proximity factors for selected microenvironments.
Residential Air Exchange Rates
Studies. Residential air exchange rate (AER) data were obtained from the following seven
studies:
Avol: Avol et al, 1998. In this study, ozone concentrations and AERs were measured at
126 residences in the greater Los Angeles metropolitan area between February and
December, 1994. Measurements were taken in four communities: Lancaster, Lake
Gregory, Riverside, and San Dimas. Data included the daily average outdoor
temperature, the presence or absence of an air conditioner (either central or room), and
the presence or absence of a swamp (evaporative) cooler. Air exchange rates were
computed based on the total house volume and based on the total house volume
corrected for the furniture. These data analyses used the corrected AERs.
RTP Panel: Williams et al, 2003a, 2003b. In this study particulate matter concentrations
and daily average AERs were measured at 37 residences in central North Carolina
during 2000 and 2001 (averaging about 23 AER measurements per residence). The
residences belong to two specific cohorts: a mostly Caucasian, non-smoking group aged
at least 50 years having cardiac defibrillators living in Chapel Hill; a group of non-
smoking, African Americans aged at least 50 years with controlled hypertension living in
a low-to-moderate SES neighborhood in Raleigh. Data included the daily average
outdoor temperature, and the number of air conditioner units (either central or room).
Every residence had at least one air conditioner unit.
RIOPA: Meng et al, 2004, Weisel et al, 2004. The Relationship of Indoor, Outdoor, and
Personal Air (RIOPA) study was undertaken to estimate the impact of outdoor sources of
air toxics to indoor concentrations and personal exposures. Volatile organic compounds,
carbonyls, fine particles and AERs were measured once or twice at 310 non-smoking
residences from summer 1999 to spring 2001. Measurements were made at residences
in Elizabeth, NJ, Houston TX, and Los Angeles CA. Residences in California were
B-200
-------
randomly selected. Residences in New Jersey and Texas were preferentially selected to
be close (< 0.5 km) to sources of air toxics. The AER measurements (generally over 48
hours) used a PMCH tracer. Data included the daily average outdoor temperature, and
the presence or absence of central air conditioning, room air conditioning, or a swamp
(evaporative) cooler.
TEACH: Chillrud at al, 2004, Kinney et al, 2002, Sax et al, 2004. The Toxic Exposure
Assessment, a Columbia/Harvard (TEACH) study was designed to characterize levels of
and factors influencing exposures to air toxics among high school students living in
inner-city neighborhoods of New York City and Los Angeles, CA. Volatile organic
compounds, aldehydes, fine particles, selected trace elements, and AER were
measured at 87 high school student's residences in New York City and Los Angeles in
1999 and 2000. Data included the presence or absence of an air conditioner (central or
room) and hourly outdoor temperatures (which were converted to daily averages for
these analyses).
Wilson 1984: Wilson et al, 1986, 1996. In this 1984 study, AER and other data were
collected at about 600 southern California homes with three seven-day tests (in March
and July 1984, and January, 1985) for each home. We obtained the data directly from
Mr. Wilson. The available data consisted of the three seven-day averages, the month,
the residence zip code, the presence or absence of a central air conditioner, and the
presence or absence of a window air conditioner. We matched these data by month and
zip code to the corresponding monthly average temperatures obtained from EPA's
SCRAM website as well as from the archives in www.wunderground.com (personal and
airport meteorological stations). Residences more than 25 miles away from the nearest
available meteorological station were excluded from the analysis. For our analyses, the
city/location was defined by the meteorological station, since grouping the data by zip
code would not have produced sufficient data for most of the zip codes.
Wilson 1991: Wilson etal, 1996. Colomeetal, 1993, 1994. In this 1991 study, AER and
other data were collected at about 300 California homes with one two-day test in the
winter for each home. We obtained the data directly from Mr. Wilson. The available data
consisted of the two-day averages, the date, city name, the residence zip code, the
presence or absence of a central air conditioner, the presence or absence of a swamp
(evaporative) cooler, and the presence or absence of a window air conditioner. We
matched these data by date, city, and zip code to the corresponding daily average
temperatures obtained from EPA's SCRAM website as well as from the archives in
www.wunderground.com (personal and airport meteorological stations). Residences
more than 25 miles away from the nearest available meteorological station were
excluded from the analysis. For our analyses, the city/location was defined by the
meteorological station, since grouping the data by zip code would not have produced
sufficient data for most of the zip codes.
Murray and Burmaster: Murray and Burmaster (1995). For this article, Murray and
Burmaster corrected and compiled nationwide residential AER data from several studies
conducted between 1982 and 1987. These data were originally compiled by the
Lawrence Berkeley National Laboratory. We acknowledge Mr. Murray's assistance in
obtaining these data for us. The available data consisted of AER measurements, dates,
cities, and degree-days. Information on air conditioner presence or absence was not
available.
Table A-1 summarizes these studies.
B-201
-------
For each of the studies, air conditioner usage, window status (open or closed), and fan status
(on or off) was not part of the experimental design, although some of these studies included
information on whether air conditioners or fans were used (and for how long) and whether
windows were closed during the AER measurements (and for how long).
As described above, in the following studies the homes were deliberately sampled from specific
subsets of the population at a given location rather than the entire population: The RTP Panel
study selected two specific cohorts of older subjects with specific diseases. The RIOPA study
was biased towards residences near air toxics sources. The TEACH study focused on inner-city
neighborhoods. Nevertheless, we included all these studies because we determined that any
potential bias would be likely to be small and we preferred to keep as much data as possible.
B-202
-------
Table A-1. Summary of Studies of Residential Air Exchange Rates
Locations
Years
Months/Seasons
Number of
Homes
Total AER
Measurements
Average
Number of
Measurements
per Home
Measurement
Duration
Measurement
Technique
Min AER Value
Max AER Value
Mean AER
Value
Min
Temperature
(C)
Avol
Lancaster, Lake
Gregory,
Riverside, San
Dimas. All in
Southern CA
1994
Feb; Mar; Apr;
May; Jun; Jul;
Aug; Sep; Oct;
Nov
86
161
1.87
Not Available
Not Available
0.01
2.70
0.80
-0.04
RTF Panel
Research Triangle
Park, NC
2000; 2001
2000 (Jun; Jul;
Aug; Sep; Oct;
Nov), 2001 (Jan;
Feb; Apr; May)
37
854
23.08
24 hour
Perflourocarbon
tracer.
0.02
21.44
0.72
-2.18
RIOPA
CA; NJ; TX
1999; 2000; 2001
1999 (July to
Dec); 2000 (all
months); 2001
(Jan and Feb)
284
524
1.85
24 to 96 hours
PMCH tracer
0.08
87.50
1.41
-6.82
TEACH
Los Angeles, CA;
New York City, NY
1999; 2000
1999 (Feb; Mar; Apr;
Jul; Aug); 2000 (Jan;
Feb; Mar; Sep; Oct)
85
151
1.78
Sample time (hours)
reported. Ranges
from about 1 to 7
days.
Perflourocarbon
tracer.
0.12
8.87
1.71
-1.36
Wilson 1984
Southern CA
1984, 1985
Mar 1984, Jul 1984, Jan
1985
581
1,362
2.34
7 days
Perflourocarbon tracer.
0.03
11.77
1.05
11.00
Wilson 1991
Southern CA
1984
Jan, Mar, Jul
288
316
1.10
7 days
Perflourocarbon tracer.
0.01
2.91
0.57
3.00
Murray
and
Burmaster
AZ, CA, CO,
CT, FL, ID,
MD, MN, MT,
NJ
1982-1987
Various
1,884
2,844
1.51
Not available
Not available
0.01
11.77
0.76
Not available
B-203
-------
Max
Temperature
(C)
Air Conditioner
Categories
Air Conditioner
Measurements
Fan Categories
Fan
Measurements
Window Open/
Closed Data
Comments
Avol
36.25
No A/C; Central
or Room A/C;
Swamp Cooler
only; Swamp +
[Central or Room]
A/C use in
minutes
Not available
Time on or off for
various fan types
during sampling
was recorded, but
not included in
database provided.
Duration open
between times
6am- 12 pm; 12pm
- 6 pm; and 6pm -
6am
RTF Panel
30.81
Central or Room
A/C (Y/N)
Not Available
Fan (Y/N)
Not Available
Windows (open /
closed along with
duration open in
inch-hours units
RIOPA
32.50
Window A/C
(Y/N); Evap
Coolers (Y/N)
Duration
measurements in
Hrs and Mins
Fan (Y/N)
Duration
measurements in
Hrs and Mins
Windows (Open /
Closed) along with
window open
duration
measurements
CA sample was a
random sample of
homes. NJ and TX
homes were
deliberately
chosen to be near
to ambient
sources.
TEACH
32.00
Central or Room A/C
(Y/N)
Not Available
Not Available
Not Available
Not Available
Restricted to inner-
city homes with high
school students.
Wilson 1984
28.00
Central A/C (Y/N);
Room A/C (Y/N);
Not Available
Not Available
Not Available
Not Available
Contemporaneous
temperature data
obtained for these
analyses from SCRAM
and
www. wunderground. com
meteorological data.
Wilson 1991
25.00
Central A/C (Y/N);
Room A/C (Y/N);
Swamp Cooler(Y/N)
Not Available
Not Available
Not Available
Not Available
Contemporaneous
temperature data
obtained for these
analyses from SCRAM
and
www.wunderground.com
meteorological data.
Murray
and
Burmaster
Not available
Not available
Not available
Not available
Not available
Not available
B-204
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We compiled the data from these seven studies to create the following variables, of which some
had missing values:
• Study
• Date
• Time - Time of the day that the AER measurement was made
• HouseJD - Residence identifier
• MeasurementJD - Uniquely identifies each AER measurement for a given study
• AER - Air Exchange Rate (per hour)
• AER_Duration - Length of AER measurement period
• Have_AC - Indicates if the residence has any type of air conditioner (A/C), either a room
A/C or central A/C or swamp cooler or any of them in combination. "Y" = "Yes." "N" =
"No."
• Type_of_AC1 - Indicates the types of A/C or swamp cooler available in each house
measured. Possible values: "Central A/C" "Central and Room A/C" "Central or Room
A/C" "No A/C" "Swamp + (Central or Room)" "Swamp Cooler only" "Window A/C"
"Wndow and Evap"
• Type_of_AC2 - Indicates if a house measured has either no A/C or some A/C. Possible
values are "No A/C" and "Central or Room A/C."
• Have_Fan - Indicates if the house studied has any fans
• Mean_Temp - Daily average outside temperature
• Min_Temp - Minimum hourly outside temperature
• Max_Temp - Maximum hourly outside temperature
• State
• City
• Location - Two character abbreviation
• Flag - Data status. Murray and Burmaster study: "Used" or "Not Used." Other studies:
"Used"; "Missing" (missing values for AER, Type_of_AC2, and/or Mean_Temp);
"Outlier".
The main data analysis was based on the first six studies. The Murray and Burmaster data were
excluded because of the absence of information on air conditioner presence. (However, a
subset of these data was used for a supplementary analysis described below.).
Based on our review of the AER data we excluded seven outlying high AER values - above 10
per hour. The main data analysis used all the remaining data that had non-missing values for
AER, Type_of_AC2, and Mean_Temp. We decided to base the A/C type variable on the broad
characterization "No A/C" versus "Central or Room A/C" since this variable could be calculated
from all of the studies (excluding Murray and Burmaster). Information on the presence or
absence of swamp coolers was not available from all the studies, and, also importantly, the
corresponding information on swamp cooler prevalence for the subsequent ozone modeling
cities was not available from the American Housing Survey. It is plausible that AER distributions
depend upon the presence or absence of a swamp cooler. It is also plausible that AER
distributions also depend upon whether the residence specifically has a central A/C, room or
window A/C, or both. However we determined to use the broader A/C type definition, which in
effect assumes that the exact A/C type and the presence of a swamp cooler are approximately
proportionately represented in the surveyed residences.
B-205
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Most of the studies had more than one AER measurement for the same house. It is reasonable
to assume that the AER varies with the house as well as other factors such as the temperature.
(The A/C type can be assumed to be the same for each measurement of the same house). We
expected the temperature to be an important factor since the AER will be affected by the use of
the available ventilation (air conditioners, windows, fans), which in turn will depend upon the
outside meteorology. Therefore it is not appropriate to average data for the same house under
different conditions, which might have been one way to account for dependence between
multiple measurements on the same house. To simplify the data analysis, we chose to ignore
possible dependence between measurements on the same house on different days and treat all
the AER values as if they were statistically independent.
Summary Statistics. We computed summary statistics for AER and its natural logarithm
LOG_AER on selected strata defined from the study, city, A/C type, and mean temperature.
Cities were defined as in the original databases, except that for Los Angeles we combined all
the data in the Los Angeles ozone modeling region, i.e. the counties of Los Angeles, Orange,
Ventura, Riverside, and San Bernardino. A/C type was defined from the Type_of_AC2 variable,
which we abbreviated as "NA" = "No A/C" and "AC" = "Central or Room A/C." The mean
temperature was grouped into the following temperature bins: -10 to 0 °C, 0 to 10 °C, 10 to 20
°C, 20 to 25 °C, 25 to 30 °C, 30 to 40 °C.(Values equal to the lower bounds are excluded from
each interval.) Also included were strata defined by study = "AH" and/or city = "All," and/or A/C
type = "AH" and/or temperature bin = "All." The following summary statistics for AER and
LOG_AER were computed:
• Number of values
• Arithmetic Mean
• Arithmetic Standard Deviation
• Arithmetic Variance
• Deciles (Min, 10th, 20th... 90th percentiles, Max)
These calculations exclude all seven outliers and results are not used for strata with 10 or fewer
values, since those summary statistics are extremely unreliable.
Examination of these summary tables clearly demonstrates that the AER distributions vary
greatly across cities and A/C types and temperatures, so that the selected AER distributions for
the modeled cities should also depend upon the city, A/C type and temperature. For example,
the mean AER for residences with A/C ranges from 0.39 for Los Angeles between 30 and 40 °C
to 1.73 for New York between 20 and 25 °C. The mean AER for residences without A/C ranges
from 0.46 for San Francisco between 10 and 20 °C to 2.29 for New York between 20 and 25 °C.
The need to account for the city as well as the A/C type and temperature is illustrated by the
result that for residences with A/C and between 20 and 25 °C, the mean AER ranges from 0.52
for Research Triangle Park to 1.73 for New York. Statistical comparisons are described below.
Statistical Comparisons. Various statistical comparisons were carried out between the
different strata, for the AER and its logarithm. The various strata are defined as in the Summary
Statistics section, excluding the "AN" cases. For each analysis, we fixed one or two of the
variables Study, City, A/C type, temperature, and tested for statistically significant differences
among other variables. The comparisons are listed in Table A-2.
Table A-2. Summary of Comparisons of Means
B-206
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Comparison
Analysis
Number.
1.
2.
3.
4.
5.
6.
Comparison
Variable(s)
"Groups
Compared"
City
Temp. Range
Type of A/C
City
City
Type of A/C
AND Temp.
Range
Stratification
Variable(s)
(not missing in
worksheet)
Type of A/C AND
Temp. Range
Study AND City
Study AND City
Type of A/C
Temp. Range
Study AND City
Total
Comparisons
12
12
15
2
6
17
Cases with significantly
different means (5 %
level)
AER
8
5
5
2
5
6
Log AER
8
5
5
2
6
6
For example, the first set of comparisons fix the Type of A/C and the temperature range; there
are twelve such combinations. For each of these twelve combinations, we compare the AER
distributions across different cities. This analysis determines whether the AER distribution is
appropriately defined by the A/C type and temperature range, without specifying the city.
Similarly, for the sixth set of comparisons, the study and city are held fixed (17 combinations)
and in each case we compare AER distributions across groups defined by the combination of
the A/C type and the temperature range.
The F Statistic comparisons compare the mean values between groups using a one way
analysis of variance (ANOVA). This test assumes that the AER or log(AER) values are normally
distributed with a mean that may vary with the comparison variable(s) and a constant variance.
We calculated the F Statistic and its P-value. P-values above 0.05 indicate cases where all the
group means are not statistically significantly different at the 5 percent level. Those results are
summarized in the last two columns of the above table "Summary of Comparisons of Means"
which gives the number of cases where the means are significantly different. Comparison
analyses 2, 3, and 6 show that for a given study and city, slightly less than half of the
comparisons show significant differences in the means across temperature ranges, A/C types,
or both. Comparison analyses 1, 4, and 5 show that for the majority of cases, means vary
significantly across cities, whether you first stratify by temperature range, A/C type, or both.
The Kruskal-Wallis Statistic comparisons are non-parametric tests that are extensions of the
more familiar Wilcoxon tests to two or more groups. The analysis is valid if the AER minus the
group median has the same distribution for each group, and tests whether the group medians
are equal. (The test is also consistent under weaker assumptions against more general
alternatives) The P-values show similar patterns to the parametric F test comparisons of the
means. Since the logarithm is a strictly increasing function and the test is non-parametric, the
Kruskal-Wallis tests give identical results for AER and Log (AER).
The Mood Statistic comparisons are non-parametric tests that compare the scale statistics for
two or more groups. The scale statistic measures variation about the central value, which is a
non-parametric generalization of the standard deviation. Specifically, suppose there is a total of
N AER or log(AER) values, summing across all the groups. These N values are ranked from 1
to N, and the j'th highest value is given a score of {j - (N+1)/2}2. The Mood statistic uses a one
way ANOVA statistic to compare the total scores for each group. Generally, the Mood statistics
show that in most cases the scale statistics are not statistically significantly different. Since the
B-207
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logarithm is a strictly increasing function and the test is non-parametric, the Mood tests give
identical results for AER and Log (AER).
Fitting Distributions. Based on the summary statistics and the statistical comparisons, the
need to fit different AER distributions to each combination of A/C type, city, and temperature is
apparent. For each combination with a minimum of 11 AER values, we fitted and compared
exponential, log-normal, normal, and Weibull distributions to the AER values.
The first analysis used the same stratifications as in the above "Summary Statistics" and
"Statistical Comparisons" sections. Results are not reported for all strata because of the
minimum data requirement of 11 values. Results for each combination of A/C type, city, and
temperature (i.e., A, C, and T) were analyzed. Each combination has four rows, one for each
fitted distribution. For each distribution we report the fitted parameters (mean, standard
deviation, scale, shape) and the p-value for three standard goodness-of-fit tests: Kolmogorov-
Smirnov (K-S), Cramer-Von-Mises (C-M), Anderson-Darling (A-D). Each goodness-of-fit test
compares the empirical distribution of the AER values to the fitted distribution. The K-S and C-M
tests are different tests examining the overall fit, while the Anderson-Darling test gives more
weight to the fit in the tails of the distribution. For each combination, the best-fitting of the four
distributions has the highest p-value and is marked by an x in the final three columns. The mean
and standard deviation (Std_Dev) are the values for the fitted distribution. The scale and shape
parameters are defined by:
• Exponential: density = a"1 exp(-x/a), where shape = mean = a
• Log-normal: density = (axV(27t)}"1 exp{ -(log x - Q2 / (2a2)}, where shape = a and scale =
Ł,. Thus the geometric mean and geometric standard deviation are given by exp(Q and
exp(a), respectively.
• Normal: density = (aV(27t)}"1 exp{ -(x - (a,)2 / (2a2)}, where mean = jo, and standard
deviation = a
• Weibull: density = (c/a) (x/a)c"1 exp{-(x/a)c}, where shape = c and scale = a
Generally, the log-normal distribution was the best-fitting of the four distributions, and so, for
consistency, we recommend using the fitted log-normal distributions for all the cases.
One limitation of the initial analysis was that distributions were available only for selected cities,
and yet the summary statistics and comparisons demonstrate that the AER distributions depend
upon the city as well as the temperature range and A/C type. As one option to address this
issue, we considered modeling cities for which distributions were not available by using the AER
distributions across all cities and dates for a given temperature range and A/C type.
Another important limitation of the initial analysis was that distributions were not fitted to all of
the temperature ranges due to inadequate data. There are missing values between temperature
ranges, and the temperature ranges are all bounded. To address this issue, the temperature
ranges were regrouped to cover the entire range of temperatures from minus to plus infinity,
although obviously the available data to fit these ranges have finite temperatures. Stratifying by
A/C type, city, and the new temperature ranges produces results for four cities: Houston (AC
and NA); Los Angeles (AC and NA); New York (AC and NA); Research Triangle Park (AC). For
each of the fitted distributions we created histograms to compare the fitted distributions with the
empirical distributions.
B-208
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AER Distributions for The First Nine Cities. Based upon the results for the above four cities
and the corresponding graphs, we propose using those fitted distributions for the three cities
Houston, Los Angeles, and New York. For another 6 of the cities to be modeled, we propose
using the distribution for one of the four cities thought to have similar characteristics to the city
to be modeled with respect to factors that might influence AERs. These factors include the age
composition of housing stock, construction methods, and other meteorological variables not
explicitly treated in the analysis, such as humidity and wind speed patterns. The distributions
proposed for these cities are as follows:
• Atlanta, GA, A/C: Use log-normal distributions for Research Triangle Park. Residences
with A/C only.
• Boston, MA: Use log-normal distributions for New York
• Chicago, IL: Use log-normal distributions for New York
• Cleveland, OH: Use log-normal distributions for New York
• Detroit, Ml: Use log-normal distributions for New York
• Houston, TX: Use log-normal distributions for Houston
• Los Angeles, CA: Use log-normal distributions for Los Angeles
• New York, NY: Use log-normal distributions for New York
• Philadelphia, PA: Use log-normal distributions for New York
Since the AER data for Research Triangle Park was only available for residences with air
conditioning, AER distributions for Atlanta residences without air conditioning are discussed
below.
