December 2009 i December 2009 EPA/600/R-08/139F 

# Integrated Science Assessment for Particulate Matter Includes Errata Sheet created on 2/10/2010 National Center for Environmental Assessment-RTP Division Office of Research and Development U.S. Environmental Protection Agency Research Triangle Park, NCDecember 2009 ii 

# Disclaimer This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.December 2009 iii 

### Errata Sheet Created 2/10/2010 for the document titled Integrated Science Assessment for Pa rticulate Matter, December 2009, Final Table or

Figure Page Erratum iv-v Table of contents for Chapte r 2 updated to correct page numbers. xv, xxiii Page numbers for Chapter 2 figures and tables corrected. 2-9 Replaced reference to “Table 6-14” in the text with “ Table 6-15 ” 2-10 Replaced reference to “Table 6-14” in the text with “ Table 6-15 ” 2-11 Replaced reference to “Table 6-14” in the text with “ Table 6-15 ” 2-12 Replaced the reference to “Figure 7-8” in the text with “ Figure 7- 7 ” (cited 3 times) Figure 2-2 2-15 Mean concentration for Laden et al. (2006, 087605 ) corrected from 14.8 to 16.4. De leted “conducted in locations where the mean annual PM 2.5 

### concentrations were <17 μg/m 3 

### ” in caption. 2-17 Replaced reference to “Table 6-17” in the text with “ Table 6-18 ” Figure 6-4 6-72 Figure replaced. Lisabeth et al. (2008, 155939 ) correctly moved to PM 2.5 

### study group. Figure 6-15 6-148 Updated HERO ID numbers. Figure 7-6 7-85 PM 2.5 

### concentrations for Laden et al. (2006, 087605 ) corrected from 17.6 to 16.4December 2009 iv 

# Table of Contents L IST OF 

## T ABLES 

## ________________________________________________________ XV 

## L IST OF 

## F IGURES 

## _______________________________________________________ XXIII 

## PM ISA P ROJECT 

## T EAM 

## _________________________________________________ XLIII 

## A UTHORS 

## , C ONTRIBUTORS 

## , R EVIEWERS 

## ______________________________________ XLVI 

## C LEAN 

## A IR 

## S CIENTIFIC 

## A DVISORY 

## C OMMITTEE FOR 

## P ARTICULATE 

## M ATTER 

## NAAQS ______ LII 

## A CRONYMS AND 

## A BBREVIATIONS 

## ____________________________________________ LIV 

## C HAPTER 

## 1. I NTRODUCTION 

## _______________________________________________ 1-2 1.1. Legislative Requirements _______________________________________________________ 16958H 

