Technical Basis for the EPA's Development of 

the Significant Impact Thresholds for PM2.5 

and Ozone

EPA-454/R-18-001 

April 2018 

Technical Basis for the EPA's Development of the Significant Impact Thresholds for 

PM2.5 and Ozone

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC
Contents

1 Bootstrap examples 6

2 Ozone results 12

2.1 2013-2015 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2 2012-2014 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3 2011-2013 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4 2010-2012 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.5 2009-2011 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.6 2008-2010 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.7 2007-2009 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.8 2006-2008 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.9 2005-2007 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.10 2004-2006 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

2.11 2003-2005 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

2.12 2002-2004 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

2.13 2001-2003 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.14 2000-2002 ozone bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3 Air quality variability results for years 2002-2013 for PM2.5 54

3.1 2013-2015 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.2 2012-2014 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

3.3 2011-2013 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

3.4 2010-2012 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.5 2009-2011 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.6 2008-2010 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

3.7 2007-2009 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

3.8 2006-2008 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

3.9 2005-2007 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

3.10 2004-2006 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

3.11 2003-2005 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

3.12 2002-2004 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

3.13 2001-2003 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

3.14 2000-2002 PM2.5 bootstrap results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

4 Comparison plots of nearby sites 124

5 Comparison of air quality variability for ozone sensitivity tests 208

5.1 All available data, no quarterly subsets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208

5.2 All available data, with quarterly subsets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

6 Analysis of temporal lag on ozone data and results from a blocked bootstrap sensitivity

analysis 212

6.1 Analysis procedure and results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212

7 Results from cluster analyses and other spatial groupings 218

7.1 Cluster analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218

7.1.1 Cluster analysis with latitude, longitude, and variability values . . . . . . . . . . . . . 218

7.1.2 Cluster analysis with time series of variability values (2014-2016) . . . . . . . . . . . . 223

7.1.3 Cluster analysis with time series of variability values (2012-2016) . . . . . . . . . . . . 227

7.2 Spatial analysis using NOAA Climate Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . 231

1
List of Figures

1 Example from site 10732003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Example from site 21700008. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Example from site 60195001. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4 Example from site 481410053. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

5 Example from site 560210001. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

6 Summary of ozone bootstrap results for 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

7 Distribution of ozone bootstrap results for 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . 14

8 Summary of ozone bootstrap results for 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

9 Distribution of ozone bootstrap results for 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . 17

10 Summary of ozone bootstrap results for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

11 Distribution of ozone bootstrap results for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . 20

12 Summary of ozone bootstrap results for 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

13 Distribution of ozone bootstrap results for 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . 23

14 Summary of ozone bootstrap results for 2011 . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

15 Distribution of ozone bootstrap results for 2011 . . . . . . . . . . . . . . . . . . . . . . . . . . 26

16 Summary of ozone bootstrap results for 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

17 Distribution of ozone bootstrap results for 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . 29

18 Summary of ozone bootstrap results for 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

19 Distribution of ozone bootstrap results for 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . 32

20 Summary of ozone bootstrap results for 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

21 Distribution of ozone bootstrap results for 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . 35

22 Summary of ozone bootstrap results for 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

23 Distribution of ozone bootstrap results for 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . 38

24 Summary of ozone bootstrap results for 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

25 Distribution of ozone bootstrap results for 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . 41

26 Summary of ozone bootstrap results for 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

27 Distribution of ozone bootstrap results for 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . 44

28 Summary of ozone bootstrap results for 2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

29 Distribution of ozone bootstrap results for 2004 . . . . . . . . . . . . . . . . . . . . . . . . . . 47

30 Summary of ozone bootstrap results for 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

31 Distribution of ozone bootstrap results for 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . 50

32 Summary of ozone bootstrap results for 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

33 Distribution of ozone bootstrap results for 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . 53

34 Summary of bootstrap results for 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

35 Distribution of bootstrap results for 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

36 Scatterplot of 50% CI bootstrap results for 2015 . . . . . . . . . . . . . . . . . . . . . . . . . 57

37 Map of the 50% CI bootstrap results for 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

38 Summary of bootstrap results for 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

39 Distribution of bootstrap results for 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

40 Scatterplot of 50% CI bootstrap results for 2014 . . . . . . . . . . . . . . . . . . . . . . . . . 62

41 Map of the 50% CI bootstrap results for 2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

42 Summary of bootstrap results for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

43 Distribution of bootstrap results for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

44 Scatterplot of 50% CI bootstrap results for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . 67

45 Map of the 50% CI bootstrap results for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

46 Summary of bootstrap results for 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

47 Distribution of bootstrap results for 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

48 Scatterplot of 50% CI bootstrap results for 2012 . . . . . . . . . . . . . . . . . . . . . . . . . 72

49 Map of the 50% CI bootstrap results for 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

50 Summary of bootstrap results for 2011 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

51 Distribution of bootstrap results for 2011 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

52 Scatterplot of 50% CI bootstrap results for 2011 . . . . . . . . . . . . . . . . . . . . . . . . . 77

2
53 Map of the 50% CI bootstrap results for 2011 . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