To avoid unusually extreme simulated AER values, we propose to set a minimum AER value of
0.01 and a maximum AER value of 10.
Obviously, we would be prefer to model each city using data from the same city, but this
approach was chosen as a reasonable alternative, given the available AER data.
AER Distributions for Sacramento and St. Louis. For these two cities, a direct mapping to
one of the four cities Houston, Los Angeles, New York, and Research Triangle Park is not
recommended because the cities are likely to be too dissimilar. Instead, we decided to use the
distribution for the inland parts of Los Angeles to represent Sacramento and to use the
aggregate distributions for all cities outside of California to represent St. Louis. The results for
the city Sacramento were obtained by combining all the available AER data for Sacramento,
Riverside, and San Bernardino counties. The results for the city St. Louis were obtained by
combining all non-California AER data.
AER Distributions for Washington DC. Washington DC was judged likely to have similar
characteristics both to Research Triangle Park and to New York City. To choose between these
two cities, we compared the Murray and Burmaster AER data for Maryland with AER data from
each of those cities. The Murray and Burmaster study included AER data for Baltimore and for
Gaithersburg and Rockville, primarily collected in March. April, and May 1987, although there is
no information on mean daily temperatures or A/C type. We collected all the March, April, and
May AER data for Research Triangle Park and for New York City, and compared those
distributions with the Murray and Burmaster Maryland data for the same three months.
The results for the means and central values show significant differences at the 5 percent level
between the New York and Maryland distributions. Between Research Triangle Park and
B-209
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Maryland, the central values and the mean AER values are not statistically significantly different,
and the differences in the mean log (AER) values are much less statistically significant than
between New York and Maryland. The scale statistic comparisons are not statistically
significantly different between New York and Maryland, but were statistically significantly
different between Research Triangle Park and Maryland. Since matching central and mean
values is generally more important than matching the scales, we propose to model Washington
DC residences with air conditioning using the Research Triangle Park distributions, stratified by
temperature:
• Washington DC, A/C: Use log-normal distributions for Research Triangle Park.
Residences with A/C only.
Since the AER data for Research Triangle Park was only available for residences with air
conditioning, the estimated AER distributions for Washington DC residences without air
conditioning are discussed below.
AER Distributions for Washington DC and Atlanta GA Residences With No A/C. For
Atlanta and Washington DC we have proposed to use the AER distributions for Research
Triangle Park. However, all the Research Triangle Park data (from the RTP Panel study) were
from houses with air conditioning, so there are no available distributions for the "No A/C" cases.
For these two cities, one option is to use AER distributions fitted to all the study data for
residences without A/C, stratified by temperature. We propose applying the "No A/C"
distributions for modeling these two cities for residences without A/C. However, since Atlanta
and Washington DC residences are expected to be better represented by residences outside of
California, we instead propose to use the "No A/C" AER distributions aggregated across cities
outside of California, which is the same as the recommended choice for the St. Louis "No A/C"
AER distributions.
A/C Type and Temperature Distributions. Since the proposed AER distribution is conditional
on the A/C type and temperature range, these values also need to be simulated using APEX in
order to select the appropriate AER distribution. Mean daily temperatures are one of the
available APEX inputs for each modeled city, so that the temperature range can be determined
for each modeled day according to the mean daily temperature. To simulate the A/C type, we
obtained estimates of A/C prevalence from the American Housing Survey. Thus for each
city/metropolitan area, we obtained the estimated fraction of residences with Central or Room
A/C (see Table A-3), which gives the probability p for selecting the A/C type "Central or Room
A/C." Obviously, 1-p is the probability for "No A/C." For comparison with Washington DC and
Atlanta, we have included the A/C type percentage for Charlotte, NC (representing Research
Triangle Park, NC). As discussed above, we propose modeling the 96-97 % of Washington DC
and Atlanta residences with A/C using the Research Triangle Park AER distributions, and
modeling the 3-4 % of Washington DC and Atlanta residences without A/C using the combined
study No A/C AER distributions.
Table A-3. Fraction of residences with central or room A/C (from American Housing
Survey)
CITY
Atlanta
Boston
Chicago
SURVEY AREA & YEAR
Atlanta, 2003
Boston, 2003
Chicago, 2003
PERCENTAGE
97.01
85.23
87.09
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Cleveland
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington DC
Research Triangle Park
Cleveland, 2003
Detroit, 2003
Houston, 2003
Los Angeles, 2003
New York, 2003
Philadelphia, 2003
Sacramento, 2003
St. Louis, 2003
Washington DC, 2003
Charlotte, 2002
74.64
81.41
98.70
55.05
81.57
90.61
94.63
95.53
96.47
96.56
Other AER Studies
We recently became aware of some additional residential and non-residential AER studies that
might provide additional information or data. Indoor / outdoor ozone and PAN distributions were
studied by Jakobi and Fabian (1997). Liu et al (1995) studied residential ozone and AER
distributions in Toronto, Canada. Weschler and Shields (2000) describes a modeling study of
ventilation and air exchange rates. Weschler (2000) includes a useful overview of residential
and non-residential AER studies.
AER Distributions for Other Indoor Environments
To estimate AER distributions for non-residential, indoor environments (e.g., offices and
schools), we obtained and analyzed two AER data sets: "Turk" (Turk et al, 1989); and "Persily"
(Persily and Gorfain 2004; Persily et al. 2005).
The earlier "Turk" data set (Turk et al, 1989) includes 40 AER measurements from offices (25
values), schools (7 values), libraries (3 values), and multi-purpose (5 values), each measured
using an SF6 tracer over two- or four-hours in different seasons of the year.
The more recent "Persily" data (Persily and Gorfain 2004; Persily et al. 2005) were derived
from the U.S. EPA Building Assessment Survey and Evaluation (BASE) study, which was
conducted to assess indoor air quality, including ventilation, in a large number of randomly
selected office buildings throughout the U.S. The data base consists of a total of 390 AER
measurements in 96 large, mechanically ventilated offices; each office was measured up to four
times over two days, Wednesday and Thursday AM and PM. The office spaces were relatively
large, with at least 25 occupants, and preferably 50 to 60 occupants. AERs were measured both
by a volumetric method and by a CO2 ratio method, and included their uncertainty estimates. For
these analyses, we used the recommended "Best Estimates" defined by the values with the lower
estimated uncertainty; in the vast majority of cases the best estimate was from the volumetric
method.
Another study of non-residential AERs was performed by Lagus Applied Technology (1995)
using a tracer gas method. That study was a survey of AERs in 16 small office buildings, 6 large
office buildings, 13 retail establishments, and 14 schools. We plan to obtain and analyze these
data and compare those results with the Turk and Persily studies.
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Due to the small sample size of the Turk data, the data were analyzed without stratification by
building type and/or season. For the Persily data, the AER values for each office space were
averaged, rather using the individual measurements, to account for the strong dependence of
the AER measurements for the same office space over a relatively short period.
Summary statistics of AER and log (AER) for the two studies are presented in Table A-4.
Table A-4. AER summary statistics for offices and other non-residential buildings
Study
Persily
Turk
Persily
Turk
Variable
AER
AER
Log(AER)
Log(AER)
N
96
40
96
40
Mean
1.9616
1.5400
0.1038
0.2544
Std Dev
2.3252
0.8808
1.1036
0.6390
Min
0.0712
0.3000
-2.6417
-1.2040
25th %ile
0.5009
0.8500
-0.6936
-0.1643
Median
1.0795
1.5000
0.0765
0.4055
75th %ile
2.7557
2.0500
1.0121
0.7152
Max
13.8237
4.1000
2.6264
1.4110
The mean values are similar for the two studies, but the standard deviations are about twice as
high for the Persily data. The proposed AER distributions were derived from the more recent
Persily data only.
Similarly to the analyses of the residential AER distributions, we fitted exponential, log-normal,
normal, and Weibull distributions to the 96 office space average AER values. The results are
shown in Table A-5.
Table A-5. Best fitting office AER distributions from the Persily et al. (2004, 2005)
Scale
1.9616
0.1038
1.9197
Shape
1.1036
0.9579
Mean
1.9616
2.0397
1.9616
1.9568
Std Dev
1.9616
3.1469
2.3252
2.0433
Distribution
Exponential
Lognormal
Normal
Weibull
P-Value
Kolmogorov-
Smirnov
0.13
0.15
0.01
P-Value
Cramer-
von
Mises
0.04
0.46
0.01
0.01
P-Value
Anderson-
Darling
0.05
0.47
0.01
0.01
(For an explanation of the Kolmogorov-Smirnov, Cramer-von Mises, and Anderson-Darling P-
values see the discussion residential AER distributions above.) According to all three goodness-
of-fit measures the best-fitting distribution is the log-normal. Reasonable choices for the lower
and upper bounds are the observed minimum and maximum AER values.
We therefore propose the following indoor, non-residential AER distributions.
• AER distribution for indoor, non-residential microenvironments: Lognormal, with scale
and shape parameters 0.1038 and 1.1036, i.e., geometric mean = 1.1094, geometric
standard deviation = 3.0150. Lower Bound = 0.07. Upper bound = 13.8.
Proximity and Penetration Factors For Outdoors, In-vehicle, and Mass Transit
For the APEX modeling of the outdoor, in-vehicle, and mass transit micro-environments, an
approach using proximity and penetration factors is proposed, as follows.
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Outdoors Near Road
Penetration factor = 1.
For the Proximity factor, we propose using ratio distributions developed from the Cincinnati
Ozone Study (American Petroleum Institute, 1997, Appendix B; Johnson et al. 1995). The field
study was conducted in the greater Cincinnati metropolitan area in August and September,
1994. Vehicle tests were conducted according to an experimental design specifying the vehicle
type, road type, vehicle speed, and ventilation mode. Vehicle types were defined by the three
study vehicles: a minivan, a full-size car, and a compact car. Road types were interstate
highways (interstate), principal urban arterial roads (urban), and local roads (local). Nominal
vehicle speeds (typically met over one minute intervals within 5 mph) were at 35 mph, 45 mph,
or 55 mph. Ventilation modes were as follows:
• Vent Open: Air conditioner off. Ventilation fan at medium. Driver's window half open.
Other windows closed.
• Normal A/C. Air conditioner at normal. All windows closed.
• Max A/C: Air conditioner at maximum. All windows closed.
Ozone concentrations were measured inside the vehicle, outside the vehicle, and at six fixed
site monitors in the Cincinnati area.
The proximity factor can be estimated from the distributions of the ratios of the outside-vehicle
ozone concentrations to the fixed-site ozone concentrations, reported in Table 8 of Johnson et
al. (1995). Ratio distributions were computed by road type (local, urban, interstate, all) and by
the fixed-site monitor (each of the six sites, as well as the nearest monitor to the test location).
For this analysis we propose to use the ratios of outside-vehicle concentrations to the
concentrations at the nearest fixed site monitor, as shown in Table A-6.
Table A-6. Ratio of outside-vehicle ozone to ozone at nearest fixed site1
Road
Type1
Local
Urban
Interstate
All
Number
of
cases1
191
299
241
731
Mean1
0.755
0.754
0.364
0.626
Standard
Deviation1
0.203
0.243
0.165
0.278
25th
Percent! le1
0.645
0.585
0.232
0.417
50th
Percentile1
0.742
0.722
0.369
0.623
75th
Percentile1
0.911
0.896
0.484
0.808
Estimated
5th
Percentile2
0.422
0.355
0.093
0.170
1. From Table 8 of Johnson etal. (1995). Data excluded if fixed-site concentration < 40
ppb.
2. Estimated using a normal approximation as Mean - 1.64 x Standard Deviation
For the outdoors-near- road microenvironment, we recommend using the distribution for local
roads, since most of the outdoors-near-road ozone exposure will occur on local roads. The
summary data from the Cincinnati Ozone Study are too limited to allow fitting of distributions,
but the 25th and 75th percentiles appear to be approximately equidistant from the median (50th
percentile). Therefore we propose using a normal distribution with the observed mean and
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standard deviation. A plausible upper bound for the proximity factor equals 1. Although the
normal distribution allows small positive values and can even produce impossible, negative
values (with a very low probability), the titration of ozone concentrations near a road is limited.
Therefore, as an empirical approach, we recommend a lower bound of the estimated 5th
percentile, as shown in the final column of the above table. Therefore in summary we propose:
• Penetration factor for outdoors, near road: 1.
• Proximity factor for outdoors, near road: Normal distribution. Mean = 0.755. Standard
Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
Outdoors, Public Garage / Parking Lot
This micro-environment is similar to the outdoors-near-road microenvironment. We therefore
recommend the same distributions as for outdoors-near-road:
• Penetration factor for outdoors, public garage / parking lot: 1.
• Proximity factor for outdoors, public garage / parking lot: Normal distribution. Mean =
0.755. Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
Outdoors, Other
The outdoors, other ozone concentrations should be well represented by the ambient monitors.
Therefore we propose:
• Penetration factor for outdoors, other: 1.
• Proximity factor for outdoors, other: 1.
In-Vehicle
For the proximity factor for in-vehicle, we also recommend using the results of the Cincinnati
Ozone Study presented in Table A-6. For this microenvironment, the ratios depend upon the
road type, and the relative prevalences of the road types can be estimated by the proportions of
vehicle miles traveled in each city. The proximity factors are assumed, as before, to be normally
distributed, the upper bound to be 1, and the lower bound to be the estimated 5th percentile.
• Proximity factor for in-vehicle, local roads: Normal distribution. Mean = 0.755. Standard
Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
• Proximity factor for in-vehicle, urban roads: Normal distribution. Mean = 0.754. Standard
Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.
• Proximity factor for in-vehicle, interstates: Normal distribution. Mean = 0.364. Standard
Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.
To complete the specification, the distribution of road type needs to be estimated for each city to
be modeled. Vehicle miles traveled (VMT) in 2003 by city (defined by the Federal-Aid urbanized
area) and road type were obtained from the Federal Highway Administration.
(http://www.fhwa.dot.gov/policy/ohim/hs03/htm/hm71.htm). For local and interstate road types,
the VMT for the same DOT categories were used. For urban roads, the VMT for all other road
types was summed (Other freeways/expressways, Other principal arterial, Minor arterial,
Collector). The computed VMT ratios for each city are shown in Table A-7.
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Table A-7. Vehicle Miles Traveled by City and Road Type in 2003 (FHWA, October 2004)
FEDERAL-AID URBANIZED
AREA
Atlanta
Boston
Chicago
Cleveland
Detroit
Houston
Los Angeles
New York
Philadelphia
Sacramento
St. Louis
Washington
FRACTION VMT BY ROAD TYPE
INTERSTATE
0.38
0.31
0.30
0.39
0.26
0.24
0.29
0.18
0.23
0.21
0.36
0.31
URBAN
0.45
0.55
0.59
0.45
0.63
0.72
0.65
0.67
0.65
0.69
0.45
0.61
LOCAL
0.18
0.14
0.12
0.16
0.11
0.04
0.06
0.15
0.11
0.09
0.19
0.08
Note that a "Federal-Aid Urbanized Area" is an area with 50,000 or more persons that at a
minimum encompasses the land area delineated as the urbanized area by the Bureau of the
Census. Urbanized areas that have been combined with others for reporting purposes are not
shown separately. The Illinois portion of Round Lake Beach-McHenry-Grayslake has been
reported with Chicago.
Thus to simulate the proximity factor in APEX, we propose to first select the road type according
to the above probability table of road types, then select the AER distribution (normal) for that
road type as defined in the last set of bullets.
For the penetration factor for in-vehicle, we recommend using the inside-vehicle to outside-
vehicle ratios from the Cincinnati Ozone Study. The ratio distributions were summarized for all
the data and for stratifications by vehicle type, vehicle speed, road type, traffic (light, moderate,
or heavy), and ventilation. The overall results and results by ventilation type are shown in Table
A-8.
Table A-8. Ratio of inside-vehicle ozone to outside-vehicle ozone1
Ventilation1
Vent Open
Normal A/C
Maximum
A/C
All
Number
of
cases1
226
332
254
812
Mean1
0.361
0.417
0.093
0.300
Standard
Deviation1
0.217
0.211
0.088
0.232
25th
Percent! le1
0.199
0.236
0.016
0.117
50th
Percentile1
0.307
0.408
0.071
0.251
75th
Percentile1
0.519
0.585
0.149
0.463
Estimated
5th
Percentile2
0.005
0.071
O.OOO3
O.OOO3
1. From Table 7 of Johnson et al.(1995). Data excluded if outside-vehicle concentration <
20 ppb.
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2. Estimated using a normal approximation as Mean - 1.64 x Standard Deviation
3. Negative estimate (impossible value) replaced by zero.
Although the data in Table A-8 indicate that the inside-to-outside ozone ratios strongly depend
upon the ventilation type, it would be very difficult to find suitable data to estimate the ventilation
type distributions for each modeled city. Furthermore, since the Cincinnati Ozone Study was
scripted, the ventilation conditions may not represent real-world vehicle ventilation scenarios.
Therefore, we propose to use the overall average distributions.
• Penetration factor for in-vehicle: Normal distribution. Mean = 0.300. Standard Deviation
= 0.232. Lower Bound = 0.000. Upper Bound = 1.
Mass Transit
The mass transit microenvironment is expected to be similar to the in-vehicle microenvironment.
Therefore we recommend using the same APEX modeling approach:
• Proximity factor for mass transit, local roads: Normal distribution. Mean = 0.755.
Standard Deviation = 0.203. Lower Bound = 0.422. Upper Bound = 1.
• Proximity factor for mass transit, urban roads: Normal distribution. Mean = 0.754.
Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.
• Proximity factor for mass transit, interstates: Normal distribution. Mean = 0.364.
Standard Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.
• Road type distributions for mass transit: See Table A-6
• Penetration factor for mass transit: Normal distribution. Mean = 0.300. Standard
Deviation = 0.232. Lower Bound = 0.000. Upper Bound = 1.
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Attachment 6: Technical Memorandum on HAPEM Near Road
Population Data Base Development (from Task 2. Near roadway
concentrations (revised))
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CONSULTING
MEMORANDUM
To: Chad Bailey and Rich Cook
From: Jonathan Cohen and Arlene Rosenbaum
Date: September 30, 2005
Re: Task 2. Near roadway concentrations (revised)
The objective of this task was to estimate the enhancement near major roadways of air toxic
pollutant concentrations from onroad motor vehicle emissions relative to concentrations at other
outdoor locations.
For this task, we reviewed several studies of near roadway concentration gradients (Cohen et
al, 2005; Kwon, 2005; Meng et al, 2004; Riediker et al, 2003; Rodes et al, 1998; Weisel et al,
2004; Zhu et al, 2002). We analyzed the available data using summary statistics and regression
modeling in order to obtain distributions of concentration ratios. One distribution describes the
ratio of concentrations within D1 meters of a major roadway to concentrations at locations
greater than D2 meters from a major roadway. A second distribution describes the ratio of
concentrations D1 - D2 meters of a major roadway to concentrations at locations greater than
D2 meters from a major roadway. We chose distances D1 = 75 m and D2 = 200 m to best
represent the near roadway concentration gradient. These ratio distributions were used in Task
3 to estimate the spatial distribution of concentrations within a census tract from the ASPEN
concentration prediction.
Rodes and Riediker Studies
In order to stratify the concentration ratios according distance from major roadways we required
concentration databases that specify those distances. EPA provided data from the Riediker et al
(2003) study of concentrations inside patrol cars, near roadways, and at fixed ambient
monitoring sites in Wake County, NC. We also evaluated the study by Rodes et al (1998), which
includes near roadway monitoring in Sacramento and Los Angeles, CA. However, in neither
study do the distances of the near roadway concentration measurements from the roadways
span the range required for this analysis. The measurements in the Riediker study were taken
within 20 feet of the roadway. The report for the Rodes study states that permission for placing
the roadside monitors was obtained from the California Transportation Agency, implying they
were located within the right-of-way of the road. The goal of this task was to estimate
concentration ratios for concentrations within about 50 to 150 m and within about 150 to 300 m
to concentrations further away from the roadway. Therefore, we determined that the results from
the Rodes and Riediker studies could not be used for the Task 2 analyses since those studies
had concentration measurements much nearer to the roadway.
Zhu et al (2002) Study
Zhu et al (2002) measured concentrations of black carbon (BC), carbon monoxide (CO), and
particle number at various distances upwind and downwind from the 710 and 405 freeways in
Los Angeles. We used these data to calculate mean CO concentrations and concentration
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ratios at different distances from the freeways. For freeway 710, the distances were 17, 20, 30,
90, 150, and 300 m downwind and 200m upwind. For freeway 405, the distances were 30, 60,
90, 150, and 300 m downwind and 300m upwind. Three measurements per day were taken at
approximately the same time (various times between 10 am and 4:30 pm) for all downwind
distances. One measurement was made on the same day at the upwind distance. We
calculated the daily average concentrations and used them to calculate the distribution of the
mean concentration upwind or downwind as a function of the distance to the freeway. These
results for each freeway, season, and overall, are shown in Table 1. The concentrations drop
very sharply as the distances increase. For Table 2, we calculated the ratios of the average
concentration between 0 and 50 m from the road to the average concentration greater than 150
m from the road and of the average concentration between 50 and 150 m from the road to the
average concentration greater than 150 m from the road. The distributions of these ratios are
shown in Table 2.
After reviewing the Zhu et al (2002) study and the analyses of Tables 1 and 2, we determined
that the results of the Zhu et al (2002) could not be used for this project because the available
ratios were only for the downwind distances, and did not represent ratios under more general
meteorological conditions.
RIOPA Study
The Relationship of Indoor, Outdoor, and Personal Air (RIOPA) study (Meng et al, 2004; Weisel
et al, 2004) was undertaken to estimate the impact of outdoor sources of air toxics to indoor
concentrations and personal exposures. Volatile organic compounds, carbonyls, fine particles
and air exchange ratios were measured once or twice at 310 non-smoking residences from
summer 1999 to spring 2001. Measurements were made at residences in Elizabeth, NJ,
Houston TX, and Los Angeles CA. Residences in California were randomly selected.