### 1-4

1H 

### 1.2. History of Reviews of the NAAQS for PM __________________________________________ 1695H 

### 1-5

12H 

### 1.3. ISA Development _____________________________________________________________ 1690H 

### 1-9

13H 

### 1.4. Document Organization _______________________________________________________ 169H 

### 1-13

14H 

### 1.5. EPA Framework for Causal Determination_________________________________________ 1692H 

### 1-14

15H 

### 1.5.1. Scientific Evidence Used in Establishing Causality____________________________ 1693H 

### 1-15

16H 

### 1.5.2. Association and Causation ______________________________________________ 1694H 

### 1-15

17H 

### 1.5.3. Evaluating Evidence for Inferring Causation _________________________________ 1695H 

### 1-15

18H 

### 1.5.4. Application of Framework for Causal Determination___________________________ 169H 

### 1-19

19H 

### 1.5.5. First Step—Determination of Causality _____________________________________ 1697H 

### 1-20

20H 

### 1.5.6. Second Step—Evaluation of Response ____________________________________ 1698H 

### 1-22

21H 

### 1.5.6.1. Effects on Human Populations ____________________________________ 169H 

### 1-22

2H 

### 1.5.6.2. Effects on Public Welfare ________________________________________ 16970H 

### 1-24

23H 

### 1.5.7. Concepts in Evaluating Adversity of Health Effects____________________________ 1697H 

### 1-24

24H 

### 1.6. Summary __________________________________________________________________ 16972H 

### 1-24

25H 

### Chapter 1 References ____________________________________________________________ 16973H 

### 1-26

26H 

## C HAPTER 

## 2. I NTEGRATIVE 

## H EALTH AND 

## W ELFARE 

## E FFECTS 

## O VERVIEW 

## _______________ 16974H 

## 2-1

27H 

### 2.1. Concentrations and Sources of Atmospheric PM_____________________________________ 16975H 

### 2-2

28H 

### 2.1.1. Ambient PM Variability and Correlations _____________________________________ 1697H 

### 2-2

29H 

### 2.1.1.1. Spatial Variability across the U.S. __________________________________ 1697H 

### 2-2

30H 

### 2.1.1.2. Spatial Variability on the Urban and Neighborhood Scales _______________ 16978H 

### 2-3

31H 

### 2.1.2. Trends and Temporal Variability ___________________________________________ 1697H 

### 2-3

32H 

### 2.1.3. Correlations between Copollutants _________________________________________ 16980H 

### 2-4

3H 

### 2.1.4. Measurement Techniques ________________________________________________ 1698H 

### 2-4

34H 

### 2.1.5. PM Formation in the Atmosphere and Removal _______________________________ 16982H 

### 2-4

35H 

### 2.1.6. Source Contributions to PM ______________________________________________ 16983H 

### 2-5

36H 

### 2.1.7. Policy-Relevant Background ______________________________________________ 16984H 

### 2-6

37H 

### 2.2. Human Exposure _____________________________________________________________ 16985H 

### 2-6

38H 

### 2.2.1. Spatial Scales of PM Exposure Assessment__________________________________ 1698H 

### 2-6

39H 

### 2.2.2. Exposure to PM Components and Copollutants _______________________________ 16987H 

### 2-7

40H 

### 2.2.3. Implications for Epidemiologic Studies ______________________________________ 1698H 

### 2-7December 2009 v 

### 2.3. Health Effects ________________________________________________________________ 2-8

42H 

### 2.3.1. Exposure to PM 2.5 

### ______________________________________________________ 1690H 

### 2-9

43H 

### 2.3.1.1. Effects of Short-Term Exposure to PM 2.5 

### _____________________________ 169H 

### 2-9

4H 

### 2.3.1.2. Effects of Long-Term Exposure to PM 2.5 

### ____________________________ 1692H 

### 2-11

45H 

### 2.3.2. Integration of PM 2.5 

### Health Effects_________________________________________ 1693H 

### 2-13

46H 

### 2.3.3. Exposure to PM 10-2.5 

### ___________________________________________________ 1694H 

### 2-18

47H 

### 2.3.3.1. Effects of Short-Term Exposure to PM 10-2.5 

### __________________________ 1695H 

### 2-18

48H 

### 2.3.4. Integration of PM 10-2.5 

### Effects_____________________________________________ 169H 

### 2-19

49H 

### 2.3.5. Exposure to UFPs _____________________________________________________ 1697H 

### 2-21

50H 

### 2.3.5.1. Effects of Short-Term Exposure to UFPs____________________________ 1698H 

### 2-21

51H 

### 2.3.6. Integration of UFP Effects _______________________________________________ 169H 

### 2-22

52H 

### 2.4. Policy Relevant Considerations _________________________________________________ 170H 

### 2-23

53H 

### 2.4.1. Potentially Susceptible Populations _______________________________________ 170H 

### 2-23

54H 

### 2.4.2. Lag Structure of PM-Morbidity and PM-Mortality Associations ___________________ 1702H 

### 2-24

5H 

### 2.4.2.1. PM-Cardiovascular Morbidity Associations __________________________ 1703H 

### 2-24

56H 

### 2.4.2.2. PM-Respiratory Morbidity Associations _____________________________ 1704H 

### 2-24

57H 

### 2.4.2.3. PM-Mortality Associations _______________________________________ 1705H 

### 2-25

58H 

### 2.4.3. PM Concentration-Response Relationship __________________________________ 1706H 

### 2-25

59H 

### 2.4.4. PM Sources and Constituents Linked to Health Effects ________________________ 170H 

### 2-26

60H 

### 2.5. Welfare Effects ______________________________________________________________ 1708H 

### 2-27

61H 

### 2.5.1. Summary of Effects on Visibility __________________________________________ 1709H 

### 2-27

62H 

### 2.5.2. Summary of Effects on Climate___________________________________________ 170H 

### 2-28

63H 

### 2.5.3. Summary of Ecological Effects of PM ______________________________________ 170H 

### 2-29

64H 

### 2.5.4. Summary of Effects on Materials__________________________________________ 1702H 

### 2-30

65H 

### 2.6. Summary of Health Effects and Welfare Effects Causal Determinations__________________ 1703H 

### 2-31

6H 

### Chapter 2 References ____________________________________________________________ 1704H 

### 2-34

67H 

## C HAPTER 

## 3. S OURCE TO 

## H UMAN 

## E XPOSURE 

## ___________________________________ 1705H 

## 3-1

68H 

### 3.1. Introduction__________________________________________________________________ 1706H 

### 3-1

69H 

### 3.2. Overview of Basic Aerosol Pr operti es _____________________________________________ 170H 

### 3-1

70H 

### 3.3. Sources, Emissions and Depositio n of Primary and Second ary PM ______________________ 1708H 

### 3-6

71H 

### 3.3.1. Emissions of Primary PM and Precursors to Secondary PM _____________________ 1709H 

### 3-8

72H 

### 3.3.2. Formation of Secondary PM _____________________________________________ 1702H 

### 3-10

73H 

### 3.3.2.1. Formation of Nitrate and Sulfate __________________________________ 1702H 

### 3-10

74H 

### 3.3.2.2. Formation of Secondary Organic Aerosol ___________________________ 1702H 

### 3-10

75H 

### 3.3.2.3. Formation of New Particles ______________________________________ 17023H 

### 3-12

76H 

### 3.3.3. Mobile Source Emissions _______________________________________________ 17024H 

### 3-13

7H 

### 3.3.3.1. Emissions from Gasoline Fueled Engines ___________________________ 17025H 

### 3-13

78H 

### 3.3.3.2. Emissions from Diesel Fueled Engines _____________________________ 17026H 

### 3-13

79H 

### 3.3.4. Deposition of PM ______________________________________________________ 1702H 

### 3-14

80H 

### 3.3.4.1. Deposition Forms ______________________________________________ 17028H 

### 3-16

81H 

### 3.3.4.2. Methods for Estimating Dry Deposition _____________________________ 17029H 

### 3-17

82H 

### 3.3.4.3. Factors Affecting Dry De position Rate s and To tals ____________________ 1703H 

### 3-18

83H 

### 3.4. Monitoring of PM ____________________________________________________________ 1703H 

### 3-20

84H 

### 3.4.1. Ambient Measurement Techniques ________________________________________ 17032H 

### 3-20

85H 

### 3.4.1.1. PM Mass_____________________________________________________ 1703H 

### 3-20

86H 

### 3.4.1.2. PM Speciation ________________________________________________ 17034H 

### 3-23

87H 

### 3.4.1.3. Multiple-Component Measurements on Individual Particles _____________ 17035H 

### 3-28

8H 

### 3.4.1.4. UFPs: Mass, Surface Area, and Number____________________________ 17036H 

### 3-29

89H 

### 3.4.1.5. PM Size Distribution ____________________________________________ 1703H 

### 3-29

90H 

### 3.4.1.6. Satellite Measurement __________________________________________ 17038H 

### 3-29

91H 

### 3.4.2. Ambient Network Design________________________________________________ 17039H 

### 3-30

92H 

### 3.4.2.1. Monitor Siting Requirements _____________________________________ 1704H 

### 3-30

93H 

### 3.4.2.2. Spatial and Tem poral Coverage___________________________________ 1704H 

### 3-31December 2009 vi 94H 

### 3.4.2.3. Network Application for Exposure Assessment with Respect to Susceptible Populations _________________________________________ 17042H 