54 Summary of bootstrap results for 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

55 Distribution of bootstrap results for 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

56 Scatterplot of 50% CI bootstrap results for 2010 . . . . . . . . . . . . . . . . . . . . . . . . . 82

57 Map of the 50% CI bootstrap results for 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

58 Summary of bootstrap results for 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

59 Distribution of bootstrap results for 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

60 Scatterplot of 50% CI bootstrap results for 2009 . . . . . . . . . . . . . . . . . . . . . . . . . 87

61 Map of the 50% CI bootstrap results for 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

62 Summary of bootstrap results for 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

63 Distribution of bootstrap results for 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

64 Scatterplot of 50% CI bootstrap results for 2008 . . . . . . . . . . . . . . . . . . . . . . . . . 92

65 Map of the 50% CI bootstrap results for 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

66 Summary of bootstrap results for 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

67 Distribution of bootstrap results for 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

68 Scatterplot of 50% CI bootstrap results for 2007 . . . . . . . . . . . . . . . . . . . . . . . . . 97

69 Map of the 50% CI bootstrap results for 2007 . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

70 Summary of bootstrap results for 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

71 Distribution of bootstrap results for 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

72 Scatterplot of 50% CI bootstrap results for 2006 . . . . . . . . . . . . . . . . . . . . . . . . . 102

73 Map of the 50% CI bootstrap results for 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

74 Summary of bootstrap results for 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

75 Distribution of bootstrap results for 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

76 Scatterplot of 50% CI bootstrap results for 2005 . . . . . . . . . . . . . . . . . . . . . . . . . 107

77 Map of the 50% CI bootstrap results for 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

78 Summary of bootstrap results for 2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

79 Distribution of bootstrap results for 2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

80 Scatterplot of 50% CI bootstrap results for 2004 . . . . . . . . . . . . . . . . . . . . . . . . . 112

81 Map of the 50% CI bootstrap results for 2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

82 Summary of bootstrap results for 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

83 Distribution of bootstrap results for 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

84 Scatterplot of 50% CI bootstrap results for 2003 . . . . . . . . . . . . . . . . . . . . . . . . . 117

85 Map of the 50% CI bootstrap results for 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

86 Summary of bootstrap results for 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

87 Distribution of bootstrap results for 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

88 Scatterplot of 50% CI bootstrap results for 2002 . . . . . . . . . . . . . . . . . . . . . . . . . 122

89 Map of the 50% CI bootstrap results for 2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

90 Comparison of PM2.5 data for sites 150031001 and 150031001 . . . . . . . . . . . . . . . . . . 125

91 Comparison of PM2.5 data for sites 180190006 and 180190006 . . . . . . . . . . . . . . . . . . 126

92 Comparison of PM2.5 data for sites 180970078 and 180970078 . . . . . . . . . . . . . . . . . . 127

93 Comparison of PM2.5 data for sites 190450019 and 190450019 . . . . . . . . . . . . . . . . . . 128

94 Comparison of PM2.5 data for sites 220330009 and 220330009 . . . . . . . . . . . . . . . . . . 129

95 Comparison of PM2.5 data for sites 271630447 and 271630447 . . . . . . . . . . . . . . . . . . 130

96 Comparison of PM2.5 data for sites 320310016 and 320310016 . . . . . . . . . . . . . . . . . . 131

97 Comparison of PM2.5 data for sites 350010023 and 350010023 . . . . . . . . . . . . . . . . . . 132

98 Comparison of PM2.5 data for sites 420950025 and 420950025 . . . . . . . . . . . . . . . . . . 133

99 Comparison of PM2.5 data for sites 421010047 and 421010047 . . . . . . . . . . . . . . . . . . 134

100 Comparison of PM2.5 data for sites 421010055 and 421010055 . . . . . . . . . . . . . . . . . . 135

101 Comparison of PM2.5 data for sites 440070022 and 440070022 . . . . . . . . . . . . . . . . . . 136

102 Comparison of PM2.5 data for sites 490353006 and 490353006 . . . . . . . . . . . . . . . . . . 137

103 Comparison of PM2.5 data for sites 100032004 and 100032004 . . . . . . . . . . . . . . . . . . 138

104 Comparison of PM2.5 data for sites 110010043 and 110010043 . . . . . . . . . . . . . . . . . . 139

105 Comparison of PM2.5 data for sites 130670003 and 130670003 . . . . . . . . . . . . . . . . . . 140

106 Comparison of PM2.5 data for sites 150011006 and 150011006 . . . . . . . . . . . . . . . . . . 141

3
107 Comparison of PM2.5 data for sites 150011012 and 150011012 . . . . . . . . . . . . . . . . . . 142

108 Comparison of PM2.5 data for sites 150012016 and 150012016 . . . . . . . . . . . . . . . . . . 143

109 Comparison of PM2.5 data for sites 150031001 and 150031001 . . . . . . . . . . . . . . . . . . 144

110 Comparison of PM2.5 data for sites 150032004 and 150032004 . . . . . . . . . . . . . . . . . . 145

111 Comparison of PM2.5 data for sites 180190006 and 180190006 . . . . . . . . . . . . . . . . . . 146

112 Comparison of PM2.5 data for sites 180970078 and 180970078 . . . . . . . . . . . . . . . . . . 147

113 Comparison of PM2.5 data for sites 190450019 and 190450019 . . . . . . . . . . . . . . . . . . 148

114 Comparison of PM2.5 data for sites 191032001 and 191032001 . . . . . . . . . . . . . . . . . . 149

115 Comparison of PM2.5 data for sites 191390015 and 191390015 . . . . . . . . . . . . . . . . . . 150

116 Comparison of PM2.5 data for sites 211110051 and 211110051 . . . . . . . . . . . . . . . . . . 151

117 Comparison of PM2.5 data for sites 220330009 and 220330009 . . . . . . . . . . . . . . . . . . 152

118 Comparison of PM2.5 data for sites 240150003 and 240150003 . . . . . . . . . . . . . . . . . . 153

119 Comparison of PM2.5 data for sites 240251001 and 240251001 . . . . . . . . . . . . . . . . . . 154

120 Comparison of PM2.5 data for sites 240290002 and 240290002 . . . . . . . . . . . . . . . . . . 155

121 Comparison of PM2.5 data for sites 240313001 and 240313001 . . . . . . . . . . . . . . . . . . 156

122 Comparison of PM2.5 data for sites 240330030 and 240330030 . . . . . . . . . . . . . . . . . . 157

123 Comparison of PM2.5 data for sites 261630001 and 261630001 . . . . . . . . . . . . . . . . . . 158

124 Comparison of PM2.5 data for sites 270031002 and 270031002 . . . . . . . . . . . . . . . . . . 159

125 Comparison of PM2.5 data for sites 270530963 and 270530963 . . . . . . . . . . . . . . . . . . 160

126 Comparison of PM2.5 data for sites 271630447 and 271630447 . . . . . . . . . . . . . . . . . . 161

127 Comparison of PM2.5 data for sites 290370003 and 290370003 . . . . . . . . . . . . . . . . . . 162

128 Comparison of PM2.5 data for sites 290470005 and 290470005 . . . . . . . . . . . . . . . . . . 163

129 Comparison of PM2.5 data for sites 290990019 and 290990019 . . . . . . . . . . . . . . . . . . 164

130 Comparison of PM2.5 data for sites 291893001 and 291893001 . . . . . . . . . . . . . . . . . . 165

131 Comparison of PM2.5 data for sites 295100007 and 295100007 . . . . . . . . . . . . . . . . . . 166

132 Comparison of PM2.5 data for sites 300490004 and 300490004 . . . . . . . . . . . . . . . . . . 167

133 Comparison of PM2.5 data for sites 300630024 and 300630024 . . . . . . . . . . . . . . . . . . 168

134 Comparison of PM2.5 data for sites 310550019 and 310550019 . . . . . . . . . . . . . . . . . . 169

135 Comparison of PM2.5 data for sites 320310016 and 320310016 . . . . . . . . . . . . . . . . . . 170

136 Comparison of PM2.5 data for sites 330050007 and 330050007 . . . . . . . . . . . . . . . . . . 171

137 Comparison of PM2.5 data for sites 330150018 and 330150018 . . . . . . . . . . . . . . . . . . 172

138 Comparison of PM2.5 data for sites 340171003 and 340171003 . . . . . . . . . . . . . . . . . . 173

139 Comparison of PM2.5 data for sites 340210008 and 340210008 . . . . . . . . . . . . . . . . . . 174

140 Comparison of PM2.5 data for sites 350010023 and 350010023 . . . . . . . . . . . . . . . . . . 175

141 Comparison of PM2.5 data for sites 360810124 and 360810124 . . . . . . . . . . . . . . . . . . 176

142 Comparison of PM2.5 data for sites 380570004 and 380570004 . . . . . . . . . . . . . . . . . . 177

143 Comparison of PM2.5 data for sites 420010001 and 420010001 . . . . . . . . . . . . . . . . . . 178

144 Comparison of PM2.5 data for sites 420030008 and 420030008 . . . . . . . . . . . . . . . . . . 179

145 Comparison of PM2.5 data for sites 420070014 and 420070014 . . . . . . . . . . . . . . . . . . 180

146 Comparison of PM2.5 data for sites 420110011 and 420110011 . . . . . . . . . . . . . . . . . . 181

147 Comparison of PM2.5 data for sites 420410101 and 420410101 . . . . . . . . . . . . . . . . . . 182

148 Comparison of PM2.5 data for sites 420450002 and 420450002 . . . . . . . . . . . . . . . . . . 183

149 Comparison of PM2.5 data for sites 420710007 and 420710007 . . . . . . . . . . . . . . . . . . 184

150 Comparison of PM2.5 data for sites 420910013 and 420910013 . . . . . . . . . . . . . . . . . . 185

151 Comparison of PM2.5 data for sites 420950025 and 420950025 . . . . . . . . . . . . . . . . . . 186

152 Comparison of PM2.5 data for sites 421010047 and 421010047 . . . . . . . . . . . . . . . . . . 187

153 Comparison of PM2.5 data for sites 421010055 and 421010055 . . . . . . . . . . . . . . . . . . 188

154 Comparison of PM2.5 data for sites 421250005 and 421250005 . . . . . . . . . . . . . . . . . . 189

155 Comparison of PM2.5 data for sites 421250200 and 421250200 . . . . . . . . . . . . . . . . . . 190

156 Comparison of PM2.5 data for sites 421255001 and 421255001 . . . . . . . . . . . . . . . . . . 191

157 Comparison of PM2.5 data for sites 421290008 and 421290008 . . . . . . . . . . . . . . . . . . 192

158 Comparison of PM2.5 data for sites 421330008 and 421330008 . . . . . . . . . . . . . . . . . . 193

159 Comparison of PM2.5 data for sites 440030002 and 440030002 . . . . . . . . . . . . . . . . . . 194

160 Comparison of PM2.5 data for sites 440070022 and 440070022 . . . . . . . . . . . . . . . . . . 195

4
161 Comparison of PM2.5 data for sites 450190048 and 450190048 . . . . . . . . . . . . . . . . . . 196