Residences in New Jersey and Texas were preferentially selected to be close (< 0.5 km) to
sources of air toxics.
Since the residences studied were at various distances from major roads, we analyzed the
results of this study to estimate the relationship between the concentration (outside the
residence) and the distance from the roadway. We obtained the relevant data from the
Appendix of Kwon (2005), who used GIS mapping to calculate distances from the residences to
major roads, gas stations, and other important emissions sources. Kwon (2005) used these data
in various regression models to estimate the concentration as a function of these distances.
Our analyses used a similar regression approach, but our modeling only used the distances to
the major roadways. For the main analyses, we excluded residences within 150 m of a gas
station to avoid confounding our analysis of roadway emissions with the effects of gas station
emissions. Unlike Kwon (2005) we chose not to eliminate any values as potential outliers, since
there were no "obvious" outlier data values.
For the preliminary analysis, all the available data, including residences within 150 m of a gas
station, were included. For each residence, two-day average pollutant concentrations were
measured for benzene, carbon tetrachloride, ethylbenzene, MTBE, PCE (perchloroethylene),
toluene, m & p-xylene, and o-xylene. We computed the distributions of the pollutant
concentrations for residences within 50 m of a major roadway, between 50 and 150 m of a
major roadway, and more than 150 m from a major roadway. These distances, from Kwon
(2005), are the distances to the nearest roadway among functional classes FC11 (urban
interstate highways), FC12 (urban other freeways), and FC14 (urban major arterials). The
results are tabulated in Table 3.
Also shown in Table 3 are the ratios of the average concentration between 0 and 50 m from the
road to the average concentration greater than 150 m from the road, and of the average
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concentration between 50 and 150 m from the road to the average concentration greater than
150 m from the road. The standard deviation of the ratio was estimated using the delta method
(first order Taylor series approximation). The CV (standard deviation divided by mean) is also
presented. Of particular interest is the finding that for all of the pollutants except toluene and the
xylenes, the mean concentrations are higher in the 50 to 150 m range than the concentrations in
the 0 to 50 m and greater than 150 m ranges. For toluene and the xylenes, the mean
concentrations decrease with distance. This preliminary analysis does not account for
seasonality and meteorology, and it also ignores the possible confounding effect of the distance
to the nearest gas station, an important emissions source.
For the main data analyses of the RIOPA data, we first removed all data from residences within
150 m of a gas station.
The attached file graphs.riopa.doc contains graphs of the concentrations versus distance to the
roadway. Four distance definitions are used, depending upon the road type: FC11 = distance to
functional class FC11, urban interstate highways; FC12 = distance to functional class FC12,
urban other freeways and expressways; FC14 = distance to functional class FC14, urban major
arterials; Min = minimum (FC11, FC12, FC14). Also shown on the graphs is a cubic regression
curve for log(concentration) against distance. The graphs show significant scatter, and no clear
tendency for concentrations to decrease with distance.
We then fitted various regression models to log (concentration) using a stepwise regression
procedure to determine the best model.
The attached Excel spreadsheet file riopa.two distance intervals.xls contains regression
analyses where the distance effect is represented by one indicator term for the 0-D1 distance
(i.e., a dummy variable that equals 1 for residences within D1 of the roadway and equals zero
for other residences), and another indicator term for the 0-D2 distance. The fitted regression
model always has an intercept and coefficients for the 0-D1 distance (to the nearest major road)
and for the 0-D2 distance. Other terms that could be in the "best" model were season indicators
(for spring, summer, or fall), wind speed, temperature, RH, Precipitation, Mixing Height,
Stability, log wind speed, log temperature, log RH, log Mixing Height, log Stability.
The spreadsheet shows results for the best-fitting models for each distance type and each pair
of distances, when either a) there are no season terms, or b) all three season terms are forced
into the model. The predicted values for the logarithm of the concentration are given by
Log(concentration) = Intercept + axlndic(O-DI) + bx|ndic(0-D2) + Meteorological terms
= Intercept + a + b + Met terms, if distance < D1
= Intercept + b + Met terms, if D1 <= distance < D2
= Intercept + Met terms, if distance >= D2
so this model is mathematically equivalent to having different coefficients for the ranges 0-D1,
D1-D2, D2- infinity.
The spreadsheet gives D1 and D2 (columns X and Y), all the coefficients, the R squared
statistic (column U), and the Akaike Information Criterion, AIC, a good-of-fit measure. For these
analyses, the AIC is a better measure of the goodness-of-fit than R squared, since the R
squared will obviously improve if you add terms to the model, but the AIC used for SAS's
regression procedure adds a penalty term to the negative log-likelihood for the number of fitted
parameters. There is no absolute scale to decide what AIC values are good, but the models with
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the lowest AIC are the best ones according to this statistic. The final column indicates the model
with the lowest AIC for each HAP.
None of these models fit the data very well. The R-Squared values range from 0.17 for MTBE,
0.25 for ethyl-benzene, 0.26 for toluene, 0.28 for m&p-xylene, 0.27 for benzene, and 0.31 for o-
xylene, which are all quite poor, but consistent with Kwon's results. For the "best" models, the
estimated values for D1 are either 25 m or 450 m.
As a second approach, we fitted models with indicator terms for 12 distance intervals instead of
2. The results are shown in the attached Excel spreadsheet riopa.all distance intervals.xls. The
same approach was used except that instead of just having indicators for 0-D1 and 0-D2, we
have indicators for each interval group: 0-25, 25-50, 50-75, 75-100, 100-150, 150-200, ... 450-
500 m. In most cases the coefficient is high for the 0-25 m group, then decreases, and then
increases again. The coefficients at the high distances are almost as high as for 0-25 m (for
MTBE they are even higher). This is not the expected pattern of coefficients decreasing with
distance, reflecting the expected tendency for concentrations to decrease with distance (if there
are no other sources).
As a third approach, we developed the models shown in the Excel spreadsheet riopa.three
times two distance intervals.xls. In this approach, instead of fitting separate two-distance models
for each functional class, we fit a model with six distance indicators, two for each functional
class, defining combinations of distances to the nearest FC11, FC12, and FC14road. Using the
same D1 and D2 for each functional class, the R squared goodness-of-fit measures are a slight
improvement over the first approach (based on a single functional class), but the AIC statistics
show no improvement after accounting for the 4 extra terms in the model.
The ability of the RIOPA regression models to predict the near roadway concentrations was
generally poor, as discussed above. This is likely due to the problem that the near roadway
concentrations are also impacted by other emissions sources that cannot be easily adjusted for.
Furthermore, the true relationship between the concentrations and the distance from the road,
meteorology and season is known to be very much more complicated than these simple
regression model formulations. In view of these findings we chose to use modeled data rather
than measured values, as discussed in the next section.
Portland Air Toxics Assessment Study
The Portland Air Toxics Assessment (PATA) Study (e.g., Cohen et al (2005)) was a recent air
toxics modeling study in the Portland area, funded by the Oregon Department of Environmental
Quality and U.S. EPA. PATA evaluated the air quality impact in Calendar Year 1999 of
emissions from over 1000 roadway links using the CALPUFF dispersion model. CALPUFF is a
non-steady state Gaussian puff model, and was selected for modeling in Portland because of its
capability for handling complex terrain and coastal interaction effects. For these Task 2
analyses, we used the CALPUFF predictions of the benzene, 1,3-butadiene, and diesel
particulate matter (PM) from major on-road sources only. We used the predicted quarterly and
annual mean concentrations together with the distances from the receptor (block group
centroid) to the nearest major road.
We restricted these analyses to block group receptors only, and to the 211 block groups in the
54 tracts that had at least one block group within 300 m of a road and at least one block group
more than 300 m from a road. The idea was to restrict the analysis to the census tracts where
the HAPEM adjustments to the ASPEN predictions would be applied. 300 m was chosen as a
maximum possible realistic value for the far distance, D2.
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The statistical regression analyses of the PATA data were similar to the regression analyses of
the RIOPA data described above. In this case stepwise regression was not needed to select the
"best" set of meteorological variables since it was not feasible to define and calculate
meteorological variables to adjust the quarterly and annual means. The quarterly/seasonal
means are for Dec-Feb, Mar-May, Jun-Aug, and Sep-Nov. In this case we only have one
distance variable, the distance from a block group centroid to the nearest road for the major
road links used in our CALPUFF modeling.
The attached file graphs.pata.doc contains graphs of annual and seasonal mean concentrations
plotted against the distance, together with a cubic regression curve for log(concentration)
against distance. The pattern of concentrations decreasing with distance is much stronger here
compared to the RIOPA plots, as should be expected since these CALPUFF modeling results
use on-road emissions only. These preliminary graphs show all block group and census tract
centroid receptors, not just the 211 block groups in the 54 tracts that had at least one block
group within 300 m of a road and at least one block group more than 300 m from a road.
The attached file pata.two.distance intervals.by season.xls shows the fitted models with two
distance terms (indicators for the distance ranges 0-D1 and 0-D2) for each season and for the
annual mean. The best models have D1 = 75 m and D2 = 250 or 300 m in most cases.
The attached file pata.two.distance intervals.quarterly mean.xls shows results from fitting the
same set of two-distance models to the entire set of quarterly means after including indicator
terms for the spring, summer, and fall quarters. The fact that the coefficients of these three
indicators are the same for all pairs of distances is perhaps unexpected, but this follows from
the facts that a) the experiment is balanced, i.e., there is one concentration for each and every
block group and season, and b) there are no interaction terms in the regression model (between
the season indicators and the distance terms). The best models have D1=75 m and 02=300 m
for all three pollutants.
The attached file pata.all.distance intervals.by season.xls shows the fitted models for each
season and the annual mean with indicator terms for the interval groups: 0-25, 25-50, 50-75, 75-
100, 100-150. 150-200, 200-250, 250-300, and >= 300. The models were fitted without an
intercept. The estimated coefficients for the distance ranges therefore equal the mean of the
log(concentration) for all block groups in the given range and season. While there is a general
tendency for the means of the logarithms to decrease with the distance from the nearest road,
the mean does not consistently decrease when the distance range is further away from the
road. This is due to the fact that different roads can have very different levels of emissions, so
that the concentration can increase if you move further away from a road A with relatively low
emissions but closer to another road B that is even further away than the road A but has very
high emissions.
The attached file pata.all.distance intervals.quarterly mean.xls shows the fitted models for the
quarterly means with indicator functions for the spring, summer, and fall seasons together with
indicator terms for all the interval groups: 0-25, 25-50, 50-75, 75-100, 100-150, 150-200, 200-
250, 250-300, and >= 300. The models were fitted without an intercept.
Generally, the two-distance regression models show that the optimal distances are D1 = 75m
and D2 = 300m. The regression models favor the higher distances for D2 because the
CALPUFF estimates for PATA continue to decrease significantly with distance from the road.
However, the R squared and AIC values are not much different between the models with D2 =
200 or 300 m. Since some other studies, including Zhu et al (2002), have shown typical zones
of influence for roadways no further than 200 m, we selected the distances D1 = 75 m and D2 =
200m.
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Concentration Ratios
As described above, we selected the distance thresholds D1 = 75 m and D2 = 200 m. Using the
same set of PATA study predicted block group concentrations at distances 0 to 75 m, 75 to 200
m, and 200 m or greater we computed the concentration ratios, using a regression approach
and an empirical approach.
We begin with the empirical approach. We considered two sets of ratios. The first set of ratios
are given by a block group concentration at distance 0-75 m divided by a block group
concentration in the same census tract at distance >= 200 m. The second set of ratios are
given by a block group concentration at distance 75-200 m divided by a block group
concentration in the same census tract at distance >= 200 m. Each set contains all such ratios
for each pollutant. For each set of ratios we fitted normal and log-normal distributions. The
results are tabulated in Table 4. In the rows marked "RAW," the number, mean, and variance of
the ratios are tabulated for each set of ratios and each pollutant. Also shown is the p-value of a
Shapiro-Wlk test of normality; higher values are evidence supporting normality. In the rows
marked "LOG" the number, mean, and variance of the logarithms of the ratios are tabulated for
each set of ratios and each pollutant. Also shown is the p-value of a Shapiro-Wlk test of
normality for the logarithms; higher values are evidence supporting normality for the logs of the
ratios, which is the same as log-normality of the ratios themselves. The log-normal distributions
fitted a little better. Using the log-normal distributions, the geometric mean is given by
exp(mean) and the geometric standard deviation is given by exp(sqrt(variance)). As well as
doing this analysis by pollutant, we also combined all the ratios for all three pollutants and
repeated the analysis. The combined distribution (shown in the Table 4 rows with pollutant =
"AH") might be a good choice for modeling some HAPs other than Benzene, 1,3-Butadiene, or
Diesel PM.
Tables 5 and 6 present the results of the regression approach whereby the log(ratio)
distributions are computed from the regression model. Table 5 uses the regressions stratified by
season, which are exactly the same as the regression models with D1 = 75 and D2 = 200 in the
spreadsheet pata.two distance intervals.by season.xls. Table 6 uses the regressions of the
quarterly means against the season indicators and the two distance indicators, which are
exactly the same as the regression models with D1 = 75 and D2 = 200 in pata.two distance
intervals.quarterly means.xls. For each pollutant, season (quarter or annual mean), and
numerator distance range, we tabulate the predicted mean and variance of the logarithm of the
ratio. Two estimates of the variance, Variancel and Variance2, are tabulated. Variance2 is the
more accurate calculation. The Appendix gives details on these regression calculations..
The regression-based estimated means and variances are both larger than the estimates from
the empirical distributions of the logarithms of the ratios. The variances are presumably higher
because the regression approach assumes that the numerator and denominator are
independent, whereas it is likely that there is a strong correlation between concentrations for
block groups that are near enough to be in the same tract. It is not obvious why the means are
higher.
Since the empirical ratio distributions only use ratios from block groups in the same census
tract, but the regression ratio distributions do not assume the numerator block groups are in the
same tract as the denominator block groups, the empirical approach is more consistent with the
intended application to ASPEN predictions. The log-normal distributions fitted a little better. We
therefore recommend using the empirical log-normal distributions for the ratio, shown in Table 4.
Another possibility is to use the set of ratios as a data set and randomly select ratios from that
B-225
-------
data set, but that approach has the disadvantage of only having a small discrete number of
possible values compared to the continuous log-normal model.
HAPEM requires specification of mimimum and maximum values for a lognormal distribution in
order to avoid unrealistic predictions. We recommend using 1.0 for the minimum value and the
95th percentile ratio as the maximum value for each ratio distribution.
References
Cohen, J.,. R. Cook, C. R. Bailey, E. Carr. 2005. Relationship between motor vehicle emissions
of hazardous pollutants, roadway proximity, and ambient concentrations on Portland, OR.
Environmental Modelling and Software 20 (2005) 7-12.
Kwon, J. 2005. Development of a RIOPA database and evaluation of the effect of proximity on
the potential residential exposure to VOCS from ambient sources. PhD. dissertation. Graduate
School, New Brunswick, Rutgers, the State University of New Jersey and the University of
Medicine and Dentistry of New Jersey
Meng, Q. Y., B. J. Turpin, L. Korn, C. P. Weisel, M. Morandi, S. Colome, J. J. Zhang, T. Stock,
D. Spektor, A. Winer, L. Zhang, J. H. Lee, R. Giovanetti, W. Cui, J. Kwon, S. Alimokhtari, D.
Shendell, J. Jones, C. Farrar, S. Maberti (2004). Influence of ambient (outdoor) sources on
residential indoor and personal PM2.s concentrations: Analyses of RIOPA data. Journal of
Exposure Analysis and Environ Epidemiology. Preprint.
Riedicker M, R. Wlliams, R. Devlin, T. Griggs, P. Bromberg. 2003. Exposure to particulate
matter, volatile organic compounds, and other air pollutants inside patrol cars. Environ. Sci.
Technol. 2003, 37, 2084-2093.
Rodes C, L. Sheldon, D. Whitaker, A. Clayton, K. Fitzgerald, J. Flanagan. Measuring
concentrations of selected air pollutants inside California vehicles. 1998. Main Study Report for
California ARE. Contract 95-339
Weisel, C. P., J. J. Zhang, B. J. Turpin, M. T. Morandi, S. Colome, T. H. Stock, D. M. Spektor, L.
Korn, A. Winer, S. Alimokhtari, J. Kwon, K. Mohan, R. Harrington, R. Giovanetti, W. Cui, M.
Afshar, S. Maberti, D. Shendell (2004). Relationship of Indoor, Outdoor and Personal Air
(RIOPA) study; study design, methods and quality assurance / control results. Journal of
Exposure Analysis and Environ Epidemiology. Preprint.
Zhu, Y, W. C.. Hinds, S. Kim, S. Shen, C. Sioutas. 2002. Study of ultrafine particles near a
major highway with heavy-duty diesel traffic. Atmospheric Environment 36 (2002) 4323-4335.
Appendix
The derivation of the regression distributions is as follows:
Suppose the regression model is written in the form
Log(conc) = intercept + slopel x Indicator (0-75 m) + slope2 x Indicator (75-200 m) + error.
where the error is normally distributed with a mean of 0 and a standard deviation of sigma, and
the errors for different block groups are independent.
Then for two block groups at distances < 75 m and > 200m, we have
B-226
-------
log(ratio) = log{conc(< 75) / cone (> 200)}
= intercept + slopel x Indicator (0-75 m) + slope2 x Indicator (75-200 m) + error (< 75m).
- (intercept + slopel x Indicator (0-75 m) + slope2 x Indicator (75-200 m) + error (> 200 m))
= intercept + slopel + error(< 75 m) - (intercept + error(> 200 m)
= slopel + error(< 75 m) - error(> 200m),
which is normally distributed with mean = slopel and variance = 2xsigmaxsigma = Variance!
The more accurate calculation takes into account the fact that the values of slopel and sigma
are unknown, but estimated from the regression model. Define Variance2 = 2xsigmaxsigma +
2xsexse, where se is the standard error of the estimated slopel. An exact calculation uses the
easily proven result that (log(ratio) - estimated slope 1}/{sqrt(Variance2)) has a t distribution. A
very accurate approximation (since the degrees of freedom are large) shows that log(ratio) has
a normal distribution with mean = slopel and variance = Variance2.
B-227
-------
1
2
3 Table 1 . Analysis of mean CO concentration versus. Distance from freeway based on Zhu
4
5
Freeway Season Distance(s)
405,710
or both
(405710)
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
405
S=Summer
W=Winter
A=AII
A
A
A
A
A
A
A
A
A
S
S
S
S
S
S
S
S
S
W
W
W
W
W
W
W
W
W
From Freeway
(m)
30
60
90
150
300
300
d <50m
50m <=d < 150m
150m <=d
30
60
90
150
300
300
d <50m
50m <=d < 150m
150m <=d
30
60
90
150
300
300
d < 50m
50m <=d < 150m
150m <=d
Upwind (U)
or
Downwind(D)
D
D
D
D
D
U
D
D
D
D
D
D
D
D
U
D
D
D
D
D
D
D
D
U
D
D
D
n
6
6
6
6
6
6
6
6
6
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
et al (2002).