### 3-36

95H 

### 3.5. Ambient PM Concentrations____________________________________________________ 17043H 

### 3-40

96H 

### 3.5.1. Spatial Distribution_____________________________________________________ 1704H 

### 3-41

97H 

### 3.5.1.1. Variability across the U.S. _______________________________________ 17045H 

### 3-42

98H 

### 3.5.1.2. Urban-Scale Variability__________________________________________ 17046H 

### 3-60

9H 

### 3.5.1.3. Neighborhood-Scale Variability ___________________________________ 1704H 

### 3-85

10H 

### 3.5.2. Temporal Variability ____________________________________________________ 17048H 

### 3-91

10H 

### 3.5.2.1. Regional Tr ends _______________________________________________ 17049H 

### 3-91

102H 

### 3.5.2.2. Seasonal Variations ____________________________________________ 1705H 

### 3-96

103H 

### 3.5.2.3. Hourly Variability_______________________________________________ 1705H 

### 3-97

104H 

### 3.5.3. Statistical Associations with Copollutants __________________________________ 17052H 

### 3-100

105H 

### 3.6. Mathematical Modeling of PM _________________________________________________ 17053H 

### 3-104

106H 

### 3.6.1. Estimating Source Contributions to PM Using Receptor Models ________________ 17054H 

### 3-104

107H 

### 3.6.1.1. Receptor Models _____________________________________________ 1705H 

### 3-104

108H 

### 3.6.2. Chemistry Transport Models ____________________________________________ 17056H 

### 3-109

109H 

### 3.6.2.1. Global Scale _________________________________________________ 1705H 

### 3-111

10H 

### 3.6.2.2. Regional Scale _______________________________________________ 17058H 

### 3-111

1H 

### 3.6.2.3. Local or Neighborhood Scale ____________________________________ 17059H 

### 3-113

12H 

### 3.6.3. Air Quality Model Evaluation for Air Concentrations __________________________ 1706H 

### 3-113

13H 

### 3.6.3.1. Ground-based Comparisons of Photochemical Dynamics______________ 1706H 

### 3-120

14H 

### 3.6.3.2. Predicted Chemistry for Nitrates and Related Compounds _____________ 17062H 

### 3-120

15H 

### 3.6.4. Evaluating Concentrations and Deposition of PM Components with CTMs ________ 17063H 

### 3-126

16H 

### 3.6.4.1. Global CTM Performance_______________________________________ 17064H 

### 3-126

17H 

### 3.6.4.2. Regional CTM Performance_____________________________________ 17065H 

### 3-127

18H 

### 3.7. Background PM ____________________________________________________________ 1706H 

### 3-139

19H 

### 3.7.1. Contributors to PRB Concentrations of PM_________________________________ 1706H 

### 3-139

120H 

### 3.7.1.1. Estimates of PRB Concentrations in Previous Assessments____________ 17068H 

### 3-140

12H 

### 3.7.1.2. Chemistry Transport Models for Predicting PRB Concentrations ________ 17069H 

### 3-142

12H 

### 3.8. Issues in Exposure Assessmen t for PM and it s Compon ents _________________________ 170H 

### 3-152

123H 

### 3.8.1. General Exposure Concepts ____________________________________________ 170H 

### 3-153

124H 

### 3.8.2. Personal and Microenvironmental Exposure Monitoring_______________________ 1702H 

### 3-155

125H 

### 3.8.2.1. New Developments in Personal Exposure Monitoring Instrumentation____ 1703H 

### 3-155

126H 

### 3.8.2.2. New Developments in Microenvironmental Exposure Monitoring Instrumentation_______________________________________________ 1704H 

### 3-156

127H 

### 3.8.3. Exposure Modeling ___________________________________________________ 1705H 

### 3-157

128H 

### 3.8.3.1. Time-Weighted Microenvironmental Models ________________________ 1706H 

### 3-157

129H 

### 3.8.3.2. Stochastic Population Exposure Model s ___________________________ 170H 

### 3-158

130H 

### 3.8.3.3. Dispersion Models ____________________________________________ 1708H 

### 3-160

13H 

### 3.8.3.4. Land Use Regression and GIS-Based Models ______________________ 1709H 

### 3-160

132H 

### 3.8.4. Exposure Assessment Studies __________________________________________ 1708H 

### 3-162

13H 

### 3.8.4.1. Micro-to-Neighborhood Scale Ambient PM Exposure _________________ 1708H 

### 3-162

134H 

### 3.8.4.2. Ambient PM Exposure Estimates from Central Site Monitoring Data _____ 17082H 

### 3-165

135H 

### 3.8.4.3. Infiltration ___________________________________________________ 17083H 

### 3-168

136H 

### 3.8.5. Multicomponent and Multipollutant PM Exposur es ___________________________ 17084H 

### 3-170

137H 

### 3.8.5.1. Exposure Issues Related to PM Composition _______________________ 17085H 

### 3-170

138H 

### 3.8.5.2. Exposure to PM and Copollutants ________________________________ 17086H 

### 3-175

139H 

### 3.8.6. Implications of Exposure Assessment Issues for Interpretation of Epidemiologic Studies_____________________________________________________________ 1708H 