162 Comparison of PM2.5 data for sites 450450015 and 450450015 . . . . . . . . . . . . . . . . . . 197

163 Comparison of PM2.5 data for sites 450630008 and 450630008 . . . . . . . . . . . . . . . . . . 198

164 Comparison of PM2.5 data for sites 482011035 and 482011035 . . . . . . . . . . . . . . . . . . 199

165 Comparison of PM2.5 data for sites 490353006 and 490353006 . . . . . . . . . . . . . . . . . . 200

166 Comparison of PM2.5 data for sites 490490002 and 490490002 . . . . . . . . . . . . . . . . . . 201

167 Comparison of PM2.5 data for sites 490570002 and 490570002 . . . . . . . . . . . . . . . . . . 202

168 Comparison of PM2.5 data for sites 530530029 and 530530029 . . . . . . . . . . . . . . . . . . 203

169 Comparison of PM2.5 data for sites 530610005 and 530610005 . . . . . . . . . . . . . . . . . . 204

170 Comparison of PM2.5 data for sites 530610020 and 530610020 . . . . . . . . . . . . . . . . . . 205

171 Comparison of PM2.5 data for sites 530611007 and 530611007 . . . . . . . . . . . . . . . . . . 206

172 Comparison of PM2.5 data for sites 550090005 and 550090005 . . . . . . . . . . . . . . . . . . 207

173 Summary of ozone bootstrap results for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

174 Summary of ozone bootstrap results for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . 211

175 Summary of ozone acf analysis for 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214

176 Summary of ozone pacf analysis for 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

177 Summary of ozone blocked-bootstrap results for 2016 . . . . . . . . . . . . . . . . . . . . . . . 216

178 Distribution of ozone blocked-bootstrap results for 2016 . . . . . . . . . . . . . . . . . . . . . 217

179 Hierarchical and K-means clusters for the annual variability for 2016 . . . . . . . . . . . . . . 221

180 Hierarchical and K-means clusters for the 24-hr variability for 2016 . . . . . . . . . . . . . . . 222

181 Hierarchical and K-means clusters for the annual variability for 2014-2016 . . . . . . . . . . . 225

182 Hierarchical and K-means clusters for the 24-hr variability for 2014-2016 . . . . . . . . . . . . 226

183 Hierarchical and K-means clusters for the annual variability for 2012-2016 . . . . . . . . . . . 229

184 Hierarchical and K-means clusters for the 24-hr variability for 2012-2016 . . . . . . . . . . . . 230

185 Comparison of variability within NOAA climate regions for 2016 . . . . . . . . . . . . . . . . 232

5
1 Bootstrap examples

Bootstrap examples from selects PM2.5 sites for the 2008-2010 DV period. Top left, top right, and middle

left plots show the distribution of daily PM concentrations for 2008, 2009, and 2010, respectively. The

vertical red line shows the annual mean and the vertical blue line shows the annual 98th percentile. Middle

left plots show sample distributions of resampled data from 2008, along with the annual mean and the 98th

percentile from each resample. The bottom left plots show the distribution of the annual DVs from the

20,000 resampled DV periods (2008-2010). The bottom right plots show the distribution of the 24-hr DVs

from the 20,000 resampled DV periods (2008-2010)

6
Figure 1: Example from site 10732003.

7
Figure 2: Example from site 21700008.

8
Figure 3: Example from site 60195001.

9
Figure 4: Example from site 481410053.

10
Figure 5: Example from site 560210001.

11
2 Ozone results

Bootstrap results for ozone data from the years 2000-2013. Each section containts a single DV period,e.g.,

the results for 2015 include data from 2013-2015.

2.1 2013-2015 ozone bootstrap results

12
Figure 6: Bootstrap results for the ozone 2015 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

13
Figure 7: Bootstrap results from the 50% CIs for the 2015 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2015 ozone DV at each site.

14
2.2 2012-2014 ozone bootstrap results

15
Figure 8: Bootstrap results for the ozone 2014 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

16
Figure 9: Bootstrap results from the 50% CIs for the 2014 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2014 ozone DV at each site.

17
2.3 2011-2013 ozone bootstrap results

18
Figure 10: Bootstrap results for the ozone 2013 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

19
Figure 11: Bootstrap results from the 50% CIs for the 2013 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2013 ozone DV at each site.

20
2.4 2010-2012 ozone bootstrap results

21
Figure 12: Bootstrap results for the ozone 2012 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

22
Figure 13: Bootstrap results from the 50% CIs for the 2012 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2012 ozone DV at each site.

23
2.5 2009-2011 ozone bootstrap results

24
Figure 14: Bootstrap results for the ozone 2011 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

25
Figure 15: Bootstrap results from the 50% CIs for the 2011 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2011 ozone DV at each site.

26
2.6 2008-2010 ozone bootstrap results

27
Figure 16: Bootstrap results for the ozone 2010 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

28
Figure 17: Bootstrap results from the 50% CIs for the 2010 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2010 ozone DV at each site.

29
2.7 2007-2009 ozone bootstrap results

30
Figure 18: Bootstrap results for the ozone 2009 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

31
Figure 19: Bootstrap results from the 50% CIs for the 2009 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2009 ozone DV at each site.

32
2.8 2006-2008 ozone bootstrap results

33
Figure 20: Bootstrap results for the ozone 2008 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

34
Figure 21: Bootstrap results from the 50% CIs for the 2008 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2008 ozone DV at each site.

35
2.9 2005-2007 ozone bootstrap results

36
Figure 22: Bootstrap results for the ozone 2007 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

37
Figure 23: Bootstrap results from the 50% CIs for the 2007 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2007 ozone DV at each site.

38
2.10 2004-2006 ozone bootstrap results

39
Figure 24: Bootstrap results for the ozone 2006 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

40
Figure 25: Bootstrap results from the 50% CIs for the 2006 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2006 ozone DV at each site.

41
2.11 2003-2005 ozone bootstrap results

42
Figure 26: Bootstrap results for the ozone 2005 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

43
Figure 27: Bootstrap results from the 50% CIs for the 2005 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2005 ozone DV at each site.

44
2.12 2002-2004 ozone bootstrap results

45
Figure 28: Bootstrap results for the ozone 2004 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

46
Figure 29: Bootstrap results from the 50% CIs for the 2004 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2004 ozone DV at each site.

47
2.13 2001-2003 ozone bootstrap results

48
Figure 30: Bootstrap results for the ozone 2003 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

49
Figure 31: Bootstrap results from the 50% CIs for the 2003 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2003 ozone DV at each site.

50
2.14 2000-2002 ozone bootstrap results

51
Figure 32: Bootstrap results for the ozone 2002 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

52
Figure 33: Bootstrap results from the 50% CIs for the 2002 ozone DVs. The top panel shows the relative

difference between the CI and the actual DV, the middle panel shows the absolute difference between the

values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the relative

difference between the 50% CIs for the 2002 ozone DV at each site.

53
3 Air quality variability results for years 2002-2013 for PM2.5

Bootstrap results for PM2.5 data from the years 2000-2015. Each section containts a single DV period,e.g.,

the results for 2015 include data from 2013-2015.