Distribution of Daily Means Across Measurement Days ||
mean
2.03
1.16
0.81
0.56
0.34
0.12
2.03
0.98
0.45
1.98
0.90
0.60
0.41
0.20
0.10
1.98
0.75
0.31
2.08
1.41
1.02
0.70
0.49
0.13
2.08
1.22
0.59
std
0.10
0.30
0.23
0.16
0.17
0.04
0.10
0.26
0.16
0.10
0.03
0.00
0.02
0.06
0.00
0.10
0.02
0.03
0.08
0.15
0.05
0.03
0.08
0.06
0.08
0.06
0.04
min
1.87
0.87
0.60
0.40
0.13
0.10
1.87
0.73
0.27
1.87
0.87
0.60
0.40
0.13
0.10
1.87
0.73
0.27
2.03
1.23
0.97
0.67
0.40
0.10
2.03
1.15
0.55
q1
2.03
0.90
0.60
0.40
0.23
0.10
2.03
0.75
0.32
1.87
0.87
0.60
0.40
0.13
0.10
1.87
0.73
0.27
2.03
1.23
0.97
0.67
0.40
0.10
2.03
1.15
0.55
median
2.03
1.08
0.78
0.55
0.32
0.10
2.03
0.96
0.44
2.03
0.90
0.60
0.40
0.23
0.10
2.03
0.75
0.32
2.03
1.50
1.03
0.70
0.50
0.10
2.03
1.23
0.62
q3
2.03
1.50
1.03
0.70
0.50
0.10
2.03
1.23
0.62
2.03
0.93
0.60
0.43
0.23
0.10
2.03
0.77
0.33
2.17
1.50
1.07
0.73
0.57
0.20
2.17
1.27
0.62
max
2.17
1.50
1.07
0.73
0.57
0.20
2.17
1.27
0.62
2.03
0.93
0.60
0.43
0.23
0.10
2.03
0.77
0.33
2.17
1.50
1.07
0.73
0.57
0.20
2.17
1.27
0.62
B-228
-------
Freeway
405,710
or both
(405710)
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
710
405710
405710
Season
S=Summer
W=Winter
A=AII
A
A
A
A
A
A
A
A
A
A
S
S
S
S
S
S
S
S
S
S
W
W
W
W
W
W
W
W
W
W
A
A
Distance(s)
From Freeway
(m)
17
20
30
90
150
200
300
d <50m
50m <=d < 150m
150m <=d
17
20
30
90
150
200
300
d <50m
50m <=d < 150m
150m <=d
17
20
30
90
150
200
300
d < 50m
50m <=d < 150m
150m <=d
17
20
C
D
D
D
D
D
U
D
D
D
D
D
D
D
D
D
U
D
D
D
D
D
D
D
D
D
U
D
D
D
D
D
D
Upwind (U)
or
Downwind(D)
1
n
7
7
7
7
7
7
7
7
7
7
5
5
5
5
5
5
5
5
5
5
2
2
2
2
2
2
2
2
2
2
7
7
Distribution of Daily Means Across Measurement Days ||
mean
2.24
1.98
1.56
0.52
0.43
0.13
0.26
1.93
0.52
0.34
2.30
2.02
1.66
0.50
0.39
0.10
0.19
1.99
0.50
0.29
2.08
1.87
1.32
0.57
0.52
0.20
0.42
1.76
0.57
0.47
2.24
1.98
std
0.19
0.17
0.25
0.09
0.07
0.05
0.12
0.14
0.09
0.09
0.19
0.19
0.22
0.10
0.04
0.00
0.06
0.10
0.10
0.03
0.12
0.05
0.07
0.00
0.02
0.00
0.02
0.00
0.00
0.00
0.19
0.17
min
2.00
1.73
1.27
0.33
0.33
0.10
0.10
1.76
0.33
0.25
2.03
1.73
1.33
0.33
0.33
0.10
0.10
1.83
0.33
0.25
2.00
1.83
1.27
0.57
0.50
0.20
0.40
1.76
0.57
0.47
2.00
1.73
q1
2.03
1.83
1.33
0.50
0.40
0.10
0.17
1.76
0.50
0.27
2.17
1.97
1.57
0.50
0.40
0.10
0.17
1.98
0.50
0.27
2.00
1.83
1.27
0.57
0.50
0.20
0.40
1.76
0.57
0.47
2.03
1.83
median
2.17
1.97
1.57
0.57
0.40
0.10
0.23
1.98
0.57
0.32
2.40
2.03
1.70
0.53
0.40
0.10
0.20
2.02
0.53
0.30
2.08
1.87
1.32
0.57
0.52
0.20
0.42
1.76
0.57
0.47
2.17
1.97
q3
2.43
2.13
1.83
0.57
0.50
0.20
0.40
2.03
0.57
0.47
2.43
2.13
1.83
0.57
0.40
0.10
0.23
2.03
0.57
0.32
2.17
1.90
1.37
0.57
0.53
0.20
0.43
1.76
0.57
0.47
2.43
2.13
max
2.47
2.23
1.87
0.57
0.53
0.20
0.43
2.10
0.57
0.47
2.47
2.23
1.87
0.57
0.43
0.10
0.27
2.10
0.57
0.33
2.17
1.90
1.37
0.57
0.53
0.20
0.43
1.76
0.57
0.47
2.47
2.23
B-229
-------
Freeway
405,710
or both
(405710)
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
405710
Season
S=Summer
W=Winter
A=AII
A
A
A
A
A
A
A
A
A
A
S
S
S
S
S
S
S
S
S
S
S
S
W
W
W
W
W
W
W
W
W
W
Distance(s)
From Freeway
(m)
30
60
90
150
200
300
300
d < 50m
50m <=d < 150m
150m <=d
17
20
30
60
90
150
200
300
300
d < 50m
50m <=d < 150m
150m <=d
17
20
30
60
90
150
200
300
300
d < 50m
C
D
D
D
D
U
D
U
D
D
D
D
D
D
D
D
D
U
D
U
D
D
D
D
D
D
D
D
D
U
D
U
D
Upwind (U)
or
Downwind(D)
1
n
13
6
13
13
7
13
6
13
13
13
5
5
8
3
8
8
5
8
3
8
8
8
2
2
5
3
5
5
2
5
3
5
Distribution of Daily Means Across Measurement Days ||
mean
1.78
1.16
0.65
0.49
0.13
0.30
0.12
1.97
0.73
0.39
2.30
2.02
1.78
0.90
0.54
0.40
0.10
0.20
0.10
1.99
0.59
0.30
2.08
1.87
1.77
1.41
0.84
0.63
0.20
0.46
0.13
1.95
std
0.30
0.30
0.22
0.13
0.05
0.15
0.04
0.13
0.30
0.13
0.19
0.19
0.24
0.03
0.09
0.03
0.00
0.06
0.00
0.09
0.15
0.03
0.12
0.05
0.42
0.15
0.25
0.10
0.00
0.07
0.06
0.18
min
1.27
0.87
0.33
0.33
0.10
0.10
0.10
1.76
0.33
0.25
2.03
1.73
1.33
0.87
0.33
0.33
0.10
0.10
0.10
1.83
0.33
0.25
2.00
1.83
1.27
1.23
0.57
0.50
0.20
0.40
0.10
1.76
q1
1.57
0.90
0.57
0.40
0.10
0.20
0.10
1.87
0.57
0.30
2.17
1.97
1.63
0.87
0.52
0.40
0.10
0.15
0.10
1.92
0.52
0.27
2.00
1.83
1.37
1.23
0.57
0.53
0.20
0.40
0.10
1.76
median
1.87
1.08
0.57
0.43
0.10
0.23
0.10
2.03
0.57
0.33
2.40
2.03
1.85
0.90
0.57
0.40
0.10
0.22
0.10
2.03
0.57
0.31
2.08
1.87
2.03
1.50
0.97
0.67
0.20
0.43
0.10
2.03
q3
2.03
1.50
0.60
0.53
0.20
0.40
0.10
2.03
0.77
0.47
2.43
2.13
1.95
0.93
0.60
0.42
0.10
0.23
0.10
2.03
0.74
0.33
2.17
1.90
2.03
1.50
1.03
0.70
0.20
0.50
0.20
2.03
max
2.17
1.50
1.07
0.73
0.20
0.57
0.20
2.17
1.27
0.62
2.47
2.23
2.03
0.93
0.60
0.43
0.10
0.27
0.10
2.10
0.77
0.33
2.17
1.90
2.17
1.50
1.07
0.73
0.20
0.57
0.20
2.17
B-230
-------
Freeway Season Distance(s) Upwind (U) || Distribution of Daily Means Across Measurement Days
405,710 S=Summer From Freeway or
or both W=Winter (m) Downwind(D) n mean std min q1 median q3 max
(405710) A=AII
405710 W 50m <=d< 150m D 5 0.96 0.36 0.57 0.57 1.15 1.23 1.27
405710 W 150m<=d D 5 0.54 0.08 0.47 0.47 0.55 0.62 0.62
B-231
-------
1
2
4
5
6
7
9
10
Table 2. CO concentration ratios based on Zhu et al (2002).
Freeway Season Distance(s) Upwind
405,710
or both
(405710)
405
405
405
405
405
405
710
710
710
710
710
710
405710
405710
405710
405710
405710
405710
S=Summer From Freeway
W=Winter (m)
(U) I Distribution of Mean (Group) / Mean (d >= 150m)
or
Downwind(D) n
mean
std
min
Across Measurement
q1 median
q3
Days I
max
A=AII
A
A
S
S
W
W
A
A
S
S
W
W
A
A
S
S
W
W
d < 50m
50m <=d < 150m
d < 50m
50m <=d < 150m
d < 50m
50m <=d < 150m
d < 50m
50m <=d < 150m
d < 50m
50m <=d < 150m
d < 50m
50m <=d < 150m
d < 50m
50m <=d < 150m
d < 50m
50m <=d < 150m
d < 50m
50m <=d < 150m
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
6
6
3
3
3
3
7
7
5
5
2
2
13
13
8
8
5
5
5.03
2.26
6.55
2.48
3.50
2.05
5.98
1.58
6.87
1.72
3.76
1.21
5.54
1.89
6.75
2.01
3.61
1.71
1.79
0.32
1.02
0.34
0.20
0.05
1.69
0.39
0.91
0.36
0.00
0.00
1.74
0.50
0.90
0.51
0.20
0.46
3.30
2.00
5.60
2.25
3.30
2.00
3.76
1.11
6.11
1.11
3.76
1.21
3.30
1.11
5.60
1.11
3.30
1.21
3.51
2.05
5.60
2.25
3.30
2.00
3.76
1.21
6.25
1.70
3.76
1.21
3.76
1.70
6.18
1.74
3.51
1.21
4.65
2.17
6.42
2.32
3.51
2.05
6.25
1.70
6.30
1.79
3.76
1.21
6.11
2.00
6.36
2.00
3.70
2.00
6.42
2.32
7.63
2.88
3.70
2.09
7.63
2.00
7.63
2.00
3.76
1.21
6.42
2.09
7.63
2.28
3.76
2.05
7.63
2.88
7.63
2.88
3.70
2.09
8.09
2.00
8.09
2.00
3.76
1.21
8.09
2.88
8.09
2.88
3.76
2.09
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1
2
3
Table 3. RIOPA Data. Analysis of mean concentrations vs. distance (d) from nearest major roadway (classes FC11, FC13, FC14).
Concentrations
Mean (group)
/Mean (d >= 150 m)
Std
Pollutant
Benzene
Benzene
Benzene
Carbon
Tetrachloride
Carbon
Tetrachloride
Carbon
Tetrachloride
Ethylbenzene
Ethylbenzene
Ethylbenzene
MTBE
MTBE
MTBE
PCE
PCE
PCE
Toluene
Toluene
Toluene
m,p,-Xylene
m,p,-Xylene
m,p,-Xylene
o-Xylene
o-Xylene
o-Xylene
Distance Group
d < 50m
50m <=d < 150m
150m <=d
d < 50m
50m <=d < 150m
150m <=d
d < 50m
50m <=d < 150m
150m<=d
d < 50m
50m <=d < 150m
150m<=d
d < 50m
50m <=d < 150m
150m<=d
d < 50m
50m <=d < 150m
150m <=d
d < 50m
50m <=d < 150m
150m<=d
d < 50m
50m <=d < 150m
150m<=d
n
12
54
117
12
54
117
12
54
117
12
54
117
12
54
117
12
54
117
12
54
117
12
54
117
mean
1.54
1.77
1.37
0.64
1.23
0.68
1.33
1.87
1.11
6.43
6.82
5.18
0.85
1.66
0.88
9.11
7.07
6.47
4.68
4.07
2.73
7.85
1.84
1.01
std
0.43
2.43
0.98
0.21
4.17
0.23
0.97
4.86
0.84
7.69
5.48
4.94
0.42
5.60
0.60
6.13
5.36
6.00
4.31
6.94
2.09
23.03
5.00
0.65
min
0.67
0.12
0.06
0.17
0.17
0.17
0.09
0.09
0.02
0.06
0.06
0.06
0.44
0.10
0.10
1.47
0.11
0.11
0.71
0.42
0.15
0.30
0.28
0.07
q1
1.25
0.82
0.63
0.53
0.56
0.56
0.81
0.42
0.46
0.89
3.22
2.09
0.59
0.50
0.49
4.69
2.36
2.59
2.18
1.72
1.43
1.02
0.58
0.59
median
1.59
1.26
1.09
0.67
0.68
0.70
1.14
1.03
0.89
4.27
5.60
3.74
0.73
0.75
0.74
7.62
5.82
4.43
4.28
2.45
2.17
1.30
0.95
0.92
q3
1.87
2.01
1.84
0.74
0.84
0.84
1.51
1.85
1.74
9.51
8.09
7.04
1.04
1.11
1.11
12.93
11.51
8.10
4.77
4.41
3.39
1.46
1.56
1.26
max
2.19
18.06
5.17
1.01
31.23
1.13
3.78
36.24
3.30
27.17
26.89
26.72
1.98
41.82
3.68
21.88
22.27
32.88
17.52
51.21
10.52
80.98
37.49
3.27
ratio
1.13
1.29
0.93
1.81
1.20
1.68
1.24
1.32
0.97
1.90
1.41
1.09
1.72
1.49
7.74
1.82
error
0.12
0.26
0.09
0.83
0.27
0.61
0.44
0.18
0.15
0.88
0.30
0.15
0.47
0.36
6.57
0.68
CV (%)
10.42
19.77
10.12
46.17
22.18
36.18
35.62
14.04
15.45
46.35
21.23
13.41
27.48
24.27
84.89
37.40
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1
2
Table 4. Empirical distributions of concentration ratios from PATA (see text). Denominator distance range is 200+ m.
Concentration
Pollutant
All
All
All
All
Benzene
Benzene
Benzene
Benzene
1 ,3-Butadiene
1 ,3-Butadiene
1 ,3-Butadiene
1 ,3-Butadiene
Diesel PM
Diesel PM
Diesel PM
Diesel PM
metric Numerator
LOG
RAW
LOG
RAW
LOG
RAW
LOG
RAW
LOG
RAW
LOG
RAW
LOG
RAW
LOG
RAW
0-75 m
0-75 m
75-200 m
75-200 m
0-75 m
0-75 m
75-200 m
75-200 m
0-75 m
0-75 m
75-200 m
75-200 m
0-75 m
0-75 m
75-200 m
75-200 m
N
99
99
306
306
33
33
102
102
33
33
102
102
33
33
102
102
P-Value for
Shapiro-Wilk
test of
Mean Variance normality
0.9267
3.2170
0.4950
2.1611
0.9071
3.1121
0.4770
2.0607
0.9708
3.3989
0.5252
2.3384
0.9023
3.1400
0.4827
2.0843
0.5352
4.8067
0.4667
5.2366
0.5137
4.3349
0.4318
3.6804
0.5767
5.6575
0.5384
8.1519
0.5455
4.6767
0.4378
3.9332
0.0008
0.0001
0.0001
0.0001
0.1423
0.0042
0.0122
0.0001
0.1110
0.0026
0.0063
0.0001
0.1541
0.0032
0.0065
0.0001
geom
mean
2.5263
1 .6404
2.4770
1.6113
2.6402
1 .6907
2.4653
1 .6204
geom
95th
stdev percentile
2.0783
1 .9802
2.0477
1 .9292
2.1371
2.0829
2.0930
1 .9380
8.4161
5.0469
8.0532
4.7492
9.2083
5.6533
8.3088
4.8119
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Table 5. Regression-based log-normal distributions of concentration ratios for each season from
PATA (see text). Denominator distance range is 200+ m. Reported values are estimated means
and variances of the logarithms of the ratios.
Numerator
Distance Range Pollutant
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
0-
75
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
75m
-200m
Benzene
Benzene
Benzene
Benzene
Benzene
Benzene
Benzene
Benzene
Benzene
Benzene
1,3-Butadiene
1 ,3-Butadiene
1,3-Butadiene
1,3-Butadiene
1,3-Butadiene
1,3-Butadiene
1,3-Butadiene
1,3-Butadiene
1,3-Butadiene
1,3-Butadiene
Diesel PM
Diesel PM
Diesel PM
Diesel PM
Diesel PM
Diesel PM
Diesel PM
Diesel PM
Diesel PM
Diesel PM
Season
Annual
Annual
Dec-Feb
Dec-Feb
Jun-Aug
Jun-Aug
Mar-May
Mar-May
Sep-Nov
Sep-Nov
Annual
Annual
Dec-Feb
Dec-Feb
Jun-Aug
Jun-Aug
Mar-May
Mar-May
Sep-Nov
Sep-Nov
Annual
Annual
Dec-Feb
Dec-Feb
Jun-Aug
Jun-Aug
Mar-May
Mar-May
Sep-Nov
Sep-Nov
Mean Variancel Variance2
1 .3504
0.6288
1.3808
0.6402
1 .4028
0.6503
1 .3528
0.6408
1.3133
0.6184
1.4577
0.6965
1 .4669
0.6969
1.5131
0.7183
1.4710
0.7143
1 .4392
0.6959
1.3163
0.6284
1 .3460
0.6394
1 .3664
0.6471
1.3251
0.6405
1 .2848
0.6177
0.8345
0.8345
0.9631
0.9631
1.0775
1.0775
0.8556
0.8556
0.7871
0.7871
1 .0006
1.0006
1.1340
1.1340
1 .2286
1 .2286
1.0223
1.0223
0.9771
0.9771
0.8449
0.8449
0.9638
0.9638
1.0973
1.0973
0.8767
0.8767
0.8065
0.8065
0.8634
0.8462
0.9965
0.9766
1.1148
1.0926
0.8853
0.8676
0.8144
0.7982
1.0353
1.0147
1.1733
1.1499
1.2711
1.2458
1.0578
1.0367
1.0109
0.9908
0.8742
0.8568
0.9972
0.9773
1.1353
1.1127
0.9070
0.8890
0.8344
0.8178
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Table 6. Regression-based log-normal distributions of quarterly mean concentration ratios from
PATA (see text). Denominator distance range is 200+ m. Reported values are estimated means
and variances of the logarithms of the ratios.
Numerator
Distance
Range Pollutant Mean Variance*! Variance2
0-75m Benzene 1.3624 0.9144 0.9224
75-200m Benzene 0.6374 0.9144 0.9176
0-75m 1,3-Butadiene 1.4726 1.0828 1.0922
75-200m 1,3-Butadiene 0.7063 1.0828 1.0866
0-75m Diesel PM 1.3306 0.9295 0.9376
75 - 200 m Diesel PM 0.6362 0.9295 0.9328
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Attachment 7: Technical Memorandum on HAPEM Near Road
Population Data Base Development (Estimating near roadway
populations and areas for HAPEM6)
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CONSULTING
MEMORANDUM
To: Chad Bailey
From: Arlene Rosenbaum and Kevin Wright
Date: December 28, 2005
Re: Estimating near roadway populations and areas for HAPEM6
PURPOSE AND BACKGROUND
In its 2001 regulation of mobile source air toxics (the "MS AT Rule") EPA's Office of
Transportation and Air Quality (OTAQ) committed to further study of the range of
concentrations to which people are exposed for consideration in future rulemaking. As part of the
Technical Analysis Plan outlined in that research, OTAQ undertook research activity looking at
the air quality in immediate proximity of busy roadways and highways. Concentrations of
pollutants directly emitted by motor vehicles show statistically significant elevation in
concentrations with increased proximity to busy roadways.
The Hazardous Air Pollutant Exposure Model (HAPEM) is a screening-level exposure model
appropriate for assessing average long-term inhalation exposures of the general population, or a
specific sub-population, over spatial scales ranging from urban to national. HAPEM uses the
general approach of tracking representatives of specified demographic groups as they move
among indoor and outdoor microenvironments and among geographic locations. The estimated
pollutant concentrations in each microenvironment visited are combined into a time-weighted
average concentration, which is assigned to members of the demographic group.
Indoor microenvironment concentrations are estimated by applying scalar factors to outdoor tract
concentrations, which are some of the required inputs. These scalar factors are derived from
published studies of concurrent concentration measurements indoors and outdoors.
In the previous version, HAPEM5, if only a single outdoor concentration is provided for each
Census tract, as is typical, this concentration is assumed to uniformly apply to the entire Census
tract. For this version, HAPEM6, we refined the model to account for the spatial variability of
outdoor concentrations within a tract due to enhanced outdoor concentrations of onroad mobile
source pollutants at locations near major roadways. The term "major roadway" is used to
describe a "Limited Access Highway", "Highway", "Major Road" or "Ramp", as defined by the
Census Feature Class Codes (CFCC). The new version of HAPEM more accurately reflects the
average and variability of exposure concentrations within each Census tract by accounting for
some of the spatial variability in the outdoor concentrations within the tract, and by extension
some of the spatial variability in indoor concentrations within the tract.
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Accomplishing this refinement to HAPEM required several activities, including the development
and implementation of an approach for creating a database of the fraction of people within each
US Census tract living near major roadways. This memorandum describes that activity.
OVERVIEW AND SPECIFICATIONS
The objective of this task was to estimate the fraction of people in each of 6 demographic groups
in each US Census tract living near major roadways.
The basic analysis was conducted at the US Census block level for populations stratified by age,
gender, and race/ethnicity. The block level data was then aggregated up to the tract level for
populations stratified by age only for use in HAPEM6.
The data bases used for this task were:
• The Environmental Sciences Research Center (ESRI) StreetMap US roadway geographic
database (which includes NavTech, GDT and TeleAtlas rectified street data)
• A geographic database of US Census block boundaries, extracted using the PCensus 2000
Census data extraction tool for Census file SF1
• A geographic data for US Census block boundaries in Puerto Rico and the US Virgin Islands
obtained from Proximity
Although the block file is an intermediate product for this project, it will be retained to facilitate
the re-specification of demographic groups for possible future analyses. Therefore, this file
contains the most resolved age-gender groups available at the block level from the US Census
STF1. The age groups for the block level data are as follows:
• 19 single-year age groups from 0-19 (PI4)
• 2 single-year age groups 20-21 (PI2)
• 16 age groups (PI2)
o 22 to 24 years
o 25 to 29 years
o 30 to 34 years
o 35 to 39 years
o 40 to 44 years
o 45 to 49 years
o 50 to 54 years
o 55 to 59 years
o 60 and 61 years
o 62 to 64 years
o 65 and 66 years
o 67 to 69 years
o 70 to 74 years
o 75 to 79 years
o 80 to 84 years
o 85 years and over.
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B-240
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The aggregated age groups for the tract level data are:
• 0-1
• 2-4
• 5-15
• 16-17
• 18-64
• 65+
The race/ethnic groups (block level only) are:
• non-Hispanic White (alone or in combination - PO10003)
• non-Hispanic Black (alone or in combination - PO 10004)
• non-Hispanic American Indian /Alaskan Native (alone or in combination - PO 10005)
• non-Hispanic Asian (alone or in combination - PO 10006)
• non-Hispanic Native Hawaiian/ Pacific Isalander (PO 10007)
• non-Hispanic other (alone or in combination - PO 10008)
• Hispanic (alone or in combination - PO 10009)
The spatial stratifications of the populations (block and tract level) are:
• Those residing within 75 meters of a major roadway
• Those residing from 75 to 200 meters from a major roadway
• Those residing at greater than 200 meters from a roadway.