### 3-176

140H 

### 3.8.6.1. Measurement Error____________________________________________ 1708H 

### 3-176

14H 

### 3.8.6.2. Model-Related Errors __________________________________________ 17089H 

### 3-177

142H 

### 3.8.6.3. Spatial Variability _____________________________________________ 1709H 

### 3-179

143H 

### 3.8.6.4. Temporal Variability ___________________________________________ 1709H 

### 3-181

14H 

### 3.8.6.5. Use of Surrogates for PM Exposure_______________________________ 17092H 

### 3-183

145H 

### 3.8.6.6. Compositional Differences ______________________________________ 17093H 

### 3-184

146H 

### 3.8.6.7. Conclusions _________________________________________________ 17094H 

### 3-184December 2009 vii 147H 

### 3.9. Summary and Conclusions ___________________________________________________ 17095H 

### 3-185

148H 

### 3.9.1. Concentrations and Sources of Atmospheric PM ____________________________ 17096H 

### 3-185

149H 

### 3.9.1.1. PM Source Characteristics______________________________________ 1709H 

### 3-185

150H 

### 3.9.1.2. Measurement Techniques ______________________________________ 17098H 

### 3-185

15H 

### 3.9.1.3. Ambient PM Variability and Correlations ___________________________ 1709H 

### 3-186

152H 

### 3.9.1.4. Temporal Variability ___________________________________________ 170H 

### 3-187

153H 

### 3.9.1.5. Correlations between Copollutants _______________________________ 170H 

### 3-188

154H 

### 3.9.1.6. Source Contributions to PM _____________________________________ 1702H 

### 3-188

15H 

### 3.9.1.7. Policy-Relevant Background ____________________________________ 1703H 

### 3-189

156H 

### 3.9.2. Human Exposure_____________________________________________________ 1704H 

### 3-189

157H 

### 3.9.2.1. Characterizing Human Exposure _________________________________ 1705H 

### 3-189

158H 

### 3.9.2.2. Spatial Scales of PM Exposure Assessment ________________________ 1706H 

### 3-190

159H 

### 3.9.2.3. Multicomponent and Multipol lutant PM Exposur es ___________________ 170H 

### 3-191

160H 

### 3.9.2.4. Implications for Epidemiologic Studies_____________________________ 1708H 

### 3-191

16H 

### Chapter 3 References ___________________________________________________________ 1709H 

### 3-193

162H 

## C HAPTER 

## 4. D OSIMETRY 

## __________________________________________________ 170H 

## 4-1

163H 

### 4.1. Introduction__________________________________________________________________ 17H 

### 4-1

164H 

### 4.1.1. Size Characterization of Inhaled Pa rticles____________________________________ 172H 

### 4-2

165H 

### 4.1.2. Structure of the Respiratory Tract __________________________________________ 173H 

### 4-3

16H 

### 4.2. Particle Deposition ____________________________________________________________ 174H 

### 4-5

167H 

### 4.2.1. Mechanisms of Deposition _______________________________________________ 175H 

### 4-6

168H 

### 4.2.2. Deposition Patterns _____________________________________________________ 176H 

### 4-7

169H 

### 4.2.2.1. Total Respiratory Tract Deposition__________________________________ 17H 

### 4-8

170H 

### 4.2.2.2. Extrathoracic Region ____________________________________________ 178H 

### 4-9

17H 

### 4.2.2.3. Tracheobronchial and Alveolar Region _____________________________ 179H 

### 4-10

172H 

### 4.2.2.4. Localized Deposition Sites _______________________________________ 1720H 

### 4-10

173H 

### 4.2.3. Interspecies Patterns of Depos ition________________________________________ 172H 

### 4-11

174H 

### 4.2.4. Biological Factors Mo dulating D epositio n ___________________________________ 172H 

### 4-11

175H 

### 4.2.4.1. Physical Activity _______________________________________________ 1723H 

### 4-12

176H 

### 4.2.4.2. Age _________________________________________________________ 1724H 

### 4-13

17H 

### 4.2.4.3. Gender ______________________________________________________ 1725H 

### 4-14

178H 

### 4.2.4.4. Anatomical Variability ___________________________________________ 1726H 

### 4-14

179H 

### 4.2.4.5. Respiratory Tr act Disease _______________________________________ 172H 

### 4-15

180H 

### 4.2.4.6. Hygroscopicity of Aerosols _______________________________________ 1728H 

### 4-16

18H 

### 4.2.5. Summary ____________________________________________________________ 1729H 

### 4-16

182H 

### 4.3. Clearance of Poorly Soluble Particles ____________________________________________ 1730H 

### 4-17

183H 

### 4.3.1. Clearance Mechanis ms and Kinetics ______________________________________ 173H 

### 4-17

184H 

### 4.3.1.1. Extrathoracic Region ___________________________________________ 1732H 

### 4-17

185H 

### 4.3.1.2. Tracheobronchial Region ________________________________________ 173H 

### 4-18

186H 

### 4.3.1.3. Alveolar Region _______________________________________________ 1734H 

### 4-19

187H 

### 4.3.2. Interspecies Patterns of Clearance and Retention ____________________________ 1735H 

### 4-19

18H 

### 4.3.3. Particle Translocation __________________________________________________ 1736H 

### 4-20

189H 

### 4.3.3.1. Alveolar Region _______________________________________________ 173H 

### 4-21

190H 

### 4.3.3.2. Olfactory Region_______________________________________________ 1738H 

### 4-22

19H 

### 4.3.4. Factors Modulati ng Clearance ___________________________________________ 1739H 

### 4-23

192H 

### 4.3.4.1. Age _________________________________________________________ 1740H 

### 4-23

193H 

### 4.3.4.2. Gender ______________________________________________________ 174H 

### 4-24

194H 

### 4.3.4.3. Respiratory Tr act Disease _______________________________________ 1742H 

### 4-24

195H 

### 4.3.4.4. Particle Overload ______________________________________________ 1743H 

### 4-25

196H 

### 4.3.5. Summary ____________________________________________________________ 174H 

### 4-25

197H 

### 4.4. Clearance of Solu ble Materials _________________________________________________ 1745H 

### 4-26

198H 

### 4.4.1. Clearance Mechanis ms and Kinetics ______________________________________ 1746H 

### 4-26

19H 

### 4.4.2. Factors Modulati ng Clearance ___________________________________________ 174H 

### 4-27

20H 

### 4.4.2.1. Age _________________________________________________________ 1748H 

### 4-28December 2009 viii 201H 

### 4.4.2.2. Physical Activity _______________________________________________ 1749H 

### 4-28

20H 

### 4.4.2.3. Disease______________________________________________________ 1750H 

### 4-28

203H 

### 4.4.2.4. Concurrent Exposures __________________________________________ 175H 

### 4-29

204H 

### 4.4.3. Summary ____________________________________________________________ 1752H 

### 4-29

205H 

### Chapter 4 References ____________________________________________________________ 1753H 

### 4-30

206H 

## C HAPTER 

## 5. P OSSIBLE 

## P ATHWAYS 

## / M ODES OF 

## A CTION 

## ___________________________ 1754H 

## 5-1

207H 

### 5.1. Pulmonary Ef fects ____________________________________________________________ 175H 

### 5-1

208H 

### 5.1.1. Reactive Oxygen Species ________________________________________________ 1756H 

### 5-1

209H 

### 5.1.2. Activation of Cell Signaling Pathways _______________________________________ 175H 

### 5-3

210H 

### 5.1.3. Pulmonary Infl ammati on _________________________________________________ 1758H 

### 5-4

21H 

### 5.1.4. Respiratory Tract Barrier Function _________________________________________ 1759H 

### 5-6

21H 

### 5.1.5. Antioxidant Defenses and Adaptive Responses _______________________________ 1760H 

### 5-6

213H 

### 5.1.6. Pulmonary F uncti on_____________________________________________________ 176H 

### 5-7

214H 

### 5.1.7. Allergic Disorders ______________________________________________________ 1762H 

### 5-8

215H 

### 5.1.8. Impaired Lung Def ense Mech anism s _______________________________________ 1763H 

### 5-8

216H 

### 5.1.9. Resolution of Inflammation/Progression or Exacerbation of Disease _______________ 1764H 

### 5-8

217H 

### 5.1.9.1. Factors Affecting the Retention of PM _______________________________ 1765H 

### 5-9

218H 

### 5.1.9.2. Factors Affecting the Balance of Pro/Anti-Inflammatory Mediators, Oxidants/Anti-Oxidants and Proteases/Anti-Proteases __________________ 176H 