3.1 2013-2015 PM2.5 bootstrap results

54
Figure 34: Bootstrap results for the 2015 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

55
Figure 35: Bootstrap results for the 2015 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

56
Figure 36: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

57
Figure 37: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2015 PM2.5 DVs.

58
3.2 2012-2014 PM2.5 bootstrap results

59
Figure 38: Bootstrap results for the 2014 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

60
Figure 39: Bootstrap results for the 2014 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

61
Figure 40: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

62
Figure 41: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2014 PM2.5 DVs.

63
3.3 2011-2013 PM2.5 bootstrap results

64
Figure 42: Bootstrap results for the 2013 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

65
Figure 43: Bootstrap results for the 2013 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

66
Figure 44: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

67
Figure 45: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2013 PM2.5 DVs.

68
3.4 2010-2012 PM2.5 bootstrap results

69
Figure 46: Bootstrap results for the 2012 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

70
Figure 47: Bootstrap results for the 2012 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

71
Figure 48: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

72
Figure 49: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2012 PM2.5 DVs.

73
3.5 2009-2011 PM2.5 bootstrap results

74
Figure 50: Bootstrap results for the 2011 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

75
Figure 51: Bootstrap results for the 2011 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

76
Figure 52: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

77
Figure 53: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2011 PM2.5 DVs.

78
3.6 2008-2010 PM2.5 bootstrap results

79
Figure 54: Bootstrap results for the 2010 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

80
Figure 55: Bootstrap results for the 2010 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

81
Figure 56: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

82
Figure 57: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2010 PM2.5 DVs.

83
3.7 2007-2009 PM2.5 bootstrap results

84
Figure 58: Bootstrap results for the 2009 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

85
Figure 59: Bootstrap results for the 2009 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

86
Figure 60: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

87
Figure 61: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2009 PM2.5 DVs.

88
3.8 2006-2008 PM2.5 bootstrap results

89
Figure 62: Bootstrap results for the 2008 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

90
Figure 63: Bootstrap results for the 2008 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

91
Figure 64: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

92
Figure 65: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2008 PM2.5 DVs.

93
3.9 2005-2007 PM2.5 bootstrap results

94
Figure 66: Bootstrap results for the 2007 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

95
Figure 67: Bootstrap results for the 2007 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

96
Figure 68: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

97
Figure 69: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2007 PM2.5 DVs.

98
3.10 2004-2006 PM2.5 bootstrap results

99
Figure 70: Bootstrap results for the 2006 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

100
Figure 71: Bootstrap results for the 2006 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

101
Figure 72: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

102
Figure 73: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2006 PM2.5 DVs.

103
3.11 2003-2005 PM2.5 bootstrap results

104
Figure 74: Bootstrap results for the 2005 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

105
Figure 75: Bootstrap results for the 2005 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

106
Figure 76: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

107
Figure 77: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2005 PM2.5 DVs.

108
3.12 2002-2004 PM2.5 bootstrap results

109
Figure 78: Bootstrap results for the 2004 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

110
Figure 79: Bootstrap results for the 2004 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

111
Figure 80: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

112
Figure 81: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2004 PM2.5 DVs.

113
3.13 2001-2003 PM2.5 bootstrap results

114
Figure 82: Bootstrap results for the 2003 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

115
Figure 83: Bootstrap results for the 2003 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

116
Figure 84: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

117
Figure 85: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2003 PM2.5 DVs.

118
3.14 2000-2002 PM2.5 bootstrap results

119
Figure 86: Bootstrap results for the 2002 PM2.5 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top two panels show the values for the DVs at the various CIs,

while the bottom two panels show the relative difference between the CI and the actual DV.

120
Figure 87: Bootstrap results for the 2002 PM2.5 DVs, showing distribution of the relative differences between

the bootstrap DVs and the actual DV at the 50%, 68%, 75%, and 95% CIs, along with the mean, median,

maximum, minimum, standard deviations of the relative differences.

121
Figure 88: Bootstrap results from the 50% CIs for PM2.5 DVs. The top two panels show the relative difference

between the CI and the actual DV and the bottom two panels show the absolute difference between the values

for the DVs at each site and the CI.

122
Figure 89: Spatial distribution of the relative difference between the CI and the actual DV from the 50%

CIs for the 2002 PM2.5 DVs.

123
4 Comparison plots of nearby sites

Comparison of PM2.5 data for paired, nearby sites for the spatial analysis conducted in Section 3.1.2.

124
Figure 90: Comparison of PM2.5 data for sites 150031001 and 150031001. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 125
Figure 91: Comparison of PM2.5 data for sites 180190006 and 180190006. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 126
Figure 92: Comparison of PM2.5 data for sites 180970078 and 180970078. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 127
Figure 93: Comparison of PM2.5 data for sites 190450019 and 190450019. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 128
Figure 94: Comparison of PM2.5 data for sites 220330009 and 220330009. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 129
Figure 95: Comparison of PM2.5 data for sites 271630447 and 271630447. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 130
Figure 96: Comparison of PM2.5 data for sites 320310016 and 320310016. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 131
Figure 97: Comparison of PM2.5 data for sites 350010023 and 350010023. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 132
Figure 98: Comparison of PM2.5 data for sites 420950025 and 420950025. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 133
Figure 99: Comparison of PM2.5 data for sites 421010047 and 421010047. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 134
Figure 100: Comparison of PM2.5 data for sites 421010055 and 421010055. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 135
Figure 101: Comparison of PM2.5 data for sites 440070022 and 440070022. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 136
Figure 102: Comparison of PM2.5 data for sites 490353006 and 490353006. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 137
Figure 103: Comparison of PM2.5 data for sites 100032004 and 100032004. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 138
Figure 104: Comparison of PM2.5 data for sites 110010043 and 110010043. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 139
Figure 105: Comparison of PM2.5 data for sites 130670003 and 130670003. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 140
Figure 106: Comparison of PM2.5 data for sites 150011006 and 150011006. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 141
Figure 107: Comparison of PM2.5 data for sites 150011012 and 150011012. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 142
Figure 108: Comparison of PM2.5 data for sites 150012016 and 150012014. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 143
Figure 109: Comparison of PM2.5 data for sites 150031001 and 150031001. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 144
Figure 110: Comparison of PM2.5 data for sites 150032004 and 150032004. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 145
Figure 111: Comparison of PM2.5 data for sites 180190006 and 180190006. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 146
Figure 112: Comparison of PM2.5 data for sites 180970078 and 180970078. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 147
Figure 113: Comparison of PM2.5 data for sites 190450019 and 190450019. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 148
Figure 114: Comparison of PM2.5 data for sites 191032001 and 191032001. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 149
Figure 115: Comparison of PM2.5 data for sites 191390015 and 191390015. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 150
Figure 116: Comparison of PM2.5 data for sites 211110051 and 211110051. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 151
Figure 117: Comparison of PM2.5 data for sites 220330009 and 220330009. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 152
Figure 118: Comparison of PM2.5 data for sites 240150003 and 240150003. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 153
Figure 119: Comparison of PM2.5 data for sites 240251001 and 240251001. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 154
Figure 120: Comparison of PM2.5 data for sites 240290002 and 240290002. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 155
Figure 121: Comparison of PM2.5 data for sites 240313001 and 240313001. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 156
Figure 122: Comparison of PM2.5 data for sites 240330030 and 240330030. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 157
Figure 123: Comparison of PM2.5 data for sites 261630001 and 261630001. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 158
Figure 124: Comparison of PM2.5 data for sites 270031002 and 270031002. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 159
Figure 125: Comparison of PM2.5 data for sites 270530963 and 270530963. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 160
Figure 126: Comparison of PM2.5 data for sites 271630447 and 271630447. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 161
Figure 127: Comparison of PM2.5 data for sites 290370003 and 290370003. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 162
Figure 128: Comparison of PM2.5 data for sites 290470005 and 290470005. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 163
Figure 129: Comparison of PM2.5 data for sites 290990019 and 290990019. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 164
Figure 130: Comparison of PM2.5 data for sites 291893001 and 291893001. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 165
Figure 131: Comparison of PM2.5 data for sites 295100007 and 295100007. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 166
Figure 132: Comparison of PM2.5 data for sites 300490004 and 300490004. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 167
Figure 133: Comparison of PM2.5 data for sites 300630024 and 300630024. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 168
Figure 134: Comparison of PM2.5 data for sites 310550019 and 310550019. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 169
Figure 135: Comparison of PM2.5 data for sites 320310016 and 320310016. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 170
Figure 136: Comparison of PM2.5 data for sites 330050007 and 330050007. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 171
Figure 137: Comparison of PM2.5 data for sites 330150018 and 330150018. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 172
Figure 138: Comparison of PM2.5 data for sites 340171003 and 340171003. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 173
Figure 139: Comparison of PM2.5 data for sites 340210008 and 340210008. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 174
Figure 140: Comparison of PM2.5 data for sites 350010023 and 350010023. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 175
Figure 141: Comparison of PM2.5 data for sites 360810124 and 360810124. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 176
Figure 142: Comparison of PM2.5 data for sites 380570004 and 380570004. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 177
Figure 143: Comparison of PM2.5 data for sites 420010001 and 420010001. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 178
Figure 144: Comparison of PM2.5 data for sites 420030008 and 420030008. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 179
Figure 145: Comparison of PM2.5 data for sites 420070014 and 420070014. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 180
Figure 146: Comparison of PM2.5 data for sites 420110011 and 420110011. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 181
Figure 147: Comparison of PM2.5 data for sites 420410101 and 420410101. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 182
Figure 148: Comparison of PM2.5 data for sites 420450002 and 420450002. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 183
Figure 149: Comparison of PM2.5 data for sites 420710007 and 420710007. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 184
Figure 150: Comparison of PM2.5 data for sites 420910013 and 420910013. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 185
Figure 151: Comparison of PM2.5 data for sites 420950025 and 420950025. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 186
Figure 152: Comparison of PM2.5 data for sites 421010047 and 421010047. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 187
Figure 153: Comparison of PM2.5 data for sites 421010055 and 421010055. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 188
Figure 154: Comparison of PM2.5 data for sites 421250005 and 421250005. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 189
Figure 155: Comparison of PM2.5 data for sites 421250200 and 421250200. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 190
Figure 156: Comparison of PM2.5 data for sites 421255001 and 421255001. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 191
Figure 157: Comparison of PM2.5 data for sites 421290008 and 421290008. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 192
Figure 158: Comparison of PM2.5 data for sites 421330008 and 421330008. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 193
Figure 159: Comparison of PM2.5 data for sites 440030002 and 440030002. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 194
Figure 160: Comparison of PM2.5 data for sites 440070022 and 440070022. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 195
Figure 161: Comparison of PM2.5 data for sites 450190048 and 450190048. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 196
Figure 162: Comparison of PM2.5 data for sites 450450015 and 450450015. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 197
Figure 163: Comparison of PM2.5 data for sites 450630008 and 450630008. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 198
Figure 164: Comparison of PM2.5 data for sites 482011035 and 482011035. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 199
Figure 165: Comparison of PM2.5 data for sites 490353006 and 490353006. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 200
Figure 166: Comparison of PM2.5 data for sites 490490002 and 490490002. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 201
Figure 167: Comparison of PM2.5 data for sites 490570002 and 490570002. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 202
Figure 168: Comparison of PM2.5 data for sites 530530029 and 530530029. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 203
Figure 169: Comparison of PM2.5 data for sites 530610005 and 530610005. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 204
Figure 170: Comparison of PM2.5 data for sites 530610020 and 530610020. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 205
Figure 171: Comparison of PM2.5 data for sites 530611007 and 530611007. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 206
Figure 172: Comparison of PM2.5 data for sites 550090005 and 550090005. Top panel shows time series for