In addition, the fraction of the area of each Census block and tract that is located within the same
distance ranges from a major roadway was determined.
PROCEDURES
For all the spatial modeling and geoprocessing operations in this study ICF utilized Arclnfo
software. Arclnfo is the most extensive version of ArcGIS 9.1, the industry's standard for
Geographic Information Systems, produced by ESRI of Redlands, CA.
Due to the size of the roadway and block geography files, most of the processing was conducted
on a county-by-county basis. The files for some counties, however, still exceeded Arclnfo's
capacity and were processed tract-by-tract. A few counties in Arizona needed special handling
because even at the tract level they exceeded Arclnfo's capacity and were disaggregated into
smaller pieces for processing.
1. Because populations are not generally evenly distributed within blocks, it was
assumed that the block populations all reside within 150 meters of any road within the
block of designation "local" or greater as defined by the Census Feature Class Codes
(CFCC). Thus, the first step was to create a 150-meter buffer around all roadways
within the block. This buffer served as a "clipped" block boundary defining the
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portion of the block containing residential populations. The block population was
assumed to be uniformly distributed within the "clipped" block boundary.
2. Next a 75-meter buffer and a 200-meter buffer were created around all major
roadways within the block. These buffers were overlaid on the "clipped" block
boundary, and the fraction of the "clipped" block area that that fell within each buffer
was calculated. This area fraction was assumed to equal the population fraction that
fell within each buffer, and the fractions were applied to each population
stratification.
3. The 75-meter buffer and the 200-meter buffer were also overlaid on the undipped
block boundary to determine the fraction of the total block area that fell with each of
the buffers.
4. The block level fractions for area and populations were then aggregated up to the tract
level, and the population stratifications were aggregated up to the 6 tract age groups
only.
RESULTS
The resulting database consists of 2 files types: (1) a block file for each state, and (2) a nation-
wide tract file.
The block files contains the following 249 fields for each block:
• block Fl PS code
• total population
• total area
• area within 75 meters of a major roadway
• area from 75 to 200 meters from a major roadway
• for each of 74 age-gender groups:
o population residing within 75 meters of a major roadway
o population residing between 75 and 200 meters from a major roadway
o population residing more than 200 meters from a major roadway
• sum of race/ethnic populations (note; this may differ slightly from the total population due to
some double-counting of persons with more than 1 race/ethnicity)
• for each of 7 race/ethnic groups:
o population residing within 75 meters of a major roadway
o population residing between 75 and 200 meters from a major roadway
o population residing more than 200 meters from a major roadway
Note that because of the limitations of the US Census data the block level populations
could not be stratified by age, gender, and race together,
The tract file contains the following 22 fields for each tract
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tract FIPS code
fraction of area within 75 meters of a major roadway
fraction of area between 75 and 200 meters from a major roadway
fraction of area more than 200 meters from a major roadway
for each of 6 age groups:
o fraction of population residing within 75 meters of a major roadway
o fraction of population residing between 75 and 200 meters from a major roadway
o fraction of population residing more than 200 meters from a major roadway
To date only a subset of states have been completely processed. For this subset state
summaries of the fraction of population living within various distances of major
roadways are presented in Table 1.
Table 1. Fraction of population residing at various distances from major roadways for
selected states.
STATE
Colorado
Georgia
New York
Distance from major roadways
< 75 meters
0.22
0.17
0.31
75 - 200 meters
0.33
0.24
0.36
> 200 meters
0.45
0.59
0.33
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Attachment 8. Technical Memorandum on the Uncertainty Analysis
Of Residential Air Exchange Rate Distributions
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INTERNATIONAL
MEMORANDUM
To: John Langstaff, EPA OAQPS
From: Jonathan Cohen, Arlene Rosenbaum, ICF International
Date: June 5, 2006
Re: Uncertainty analysis of residential air exchange rate distributions
This memorandum describes our assessment of some of the sources of the uncertainty of city-
specific distributions of residential air exchange rates that were fitted to the available study data.
City-specific distributions for use with the APEX ozone model were developed for 12 modeling
cities, as detailed in the memorandum by Cohen, Mallya and Rosenbaum, 200529 (Appendix A
of this report). In the first part of the memorandum, we analyze the between-city uncertainty by
examining the variation of the geometric means and standard deviations across cities and studies.
In the second part of the memorandum, we assess the within-city uncertainty by using a
bootstrap distribution to estimate the effects of sampling variation on the fitted geometric means
and standard deviations for each city. The bootstrap distributions assess the uncertainty due to
random sampling variation but do not address uncertainties due to the lack of representativeness
of the available study data, the matching of the study locations to the modeled cities, and the
variation in the lengths of the AER monitoring periods.
Variation of geometric means and standard deviations across cities and studies
The memorandum by Cohen, Mallya and Rosenbaum, 200530 (Attachment 5 of this report)
describes the analysis of residential air exchange rate (AER) data that were obtained from seven
studies. The AER data were subset by location, outside temperature range, and the A/C type, as
defined by the presence or absence of an air conditioner (central or window). In each case we
chose to fit a log-normal distribution to the AER data, so that the logarithm of the AER for a
given city, temperature range, and A/C type is assumed to be normally distributed. If the AER
data has geometric mean GM and geometric standard deviation GSD, then the logarithm of the
AER is assumed to have a normal distribution with mean log(GM) and standard deviation
log(GSD).
Table D-l shows the assignment of the AER data to the 12 modeled cities. Note that for Atlanta,
GA and Washington DC, the Research Triangle Park, NC data for houses with A/C was used to
represent the AER distributions for houses with A/C, and the non-California data for houses
without A/C was used to represent the AER distributions for houses without A/C. Sacramento,
CA AER distributions were estimated using the AER data from the inland California counties of
29 Cohen, I, H. Mallya, and A. Rosenbaum. 2005. Memorandum to John Langstaff. EPA 68D01052, Work
Assignment 3-08. Analysis of Air Exchange Rate Data. September 30, 2005.
30 Op. Cit.
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Sacramento, Riverside, and San Bernardino; these combined data are referred to by the City
Name "Inland California." St Louis, MO AER distributions were estimated using the AER data
from all states except for California and so are referred to be the City Name "Outside
California."
Table D-l. Assignment of Residential AER distributions to modeled cities
Modeled city
Atlanta, GA, A/C
Atlanta, GA, no A/C
Boston, MA
Chicago, IL
Cleveland, OH
Detroit, MI
Houston, TX
Los Angeles, CA
New York, NY
Philadelphia, PA
Sacramento
St. Louis
Washington, DC, A/C
Washington, DC, no
A/C
AER distribution
Research Triangle Park, A/C only
All non-California, no A/C ("Outside
California")
New York
New York
New York
New York
Houston
Los Angeles
New York
New York
Inland parts of Los Angeles ("Inland
California")
All non-California ("Outside California")
Research Triangle Park, A/C only
All non-California, no A/C ("Outside
California")
It is evident from Table D-l that for some of the modeled cities, some potentially large
uncertainty was introduced because we modeled their AER distributions using available data
from another city or group of cities thought to be representative of the first city on the basis of
geography and other characteristics. This was necessary for cities where we did not have any or
sufficient AER data measured in the same city that also included the necessary temperature and
A/C type information. One way to assess the impact of these assignments on the uncertainty of
the AER distributions is to evaluate the variation of the fitted log-normal distributions across the
cities with AER data. In this manner we can examine the effect on the AER distribution if a
different allocation of study data to the modeled cities had been used.
Even for the cities where we have study AER data, there is uncertainty about the fitted AER
distributions. First, the studies used different measurement and residence selection methods. In
some cases the residences were selected by a random sampling method designed to represent the
entire population. In other cases the residences were selected to represent sub-populations. For
example, for the RTF study, the residences belong to two specific cohorts: a mostly Caucasian,
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non-smoking group aged at least 50 years having cardiac defibrillators living in Chapel Hill; a
group of non-smoking, African Americans aged at least 50 years with controlled hypertension
living in a low-to-moderate SES neighborhood in Raleigh. The TEACH study was restricted to
residences of inner-city high school students. The RIOPA study was a random sample for Los
Angeles, but was designed to preferentially sample locations near major air toxics sources for
Elizabeth, NJ and Houston TX. Furthermore, some of the studies focused on different towns or
cities within the larger metropolitan areas, so that, for example, the Los Angeles data from the
Avol study was only measured in Lancaster, Lake Gregory, Riverside, and San Dimas but the
Los Angeles data from the Wilson studies were measured in multiple cities in Southern
California. One way to assess the uncertainty of the AER distributions due to variations of study
methodologies and study sampling locations is to evaluate the variation of the fitted log-normal
distributions within each modeled city across the different studies.
We evaluated the variation between cities, and the variation within cities and between studies, by
tabulating and plotting the AER distributions for all the study/city combinations. Since the
original analyses by Cohen, Mallya and Rosenbaum, 2005 clearly showed that the AER
distribution depends strongly on the outside temperature and the A/C type (whether or not the
residence has air conditioning), this analysis was stratified by the outside temperature range and
the A/C type. Otherwise, study or city differences would have been confounded by the
temperature and A/C type differences and you would not be able to tell how much of the AER
difference was due to the variation of temperature and A/C type across cities or studies. In order
to be able to compare cities and studies we could not use different temperature ranges for the
different modeled cities as we did for the original AER distribution modeling. For these analyses
we stratified the temperature into the ranges <= 10, 10-20, 20-25, and >25 °C and categorized the
A/C type as "Central or Window A/C" versus 'No A/C," giving 8 temperature and A/C type
strata.
Table D-2 shows the geometric means and standard deviations by city and study. These
geometric mean and standard deviation pairs are plotted in Figure D-l through D-8. Each figure
shows the variation across cities and studies for a given temperature range and A/C type. The
results for a city with only one available study are shown with a blank study name. For cities
with multiple studies, results are shown for the individual studies and the city overall distribution
is designated by a blank value for the study name.
Table D-2. Geometric means and standard deviations by city and study.
A/C Type
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Temperature
<=10
<= 10
<=10
<=10
<= 10
<=10
<= 10
<=10
<=10
<= 10
10-20
City
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
Research Triangle Park
Sacramento
San Francisco
Stockton
Arcata
Study*
Avol
RIOPA
Wilson 1991
N
2
5
2
1
2
20
157
3
2
7
1
Geo Mean
0.32
0.62
0.72
0.31
0.77
0.71
0.96
0.38
0.43
0.48
0.17
Geo Std Dev**
1.80
1.51
1.22
1.12
2.02
1.81
1.82
1.00
1.64
B-247
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Table D-2. Geometric means and standard deviations by city and study.
A/C Type
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
Temperature
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
>25
>25
>25
>25
>25
>25
>25
>25
>25
<= 10
<=10
<= 10
<= 10
<=10
<= 10
<=10
<= 10
City
Bakersfield
Fresno
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Redding
Research Triangle Park
Sacramento
San Diego
San Francisco
Santa Maria
Stockton
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Red Bluff
Research Triangle Park
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Research Triangle Park
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Study*
Avol
RIOPA
TEACH
Wilson 1984
Wilson 1991
RIOPA
TEACH
Avol
RIOPA
Wilson 1984
RIOPA
TEACH
Avol
RIOPA
Wilson 1984
RIOPA
TEACH
Avol
RIOPA
Wilson 1991
RIOPA
TEACH
N
2
8
13
716
33
11
1
634
37
5
4
1
1
320
7
23
5
1
4
20
273
32
26
215
37
20
17
2
196
79
114
25
10
79
19
14
5
145
13
18
14
2
2
48
44
4
Geo Mean
0.36
0.30
0.42
0.59
0.48
0.60
0.68
0.59
0.64
1.36
1.20
2.26
0.31
0.56
0.26
0.41
0.42
0.23
0.73
0.47
1.10
0.61
0.90
1.23
1.11
0.93
1.37
0.61
0.40
0.43
0.72
0.37
0.94
0.86
1.24
1.23
1.29
0.38
0.66
0.54
0.51
0.72
0.60
1.02
1.04
0.79
Geo Std Dev**
1.34
1.62
2.19
1.90
1.87
1.87
1.89
2.11
2.34
2.53
1.91
1.67
1.55
1.25
1.42
1.94
2.36
1.95
2.42
2.33
2.74
2.91
2.52
3.20
1.89
2.17
2.60
3.10
1.71
2.33
2.18
2.28
2.04
1.71
1.68
3.09
3.60
1.11
1.00
2.14
2.20
1.28
B-248
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Table D-2. Geometric means and standard deviations by city and study.
A/C Type
NoA/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
No A/C
Temperature
<= 10
<=10
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
10-20
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
20-25
>25
>25
>25
>25
>25
>25
>25
>25
>25
City
Sacramento
San Francisco
Bakersfield
Fresno
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
Sacramento
San Diego
San Francisco
Santa Maria
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Red Bluff
Houston
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
Study*
Avol
RIOPA
TEACH
Wilson 1984
Wilson 1991
Avol
RIOPA
Wilson 1984
RIOPA
TEACH
Avol
RIOPA
TEACH
Wilson 1984
RIOPA
TEACH
N
3
9
1
4
28
390
23
87
9
241
30
59
1
49
15
2
10
148
19
38
91
26
19
7
1
2
25
6
4
3
12
6
3
3
Geo Mean
0.58
0.39
0.85
0.90
0.63
0.75
0.78
0.78
2.32
0.70
0.75
0.79
1.09
0.47
0.34
0.27
0.92
1.37
0.95
.30
.52
.62
.50
.99
0.55
0.92
0.99
1.56
1.33
0.86
0.74
1.54
1.73
1.37
Geo Std Dev**
1.30
1.42
2.42
2.92
2.09
2.55
1.96
2.05
2.06
1.82
2.04
1.95
3.05
1.23
2.41
2.28
1.87
2.11
2.40
2.24
2.30
2.11
3.96
1.97
1.36
1.37
1.02
2.29
1.65
2.00
1.38
* For a given city, if AER data were available from only one study, then the study name is missing.
for two or more studies, then the overall city distribution is shown in the row where the study name
distributions by study and city are shown in the rows with a specific study name.
** The geometric standard deviation is undefined if the sample size equals 1.
If AER data were available
is missing, and the
In general, there is a relatively wide variation across different cities. This implies that the AER
modeling results would be very different if the matching of modeled cities to study cities was
changed, although a sensitivity study using the APEX model would be needed to assess the
impact on the ozone exposure estimates. In particular the ozone exposure estimates may be
sensitive to the assumption that the St. Louis AER distributions can be represented by the
combined non-California AER data. One way to address this is to perform a Monte Carlo
analysis where the first stage is to randomly select a city outside of California, the second stage
picks the A/C type, and the third stage picks the AER value from the assigned distribution for the
B-249
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city, A/C type and temperature range. Note that this will result in a very different distribution to
the current approach that fits a single log-normal distribution to all the non-California data for a
given temperature range and A/C type. The current approach weights each data point equally, so
that cities like New York with most of the data values get the greatest statistical weight. The
Monte Carlo approach gives the same total statistical weight for each city and fits a mixture of
log-normal distributions rather than a single distribution.
In general, there is also some variation within studies for the same city, but this is much smaller
than the variation across cities. This finding tends to support the approach of combining different
studies. Note that the graphs can be deceptive in this regard because some of the data points are
based on very small sample sizes (N); those data points are less precise and the differences
would not be statistically significant. For example, for the No A/C data in the range 10-20 °C,
the Los Angeles TEACH study had a geometric mean of 2.32 based on only nine AER values,
but the overall geometric mean, based on 390 values, was 0.75 and the geometric means for the
Los Angeles Avol, RIOPA, Wilson 1984, and Wilson 1991 studies were each close to 0.75. One
noticeable case where the studies show big differences for the same city is for the A/C houses in
Los Angeles in the range 20-25 °C where the study geometric means are 0.61 (Avol, N=32), 0.90
(RIOPA, N=26) and 1.23 (Wilson 1984, N=215).
Bootstrap analyses
The 39 AER subsets defined in the Cohen, Mallya, and Rosenbaum, 2005 memorandum
(Appendix A of this report) and their allocation to the 12 modeled cities are shown in Table D-3.
To make the distributions sufficiently precise in each AER subset and still capture the variation
across temperature and A/C type, different modeled cities were assigned different temperature
range and A/C type groupings. Therefore these temperature range groupings are sometimes
different to those used to develop Table D-2 and Figure D-l through D-8.
Table D-3. AER subsets by city, A/C type, and temperature range.
Subset City
Name
Houston
Houston
Houston
Houston
Houston
Houston
Inland California
Inland California
Inland California
Inland California
Study Cities
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Sacramento, Riverside,
and San Bernardino
counties, CA
Sacramento, Riverside,
and San Bernardino
counties, CA
Sacramento, Riverside,
and San Bernardino
counties, CA
Sacramento, Riverside,
and San Bernardino
counties, CA
Represents
Modeled Cities:
Houston, TX
Houston, TX
Houston, TX
Houston, TX
Houston, TX
Houston, TX
Houston, TX
Sacramento, CA
Sacramento, CA
Sacramento, CA
Sacramento, CA
A/C Type
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
No A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
Temperature
Range (°C)
<=20
20-25
25-30
>30
<=10
10-20
>20
<=25
>25
<=10
10-20
B-250
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Table D-3. AER subsets by city, A/C type, and temperature range.
Subset City
Name
Inland California
Inland California
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
New York City
Study Cities
Sacramento, Riverside,
and San Bernardino
counties, CA
Sacramento, Riverside,
and San Bernardino
counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
Los Angeles, Orange,
Riverside, San
Bernardino, and
Ventura counties, CA
New York, NY
New York, NY
New York, NY
New York, NY
Represents
Modeled Cities:
Sacramento, CA
Sacramento, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
A/C Type
NoA/C
No A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
No A/C
No A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
Temperature
Range (°C)
20-25
>25
<=20
20-25
25-30
>30
<=10
10-20
20-25
>25
<=10
10-25
>25
<=10
B-251
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Table D-3. AER subsets by city, A/C type, and temperature range.
Subset City
Name
New York City
New York City
Outside California
Outside California
Outside California
Outside California
Outside California
Outside California
Outside California
Outside California
Research Triangle Park
Research Triangle Park
Research Triangle Park
Research Triangle Park
Study Cities
New York, NY
New York, NY
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Cities outside CA
Research Triangle
Park, NC
Research Triangle
Park, NC
Research Triangle
Park, NC
Research Triangle
Park, NC
Represents
Modeled Cities:
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
Boston, MA,
Chicago, IL,
Cleveland, OH,
Detroit, MI,
New York, NY,
Philadelphia, PA
St. Louis, MO
St. Louis, MO
St. Louis, MO
St. Louis, MO
St. Louis, MO
St. Louis, MO
Atlanta, GA
Washington DC
St. Louis, MO
Atlanta, GA
Washington DC
St. Louis, MO
Atlanta, GA
Washington DC
Atlanta, GA
Washington DC
Atlanta, GA
Washington DC
Atlanta, GA
Washington DC
Atlanta, GA
Washington DC
A/C Type
NoA/C
No A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
No A/C
No A/C
No A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Central or Room A/C
Temperature
Range (°C)
10-20
>20
<=10
10-20
20-25
25-30
>30
<=10
10-20
>20
<=10
10-20
20-25
>25
The GM and GSD values that define the fitted log-normal distributions for these 39 AER subsets
are shown in Table D-4. Examples of these pairs are also plotted in Figures D-9 through D-19, to
be further described below. Each of the example figures D-9 through D-19 corresponds to a
single GM/GSD "Original Data" pair. The GM and GSD values for the "Original Data" are at
the intersection of the horizontal and vertical lines that are parallel to the x- and y-axes in the
figures.
B-252
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Table D-4. Geometric means and standard deviations for AER subsets by city, A/C type,
and temperature range.
Subset City
Name
Houston
Houston
Houston
Houston
Houston
Houston
Inland California
Inland California
Inland California
Inland California
Inland California
Inland California
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
New York City
New York City
New York City
New York City
New York City
New York City
Outside California
Outside California
Outside California
Outside California
A/C Type
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
No A/C
No A/C
No A/C
Central or Room
A/C
Central or Room
A/C
No A/C
No A/C
No A/C
No A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
No A/C
No A/C
No A/C
No A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
No A/C
No A/C
No A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Temperature
Range (°C)
<=20
20-25
25-30
>30
<=10
10-20
>20
<=25
>25
<=10
10-20
20-25
>25
<=20
20-25
25-30
>30
<=10
10-20
20-25
>25
<=10
10-25
>25
<=10
10-20
>20
<=10
10-20
20-25
25-30
N
15
20
65
14
13
28
12
226
83
17
52
13
14
721
273
102
12
18
390
148
25
20
42
19
48
59
32
179
338
253
219
Geometric
Mean
0.4075
0.4675
0.4221
0.4989
0.6557
0.6254
0.9161
0.5033
0.8299
0.5256
0.6649
1.0536
0.8271
0.5894
1.1003
0.8128
0.2664
0.5427
0.7470
1.3718
0.9884
0.7108
1.1392
1.2435
1.0165
0.7909
1.6062
0.9185
0.5636
0.4676
0.4235
Geometric
Standard
Deviation
2.1135
1.9381
2.2579
1.7174
1.6794
2.9162
2.4512
1.9210
2.3534
3.1920
2.1743
1.7110
2.2646
1.8948
2.3648
2.4151
2.7899
3.0872
2.0852
2.2828
1.9666
2.0184
2.6773
2.1768
2.1382
2.0417
2.1189
1.8589
1.9396
2.2011
2.0373
B-253
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Table D-4. Geometric means and standard deviations for AER subsets by city, A/C type,
and temperature range.