### 5-9

219H 

### 5.1.9.3. Pre-Existing Disease ___________________________________________ 176H 

### 5-10

20H 

### 5.1.10. Pulmonary DN A Damage _______________________________________________ 1768H 

### 5-10

21H 

### 5.1.11. Epigenetic Changes ___________________________________________________ 1769H 

### 5-10

2H 

### 5.1.12. Lung Development ____________________________________________________ 170H 

### 5-11

23H 

### 5.2. Systemic Inflammation ________________________________________________________ 17H 

### 5-12

24H 

### 5.2.1. Endothelial Dysfunction and Altered Vasoreactivity ___________________________ 172H 

### 5-13

25H 

### 5.2.2. Activation of Coagulation and Acute Phase Response_________________________ 173H 

### 5-14

26H 

### 5.2.3. Atherosclerosis _______________________________________________________ 174H 

### 5-15

27H 

### 5.3. Activation of the Autonomic Nerv ous System by Pu lmonary Re flex es ___________________ 175H 

### 5-16

28H 

### 5.4. Translocation of UFPs or Soluble PM Components__________________________________ 176H 

### 5-17

29H 

### 5.5. Disease of the Cardiovascular and Other Organ Systems ____________________________ 17H 

### 5-18

230H 

### 5.6. Acute and Chronic Responses__________________________________________________ 178H 

### 5-19

231H 

### 5.7. Results of New Inhalation Studies which Contribute to Modes of Action__________________ 179H 

### 5-19

23H 

### 5.8. Gaps in Knowledge __________________________________________________________ 1780H 

### 5-22

23H 

### Chapter 5 References ____________________________________________________________ 178H 

### 5-23

234H 

## C HAPTER 

## 6. I NTEGRATED 

## H EALTH 

## E FFECTS OF 

## S HORT 

## -T ERM 

## PM E XPOSURE 

## ___________ 1782H 

## 6-1

235H 

### 6.1. Introduction__________________________________________________________________ 1783H 

### 6-1

236H 

### 6.2. Cardiovascular and Systemic Effects______________________________________________ 1784H 

### 6-2

237H 

### 6.2.1. Heart Rate and Heart Rate Variability _______________________________________ 1785H 

### 6-2

238H 

### 6.2.1.1. Epidemiologic Studies ___________________________________________ 1786H 

### 6-2

239H 

### 6.2.1.2. Controlled Human Exposure Studies ________________________________ 178H 

### 6-8

240H 

### 6.2.1.3. Toxicological Studies ___________________________________________ 178H 

### 6-10

241H 

### 6.2.2. Arrhythmia ___________________________________________________________ 1789H 

### 6-13

24H 

### 6.2.2.1. Epidemiologic Studies __________________________________________ 1790H 

### 6-13

243H 

### 6.2.2.2. Toxicological Studies ___________________________________________ 179H 

### 6-18

24H 

### 6.2.3. Ischemia ____________________________________________________________ 1792H 

### 6-20

245H 

### 6.2.3.1. Epidemiologic Studies __________________________________________ 1793H 

### 6-20

246H 

### 6.2.3.2. Controlled Human Exposure Studies _______________________________ 1794H 

### 6-22

247H 

### 6.2.3.3. Toxicological Studies ___________________________________________ 1795H 

### 6-23

248H 

### 6.2.4. Vasomotor Function____________________________________________________ 1796H 

### 6-24December 2009 ix 249H 

### 6.2.4.1. Epidemiologic Studies __________________________________________ 179H 

### 6-24

250H 

### 6.2.4.2. Controlled Human Exposure Studies _______________________________ 1798H 

### 6-26

251H 

### 6.2.4.3. Toxicological Studies ___________________________________________ 179H 

### 6-29

25H 

### 6.2.5. Blood Pressure _______________________________________________________ 1720H 

### 6-33

253H 

### 6.2.5.1. Epidemiologic Studies __________________________________________ 1720H 

### 6-33

254H 

### 6.2.5.2. Controlled Human Exposure Studies _______________________________ 1720H 

### 6-36

25H 

### 6.2.5.3. Toxicological Studies ___________________________________________ 17203H 

### 6-37

256H 

### 6.2.6. Cardiac Contractility ___________________________________________________ 17204H 

### 6-38

257H 

### 6.2.6.1. Toxicological Studies ___________________________________________ 17205H 

### 6-38

258H 

### 6.2.7. Systemic Inflammation _________________________________________________ 17206H 

### 6-39

259H 

### 6.2.7.1. Epidemiologic Studies __________________________________________ 1720H 

### 6-40

260H 

### 6.2.7.2. Controlled Human Exposure Studies _______________________________ 17208H 

### 6-44

261H 

### 6.2.7.3. Toxicological Studies ___________________________________________ 17209H 

### 6-46

26H 

### 6.2.8. Hemostasis, Thrombosis and Coagulatio n Factors____________________________ 1720H 

### 6-47

263H 

### 6.2.8.1. Epidemiologic Studies __________________________________________ 172H 

### 6-47

264H 

### 6.2.8.2. Controlled Human Exposure Studies _______________________________ 172H 

### 6-48

265H 

### 6.2.8.3. Toxicological Studies ___________________________________________ 1723H 

### 6-50

26H 

### 6.2.9. Systemic and Cardiovascular Oxidative Stress_______________________________ 1724H 

### 6-52

267H 

### 6.2.9.1. Epidemiologic Studies __________________________________________ 1725H 

### 6-52

268H 

### 6.2.9.2. Controlled Human Exposure Studies _______________________________ 1726H 

### 6-53

269H 

### 6.2.9.3. Toxicological Studies ___________________________________________ 172H 

### 6-54

270H 

### 6.2.10. Hospital Admissions and Emergency Department Visits________________________ 1728H 

### 6-56

271H 

### 6.2.10.1. All Cardiovascular Disease ______________________________________ 1729H 

### 6-60

27H 

### 6.2.10.2. Cardiac Diseases ______________________________________________ 1720H 

### 6-64

273H 

### 6.2.10.3. Ischemic Heart Disease _________________________________________ 172H 

### 6-64

274H 

### 6.2.10.4. Acute Myocardial Infarction ______________________________________ 172H 

### 6-67

275H 

### 6.2.10.5. Congestive Heart Failure ________________________________________ 1723H 

### 6-68

276H 

### 6.2.10.6. Cardiac Arrhythmias____________________________________________ 1724H 

### 6-69

27H 

### 6.2.10.7. Cerebrovascular Disease ________________________________________ 1725H 

### 6-70

278H 

### 6.2.10.8. Peripheral Vascular Disease _____________________________________ 1726H 

### 6-72

279H 

### 6.2.10.9. Copollutant Models_____________________________________________ 172H 

### 6-72

280H 

### 6.2.10.10. Concentration Response _____________________________________ 1728H 

### 6-75

281H 

### 6.2.10.11. Out of Hospital Cardiac Arrest _________________________________ 1729H 

### 6-76

28H 

### 6.2.11. Cardiovascular Mortality ________________________________________________ 17230H 

### 6-77

283H 

### 6.2.12. Summary and Causal Determinations______________________________________ 1723H 

### 6-78

284H 

### 6.2.12.1. PM 2.5 

### ________________________________________________________ 1723H 

### 6-78

285H 