both sites for years 2012-2014. Bottom panel shows scatter plot of paired data, along with slope for the

linear regression and correlation coefficient (r2), with data points are colored by month. 207
5 Comparison of air quality variability for ozone sensitivity tests

Results from the ozone sensitivity analysis discussed in Section 2.2.3.

5.1 All available data, no quarterly subsets

208
Figure 173: Bootstrap results for the ozone 2013 DVs, showing the 50%, 68%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

209
5.2 All available data, with quarterly subsets

210
Figure 174: Bootstrap results for the ozone 2013 DVs, showing the 50%, 65%, 75%, and 95% CIs, along with

the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the middle panel

shows the relative difference between the CI and the actual DV, and the bottom panel shows the distribution

of the relative differences between the CI and the actual DV.

211
6 Analysis of temporal lag on ozone data and results from a

blocked bootstrap sensitivity analysis

This section presents results from an analysis to examine the temporal correlation of air quality levels, i.e.,

the tendency of high concentration days to occur after other days with high concentrations. Such behavior,

if present, would be a function of both emission trends (e.g., weekday traffic versus weekend traffic) and

meteorology (e.g., high pressure systems often hinder the transport of pollutants and also accompany higher

temperatures, which tend to increase the formation of ozone). The primary motivation for this assessment

is to determine whether the implementation of a block bootstrap procedure is needed for the bootstrapping

analysis described in Section 2.2.3 in order to account for possible temporal correlation, and if so, what is the

appropriate block size. If not properly accounted for, correlation can affect the assessment of uncertainty (i.e.,

the standard errors used to calculate confidence intervals). While in this analysis confidence intervals were

constructed using empirical percentiles, it is important to consider whether autocorrelation may be affecting

the distributional characteristics of the bootstrapped data. Thus a sensitivity analysis is considered.

A block bootstrap method can be used in the presence of autocorrelation to replicate the correlation

structure in the data. Blocks are designed such that the dependence between adjacent or closely spaced

measurements is contained within a block, and there is induced independence between measurements in

adjacent blocks. Block size selection can be tricky, as the blocks should be large enough to induce independence but small enough to retain important characteristics of the data, including natural variation and

overall trends (i.e., the variance-bias trade-off for avoiding over-smoothing). There is no one agreed upon

method for selection of block size for bootstrapping procedures. Many considerations can come into play,

including practical issues and subject-matter scientific expertise. The analysis presented here first attempts

to determine the ”length of lag” in the ambient ozone data (i.e., how long do correlations of concentrations

between MDA8 values persist). Based on the lag analysis, a secondary ”blocked” bootstrap analysis was

completed which sampled blocks of days corresponding to the lag found in the initial analysis. Ultimately,

a 7-day lag was selected from the lag analysis. The resultant bootstrap results were similar to the original

non-parametric bootstrap, which sampled individual days rather than blocks of 7-days.

6.1 Analysis procedure and results

The R software package [R Core Team, 2017] was used to conduct the lag analysis. The acf (autocorrelation

function) and pacf (partial autocorrelation function) were used to determine the autocorrelation of the time

series of MDA8 values at each measurement site for all data available from 2016. The results from the

network-wide correlations were summarized in Figures 175 and 176.

The results from the acf analysis suggest that autocorrelations drop off after lag 3, as the mean and

median correlation coefficients at 4, 5 and 6 days lag are equivalent (top panel of Figure 175). While the

correlations are still within the 95% confidence interval returned from acf out to the 6-day lag, the fact that

the distribution of the differences between the individual correlation and the confidence intervals (middle

and bottom panel of Figure 175) are virtually identical starting at 3 days of lag suggest that correlations

at this level would be found at any lag period. The pacf analysis accounts for the autocorrelation found

in the previous lag periods (i.e., the correlation found for the 2-day lag removes the correlation found from

the 1-day lag). The results from this analysis suggest that the autocorrelation is only significant to one day.

Taking these results into account, a 3-day lag should appropriately account for any autocorrelation in the

ozone data. This is implemented in the bootstrapping analysis via a 7-day block size to account for +- 3 days

surrounding the sampled daily value. Thus, prior to bootstrapping, the data is grouped into fixed blocks of

size n  ̄7 and the sampline with replacement is performed on the blocks. This is also consistent with block

sizes used in Inoue and Shintani [2006] and Hall and Horowitz [1996]. A 7-day block size also addresses the

consideration of weekly (7-day) pollution patterns across weekday to weekend that may exist.

A second bootstrap analysis was completed for the 2016 ozone data using a block sampling method,

with the 7-day block sample size, in order to determine the effect of possible temporal autocorrelation on

the bootstrap confidence intervals. The analysis was conducted with the R ”boot” package with the tsboot

(time seties bootstrap) package with block resampling with fixed block lengths. Simple blocking, rather

than overlapping blocks of randomly varying widths, should suffice for initial consideration of possible effect

of dependence on bootstrapped confidence intervals (Lahiri [1999] and Andrews [2002]). The results from

212
this bootstrap approach are shown in Figures 177 and 178 (which can be compared to the results from the

non-paremetric bootstrap in Sections 3.1 and 4.2 in the main document). The results detailed in Figure 178

indicate slightly greater variability in the blocked bootstrap result, with the mean variability from the blocked

bootstrap was 1.62%, versus 1.42% from the non-parametric bootstrap, and the median was 1.55%, versus

1.47% from the non-parametric bootstrap. while there are a few sites with notable larger variability, as with

the non-parametric bootstrap, there is no large-scale trend in the variability. The only location of note is

perhaps the Uinta Basin in Utah, where a cluster of sites are grayed out in the map, indicating variability

greater than the color scale. These sites have the highest variability from the blocked bootstrap. The Uinta

Basin is known to have a unique patter in high-ozone days, with the maximum concentrations occurring in

the winter during unique meteorological events, such that the high days are always clustered together. As a

result, this highly unique ozone pattern has distinctly different results in the blocked bootstrap as compared

to the non-parametric bootstrap.