Subset City
Name
Outside California
Outside California
Outside California
Outside California
Research Triangle
Park
Research Triangle
Park
Research Triangle
Park
Research Triangle
Park
A/C Type
Central or Room
A/C
No A/C
No A/C
No A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Central or Room
A/C
Temperature
Range (°C)
>30
<=10
10-20
>20
<=10
10-20
20-25
>25
N
24
61
87
44
157
320
196
145
Geometric
Mean
0.5667
0.9258
0.7333
1.3782
0.9617
0.5624
0.3970
0.3803
Geometric
Standard
Deviation
1.9447
2.0836
2.3299
2.2757
1.8094
1.9058
1.8887
1.7092
To evaluate the uncertainty of the GM and GSD values, a bootstrap simulation was performed,
as follows. Suppose that a given AER subset has N values. A bootstrap sample is obtained by
sampling N times at random with replacement from the N AER values. The first AER value in
the bootstrap sample is selected randomly from the N values, so that each of the N values is
equally likely. The second, third, ..., N'th values in the bootstrap sample are also selected
randomly from the N values, so that for each selection, each of the N values is equally likely.
The same value can be selected more than once. Using this bootstrap sample, the geometric
mean and geometric standard deviation of the N values in the bootstrap sample was calculated.
This pair of values is plotted as one of the points in a figure for that AER subset. 1,000 bootstrap
samples were randomly generated for each AER subset, producing a set of 1,000 geometric mean
and geometric standard deviation pairs, which were plotted in example Figures D-9 through D-
19.
The bootstrap distributions display the part of the uncertainty of the GM and GSD that is entirely
due to random sampling variation. The analysis is based on the assumption that the study AER
data are a random sample from the population distribution of AER values for the given city,
temperature range, and A/C type. On that basis, the 1,000 bootstrap GM and GSD pairs estimate
the variation of the GM and GSD across all possible samples of N values from the population.
Since each GM, GSD pair uniquely defines a fitted log-normal distribution, the pairs also
estimate the uncertainty of the fitted log-normal distribution. The choice of 1,000 was made as a
compromise between having enough pairs to accurately estimate the GM, GSD distribution and
not having too many pairs so that the graph appears as a smudge of overlapped points. Note that
even if there were infinitely many bootstrap pairs, the uncertainty distribution would still be an
estimate of the true uncertainty because the N is finite, so that the empirical distribution of the N
measured AER values does not equal the unknown population distribution.
In most cases the uncertainty distribution appears to be a roughly circular or elliptical geometric
mean and standard deviation region. The size of the region depends upon the sample size and on
the variability of the AER values; the region will be smallest when the sample size N is large
B-254
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and/or the variability is small, so that there are a large number of values that are all close
together.
The bootstrap analyses show that the geometric standard deviation uncertainty for a given
CMSA/air-conditioning-status/temperature-range combination tends to have a range of at most
from "fitted GSD-1.0 hr"1" to "fitted GSD+1.0 hr"1", but the intervals based on larger AER
sample sizes are frequently much narrower. The ranges for the geometric means tend to be
approximately from "fitted GM-0.5 hr"1" to "fitted GM+0.5 hr"1", but in some cases were much
smaller.
The bootstrap analysis only evaluates the uncertainty due to the random sampling. It does not
account for the uncertainty due to the lack of representativeness, which in turn is due to the fact
that the samples were not always random samples from the entire population of residences in a
city, and were sometimes used to represent different cities. Since only the GM and GSD were
used, the bootstrap analyses does not account for uncertainties about the true distributional
shape, which may not necessarily be log-normal. Furthermore, the bootstrap uncertainty does not
account for the effect of the calendar year (possible trends in AER values) or of the uncertainty
due to the AER measurement period; the distributions were intended to represent distributions of
24 hour average AER values although the study AER data were measured over a variety of
measurement periods.
To use the bootstrap distributions to estimate the impact of sample size on the fitted distributions,
a Monte Carlo approach could be used with the APEX model. Instead of using the Original Data
distributions, a bootstrap GM, GSD pair could be selected at random and the AER value could be
selected randomly from the log-normal distribution with the bootstrap GM and GSD.
B-255
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4.0—
3.5—
Figure D-1
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: Central or Room A/C
Temperature Range: <= 10 Degrees Celsius
Q
•o
,0
^H
"5
o
3.0—
2.5—
2.0—
1.5—
1.0—
E
AG F
T
B
cn
H
III 1
0.5
1.0 1.5 2.0
Geometric Mean
2.5
3.0
AAAHouston
E E ENewYorkCity
I I I Stockton
B B BLosAngeles C C CLosAngeles-Avol
F F FResearchTnanglePark GGGSacramento
DDDLosAngeles-Wilson 1991
H H Ffs anFranci sco
-------
Figure D-2
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: Central or Room A/C
Temperature Range: 10-20 Degrees Celsius
4.0-
3.5-
-------
Figure D-3
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
Q
T3
-t—»
(Si
•c
"5
o
-------
Q
T3
-t—»
(Si
•c
"5
o
25 Degrees Celsius
A
D
H
0.0
0.5
1.0 1.5
Geometric Mean
2.0
2.5
3.0
AAAHouston B B BLosAngeles
E E ELosAngeles-Wilsonl984 F F FNewYorkCity
I I I ResearchTnanglePark
C C CLosAngeles-Avol D D DLosAngeles-RIOPA
GGGNewYorkCity-RIOPA HHHNewYorkCity-TEACH
B-259
-------
Q
T3
-t— »
(Si
•c
"5
o
-------
Figure D-6
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: No A/C
Temperature Range: 10-20 Degrees Celsius
Q
T3
-t—»
(Si
•c
"5
o
-------
Figure D-7
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: No A/C
Temperature Range: 20-25 Degrees Celsius
4.0—
3.5—
CD
Q 3.0H
.g 2.5—
"CD
o 2 0—
CD ^••V I
O
1.5—
1.0—
A
D
H
0.0
0.5
1.0 1.5
Geometric Mean
2.0
2.5
3.0
AAAHouston B B BLosAngeles
E E ELosAngeles-Wilson 1984 F F FNewYorkCity
C C CLosAngeles-Avol
G G GNewYorkCity-RIOP A
D D DLosAngeles-RIOPA
H H HNewYorkCity-TEACH
B-262
-------
Q
T3
-t— »
(Si
•c
"5
o
(U
O
4.0—
3.5—
3.0H
1.5—
1.0—
Figure D-8
Geometric mean and standard deviation of air exchange rate
For different cities and studies
Air Conditioner Type: No A/C
Temperature Range: > 25 Degrees Celsius
B
H
G
DI C
E
1 1 1
0.0 0.5 1.0 1.5 2.0 2
5 3.0
Geometric Mean
AAAHouston B B BLosAngeles C C CLosAngeles-Avol DDDLosAngeles-RIOPA
E E ELosAngeles-TEACH F F FLosAngeles-Wilsonl984 GGGNewYorkCity HHHNewYorkCity-RIOPA
I I iNewYorkCity-TEACH
B-263
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Figure D-9
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Houston
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
4.0—
3.5 —
Q 3.0—
"3
.g 2.5—1
•s
8 2.0—
O
1.5 —
1.0—
0.0
1
0.5
1
1.0
1.5
Geometric Mean
2.0
2.5
1
3.0
Bootstrapped Data +++Original Data
B-264
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Figure D-10
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Houston
Air Conditioner Type: No A/C
Temperature Range:
20 Degrees Celsius
4.0—
3.5 —
1 3.0-
Geometric Std
K> K>
o u<
1 1
1.5 —
1.0—
.
• "t"*2&
*
.
^id%---
Pp^'-M^.
1 1 1 1 1 1 1
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
••Bootstrapped Data +++Original Data
B-265
-------
Figure D-11
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Inland California
Air Conditioner Type: Central or Room A/C
Temperature Range: <=25 Degrees Celsius
4.0—
3.5-
3.0H
"3
oo
| 2.5-
I
8 2.0— |
o
1.5 —
1.0—
0.5
\
1.0
1.5 2.0
Geometric Mean
2.5
3.0
••Bootstrapped Data +++Original Data
B-266
-------
Figure D-12
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Inland California
Air Conditioner Type: No A/C
Temperature Range: 20-25 Degrees Celsius
4.0—
3.5-
Q 3.0—
"3
oo
| 2.5-
I
8 2.0—1
o
1.5 —
1.0—
1
0.5
1
1.0
1.5 2.0
Geometric Mean
2.5
1
3.0
••Bootstrapped Data +++Original Data
B-267
-------
Figure D-13
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Los Angeles
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
4.0—
3.5-
3.0H
•3
w
| 2.5-
I
8 2.0— |
o
1.5 —
1.0—
\
0.5
\
1.0
1.5 2.0
Geometric Mean
2.5
3.0
••Bootstrapped Data +++Original Data
B-268
-------
Figure D-14
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Los Angeles
Air Conditioner Type: No A/C
Temperature Range: 20-25 Degrees Celsius
4.0—
3.5-
I 3.0H
•3
00
•I 2-5~
1
8 2.0—1
o
1.5 —
1.0—
\
0.5
\
1.0
1.5 2.0
Geometric Mean
2.5
\^
3.0
••Bootstrapped Data +++Origiiial Data
B-269
-------
Figure D-15
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: New York City
Air Conditioner Type: Central or Room A/C
Temperature Range: 10-25 Degrees Celsius
4.0—
3.5 —
1 3.0-
Geometric Std
K> K>
o Lo
1 1
1.5 —
1.0—
•
••• V'^Ł
"'• '-^'^^^
. -:;J^
••fc. :'
S^SjVjI^M.- " . •
isSC*>""iHv-.'-
^^•.•-•;. ••". ' •
"./".. ***•'•.•.
i i i i i i i
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
••Bootstrapped Data +++Original Data
B-270
-------
Figure D-16
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: New York City
Air Conditioner Type: No A/C
Temperature Range: >20 Degrees Celsius
4.0—
3.5-
I 3.0H
•3
00
•c 2-5~
1
8 2.0—1
o
1.5 —
1.0—
\
0.5
\
1.0
1.5 2.0
Geometric Mean
2.5
\^
3.0
••Bootstrapped Data +++Original Data
B-271
-------
Figure D-17
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Outside California
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
4.0—
3.5-
3.0H
•3
oo
| 2.5-
I
8 2.0— |
o
1.5 —
1.0—
\
0.5
\
1.0
1.5 2.0
Geometric Mean
2.5
3.0
••Bootstrapped Data +++Original Data
B-272
-------
Figure D-18
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Outside California
Air Conditioner Type: No A/C
Temperature Range:
20 Degrees Celsius
4.0—
3.5-
I 3.0H
3
00
•c 2-5~
1
8 2.0—1
o
1.5 —
1.0—
\
0.5
\
1.0
1.5
Geometric Mean
2.0
2.5
\^
3.0
••Bootstrapped Data +++Original Data
B-273
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Figure D-19
Geometric mean and standard deviation of air exchange rate
Bootstrapped distributions for different cities
City: Research Triangle Park
Air Conditioner Type: Central or Room A/C
Temperature Range: 20-25 Degrees Celsius
Q
"2
Geometric Ł
4.0—
3.5 —
3.0—
2.5 —
2.0—
1.5 —
1.0—
i
'
i
f
1 1 1 1 1 1 1
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Geometric Mean
•Bootstrapped Data +++Original Data
B-274
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Attachment 9. Technical Memorandum on the Distributions of Air
Exchange Rate Averages Over Multiple Days
-------
INTERNATIONAL
MEMORANDUM
To: John Langstaff, EPA OAQPS
From: Jonathan Cohen, Arlene Rosenbaum, ICF International
Date: June 8, 2006
Re: Distributions of air exchange rate averages over multiple days
As detailed in the memorandum by Cohen, Mallya and Rosenbaum, 200531 (Appendix A of this
report) we have proposed to use the APEX model to simulate the residential air exchange rate
(AER) using different log-normal distributions for each combination of outside temperature
range and the air conditioner type, defined as the presence or absence of an air conditioner
(central or room).
Although the averaging periods for the air exchange rates in the study databases varied from one
day to seven days, our analyses did not take the measurement duration into account and treated
the data as if they were a set of statistically independent daily averages. In this memorandum we
present some analyses of the Research Triangle Park Panel Study that show extremely strong
correlations between consecutive 24-hour air exchange rates measured at the same house. This
provides support for the simplified approach of treating all averaging periods as if they were 24-
hour averages.
In the current version of the APEX model, there are several options for stratification of time
periods with respect to AER distributions, and for when to re-sample from a distribution for a
given stratum. The options selected for this current set of simulations resulted in a uniform AER
for each 24-hour period and re-sampling of the 24-hour AER for each simulated day. This re-
sampling for each simulated day implies that the simulated AERs on consecutive days in the
same microenvironment are statistically independent. Although we have not identified sufficient
data to test the assumption of uniform AERs throughout a 24-hour period, the analyses described
in this memorandum suggest that AERs on consecutive days are highly correlated. Therefore, we
performed sensitivity simulations to assess the impact of the assumption of temporally
independent air exchange rates, but found little difference between APEX predictions for the two
scenarios (i.e., temporally independent and autocorrelated air exchange rates).
31 Cohen, I, H. Mallya, and A. Rosenbaum. 2005. Memorandum to John Langstaff. EPA 68D01052, Work
Assignment 3-08. Analysis of Air Exchange Rate Data. September 30, 2005.
B-276
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Distributions of multi-day averages from the RTF Panel Study
The RTF Panel study included measurements of 24-hour averages at 38 residences for up to four
periods of at least seven days. These periods were in different seasons and/or calendar years.
Daily outside temperatures were also provided. All the residences had either window or room air
conditioners or both. We used these data to compare the distributions of daily averages taken
over 1, 2, 3, .. 7 days.
The analysis is made more complicated because the previous analyses showed the dependence of
the air exchange rate on the outside temperature, and the daily temperatures often varied
considerably. Two alternative approaches were employed to group consecutive days. For the first
approach, A, we sorted the data by the HOUSE_ID number and date and began a new group of
days for each new HOUSE_ID and whenever the sorted measurement days on the same
HOUSE_ID were 30 days or more apart. In most cases, a home was measured over four different
seasons for seven days, potentially giving 38 x 4 = 152 groups; the actual number of groups was
124. For the second approach, B, we again sorted the data by the HOUSE_ID number and date,
but this time we began a new group of days for each new HOUSE_ID and whenever the sorted
measurement days on the same HOUSE_ID were 30 days or more apart or were for different
temperature ranges. We used the same four temperature ranges chosen for the analysis in the
Cohen, Mallya, and Rosenbaum, 2005, memorandum (Appendix A): <= 10, 10-20, 20-25, and >
25 °C. For example, if the first week of measurements on a given HOUSE_ID had the first three
days in the <= 10 °C range, the next day in the 10-20 °C range, and the last three days in the <=
10 °C range, then the first approach would treat this as a single group of days. The second
approach would treat this as three groups of days, i.e., the first three days, the fourth day, and the
last three days. Using the first approach, the days in each group can be in different temperature
ranges. Using the second approach, every day in a group is in the same temperature range. Using
the first approach we treat groups of days as being independent following a transition to a
different house or season. Using the second approach we treat groups of days as being
independent following a transition to a different house or season or temperature range.
To evaluate the distributions of multi-day air exchange rate (AER) averages, we averaged the
AERs over consecutive days in each group. To obtain a set of one-day averages, we took the
AERs for the first day of each group. To obtain a set of two-day averages, we took the average
AER over the first two days from each group. We continued this process to obtain three-, four-,
five-, six-, and seven-day averages. There were insufficiently representative data for averaging
periods longer then seven days. Averages over non-consecutive days were excluded. Each
averaging period was assigned the temperature range using the average of the daily temperatures
for the averaging period. Using Approach A, some or all of the days in the averaging period
might be in different temperature ranges than the overall average. . Using Approach B, every day
is in the same temperature range as the overall average. For each averaging period and
temperature range, we calculated the mean, standard deviation, and variance of the period
average AER and of its natural logarithm. Note than the geometric mean equals e raised to the
power Mean log (AER) and the geometric standard deviation equals e raised to the power Std
Dev log (AER). The results are shown in Tables E-l (Approach A) and E-2 (Approach B).
B-277
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Table E-l. Distribution of AER averaged over K days and its logarithm. Groups defined by Approach A.
Temperature
(°C)
<=10
<= 10
<= 10
<=10
<= 10
<=10
<= 10
10-20
10-20
10-20
10-20
10-20
10-20
10-20
20-25
20-25
20-25
20-25
20-25
20-25
20-25
>25
>25
>25
>25
>25
>25
>25
K
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
Groups
35
30
28
28
24
24
29
48
55
51
50
53
49
34
32
28
27
17
17
17
14
9
11
12
23
23
23
17
Mean
AER
1.109
1.149
1.065
1.081
1.103
1.098
1.054
0.652
0.654
0.641
0.683
0.686
0.677
0.638
0.500
0.484
0.495
0.536
0.543
0.529
0.571
0.470
0.412
0.411
0.385
0.390
0.399
0.438
Mean
log(AER)
-0.066
-0.009
-0.088
-0.090
-0.082
-0.083
-0.109
-0.659
-0.598
-0.622
-0.564
-0.546
-0.533
-0.593
-1.005
-0.972
-0.933
-0.905
-0.905
-0.899
-0.889
-1.058
-1.123
-1.036
-1.044
-1.028
-1.010
-0.950
Std Dev
AER
0.741
0.689
0.663
0.690
0.754
0.753
0.704
0.417
0.411
0.416
0.440
0.419
0.379
0.343
0.528
0.509
0.491
0.623
0.672
0.608
0.745
0.423
0.314
0.243
0.176
0.175
0.193
0.248
Std Dev
log(AER)
0.568
0.542
0.546
0.584
0.598
0.589
0.556
0.791
0.607
0.603
0.619
0.596
0.544
0.555
0.760
0.623
0.604
0.652
0.649
0.617
0.683
0.857
0.742
0.582
0.429
0.425
0.435
0.506
Variance
AER
0.549
0.474
0.440
0.476
0.568
0.567
0.496
0.174
0.169
0.173
0.194
0.175
0.144
0.118
0.279
0.259
0.241
0.389
0.452
0.370
0.555
0.179
0.098
0.059
0.031
0.031
0.037
0.061
Variance
log(AER)
0.322
0.294
0.298
0.341
0.358
0.347
0.309
0.625
0.368
0.363
0.384
0.355
0.296
0.308
0.577
0.388
0.365
0.425
0.421
0.381
0.466
0.734
0.551
0.339
0.184
0.181
0.189
0.256
Using both approaches, Tables E-l and E-2 show that the mean values for the AER and its
logarithm are approximately constant for the same temperature range but different averaging
periods. This is expected if the daily AER values all have the same statistical distribution,
regardless of whether or not they are independent. More interesting is the observation that the
standard deviations and variances are also approximately constant for the same temperature
range but different averaging periods, except for the data at > 25 °C where the standard
deviations and variances tend to decrease as the length of the averaging period increases. If the
daily AER values were statistically independent, then the variance of an average over K days is
given by Var / K, where Var is the variance of a single daily AER value. Clearly this formula
does not apply. Since the variance is approximately constant for different values of K in the same
temperature range (except for the relatively limited data at > 25 °C), this shows that the daily
AER values are strongly correlated. Of course the correlation is not perfect, since otherwise the
AER for a given day would be identical to the AER for the next day, if the temperature range
were the same, which did not occur.
Table E-2. Distribution of AER averaged over K days and its logarithm. Groups defined by Approach B.
B-278
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Temperature
(°C)
<=10
<= 10
<=10
<= 10
<=10
10-20
10-20
10-20
10-20
10-20
10-20
10-20
20-25
20-25
20-25
20-25
>25
>25
>25
>25
>25
>25
>25
K
1
2
3
4
5
1
2
3
4
5
6
7
1
2
3
4
1
2
3
4
5
6
7
Groups
62
41
32
17
5
109
81
63
27
22
12
6
107
63
23
3
54
32
23
12
12
6
6
Mean
AER
1.125
1.059
1.104
1.292
1.534
0.778
0.702
0.684
0.650
0.629
0.614
0.720
0.514
0.511
0.577
1.308
0.488
0.486
0.427
0.401
0.410
0.341
0.346
Mean
log(AER)
-0.081
-0.063
-0.040
0.115
0.264
-0.482
-0.532
-0.540
-0.626
-0.660
-0.679
-0.587
-0.915
-0.930
-0.837
-0.484
-0.949
-0.900
-0.970
-1.029
-1.003
-1.164
-1.144
Std Dev
AER
0.832
0.595
0.643
0.768
1.087
0.579
0.451
0.409
0.414
0.417
0.418
0.529
0.518
0.584
0.641
1.810
0.448
0.351
0.218
0.207
0.207
0.129
0.125
Std Dev
log(AER)
0.610
0.481
0.530
0.531
0.608
0.721
0.603
0.580
0.663
0.654
0.638
0.816
0.639
0.603
0.659
1.479
0.626
0.595
0.506
0.509
0.507
0.510
0.494
Variance
AER
0.692
0.355
0.413
0.590
1.182
0.336
0.204
0.167
0.171
0.174
0.175
0.280
0.269
0.341
0.411
3.277
0.201
0.123
0.048
0.043
0.043
0.017
0.016
Variance
log(AER)
0.372
0.231
0.281
0.282
0.370
0.520
0.363
0.336
0.440
0.428
0.407
0.667
0.409
0.364
0.434
2.187
0.392
0.354
0.256
0.259
0.257
0.261
0.244
These arguments suggest that, based on the RTF Panel study data, to a reasonable
approximation, the distribution of an AER measurement does not depend upon the length of the
averaging period for the measurement, although it does depend upon the average temperature.
This supports the methodology used in the Cohen, Mallya, and Rosenbaum, 2005, analyses that
did not take into account the length of the averaging period.