### 6.2.12.2. PM 10-2.5 

### ______________________________________________________ 1723H 

### 6-81

286H 

### 6.2.12.3. UFPs________________________________________________________ 17234H 

### 6-83

287H 

### 6.3. Respiratory Effects ___________________________________________________________ 17235H 

### 6-84

28H 

### 6.3.1. Respiratory Symptoms and Medication Use _________________________________ 17236H 

### 6-84

289H 

### 6.3.1.1. Epidemiologic Studies __________________________________________ 1723H 

### 6-84

290H 

### 6.3.1.2. Controlled Human Exposure Studies _______________________________ 17238H 

### 6-93

291H 

### 6.3.2. Pulmonary F uncti on____________________________________________________ 17239H 

### 6-94

29H 

### 6.3.2.1. Epidemiologic Studies __________________________________________ 17240H 

### 6-95

293H 

### 6.3.2.2. Controlled Human Exposure Studies _______________________________ 1724H 

### 6-98

294H 

### 6.3.2.3. Toxicological Studies ___________________________________________ 1724H 

### 6-99

295H 

### 6.3.3. Pulmonary Infl ammati on _______________________________________________ 17243H 

### 6-101

296H 

### 6.3.3.1. Epidemiologic Studies _________________________________________ 1724H 

### 6-101

297H 

### 6.3.3.2. Controlled Human Exposure Studies ______________________________ 17245H 

### 6-104

298H 

### 6.3.3.3. Toxicological Studies __________________________________________ 17246H 

### 6-106

29H 

### 6.3.4. Pulmonary Oxidat ive Resp onses ________________________________________ 1724H 

### 6-110

30H 

### 6.3.4.1. Controlled Human Exposure Studies ______________________________ 17248H 

### 6-111

301H 

### 6.3.4.2. Toxicological Studies __________________________________________ 17249H 

### 6-112

302H 

### 6.3.5. Pulmonary Injury _____________________________________________________ 17250H 

### 6-114

30H 

### 6.3.5.1. Epidemiologic Studies _________________________________________ 1725H 

### 6-114

304H 

### 6.3.5.2. Controlled Human Exposure Studies ______________________________ 1725H 

### 6-114

305H 

### 6.3.5.3. Toxicological Studies __________________________________________ 17253H 

### 6-115

306H 

### 6.3.6. Allergic Responses ___________________________________________________ 17254H 

### 6-122December 2009 x 307H 

### 6.3.6.1. Epidemiologic Studies _________________________________________ 1725H 

### 6-122

308H 

### 6.3.6.2. Controlled Human Exposure Studies ______________________________ 17256H 

### 6-122

309H 

### 6.3.6.3. Toxicological Studies __________________________________________ 1725H 

### 6-123

310H 

### 6.3.7. Host Defense________________________________________________________ 17258H 

### 6-129

31H 

### 6.3.7.1. Epidemiologic Studies _________________________________________ 17259H 

### 6-129

312H 

### 6.3.7.2. Toxicological Studies __________________________________________ 17260H 

### 6-129

31H 

### 6.3.8. Respiratory ED Visits, Hospital Admissions and Physician Visits ________________ 1726H 

### 6-132

314H 

### 6.3.8.1. All Respiratory Diseases _______________________________________ 1726H 

### 6-133

315H 

### 6.3.8.2. Asthma _____________________________________________________ 17263H 

### 6-137

316H 

### 6.3.8.3. Chronic Obstructive Pulmonary Disease ___________________________ 17264H 

### 6-142

317H 

### 6.3.8.4. Pneumonia and Respiratory Infections ____________________________ 17265H 

### 6-143

318H 

### 6.3.8.5. Copollutant Models____________________________________________ 1726H 

### 6-147

319H 

### 6.3.9. Respiratory Mortality __________________________________________________ 1726H 

### 6-149

320H 

### 6.3.10. Summary and Causal Determinations_____________________________________ 17268H 

### 6-149

321H 

### 6.3.10.1. PM 2.5 

### _______________________________________________________ 17269H 

### 6-149

32H 

### 6.3.10.2. PM 10-2.5 

### _____________________________________________________ 1720H 

### 6-152

32H 

### 6.3.10.3. UFPs_______________________________________________________ 172H 

### 6-153

324H 

### 6.4. Central Nervous System Effects _______________________________________________ 172H 

### 6-154

325H 

### 6.4.1. Epidemiologic Studies _________________________________________________ 1723H 

### 6-154

326H 

### 6.4.2. Controlled Human Exposure Studies _____________________________________ 1724H 

### 6-155

327H 

### 6.4.3. Toxicologica l Studies __________________________________________________ 1725H 

### 6-155

328H 

### 6.4.3.1. Urban Air ___________________________________________________ 1726H 

### 6-155

329H 

### 6.4.3.2. CAPs ______________________________________________________ 172H 

### 6-156

30H 

### 6.4.3.3. Diesel Exhaust _______________________________________________ 1728H 

### 6-156

31H 

### 6.4.3.4. Summary of Toxicological Study Findings of CNS Effects______________ 1729H 

### 6-157

32H 

### 6.4.4. Summary and Causal Determination _____________________________________ 17280H 

### 6-157

3H 

### 6.5. Mortality __________________________________________________________________ 1728H 

### 6-158

34H 

### 6.5.1. Summary of Findings from 2004 PM AQCD ________________________________ 1728H 

### 6-158

35H 

### 6.5.2. Associations of Mortality and Short-Term Exposure to PM _____________________ 17283H 

### 6-159

36H 

### 6.5.2.1. PM 10 

### _______________________________________________________ 17284H 

### 6-160

37H 

### 6.5.2.2. PM 2.5 

### _______________________________________________________ 17285H 

### 6-174

38H 

### 6.5.2.3. Thoracic Coarse Particles (PM 10-2.5 

### )_______________________________ 17286H 

### 6-184

39H 

### 6.5.2.4. Ultrafine Particles _____________________________________________ 1728H 

### 6-190

340H 

### 6.5.2.5. Chemical Comp onents of PM____________________________________ 1728H 

### 6-191

341H 

### 6.5.2.6. Source-Apportioned PM Analyses ________________________________ 17289H 

### 6-196

342H 

### 6.5.2.7. Investigation of Concentration-Response Relationship ________________ 17290H 

### 6-197

34H 

### 6.5.3. Summary and Causal Determinations_____________________________________ 1729H 

### 6-200

34H 

### 6.5.3.1. PM 2.5 

### _______________________________________________________ 1729H 

### 6-200

345H 

### 6.5.3.2. PM 10-2.5 

### _____________________________________________________ 17293H 

### 6-201

346H 

### 6.5.3.3. UFPs_______________________________________________________ 17294H 

### 6-202

347H 

### 6.6. Attribution of Ambient PM Health Effects to Specific Constituents or Sources ____________ 17295H 

### 6-202

348H 

### 6.6.1. Evaluation Approach __________________________________________________ 17296H 

### 6-202

349H 

### 6.6.2. Findings ____________________________________________________________ 1729H 

### 6-203

350H 

### 6.6.2.1. Epidemiologic Studies _________________________________________ 17298H 

### 6-203

351H 

### 6.6.2.2. Controlled Human Exposure Studies ______________________________ 1729H 

### 6-206

352H 