213
Figure 175: Mean (red lines) and median (black lines) correlations from the acf analysis for ozone data from

2016. 214
Figure 176: Mean (red lines) and median (black lines) correlations from the pacf analysis for ozone data

from 2016. 215
Figure 177: Blocked-bootstrap results for the ozone 2016 DVs, showing the 50%, 68%, 75%, and 95% CIs,

along with the mean and median bootstrap DVs. The top panel shows the DVs at the various CIs, the

middle panel shows the relative difference between the CI and the actual DV, and the bottom panel shows

the distribution of the relative differences between the CI and the actual DV.

216
Figure 178: Blocked-bootstrap results from the 50% CIs for the 2016 ozone DVs. The top panel shows the

relative difference between the CI and the actual DV, the middle panel shows the absolute difference between

the values for the DVs at each site and the CI, and the bottom panel shows the spatial distribution of the

relative difference between the 50% CIs for the 2015 ozone DV at each site.

217
7 Results from cluster analyses and other spatial groupings

This section presents results from several cluster analyses and other analyses conducted to examine the

presence of spatial groupings or trends. If strong correlation in the variability can be found in natural

spatial groupings, there may be reason to consider the variability at a regional, rather than national, level.

The primary purpose of this analysis is to attempt to identify natural spatial groupings and determine if

there strong correlations in the variability within these spatial groupings and if the variability between spatial

groupings are significantly different. Since there is no clear pathway to determine the spatial correlations,

the analysis presented here consists of several iterations of cluster analysis as well as an analysis of variability

based on well-established climate regions to explore this issue from various perspectives.

7.1 Cluster analyses

Cluster analysis is an analysis technique that attempts to group data by similar characteristics of the data

in question. This is generally done by assigning quantitative values to each characteristic and measuring

and minimizing the ”distance” between the existing clusters. The ”distance” parameter can be calculated in

a variety of ways, but the most common (and the one used here) is simply the Euclidian distance between

the input variables. Two types of clustering algorithms are applied, a K-means algorithm and a hierarchical

algorithm. The K-mean algorithm uses a pre-determined number of clusters and initially randomly assigns all

items to clusters. The distance between cluster centers and all individuals are calculated, then individuals

are reassigned to their closest cluster. The algorithm repeats a set number of times or until a minimum

convergence threshold is reached. Hierarchical algorithms do not use a predetermined a number of clusters,

but instead start with each individual as part of their own cluster. The first step in a hierarchical analysis

combines the two closest clusters (which are just the two closest members at the first step). Each subsequent

step combines the next closest clusters, until only 2 clusters are left. The R software package [R Core Team,

2017] was used to conduct the cluster analysis, using the kmeans and hclust functions. The analysis was

performed on the results from the 2014-2016 PM variability results, as described in the following sections.

7.1.1 Cluster analysis with latitude, longitude, and variability values

This cluster analysis used the latitude, longitude (both in degrees eastwest and northsouth), and the relative

variability (as a percentage of the site’s DV). Thus, the distance between individuals and clusters is defined

as the difference between the latitude, longitude, and relative variability. Since the longitudes and latitude

varies on a much larger scale between sites (longitude ranges from -64 to -160 degrees, latitude ranges from

17 to 64 degrees) than the relative variability (0-5 percent for the annual and 0-75 percent for the daily

DVs), the spatial input component will have a greater impact on the resulting than the site-level variability

(clusters for the annual and daily DVs were computed separately). That is, the spatial closeness will be the

primary factor in forming these clusters, but the analysis will then try to group nearby sites with similar

levels of variability. Hierarchical and K-mean clustering were applied independently.

The clusters formed from this analysis is shown in Figures 179 and 180 and statistics are summarized in

Tables 1- 4. The K-means analysis used 10 clusters, which was picked based on the number of EPA Regions.

The figure also shows the hierarchical cluster results at 10 clusters for comparison. The clusters from the

hierarchical analysis have relatively little recognizable geographic correlation. For example, cluster 1 (orange

circles in both the annual and 24-hr figures) consists of a group of sites over California and Arizona and a

group over the south eastern US (Florida, Georgia, Alabama), with a major discontinuity in this grouping,

with no data points in New Mexico, Texas, Louisiana, and Mississippi. Table 1 and 2 show the statistics

from the hierarchical clusters for the annual and 24-hr standards. The table includes a comparison of the

mean variability from each cluster to the mean from the entire dataset using a Welch Modified Two-Sample

t-Test (determined from the tsum.test function from the BSDA package in R) to determine if the means

are significantly different. For the annual standard, the p values are all fairly high, with the smallest value

just over 0.1, which is well above the nominal p value of 0.5 typically identified as an indicator that the

means may be different. For the 24-hr standard, there are 2 clusters with p values less than 0.05. Cluster

7 has a p value of 0.02, which may be different than the annual mean, but the sites in this cluster (light

blue squares with an ”x”) are spread across the country, i.e., they are not spatially distinct. Cluster 8 has

the smallest p value (0.006) and has the smallest mean variability. For the most part, this cluster is in the

218
same region (purple asterisk), in the eastern US, from North Carolina up to New York. However, this cluster

is interspersed with several other clusters. Thus, while it has distinct variability values and is spatially

correlated, it is not spatially distinct.

The results from the K-mean cluster analysis are starkly different from the hierarchical analysis. The

clusters are all geographically distinct and the results of the t-test indicate that several of the clusters are

distinctly different from the mean dataset. For the annual standard, half of the clusters have p-values less

than 0.05 (2, 3, 4, 8, and 10) while 7 clusters have p-values less than 0.05 for the daily standard (1, 2, 3,

4, 5, 6, and 9, though cluster 1 and 9 only have a few members and cluster 2 is close enough to 0.05 to

discount as significantly different, leaving only 4 clusters of note). On the surface, this suggests there are

regional differences in the variability. However, the differences between the results from the annual and daily

standards suggest the result is less certain. For example, cluster 4 in the annual analysis stands out as having

the largest mean variability and a very small p-value, suggesting the variability in this subset is significantly

different from the mean dataset. However, these sites are part of a larger cluster in the daily results (cluster

8), which include California sites, and has lower mean variability than the mean from the dataset (though

not significantly different, with a p-value of 0.31). Another example of inconsistency between the annual and

daily results is cluster 3 in the annual results, which roughly correlates to cluster 6 in the daily results. In

this case, the clusters represent approximately the same geographic region. However, for the annual result,

cluster 3 has mean variability that appears to be significantly higher than the mean dataset’s (p-value of

0.016), but significantly lower mean variability for the daily standard than the mean dataset’s (p-value of

0.018). Thus, these particular geographic areas have higher than average variability in the long-term, but

lower than average variability in the short-term. The inconsistent results from the K-means analysis make it

difficult draw specific conclusions about the geographic nature of the variability as estimated by this analysis.