The above argument suggests that the assumption that daily AER values are statistically
independent is not justified. Statistical modeling of the correlation structure between consecutive
daily AER values is not easy because of the problem of accounting for temperature effects, since
temperatures vary from day to day. In the next section we present some statistical models of the
daily AER values from the RTF Panel Study.
Statistical models of AER auto-correlations from the RTF Panel Study
We used the MIXED procedure from SAS to fit several mixed models with fixed effects and
random effects to the daily values of AER and log(AER). The fixed effects are the population
average values of AER or log(AER), and are assumed to depend upon the temperature range.
The random effects have expected values of zero and define the correlations between pairs of
measurements from the same Group, where the Groups are defined either using Approach A or
Approach B above. As described above, a Group is a period of up to 14 consecutive days of
measurements at the same house. For these mixed model analyses we included periods with one
or more missing days. For all the statistical models, we assume that AER values in different
B-279
-------
Groups are statistically independent, which implies that data from different houses or in different
seasons are independent.
The main statistical model for AER was defined as follows:
AER = Mean(Temp Range) + A(Group, Temp Range)
+ B(Group, Day Number) + Error(Group, Day Number)
Mean(Temp Range) is the fixed effects term. There is a different overall mean value for each of
the four temperature ranges.
A(Group, Temp Range) is the random effect of temperature. For each Group, four error terms are
independently drawn from four different normal distributions, one for each temperature range.
These normal distributions all have mean zero, but may have different variances. Because of this
term, there is a correlation between AER values measured in the same Group of days for a pair
of days in the same temperature range.
B(Group, Day Number) is the repeated effects term. The day number is defined so that the first
day of a Group has day number 1, the next calendar day has day number 2, and so on. In some
cases AER's were missing for some of the day numbers. B(Group, Day Number) is a normally
distributed error term for each AER measurement. The expected value (i.e., the mean) is zero.
The variance is V. The covariance between B(Group g, day i) and B(Group h, day j) is zero for
days in different Groups g and h, and equals V x exp(d x i-j|) for days in the same Group. V and
d are fitted parameters. This is a first order auto-regressive model. Because of this term, there is a
correlation between AER values measured in the same Group of days, and the correlation
decreases if the days are further apart.
Finally, Error(Group, Day Number) is the Residual Error term. There is one such error term for
every AER measurement, and all these terms are independently drawn from the same normal
distribution, with mean 0 and variance W.
We can summarize this rather complicated model as follows. The AER measurements are
uncorrelated if they are from different Groups. If they are in the same Group, they have a
correlation that decreases with the day difference, and they have an additional correlation if they
are in the same temperature range.
Probably the most interesting parameter for these models is the parameter d, which defines the
strength of the auto-correlation between pairs of days. This parameter d lies between -1 (perfect
negative correlation) and +1 (perfect positive correlation) although values exactly equal to +1 or
-1 are impossible for a stationary model. Negative values of d would be unusual since they
would imply a tendency for a high AER day to be followed by a low AER day, and vice versa.
The case d=0 is for no auto-correlation.
Table E-3 gives the fitted values of d for various versions of the model. The variants considered
were:
• model AER or log(AER)
• include or exclude the term A(Group, Temp Range) (the "random" statement in the SAS
code)
B-280
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• use Approach A or Approach B to define the Groups
Since Approach B forces the temperature ranges to be the same for very day in a Group, the
random temperature effect term is difficult to distinguish from the other terms. Therefore this
term was not fitted using Approach B.
Table E-3. Autoregressive parameter d for various statistical models for the RTF Panel
Study AERs.
Dependent variable
AER
AER
AER
Log(AER)
Log(AER)
Log(AER)
Include A(Group,
Temp Range)?
Yes
No
No
Yes
No
No
Approach
A
A
B
A
A
B
d
0.80
0.82
0.80
0.87
0.87
0.85
In all cases, the parameter d is 0.8 or above, showing the very strong correlations between AER
measurements on consecutive or almost consecutive days.
Impact of accounting for daily average AER auto-correlation
In the current version of the APEX model, there are several options for stratification of time
periods with respect to AER distributions, and for when to re-sample from a distribution for a
given stratum. The options selected for this current set of simulations resulted in a uniform AER
for each 24-hour period and re-sampling of the 24-hour AER for each simulated day. This re-
sampling for each simulated day implies that the simulated AERs on consecutive days in the
same microenvironment are statistically independent. Although we have not identified sufficient
data to test the assumption of uniform AERs throughout a 24-hour period, the analyses described
in this memorandum suggest that AERs on consecutive days are highly correlated.
Therefore, in order to determine if bias was introduced into the APEX estimates with respect to
either the magnitudes or variability of exposure concentrations by implicitly assuming
uncorrelated air exchange rates, we re-ran the 2002 base case simulations using the option to not
re-sample the AERs. For this option APEX selects a single AER for each
microenvironment/stratum combination and uses it throughout the simulation.
The comparison of the two scenarios indicates little difference in APEX predictions, probably
because the AERs pertain only to indoor microenvironments and for the base cases most
exposure to elevated concentrations occurs in the "other outdoors" microenvironment. Figures E-
1 and E-2 below present the comparison for exceedances of 8-hour average concentration during
moderate exertion for active person in Boston and Houston, respectively.
Figure E-l
B-281
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200
Air Exchange Rate Resampling Sensitivity:
Days/Person with Exceedances of
8-Hour Average Exposure Concentration During Moderate Exertion
-Active Persons, Boston, 2002-
20 40 60
Cumulative Percentile
80
100
-base-. 01
-rsoff- .01
base -.02
rsoff- .02
-base -.03
-rsoff- .03
-base - .04
-rsoff- .04
base -.05
rsoff- .05
Figure E-2
Air Exchange Rate Resampling Sensitivity:
Days/Person with Exceedances of
8-Hour Average Exposure Concentration During Moderate Exertion
-Active Persons, Houston, 2002-
re
20 40 60
Cumulative Percentile
80
100
-base-. 01
-rsoff-.01
base -.02
rsoff-.02
-base -.03
-rsoff- .03
-base - .04
-rsoff-.04
base -.05
rsoff-.05
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B-283
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Appendix C
Nitrogen Dioxide Health Risk Assessment for
Atlanta, GA
November 2008
Prepared for
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
Prepared by
Ellen Post
Jin Huang
Andreas Maier
Hardee Mahoney
Abt Associates Inc.
Bethesda, MD
Work funded through
Contract No. EP-W-05-022
Work Assignments 2-62 & 3-62
Harvey Richmond, Work Assignment Manager
Catherine Turner, Project Officer
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DISCLAIMER
This report is being furnished to the U.S. Environmental Protection Agency
(EPA) by Abt Associates Inc. in partial fulfillment of Contract No. EP-W-05-022, Work
Assignments 2-62 and 3-62. Any opinions, findings, conclusions, or recommendations
are those of the authors and do not necessarily reflect the views of the EPA or Abt
Associates. Any questions concerning this document should be addressed to Harvey
Richmond, U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards, C504-06, Research Triangle Park, North Carolina 27711 (email:
richmond.harvey@epa.gov).
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Table of Contents
1 INTRODUCTION 1-1
2 PRELIMINARY CONSIDERATIONS 2-1
2.1 The Broad Empirical Basis for a Relationship Between NC>2 and Adverse
Health Effects 2-1
2.2 Basic Structure of the Risk Assessment 2-1
2.3 Air Quality Considerations 2-2
3 METHODS 3-1
3.1 General approach 3-1
3.2 Selection of health endpoint(s) 3-5
3.3 Selection of urban area(s) and epidemiological studies 3-5
3.4 Selection of concentration-response functions 3-7
3.5 Air quality considerations 3-8
3.6 Baseline health effects incidence 3-9
3.7 Summary of determinants of theNO2 risk assessment 3-10
3.8 Addressing uncertainty and variability 3-10
3.8.1 Concentration-response functions 3-16
3.8.1.1 Uncertainty associated with the appropriate model form 3-16
3.8.1.2 Uncertainty associated with the estimated concentration-
response functions in the study location 3-17
3.8.1.3 Applicability of concentration-response functions in different
locations and/or time periods 3-19
3.8.1.4 Extrapolation beyond observed air quality levels 3-19
3.8.2 The air quality data 3-20
3.8.2.1 Adequacy of NC^air quality data 3-20
3.8.2.2 Simulation of reductions in NC>2 concentrations to just meet
the current or an alternative standard 3-21
3.8.3 Baseline health effects incidence 3-21
3.8.3.1 Quality of incidence data 3-21
3.8.3.2 Lack of daily health effects incidences 3-22
4 RESULTS 4-1
5 REFERENCES 5-1
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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 3-8
Table 3-2. Key Uncertainties in theNOi Risk Assessment 3-13
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 4-2
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 4-3
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 4-4
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 NOi Concentrations 4-5
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 4-6
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 4-7
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 4-8
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 NOi Concentrations 4-9
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 4-10
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List of Figures
Figure 3-1. Major Components of NOi Health Risk Assessment Based on
Epidemiology Studies 3-2
Figure 4-1. Incidence of Respiratory-Related Emergency Department Visits in
Atlanta, GA Under Different Air Quality Scenarios, Based on Adjusting
2005, 2006, and 2007 NO2 Concentrations 4-11
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Nitrogen Dioxide Health Risk Assessment for Atlanta, GA
1 INTRODUCTION
The U.S. Environmental Protection Agency (EPA) is presently conducting a
review of the national ambient air quality standards (NAAQS) for nitrogen dioxide
(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 NO2 NAAQS review is presented in the
"Integrated Review Plan for the Primary National Ambient Air Quality Standard for
Nitrogen Dioxide" (U.S. EPA, 2007a). The plan discusses the preparation of two key
components in the NAAQS review process: an Integrated Science Assessment (ISA) and
risk/exposure assessments. The ISA critically evaluates and integrates scientific
information on the health effects associated with exposure to oxides of nitrogen (NOx) in
the ambient air. The risk/exposure assessments develop qualitative characterization and
quantitative estimates, where judged appropriate, of human exposure and health risk and
related variability and uncertainties, drawing upon the information summarized in the
ISA.
In early March 2008, EPA's National Center for Environmental Assessment
released a second draft of the "Integrated Science Assessment for Oxides of Nitrogen -
Health Criteria (Second External Review Draft)," henceforth referred to as the draft ISA
(U.S. EPA, 2008a). EPA's Office of Air Quality Planning and Standards (OAQPS) in
early April released a first draft of its "Risk and Exposure Assessment to Support the
Review of the NO2 Primary National Ambient Air Quality Standard," henceforth referred
to as the 1st draft REA (U.S. EPA, 2008b). Both of these documents were reviewed by
the CASAC NO2 Panel on May 1-2, 2008.
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."
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As a result of the May 2008 CAS AC NC>2 Panel review and in response to advice
offered by the CASAC Panel, OAQPS decided to expand the health risk assessment to
include a quantitative assessment of 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 paniculate (e.g., nitrate) species. As in past
NAAQS reviews, NC>2 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 NC>2 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 63 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.
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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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2 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.
2.1 The Broad Empirical Basis for a Relationship Between NOi 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).
2.2 Basic Structure of the Risk Assessment
The general approach used in this risk assessment, as in the risk assessment that was
part of the recent PM NAAQS review, relies upon C-R functions that have been estimated in
epidemiological studies. Since these studies estimate C-R functions using ambient air quality
data from fixed-site, population-oriented monitors, the appropriate application of these
functions in 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
quality simulated to reflect attainment of the current and alternative NC>2 ambient standards.
4 The current NO2 standard is met in all locations in the United States. The risks associated with just meeting
the current standard are therefore greater than the risks associated with "as is" NO2 concentrations, which are
lower than NO2 concentrations simulated to just meet the current standard.
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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 NC>2 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 NC>2 risk assessment are discussed in section 3 below. The risk
assessment was implemented within a new probabilistic version of TRIM.Risk, the
component of EPA's Total Risk Integrated Methodology (TRIM) model that estimates human
health risks.5
2.3 Air Quality Considerations
The risk assessment includes risk estimates 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 NC>2 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
NC>2 standards, it is necessary to estimate the distribution of hourly NC>2 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 NC>2 concentrations that would occur if the current
standard were just met in the risk assessment location. Estimated design values were used to
determine the (upward) adjustment necessary to just meet the current NO2 standard. The
adjustment was then applied to each year of data (2006 and 2007) to estimate risks in each of
these individual years. For alternative 1-hour daily maximum standards, staff specified the
form as being the 3-year average of the 98th (or 99th) percentile of the daily maximum 1-hour
concentrations. Thus, the three-year period including 2005 to 2007 was used for analyses
involving alternative 1-hour standards. Estimated design values were used to determine the
5 TRIM.Risk was most recently applied to EPA's O3 health risk assessment. A User's Guide for the Application
of TRIM.Risk to the O3 health risk assessment (Abt Associates, 2007b) is available online at:
http://epa.gov/ttn/fera/data/trim/trimrisk ozone ra userguide 8-6-07.pdf.
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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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3 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).
3.1 General approach
As in the PM risk assessment (Abt Associates, 2005) and part of the recently
completed Os 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
NO2 air quality for a specific urban area with C-R functions derived from
epidemiological studies and baseline incidence data for a specific health endpoint to
derive estimates of the incidence of the health endpoint attributable to ambient 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 NC>2 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 3-1. 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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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,
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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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3.2 Selection of health endpoint(s)
As discussed in section 3.1.6 of the final ISA, many studies have observed positive
associations between ambient NO2 concentrations and ED visits and hospitalizations for
all respiratory diseases and asthma, and these associations appear to be generally robust
and independent of the effects of ambient particles or gaseous co-pollutants. Noting that
exposure to NO2 has been found to result in host defense and immune system changes,
airway inflammation, and airway responsiveness, the final ISA concludes that "while not
providing specific mechanistic data linking exposure to ambient NO2 and respiratory
hospitalization or ED visits for asthma, these findings provide plausibility and coherence
for such a relationship" (section 3.1.6.5, p. 3-41).
In summarizing the evidence for a relationship between short-term exposure to NO2
and respiratory health effects, the final ISA notes that "the body of evidence from
epidemiologic studies has grown substantially since the 1993 AQCD and provided
scientific evidence that short-term exposure to NO2 is associated with a broad range of
respiratory morbidity effects, including altered lung host defense, inflammation, airway
hyperresponsiveness, respiratory symptoms, lung function decrements, and ED visits and
hospital admissions for respiratory diseases" (section 3.1.7, p. 3-41). For this risk
assessment, we are focusing on respiratory ED visits.
3.3 Selection of urban area(s) and epidemiological studies
Several objectives were considered in selecting potential urban areas for which to
conduct the risk assessment. An urban area was considered if:
• it had sufficient air quality data for the 3-year period under consideration;
• it was a location where at least one C-R function for the selected health
endpoint had been estimated by a study that satisfied the study selection
criteria; and
• it had available relatively recent location-specific baseline incidence data,
specific to International Classification of Disease (ICD) codes, or an
equivalent illness classification system.
C-R functions for respiratory ED visits have been estimated in two urban areas in
the United States - Atlanta and New York City. The selection of an urban area to include
in the risk assessment depends in part on the decision of which epidemiological studies to
use. An epidemiological study was considered if:
• it was a published, peer-reviewed study that had been evaluated in the final ISA
for the pollutant of interest and judged adequate by EPA staff for purposes of
inclusion in the risk assessment based on that evaluation;
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• it directly measured, rather than estimated, the pollutant of interest on a reasonable
proportion of the days in the study; and
• it either did not rely on Generalized Additive Models (GAMs) using the S-Plus
software to estimate C-R functions or it appropriately re-estimated these functions
using revised methods.7
• it preferably included both single- and multi-pollutant models.
Six U.S. studies focused on ED visits and/or hospital admissions. Three of these
(Peel et al., 2005 and Tolbert et al., 2007 in Atlanta; Ito et al., 2007 in New York City)
evaluated associations with NO2 using multi-pollutant models as well as single-pollutant
models. Tolbert et al. (2007), which updated Peel et al. (2005), evaluated ED visits
among all ages in Atlanta, GA during the period of 1993 to 2004. Using single pollutant
models, the authors reported a 2% (95% CI: 1%, 2.9%) increase in respiratory ED visits
associated with a 23-ppb increase in 1-h maximum NO2 levels. In a two-pollutant model
with CO, NO2 was positive and still statistically significant (RR = 1.017, 95% CI =1.006,
1.029). In two-pollutant models with PMio and 63, and in a three-pollutant model with
both PMio and 63, 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, 2008a).
The Atlanta study (Peel et al., 2005 and Tolbert et al., 2007) spanned 12 years,
and collected NO2 monitor data on 4,351 out of a possible 4,384 days - over 99 percent
of the days. It satisfies all of the criteria listed above for study selection.
In the study by Ito and colleagues, investigators evaluated ED visits for asthma in
New York City during the years 1999 to 2002. The authors found a 12 % (95% CI: 7%,
15%) increase in risk per 20 ppb increase in 24-hour ambient NO2. Risk estimates were
robust and remained statistically significant in multi-pollutant models that included
PM2.5, O3, CO, and SO2.
Due to time and resource constraints, EPA staff selected the Atlanta area and the
study by Tolbert et al. to conduct a focused risk assessment for ED visits. Considerations
that influenced this choice were the longer time period and more comprehensive coverage
of emergency departments in the Tolbert et al. study, the ready availability of baseline
incidence data from the authors of this study, and the EPA 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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3.4 Selection of concentration-response functions
Studies often report more than one estimated C-R function for the same location
and health endpoint. Sometimes models including different sets of co-pollutants are
estimated in a study; sometimes different lags are estimated.
Tolbert et al. (2007) estimated C-R functions in which NC>2 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 (PMi0, O3, 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 NC>2 in single pollutant models, risks attributed to NC>2 might be overestimated where
C-R functions are based on single pollutant models. However, if co-pollutants are highly
correlated with NC>2, their inclusion in an NC>2 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 NC>2 exposure studies may not capture the possible impact of long-term
exposures to NC>2 is not known. A number of epidemiologic studies have examined the
effects of long-term exposure to NC>2 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 NC>2 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
NC>2 exposures, if any, and this uncertainty should be kept in mind as one considers the
results from the short-term exposure NC>2 risk assessment.
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3.5 Air quality considerations
Air quality considerations are discussed briefly in section 2 of this document and
in chapter 6 of the draft REA. Here we describe those air quality considerations that are
directly relevant to the estimation of health risks in the NO2 risk assessment.
In the first part of the risk assessment, we estimate the incidence of the health
effect associated with "as is" levels of NO2 (or equivalently, the change in health effect
incidence, 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 3-1. Mean and 98th and 99th Percentiles of the Distributions of 1-Hour Daily Maximum NO2
Concentrations (in ppm) at the Georgia Tech Monitor: 2005,2006, and 2007
Year
2005
2006
2007
Mean
0.0351
0.0364
0.0327
98th Percentile
0.0764
0.0660
0.0684
99th Percentile
0.0794
0.0694
0.0780
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Because Tolbert et al. (2007) estimated a relationship between daily respiratory-
related ED visits and the 3-day moving average (i.e., NC>2 levels on the same day, the
previous day, and the day before that) of daily 1-hour maximum NC>2 concentrations, we
calculated daily 1-hour maximum NO2 concentrations at the monitor. Because our lower
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.
3.6 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, 2008a). The study notes that there are 42 hospitals with EDs in the
20-county Atlanta MSA. Of these, 41 were able to provide incidence data for at least part
of the study period (1993 - 2004). For purposes of the 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, 2008a).8
There were 38 hospitals operating in the Atlanta MSA in 2004, of which 37
reported data. The study authors estimate that the missing respiratory ED visits, from the
single hospital that declined to report data, account for only 2.3 percent of the total
number for 2004, so that the 2004 baseline incidence (121,818) includes an estimated
97.7 percent of the respiratory ED visits in Atlanta that year (Tolbert, 2008b). Thus,
although the understatement of baseline incidence will result in a downward bias in our
estimates of NO2-related risk, that bias will be very small.
To check on the variability of respiratory ED visits across the years, the study authors provided a table of
ED visits, including respiratory ED visits in particular, for years 2002-2004 among the 33 hospitals that
contributed data each year. The average annual number of respiratory ED visits in those 33 hospitals
during the three-year period (2002 - 2004) was 124,979. The number for those 33 hospitals in 2004 was
114,475, or about 92 percent of the 3-year average (Tolbert, 2008b).
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The specific definition of "respiratory-related" ED visits used in Tolbert et al.
(2007) included ED visits with the following respiratory illnesses as the primary
diagnosis (specified by ICD-9 diagnostic codes): asthma (493, 786.07, and 786.09),
COPD (491, 492, and 496), upper respiratory illness (460 - 465, 460.0, and 477),
pneumonia (480 - 486), and bronchiolitis (466.1, 466.11, and 466.19). The baseline
incidence given above - 121,818 - is thus a count of all ED visits with one of these 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.
3.7 Summary of determinants of the NOi risk assessment
The determinants of the NC>2 risk assessment can be summarized as follows:
• Health endpoint: respiratory ED visits among all ages
• Assessment location: Atlanta MSA
• Epidemiological study: Tolbert et al. (2007)
• C-R functions:
o a single-pollutant C-R function,
o two-pollutant C-R functions (with CO, PMio, and 63), 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).
• 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.
• Baseline incidence: an estimate of the baseline incidence of respiratory ED visits
in Atlanta, GA in 2004 (the most recent year in the study) was obtained (Tolbert,
2008a). The estimate, 121,818 respiratory ED visits in 2004, was based on 37
hospitals that reported data (out of 38 hospitals operating) that year (Tolbert,
2008b).
3.8 Addressing uncertainty and variability
Any estimation of risk associated with "as is" 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,
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sometimes quantitatively. For example, the statistical uncertainty surrounding the
estimated NO2 coefficients in the C-R functions is reflected in confidence intervals
provided for the risk estimates.