### 6.6.2.3. Toxicological Studies __________________________________________ 1730H 

### 6-206

35H 

### 6.6.3. Summary by Health Effects _____________________________________________ 1730H 

### 6-210

354H 

### 6.6.4. Conclusion__________________________________________________________ 17302H 

### 6-211

35H 

### Chapter 6 References ___________________________________________________________ 1730H 

### 6-213

356H 

## C HAPTER 

## 7. I NTEGRATED 

## H EALTH 

## E FFECTS OF 

## L ONG 

## -T ERM 

## PM E XPOSURE 

## ____________ 17304H 

## 7-1

357H 

### 7.1. Introduction__________________________________________________________________ 17305H 

### 7-1

358H 

### 7.2. Cardiovascular and Systemic Effects______________________________________________ 17306H 

### 7-1

359H 

### 7.2.1. Atherosclerosis ________________________________________________________ 1730H 

### 7-2

360H 

### 7.2.1.1. Epidemiologic Studies ___________________________________________ 17308H 

### 7-2December 2009 xi 361H 

### 7.2.1.2. Toxicological Studies ____________________________________________ 17309H 

### 7-4

362H 

### 7.2.2. Venous Thromboembolism _______________________________________________ 1730H 

### 7-6

36H 

### 7.2.2.1. Epidemiologic Studies ___________________________________________ 173H 

### 7-7

364H 

### 7.2.3. Metabolic Syndromes ___________________________________________________ 1732H 

### 7-7

365H 

### 7.2.3.1. Epidemiologic Studies ___________________________________________ 173H 

### 7-7

36H 

### 7.2.3.2. Toxicological Studies ____________________________________________ 1734H 

### 7-7

367H 

### 7.2.4. Systemic Inflammation, Immune Function, and Blood Coagulation ________________ 1735H 

### 7-8

368H 

### 7.2.4.1. Epidemiologic Studies ___________________________________________ 1736H 

### 7-8

369H 

### 7.2.4.2. Toxicological Studies ____________________________________________ 173H 

### 7-8

370H 

### 7.2.5. Renal and Vascular Function______________________________________________ 1738H 

### 7-9

371H 

### 7.2.5.1. Epidemiologic Studies __________________________________________ 1739H 

### 7-10

372H 

### 7.2.5.2. Toxicological Studies ___________________________________________ 17320H 

### 7-11

37H 

### 7.2.6. Autonomic Functi on____________________________________________________ 1732H 

### 7-12

374H 

### 7.2.6.1. Toxicological Studies ___________________________________________ 1732H 

### 7-12

375H 

### 7.2.7. Cardiac changes ______________________________________________________ 1732H 

### 7-12

376H 

### 7.2.7.1. Toxicologica l studies ___________________________________________ 17324H 

### 7-12

37H 

### 7.2.8. Left Ventricular Mass and Function ________________________________________ 17325H 

### 7-13

378H 

### 7.2.9. Clinical Outcomes in Epidemiologic Studies _________________________________ 17326H 

### 7-13

379H 

### 7.2.10. Cardiovascular Mortality ________________________________________________ 1732H 

### 7-17

380H 

### 7.2.11. Summary and Causal Determinations______________________________________ 17328H 

### 7-18

381H 

### 7.2.11.1. PM 2.5 

### ________________________________________________________ 17329H 

### 7-18

382H 

### 7.2.11.2. PM 10-2.5 

### ______________________________________________________ 1730H 

### 7-19

38H 

### 7.2.11.3. UFPs________________________________________________________ 173H 

### 7-19

384H 

### 7.3. Respiratory Effects ___________________________________________________________ 1732H 

### 7-20

385H 

### 7.3.1. Respiratory Symptoms an d Disease Incidence ______________________________ 173H 

### 7-20

386H 

### 7.3.1.1. Epidemiologic Studies __________________________________________ 1734H 

### 7-20

387H 

### 7.3.2. Pulmonary F uncti on____________________________________________________ 1735H 

### 7-26

38H 

### 7.3.2.1. Epidemiologic Studies __________________________________________ 1736H 

### 7-26

389H 

### 7.3.2.2. Toxicological Studies ___________________________________________ 173H 

### 7-30

390H 

### 7.3.3. Pulmonary Infl ammati on ________________________________________________ 1738H 

### 7-32

391H 

### 7.3.3.1. Epidemiologic Studies __________________________________________ 1739H 

### 7-32

392H 

### 7.3.3.2. Toxicological Studies ___________________________________________ 17340H 

### 7-32

39H 

### 7.3.4. Pulmonary Oxidat ive Resp onse __________________________________________ 1734H 

### 7-34

394H 

### 7.3.4.1. Toxicological Studies ___________________________________________ 17342H 

### 7-34

395H 

### 7.3.5. Pulmonary Injury ______________________________________________________ 1734H 

### 7-35

396H 

### 7.3.5.1. Toxicological Studies ___________________________________________ 1734H 

### 7-35

397H 

### 7.3.6. Allergic Responses ____________________________________________________ 17345H 

### 7-38

398H 

### 7.3.6.1. Epidemiologic Studies __________________________________________ 17346H 

### 7-38

39H 

### 7.3.6.2. Toxicological Studies ___________________________________________ 1734H 

### 7-39

40H 

### 7.3.7. Host Defense_________________________________________________________ 17348H 

### 7-40

401H 

### 7.3.7.1. Epidemiologic Studies __________________________________________ 17349H 

### 7-40

402H 

### 7.3.7.2. Toxicological Studies ___________________________________________ 17350H 

### 7-40

403H 

### 7.3.8. Respiratory Mortality ___________________________________________________ 1735H 

### 7-41

40H 

### 7.3.9. Summary and Causal Determinations______________________________________ 17352H 

### 7-42

405H 

### 7.3.9.1. PM 2.5 

### ________________________________________________________ 1735H 

### 7-42

406H 

### 7.3.9.2. PM 10-2.5 

### ______________________________________________________ 17354H 

### 7-43

407H 

### 7.3.9.3. UFPs________________________________________________________ 1735H 

### 7-44

408H 

### 7.4. Reproductive, Developmental, Prenatal and Neonatal Outcomes_______________________ 17356H 

### 7-44

409H 

### 7.4.1. Epidemiologic Studies __________________________________________________ 1735H 

### 7-44

410H 

### 7.4.1.1. Low Birth Weight ______________________________________________ 17358H 

### 7-45

41H 

### 7.4.1.2. Preterm Birth _________________________________________________ 17359H 

### 7-48

412H 

### 7.4.1.3. Growth Restriction _____________________________________________ 17360H 

### 7-51

413H 

### 7.4.1.4. Birth Defects __________________________________________________ 1736H 

### 7-52

41H 

### 7.4.1.5. Infant Mortality ________________________________________________ 17362H 

### 7-53

415H 

### 7.4.1.6. Decrements in Sperm Quality ____________________________________ 1736H 

### 7-58

416H 

### 7.4.2. Toxicologica l Studies ___________________________________________________ 17364H 

### 7-58

417H 

### 7.4.2.1. Female Reproductive Effects _____________________________________ 17365H 

### 7-59December 2009 xii 418H 

### 7.4.2.2. Male Reproductive Effects _______________________________________ 1736H 

### 7-60

419H 