Table 1: Comparison of hierarchical clusters for lat-long-annual variability

n.sites grp mean median sd ann.pval

107 1 1.628 1.538 0.7296 0.1969

44 2 1.917 1.786 0.7377 0.1052

43 3 1.759 1.724 0.7835 0.7972

57 4 1.759 1.744 0.5889 0.7024

43 5 1.659 1.630 0.5025 0.4108

44 6 1.745 1.765 0.4914 0.8207

73 7 1.736 1.754 0.5604 0.8929

26 8 1.674 1.714 0.6251 0.6762

49 9 1.816 1.744 0.7380 0.4171

38 10 1.660 1.453 0.8483 0.6384

524 All 1.727 1.705 0.6700 1.0000

Table 2: Comparison of hierarchical clusters for lat-long-24-hr variability

n.sites grp mean median sd TF.pval

107 1 5.985 3.846 8.882 0.718242

44 2 6.396 5.013 5.046 0.365465

43 3 6.339 5.556 6.332 0.501141

57 4 5.150 4.054 3.758 0.370173

43 5 6.192 4.762 6.745 0.618803

44 6 5.098 4.692 3.403 0.336031

73 7 4.720 4.167 2.598 0.021686

26 8 4.338 4.006 1.917 0.005972

49 9 5.989 4.545 8.297 0.787386

38 10 6.217 4.583 6.185 0.594067

524 all 5.659 4.447 6.163 1.000000

219
Table 3: Comparison of K-means clusters for lat-long-annual variability

n.sites grp mean median sd ann.pval

61 1 1.634 1.613 0.5826 2.508e-01

26 2 1.425 1.291 0.5835 1.618e-02

96 3 1.863 1.796 0.4657 1.599e-02

26 4 2.487 2.395 0.7843 4.550e-05

58 5 1.661 1.658 0.5383 3.941e-01

38 6 1.619 1.695 0.6162 3.044e-01

57 7 1.573 1.471 0.5912 7.068e-02

66 8 1.423 1.373 0.4362 2.550e-06

57 9 1.663 1.471 0.8888 6.006e-01

39 10 2.266 2.174 0.7903 1.556e-04

524 all 1.727 1.705 0.6700 1.000e+00

Table 4: Comparison of K-means clusters for lat-long-24-hr variability

n.sites grp mean median sd TF.pval

3 1 61.111 58.333 12.729 1.701e-02

59 2 4.794 4.762 2.367 3.604e-02

44 3 9.083 8.477 3.979 2.320e-06

104 4 3.807 3.333 2.131 8.750e-08

33 5 4.041 2.941 3.060 9.146e-03

109 6 4.751 4.545 2.849 1.831e-02

38 7 4.994 4.762 2.825 2.154e-01

68 8 5.165 4.202 3.342 3.111e-01

7 9 30.768 30.952 9.002 3.049e-04

59 10 5.020 3.846 3.136 1.938e-01

524 all 5.659 4.447 6.163 1.000e+00

220
Figure 179: Hierarchical and K-means clusters for the annual variability for 2016.

221
Figure 180: Hierarchical and K-means clusters for the 24-hr variability for 2016.

222
7.1.2 Cluster analysis with time series of variability values (2014-2016)

This cluster analysis used the relative variability (as a percentage of the site’s DV) from each site over 3

DV periods (2014-2016). Thus, the distance between individuals and clusters is defined as the difference

between each year’s variability values (i.e., the variability from 2014 data, the variability from 2015 data,

the variability from 2016 data) for a particular standard. Unlike the previous analysis, all input variables

are on the same scale, such that no one parameter is driving the cluster formation. Therefore, this analysis

attempts to group sites with similar levels of variability over time in order to see if those variability trends have

spatial correlation. Since this approach incorporates the variability over time, it reflects the final composite

variability value determined in the main analysis, which is the average over 3 DV periods. Hierarchical and

K-mean clustering were applied independently.

The clusters formed from this analysis is shown in Figures 181 and 182 and statistics are summarized

in Tables 5- 8. The K-means analysis used 10 clusters, which was picked based on the number of EPA

Regions. The figure also shows the hierarchical cluster results at 10 clusters for comparison. As with the

latlongvariability analysis presented in the previous section, the clusters from the hierarchical analysis have

relatively little recognizable geographic correlation. However, most of the clusters have mean variability levels

that are distinctly different from the mean dataset (note that the mean values presented here represent the

mean from all years), though this approach also resulted in more clusters with very few members, such that

the annual results only had 5 clusters with 20 or more members and the 24-hr results only had 3 clusters

with 20 or more members. The spatial distribution of the results from the K-means analysis was similar to

the hierarchical results, in that relatively little recognizable geographic correlation. However, the K-mean

algorithm resulted in more meaningful clusters in terms of number of members and the statistical significance.

Thus, while the cluster analysis conducted with the 3-year variability trends resulted in groups that were

distinct with respect to their variability levels, it showed essentially no spatial correlation, suggesting that

geographic differences in variability do not need to be taken into account.

Table 5: Comparison of hierarchical clusters for 2014-2016 variability, annual

n ̇sites grp mean median sd ann ̇pval

86 1 1.027 1.053 0.2387 1.418e-49

84 2 1.475 1.449 0.2964 9.408e-08

30 3 2.422 2.381 0.2443 2.756e-19

147 4 1.877 1.852 0.2730 1.498e-05

20 5 1.488 1.559 0.4272 3.258e-02

7 6 2.185 2.439 0.8897 2.129e-01

12 7 3.017 3.008 0.3734 3.841e-08

9 8 3.099 3.125 0.4706 1.585e-05

1 9 4.843 5.128 0.8896 NA

2 10 3.987 4.139 0.5126 9.888e-02

398 11 1.716 1.666 0.5855 1.000e+00

Table 6: Comparison of hierarchical clusters for 2014-2016 variability, 24-hr

n ̇sites grp mean median sd TF ̇pval

272 1 3.630 3.333 1.662 1.499e-08

49 2 8.034 7.895 2.365 1.311e-11

58 3 4.848 4.545 2.134 7.556e-01

9 4 11.821 11.765 2.320 1.106e-05

4 5 9.097 5.409 7.870 3.701e-01

1 6 18.480 20.312 4.399 NA

1 7 55.556 75.000 41.107 NA

1 8 25.966 29.630 7.354 NA

2 9 13.220 14.460 6.831 3.363e-01

1 10 41.250 46.667 20.340 NA

223
n ̇sites grp mean median sd TF ̇pval

398 11 4.957 4.129 4.143 1.000e+00

Table 7: Comparison of K-means clusters for 2014-2016 variability, annual

n ̇sites grp mean median sd ann ̇pval

23 1 1.027 1.053 0.2387 1.068e-14

3 2 1.475 1.449 0.2964 2.939e-01

61 3 2.422 2.381 0.2443 1.781e-38

66 4 1.877 1.852 0.2730 3.753e-04

48 5 1.488 1.559 0.4272 1.382e-03

5 6 2.185 2.439 0.8897 3.038e-01

68 7 3.017 3.008 0.3734 4.822e-50

28 8 3.099 3.125 0.4706 3.779e-16

27 9 4.843 5.128 0.8896 9.066e-17

69 10 3.987 4.139 0.5126 2.528e-56

398 11 1.716 1.666 0.5855 1.000e+00

Table 8: Comparison of K-means clusters for 2014-2016 variability, 24-hr

n ̇sites grp mean median sd TF ̇pval

2 1 3.630 3.333 1.662 4.577e-01

59 2 8.034 7.895 2.365 2.016e-13

44 3 4.848 4.545 2.134 7.770e-01

104 4 11.821 11.765 2.320 4.609e-65

37 5 9.097 5.409 7.870 3.098e-03

46 6 18.480 20.312 4.399 1.172e-26

2 7 55.556 75.000 41.107 3.319e-01

22 8 25.966 29.630 7.354 6.561e-12

73 9 13.220 14.460 6.831 7.360e-16

9 10 41.250 46.667 20.340 6.812e-04

398 11 4.957 4.129 4.143 1.000e+00

224
Figure 181: Hierarchical and K-means clusters for the annual variability for 2014-2016.

225
Figure 182: Hierarchical and K-means clusters for the 24-hr variability for 2014-2016.

226
7.1.3 Cluster analysis with time series of variability values (2012-2016)

This cluster analysis used the relative variability (as a percentage of the site’s DV) from each site over 5

DV periods (2012-2016). Thus, the distance between individuals and clusters is defined as the difference

between each year’s variability value (i.e., the variability from 2012 data, ..., the variability from 2016 data)

for a particular standard. Unlike the previous analysis, all input variables are on the same scale, such that

no one parameter is driving the cluster formation. Therefore, this analysis attempts to group sites with

similar levels of variability over time in order to see if those variability trends have spatial correlation. Since

this approach incorporates the variability over time, it partly reflects the final composite variability value

determined in the main analysis, which is the average over 3 DV periods. The extended period is evaluated

in addition to the 3 DV periods presented above in order to improve correlations that may exist with a longer

data record. Hierarchical and K-mean clustering were applied independently.