Variability refers to the heterogeneity in a population or parameter. Even if there
is no uncertainty surrounding inputs to the analysis, there may still be variability. For
example, there may be variability among C-R functions describing the relationship
between NO2 and respiratory ED visits across urban areas. This variability does not imply
uncertainty about the C-R function in any of the urban areas, but only that these functions
are different in the different locations, reflecting differences in the populations and/or
other factors that may affect the relationship between NO2 and respiratory ED visits. In
general, it is possible to have uncertainty but no variability (if, for instance, there is a
single parameter whose value is uncertain) or variability but little or no uncertainty (for
example, people's heights vary considerably but can be accurately measured with little
uncertainty).
The NO2 risk assessment addresses variability-related concerns by using location-
specific inputs (i.e., location-specific C-R function, baseline incidence data and air
quality data). Because the NO2 risk assessment focuses on only a single urban area, it
does not attempt to portray a larger picture of risk than is relevant to the selected
assessment area.
Temporal variability is more difficult to address, because the risk assessment
focuses on some unspecified time in the future. To minimize the degree to which values
of inputs to the analysis may be different from the values of those inputs at that
unspecified time, we used recent input data - for example, year 2005 through 2007 air
quality data and recent (2004) baseline incidence data. However, future changes in
inputs have not been predicted (e.g., future baseline incidences). To address the impact
of variability in NO2 concentrations from one year to another, we carried out the risk
assessment for the years in the three-year period under consideration - 2005, 2006, and
2007 - separately.
A number of important sources of uncertainty in the NO2 risk assessment are
addressed where possible. The following are among the major sources of uncertainty:
• Uncertainties related to estimating the C-R functions, including
o uncertainty about the extent to which the association between NO2 and the
health endpoint actually reflects a causal relationship.
o uncertainty surrounding estimates of NO2 coefficients in the C-R functions
used in the analyses.
o uncertainty about the specification of the model (including the shape of
the C-R relationship), particularly whether or not there is a threshold
below which no response occurs.
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o uncertainty related to the transferability of 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
• 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 NO2 standards.
• Uncertainties associated with use of baseline health effects incidence information
- e.g., the extent to which the baseline incidence estimate is downward biased by
the lack of data for 6 of the 42 hospitals in the Atlanta MSA.
The specific sources of uncertainty in the NO2 risk assessment are described in detail
below and are summarized in Table 3-2.
9 Uncertainty about transferability of C-R functions often results not only from differences between the
study and risk assessment time periods, but also between the study and risk assessment locations. Because
the NO2 risk assessment is being conducted in the same location as the study from which the C-R functions
were obtained, this is not a problem here.
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Table 3-2. Key Uncertainties in the NO2 Risk Assessment
Uncertainty
Level of
Uncertainty
Direction of Bias
Comments
Causality
low
Upward, if
causality
assumption isn't
true.
Statistical association does not prove causation. However, the risk
assessment considers only a health endpoint for which the overall weight of
the evidence supports the assumption that NC>2 is likely causally related
based on the totality of the health effects evidence. If the assumption of a
causal relationship is incorrect, then a positive estimated coefficient in the C-
R function would be upward biased, since it is greater than zero.
Empirically
estimated C-R
relations
medium
No obvious bias, if
C-R model is
correctly specified.
Otherwise,
unclear.
Because C-R functions are empirically estimated, there is uncertainty
surrounding these estimates. If the model is correctly specified, there is no
bias in the coefficient estimates. If the model is mis-specified, there can be
bias. Omitted confounding variables, for example, could cause upward bias
in the estimated NO2 coefficients if the omitted variables are positively
correlated with both NO2 and the health effect. 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.
Functional form of
C-R relation
medium
Unclear
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. If the "true" functional relationship between
NO2 and a health effect is different from the one specified, there can be bias
in the resulting estimates of effect. The direction of the bias will depend on
how the specified model differs from reality. For example, if the specified
C-R function is log-linear down to 0 ug/m3, but there is actually a threshold
in the true relationship, then the effect will be overstated by the model
corresponding to levels of NO2 below the threshold.
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Uncertainty
Level of
Uncertainty
Direction of Bias
Comments
Lag structure of C-
R relation
low
Downward, if
important lags are
omitted (e.g., if C-
R function
includes a single
lag, while "truth"
is a distributed
lag).
Unclear, if C-R
function includes a
single lag, but it's
the wrong lag.
The actual lag structure for short-term NO2 exposures is uncertain. Omitted
lags could cause an underestimation in the predicted incidence associated
with a given reduction in NO2 concentrations. The level of uncertainty (in
the sense of the impact of the uncertainty) may depend on the situation. For
example, suppose the health effect is actually affected largely by same-day
NO2 concentrations but the model (incorrectly) includes only a 1-day lag. In
this case, the impact on the outcome of the analysis may be minimal if, as is
likely, there is a high degree of autocorrelation in NO2 concentrations from
day to day (so that yesterday's NO2 level would act as a good proxy for
today's NO2 level). If, on the other hand, there is a distributed lag - e.g., if
risk of the health effect on day t depends on NO2 concentrations for the
entire week leading up to day t - and the model includes only a single lag,
then the understatement of effect could be substantial.
Transferability of
C-R relations
low
No obvious bias.
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.
Extrapolation of C-
R relations beyond
the range of
observed NO2 data
low
Unclear.
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.
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Uncertainty
Level of
Uncertainty
Direction of Bias
Comments
Adequacy of
ambient NO2
monitors as
surrogate for
population
exposure
low
No obvious bias.
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.
Adjustment of air
quality distributions
to simulate just
meeting current and
alternative NO2
standards.
medium
Could be in either
direction.
The pattern and extent of daily reductions in NO2 concentrations that would
result if the current NO2 standard or alternative NO2 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 NO2 standard that would
depend on the nature of future growth in NO2 emissions, if any, and future
air quality control strategies.
Baseline health
effects data
low
Small downward
bias.
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 NO2. This source of uncertainty is relatively minor in the NO2
risk assessment, however, because a baseline incidence estimate has been
obtained from the study authors for the assessment area. There is a small
downward bias to this estimate, because it is based on 37 of the 38 hospitals
operating in the Atlanta study area in 2004; the study authors estimate that
respiratory ED visits at those 37 hospitals comprise about 98% of the total
for that year (Tolbert, 2008b). The estimated baseline incidence for
respiratory ED visits in 2004 also appears to be roughly 8% lower than the
average baseline incidence observed during the period from 2002 to 2004.
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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.
3.8.1 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.
3.8.1.1 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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3.8.1.2 Uncertainty associated with the estimated concentration-response functions
in the study location
The uncertainty associated with an estimate of the NC>2 coefficient in a C-R
function reported by a study depends on the sample size and the study design. The final
ISA has evaluated the substantial body of NC>2 epidemiological studies. In general,
critical considerations in evaluating the design of an epidemiological study include the
adequacy of the measurement of ambient NC>2, the adequacy of the health effects
incidence data, and the consideration of potentially important health determinants and
potential confounders and effect modifiers such as:
• other pollutants;
• weather variables (e.g., temperature extremes);
• exposure to other health risks, such as smoking and occupational exposure; and
• demographic characteristics, including age, sex, socioeconomic status, and access to
medical care.
The possible confounding effect of co-pollutants, including other criteria air
pollutants, has often been noted as a problem in air pollutant risk assessments,
particularly when these other pollutants are highly correlated with the pollutant of
interest. As noted above, if other pollutants are included in the model and are highly
correlated with NC>2, this will inflate the variance of the estimators of the pollutant
coefficients, making them more unstable. However, if other pollutants are causally
related to the health effect, are correlated with NC>2, and are omitted from the model, then
the resulting single-pollutant model will falsely attribute to NC>2 some of the effect of
these other pollutants. 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. Given the advantages and disadvantages of both single- and multi-
pollutant models, we report risk estimates based on both the single- and multi-pollutant
models from Tolbert et al. (2007). The issue of possible confounding by co-pollutants is
discussed in more detail in the final ISA.
The main reason to use a multi-pollutant model is to avoid the potential upward
bias in the NC>2 coefficient that may result if other pollutants that are causally related to
the health effect are omitted from the model. It might be argued that if all the pollutants
in a multi-pollutant model are causally related to the health effect we should consider the
changes that would occur in all of the pollutants, rather than only the changes in NC>2, as
a result of an NC>2 standard being implemented, since considering only the changes in
NC>2 will tend to understate the full benefit of just meeting an alternative standard. If one
were evaluating total benefits to be derived from an implementation plan for an area, then
the total reduction in health effects resulting from reduction in levels for all of the
pollutants would be of interest. However, for the purposes of evaluating the adequacy of
the current and alternative NC>2 NAAQS, considering the health gains associated with
reductions attributable to lower levels of not just NC>2, but other pollutants such as PM2.5
and 63, would effectively result in double counting. This double counting would occur
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because the health gains associated with reductions in these other pollutants should
already have been taken into account in assessments conducted to inform decisions on
NAAQS for the other pollutants. Thus, when co-pollutant models are applied in the
current risk assessment, only the risks resulting from reductions in NC>2 ambient
concentrations are considered; risks that might result from reduction of other pollutants
are not considered in this analysis.
One of the criteria for selecting studies addresses the adequacy of the
measurement of ambient NC>2. This criterion was that NC>2 was directly measured, rather
than estimated, on a reasonable proportion of the days in the study. This criterion was
designed to minimize error in the estimated NC>2 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 NC>2 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 NC>2 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 NC>2 is not the causal agent,
however, then there is a problem of confounding co-pollutants or other factors, so that
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
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produce overestimates of the NO2 effect, if some of the effects of other pollutants
(omitted from the model) are falsely attributed to NO2. Statistical estimates of an NO2
effect based on a multi-pollutant model can be more uncertain, and even statistically
insignificant, if the co-pollutants included in the model are highly correlated with NO2.
As a result of these considerations, we report risk estimates based on both the single-
pollutant and multi-pollutant models from Tolbert et al. (2007).
3.8.1.3 Applicability of concentration-response functions in different locations
and/or time periods
The relationship between ambient NO2 concentration and the incidence of a given
health endpoint in the population (the population health response) depends on (1) the
relationship between ambient NO2 concentration and personal exposure to ambient
generated NO2 and (2) the relationship between personal exposure to ambient-generated
NO2 and the population health response. Both of these are likely to vary to some degree
from one location and/or time period to another. The relationship between ambient NO2
concentration and personal exposure to ambient-generated NO2 will depend on patterns
of behavior, such as the amount of time spent outdoors, as well as on factors affecting the
extent to which ambient-generated NO2 infiltrates into indoor environments. The
relationship between personal exposure to ambient-generated NO2 and the population
health response will depend on the population exposed. Exposed populations may differ
from one location and/or time period to another in characteristics that are likely to affect
their susceptibility to NO2 air pollution. For instance, people with preexisting conditions
such as asthma are probably more susceptible to the adverse effects of exposure to NO2,
and populations may vary from one location and/or time period to another in the
prevalence of specific diseases. Also, some age groups may be more susceptible than
others, and population age distributions may also vary both spatially and temporally. In
the NO2 risk assessment we avoid the uncertainty associated with inter-locational
differences, however, by using C-R functions that were estimated in the assessment area.
In addition, although we cannot completely eliminate possible temporal changes, we
minimize the uncertainty associated with such changes by using relatively recent baseline
incidence data.
3.8.1.4 Extrapolation beyond observed air quality levels
Although a C-R function describes the relationship between ambient NO2 and a
given health endpoint for all possible NO2 levels (potentially down to zero), the
estimation of a C-R function is based on real ambient NO2 values that are limited to the
range of NO2 concentrations in the location in which the study was conducted. Thus,
uncertainty in the shape of the estimated C-R function increases considerably outside the
range of NO2 concentrations observed in the study.
Because we are interested in the effects of NO2 down to 0 ppm, the NO2 risk
assessment assumes that the estimated C-R functions adequately represent the true C-R
relationship down to 0 ppm in the assessment location. However, although the observed
NO2 concentrations in Tolbert et al. (2007) did not go down to 0 ppm, the study authors
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reported the minimum 1-hour NC>2 level observed in their study to be 1 ppb (or 0.001
ppm) (and the maximum to be 181 ppb), so the uncertainty resulting from extrapolation
to levels below those air quality levels observed in the study should be minimal.
The C-R relationship may also be less certain towards the upper end of the
concentration range being considered in a risk assessment, particularly if the NC>2
concentrations in the assessment location/time period exceed the NC>2 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 NC>2 concentrations typically
observed in epidemiological studies, it may not be log-linear over the entire range of NC>2
levels at the location considered in the NC>2 risk assessment. However, because the study
was carried out in the risk assessment location and is relatively recent, the uncertainty
resulting from extrapolation to levels above those air quality levels observed in the study
should similarly be minimal.
3.8.2 The air quality data
3.8.2.1 Adequacy of NOi air quality data
Ideally, the measurement of average hourly ambient NC>2 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 NC>2
concentrations in the assessment location as well. If, however, the measurement of
average hourly ambient NC>2 concentrations in the study location is biased, unbiased risk
predictions in the assessment location are still possible if the measurement of average
hourly ambient NC>2 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 NC>2 monitor as was used in Tolbert et al. (2007), the estimates of risk should
avoid any bias as a result of the monitor estimates of average hourly ambient NC>2
concentrations in the risk assessment location.
Another potential source of uncertainty is missing air quality data. Although NC>2
concentrations were not available for all hours of the 3-year period chosen for the NC>2
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 NC>2 levels in the available data are similar to ambient NC>2 levels in
those other years. A substantial difference between NC>2 levels in the years used in the
risk assessment and NC>2 levels in the other years could imply a substantial difference in
predicted incidences of health effects.
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3.8.2.2 Simulation of reductions in NO2 concentrations to just meet the current or
an alternative standard
The pattern of hourly NO2 concentrations that would result if the current NO2
standard or an alternative standard were just met in the assessment location is, of course,
not known. This therefore adds uncertainty to estimates of risk when NO2 concentrations
just meet a specified standard.
Although the health risk assessment uses air quality data from three years, 2005,
2006, and 2007, it simulates just attaining a standard in each year separately, since we are
estimating annual reduced health risks. Design values based on the most recent 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.
3.8.3 Baseline health effects incidence
The C-R functions used in the NO2 risk assessment are log-linear (see equation 3-
1 in section 3.1). Given this functional form, the percent change in incidence of a health
effect corresponding to a change in NO2 depends only on the change in NO2 levels (and
not the actual value of either the initial or final NO2 concentration). This percent change
is multiplied by a baseline incidence, 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.
3.8.3.1 Quality of incidence data
As noted in section 3.7 above, we obtained an estimate of the baseline incidence
of respiratory ED visits in Atlanta, GA (Tolbert, 2008a). There are 42 hospitals with EDs
in the 20-county Atlanta MSA, but not all 42 contributed incidence data in all of the years
of the Tolbert study (1993 - 2004). We used the most recent year of the study (2004),
which had an estimate of baseline incidence of respiratory ED visits in Atlanta based on
data from 37 of the 38 hospitals operating in the Atlanta study area in that year. The
study authors estimate that respiratory ED visits at those 37 hospitals comprise about 98
percent of the total for that year (Tolbert, 2008b). The estimate of baseline incidence in
2004, which is used as the estimate of baseline incidence in the NO2 risk assessment for
2005 - 2007, is thus a slight underestimate, resulting in a similarly slight downward bias
in the estimates of NO2-related respiratory ED visits.
A minor uncertainty surrounding hospital or ED visit baseline incidence estimates
sometimes arises if these estimates are based on the reporting of hospitals within an
assessment area. Hospitals report the numbers of ICD code-specific discharges in a given
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year. If people from outside the assessment area use these hospitals or EDs, and/or if
residents of the assessment area use hospitals or EDs outside the assessment area, these
rates will not accurately reflect the numbers of residents of the assessment area who were
admitted to the hospital or ED for specific illnesses during the year, the rates that are
desired for the risk assessment. This problem is partially avoided in Tolbert et al. (2007)
because only residents of the Atlanta MSA, determined by residential zip code at the time
of the ED visit, were included in the study. To the extent that residents of the Atlanta
MSA visited EDs outside the Atlanta MSA, this would tend to downward bias the
estimates of 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 uncertainly.
3.8.3.2 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.
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4 RESULTS
Results are expressed as (1) incidence of respiratory-related ED visits, (2)
incidence of respiratory-related ED visits per 100,000 population, and (3) percent of total
incidence of respiratory-related ED visits. Each form of result is shown in three tables,
one for each of the three years (2005, 2006, and 2007) of air quality data used in the
analysis. As noted in section 2.3, because the current annual average standard is based on
two years, the adjustment to simulate just meeting the current standard was applied only
to two years, 2006 and 2007. Therefore, results tables for 2005 do not include results
associated with just meeting the current standard. The alternative 1-hour daily maximum
standards, in contrast, have the form of the 3-year average of the 98th (or 99th) percentile
of the daily maximum 1-hour concentrations. Thus, the adjustment to simulate just
meeting these alternative 1-hour daily maximum standards was applied to each of the
three years, 2005, 2006 and 2007. Therefore, results tables for 2006 and 2007 include
results associated with just meeting the alternative 1-hour daily maximum standards as
well as results associated with just meeting the current standard. All results tables
include results associated with "as is" 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.
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Table 4-1. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2 Concentrations
that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations*
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.
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November 2008
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Table 4-2. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2 Concentrations
that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations*
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.
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November 2008
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Table 4-3. Estimated Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2 Concentrations
that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations*
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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November 2008
-------
Table 4-4. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO2 Concentrations and
NO2 Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations*
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.
4-5
November 2008
-------
Table 4-5. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO2 Concentrations and
NO2 Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations*
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.
4-6
November 2008
-------
Table 4-6. Estimated Incidence of Respiratory Emergency Department Visits per 100,000 Population Associated with "As Is" NO2 Concentrations and
NO2 Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations*
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.
4-7
November 2008
-------
Table 4-7. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2
Concentrations that Just Meet Alternative Standards in Atlanta, GA, Based on Adjusting 2005 NO2 Concentrations*
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.
4-8
November 2008
-------
Table 4-8. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2
Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2006 NO2 Concentrations*
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.
4-9
November 2008
-------
Table 4-9. Estimated Percent of Total Incidence of Respiratory Emergency Department Visits Associated with "As Is" NO2 Concentrations and NO2
Concentrations that Just Meet the Current and Alternative Standards in Atlanta, GA, Based on Adjusting 2007 NO2 Concentrations*
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.
4-10
November 2008
-------
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*
12000
"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 98th 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.
4-11
November 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. 4-12 November 2008
-------
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Urban Areas. Prepared for Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency, Research Triangle Park, NC. June 2005. Available
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Abt Associates Inc. 2007a. Ozone Health Risk Assessment for Selected Urban Areas.
Prepared for Office of Air Quality Planning and Standards, U.S. Environmental
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Ito, K. 2007. Association between coarse particles and asthma emergency department
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PB, Frumkin H. 2005. Ambient air pollution and respiratory emergency department
visits. Epidemiology. 16:164-174.
Tolbert, P. 2008a. Personal communication (email) to H. Richmond, U.S. EPA -
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(2007)," May 30.
Tolbert, P. 2008b. Personal communication (email) to H. Richmond, U.S. EPA -
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incidence data for the NO2 risk and exposure assessment," November 20, 2008.
Tolbert, PE, Klein M, Peel JL, Sarnat SE, Sarnat JA. 2007. Multipollutant modeling
issues in a study of ambient air quality and emergency department visits in Atlanta. J
Expos Sci Environ Epidemiol. 17S2:S29-35.
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U.S. EPA. 2005. Review of the National Ambient Air Quality Standards for Particulate
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-------
Office of Air Quality Planning and Standards, Research Triangle Park, NC. June.
Available online at: http://www.epa.gov/ttn/naaqs/standards/pm/s_pm crsp.html
U.S. EPA. 2007a. Integrated Review Plan for the Primary National Ambient Air Quality
Standard for Nitrogen Dioxide. Office of Air Quality Planning and Standards, Research
Triangle Park, NC. Draft. August 2007. Available online at:
http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html
U.S. EPA. 2007b. Nitrogen Dioxide Health Assessment Plan: Scope and Methods for
Exposure and Risk Assessment. Draft. September 2007. Available online at:
http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_pd.html.
U.S. EPA. 2007c. Air Trends. Nitrogen Dioxide. Office of Air Quality Planning and
Standards, Research Triangle Park, NC. Available online at:
http://www.epa.gov/airtrends/nitrogen.html.
U.S. EPA. 2008a. Integrated Science Assessment for Oxides of Nitrogen - Health
Criteria (Second External Review Draft). Available online at:
http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_isi.html
U.S. EPA, 2008b. Risk and Exposure Assessment to Support the Review of the NO2
Primary National Ambient Air Quality Standard (First Draft). Available online at:
http://www.epa.gov/ttn/naaqs/standards/nox/s_nox_cr_rea.html
U.S. EPA. 2008c. Integrated Science Assessment for Oxides of Nitrogen - Health
Criteria (Final Report). National Center for Environmental Assessment, Washington, DC,
EPA/600/R-08/071, 2008. Available online at:
http://cfpub. epa.gov/ncea/cfm/recordisplay. cfm?deid= 194645
U.S. EPA. 2008d. Risk and Exposure Assessment to Support the Review of the NO2
Primary National Ambient Air Quality Standard (Second Draft). Office of Air Quality
Planning and Standards, Research Triangle Park, NC. Available online at:
http://www.epa.gOv/ttn/naaqs/standards/nox/s nox cr rea.html
Abt Associates Inc. 5-2 November 2008
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United States Office of Air Quality Planning and Standards EPA-452/R-08-008b
Environmental Protection Health and Environmental Impacts Division November 2008
Agency Research Triangle Park, NC
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