The clusters formed from this analysis is shown in Figures 183 and 184 and statistics are summarized in

Tables 9- 12. The K-means analysis used 10 clusters, which was picked based on the number of EPA Regions.

The figure also shows the hierarchical cluster results at 10 clusters for comparison. The results from this

analysis are fairly similar to the results using the 3 DV periods. The clusters are not spatially distinct;

many clusters have few members, though most have distinct variability levels. Thus, while the cluster

analysis conducted with the 5-year variability trends resulted in groups that were distinct with respect to

their variability levels, it showed essentially no spatial correlation, suggesting that geographic differences in

variability do not need to be taken into account.

Table 9: Comparison of hierarchical clusters for 2012-2016 variability, annual

n ̇sites grp mean median sd ann ̇pval

76 1 1.138 1.099 0.2967 1.272e-27

30 2 2.292 2.273 0.3028 6.841e-12

55 3 1.533 1.531 0.3474 9.338e-04

105 4 1.809 1.802 0.3136 6.702e-02

1 5 3.295 2.632 1.2706 NA

16 6 2.929 2.871 0.3333 1.926e-11

2 7 3.691 3.562 1.0601 2.315e-01

1 8 3.117 3.670 0.8943 NA

1 9 4.096 4.348 0.5319 NA

3 10 2.185 2.232 0.7266 3.884e-01

290 11 1.727 1.678 0.5550 1.000e+00

Table 10: Comparison of hierarchical clusters for 2012-2016 variability, 24-hr

n ̇sites grp mean median sd TF ̇pval

193 1 3.460 3.226 1.469 6.142e-09

25 2 7.603 7.143 2.917 1.610e-04

32 3 5.251 4.762 2.776 5.508e-01

33 4 7.187 7.143 2.787 1.074e-04

1 5 36.947 10.000 37.861 NA

1 6 13.357 7.692 10.734 NA

2 7 14.391 12.812 5.456 2.455e-01

1 8 36.444 10.000 39.143 NA

1 9 23.033 28.571 9.620 NA

1 10 27.333 18.750 23.875 NA

290 11 4.927 4.132 3.802 1.000e+00

227
Table 11: Comparison of K-means clusters for 2012-2016 variability, annual

n ̇sites grp mean median sd ann ̇pval

46 1 1.138 1.099 0.2967 1.113e-18

39 2 2.292 2.273 0.3028 5.306e-15

56 3 1.533 1.531 0.3474 8.645e-04

51 4 1.809 1.802 0.3136 1.358e-01

34 5 3.295 2.632 1.2706 2.922e-08

3 6 2.929 2.871 0.3333 2.210e-02

3 7 3.691 3.562 1.0601 8.448e-02

21 8 3.117 3.670 0.8943 5.988e-07

21 9 4.096 4.348 0.5319 5.424e-16

16 10 2.185 2.232 0.7266 2.442e-02

290 11 1.727 1.678 0.5550 1.000e+00

Table 12: Comparison of K-means clusters for 2012-2016 variability, 24-hr

n ̇sites grp mean median sd TF ̇pval

1 1 3.460 3.226 1.469 NA

72 2 7.603 7.143 2.917 1.189e-09

67 3 5.251 4.762 2.776 4.262e-01

20 4 7.187 7.143 2.787 2.264e-03

26 5 36.947 10.000 37.861 2.218e-04

75 6 13.357 7.692 10.734 2.885e-09

2 7 14.391 12.812 5.456 2.455e-01

3 8 36.444 10.000 39.143 2.978e-01

2 9 23.033 28.571 9.620 2.285e-01

22 10 27.333 18.750 23.875 2.491e-04

290 11 4.927 4.132 3.802 1.000e+00

228
Figure 183: Hierarchical and K-means clusters for the annual variability for 2012-2016.

229
Figure 184: Hierarchical and K-means clusters for the 24-hr variability for 2012-2016.

230
7.2 Spatial analysis using NOAA Climate Regions

The National Oceanic and Atmospheric Administration (NOAA) has identified 9 ”climate regions” [Thomas

and Koss, 1984], which have been identified to have distinct climatologically characteristics (more information

available at the NOAA website). This spatial grouping thus represents an independent spatial grouping with

which to evaluate regional variability characteristics. The mean annual and 24-hr variability values from sites

within these regions are compared in Figure 185 and detailed in Tables 13 and 14. The Pacific Northwest

(region 4) and the Central Northwest (region 9) stand out as having higher variability, which was seen in the

first K-means cluster analysis (using latitude, longitude, and the variability). The p-values for the annual

results are less than 0.05 for these two regions (though the p-value for region 9 is just barely less than 0.05

and the p-value for region 4 is still relatively large). The p-values for these two regions from the 24-hr results

are well above the nominal value of 0.05 and so are not significantly different from the mean dataset. Thus,

the results again make it difficult draw specific conclusions about the geographic nature of the variability

as estimated by this analysis, though the overall interpretation of these results is that most regions are not

significantly different from the mean dataset.

Table 13: Comparison of variability within NOAA climate regions, annual

Region Number n sites ann mean ann median ann sd ann pval

1 96 1.724119413 1.724137931 0.538832598 0.986301656

2 69 1.864963958 1.875 0.501458469 0.547340281

3 94 1.457900874 1.388888889 0.545905166 0.245378188

4 22 2.473011166 2.405978785 0.845285667 0.013781951

5 39 1.719076105 1.704545455 0.622791499 0.970268689

6 70 1.563384611 1.583124478 0.459051349 0.469019357

7 25 1.623411728 1.595744681 0.621819606 0.676247393

8 66 1.610690042 1.405159932 0.84597489 0.627212461

9 42 2.255001105 2.198067633 0.808280411 0.047892639

All 523 1.727970703 1.727970703 0.670071968 1

Table 14: Comparison of variability within NOAA climate regions, 24-hr

Region Number n sites TF mean TF median TF sd TF pval

1 96 5.861069073 4.545454545 6.966363904 0.924845491

2 69 4.690627066 4.545454545 1.965280928 0.632945983

3 94 3.628720758 3.125 2.092141946 0.326832786

4 22 10.13095985 7.417582418 11.55838373 0.165608839

5 39 5.280129178 5 3.051945505 0.853550834

6 70 5.893319944 4.653679654 5.146873277 0.911597545

7 25 5.383605813 4 3.836720273 0.896970478

8 66 5.93939837 3.923076923 10.5914199 0.90680014

9 42 8.69767221 7.692307692 3.979459168 0.166016078

All 523 5.660892679 4.347826087 6.168440212 1

231
Figure 185: Comparison of variability within NOAA climate regions for 2016.

232
References

D. W. Andrews. The Block-block Bootstrap: Improved Asymptotic Refinements. Cowles Foundation Discussion Papers 1370, Cowles Foundation for Research in Economics, Yale University, May 2002. URL

https://ideas.repec.org/p/cwl/cwldpp/1370.html.

P. Hall and J. L. Horowitz. Bootstrap critical values for tests based on generalized-method-of-moments

estimators. Econometrica, 64(4):891–916, 1996. ISSN 0165-0270. doi: 10.2307/2171849. URL http:

//www.jstor.org/stable/2171849.

A. Inoue and M. Shintani. Bootstrapping gmm estimators for time series. Journal of Econometrics,

133(2):531–555, 2006. URL http://EconPapers.repec.org/RePEc:eee:econom:v:133:y:2006:i:2:p:

531-555.

S. N. Lahiri. Theoretical comparisons of block bootstrap methods. The Annals of Statistics, 27(1):386–404,

1999.

R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical

Computing, Vienna, Austria, 2017.

R. K. Thomas and W. J. Koss. Regional and national monthly, seasonal, and annual temperature weighted

by area, 1895-1983. Historical Climatology Series 4-3, 1984.

233

United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC

Publication No. EPA-454/R-18-001

April, 2018