### 7.4.2.3. Multiple Generation Effects ______________________________________ 1736H 

### 7-62

420H 

### 7.4.2.4. Receptor Mediated Effects _______________________________________ 17368H 

### 7-63

421H 

### 7.4.2.5. Developmental Effects __________________________________________ 17369H 

### 7-63

42H 

### 7.4.3. Summary and Causal Determinations______________________________________ 1730H 

### 7-67

423H 

### 7.4.3.1. PM 2.5 

### ________________________________________________________ 173H 

### 7-67

42H 

### 7.4.3.2. PM 10-2.5 

### ______________________________________________________ 1732H 

### 7-68

425H 

### 7.4.3.3. UFPs________________________________________________________ 173H 

### 7-68

426H 

### 7.5. Cancer, Mutagenicity, and Genotoxicity ___________________________________________ 1734H 

### 7-68

427H 

### 7.5.1. Epidemiologic Studies __________________________________________________ 1735H 

### 7-69

428H 

### 7.5.1.1. Lung Cancer Mortality and Incidence_______________________________ 1736H 

### 7-70

429H 

### 7.5.1.2. Other Cancers ________________________________________________ 173H 

### 7-73

430H 

### 7.5.1.3. Markers of Exposure or Susceptibility ______________________________ 1738H 

### 7-73

431H 

### 7.5.2. Toxicologica l Studies ___________________________________________________ 1739H 

### 7-75

432H 

### 7.5.2.1. Mutagenesis and Genotoxicity ____________________________________ 17380H 

### 7-76

43H 

### 7.5.2.2. Carcinogenesis________________________________________________ 1738H 

### 7-79

43H 

### 7.5.2.3. Summary of Toxico logical Studies _________________________________ 17382H 

### 7-80

435H 

### 7.5.3. Epigenetic Studies and Other Heritable DNA mutations ________________________ 1738H 

### 7-80

436H 

### 7.5.4. Summary and Causal Determinations______________________________________ 17384H 

### 7-81

437H 

### 7.5.4.1. PM 2.5 

### ________________________________________________________ 17385H 

### 7-81

438H 

### 7.5.4.2. PM 10-2.5 

### ______________________________________________________ 17386H 

### 7-82

439H 

### 7.5.4.3. UFPs________________________________________________________ 1738H 

### 7-82

40H 

### 7.6. Mortality ___________________________________________________________________ 1738H 

### 7-82

41H 

### 7.6.1. Recent Studies of Long-Term Exposure to PM and Mortality ____________________ 17389H 

### 7-84

42H 

### 7.6.2. Composition and Source-Oriented Analyses of PM ___________________________ 17390H 

### 7-89

43H 

### 7.6.3. Within-City Effects of PM Exposure________________________________________ 1739H 

### 7-90

4H 

### 7.6.4. Effects of Different Long-term Exposure Windows ____________________________ 17392H 

### 7-92

45H 

### 7.6.5. Summary and Causal Determinations______________________________________ 1739H 

### 7-95

46H 

### 7.6.5.1. PM 2.5 

### ________________________________________________________ 17394H 

### 7-95

47H 

### 7.6.5.2. PM 10-2.5 

### ______________________________________________________ 17395H 

### 7-97

48H 

### 7.6.5.3. UFPs________________________________________________________ 17396H 

### 7-97

49H 

### Chapter 7 References ____________________________________________________________ 1739H 

### 7-98

450H 

## C HAPTER 

## 8. P OPULATIONS 

## S USCEPTIBLE TO 

## PM- RELATED 

## H EALTH 

## E FFECTS 

## ___________ 17398H 

## 8-1

451H 

### 8.1. Potentially Susceptible Populations _______________________________________________ 1739H 

### 8-3

452H 

### 8.1.1. Lifestage _____________________________________________________________ 1740H 

### 8-3

453H 

### 8.1.1.1. Older Adults ___________________________________________________ 1740H 

### 8-3

45H 

### 8.1.1.2. Children ______________________________________________________ 17402H 

### 8-5

45H 

### 8.1.2. Pregnancy and Developmental Effects ______________________________________ 17403H 

### 8-5

456H 

### 8.1.3. Gender _______________________________________________________________ 1740H 

### 8-6

457H 

### 8.1.4. Race/Ethnicity _________________________________________________________ 17405H 

### 8-7

458H 

### 8.1.5. Gene-Environment Interaction ____________________________________________ 17406H 

### 8-7

459H 

### 8.1.6. Pre-Existing Disease ____________________________________________________ 1740H 

### 8-9

460H 

### 8.1.6.1. Cardiovascular Diseases _________________________________________ 17408H 

### 8-9

461H 

### 8.1.6.2. Respiratory Illnesses ___________________________________________ 17409H 

### 8-12

462H 

### 8.1.6.3. Respiratory Contributions to Cardiovascular Effects ___________________ 1740H 

### 8-13

463H 

### 8.1.6.4. Diabetes and Obesity ___________________________________________ 174H 

### 8-13

46H 

### 8.1.7. Socioeconomic Status __________________________________________________ 1742H 

### 8-14

465H 

### 8.1.8. Summary ____________________________________________________________ 1743H 

### 8-15

46H 

### Chapter 8 References ____________________________________________________________ 174H 

### 8-17

467H 

## C HAPTER 

## 9. W ELFARE 

## E FFECTS 

## ____________________________________________ 1745H 

## 9-1

468H 

### 9.1. Introduction__________________________________________________________________ 1746H 

### 9-1

469H 

### 9.2. Effects on Visibility ____________________________________________________________ 174H 

### 9-1December 2009 xiii 470H 

### 9.2.1. Introduction ___________________________________________________________ 1748H 

### 9-1

471H 

### 9.2.2. Background ___________________________________________________________ 1749H 

### 9-2

472H 

### 9.2.2.1. Non-PM Visibility Effects _________________________________________ 17420H 

### 9-5

473H 

### 9.2.2.2. PM Visibility Effects _____________________________________________ 1742H 

### 9-5

47H 

### 9.2.2.3. Direct Optical Measurements ______________________________________ 1742H 

### 9-8

475H 

### 9.2.2.4. Value of Good Visual Air Quality __________________________________ 17423H 

### 9-10

476H 

### 9.2.3. Monitoring and Assessment _____________________________________________ 1742H 

### 9-10

47H 

### 9.2.3.1. Aerosol Properties _____________________________________________ 17425H 

### 9-11

478H 

### 9.2.3.2. Spatial Patterns _______________________________________________ 17426H 

### 9-16

479H 

### 9.2.3.3. Urban and Regional Patterns _____________________________________ 1742H 

### 9-23

480H 

### 9.2.3.4. Temporal Trends ______________________________________________ 17428H 

### 9-31

481H 

### 9.2.3.5. Causes of Haze _______________________________________________ 17429H 

### 9-37

482H 

### 9.2.4. Urban Visibility Valuation and Preference ___________________________________ 17430H 

### 9-65

483H 

### 9.2.4.1. Urban Visibility Preference Studies ________________________________ 1743H 

### 9-67

48H 

### 9.2.4.2. Denver, Colorado Urban Visibility Preference Study ___________________ 17432H 

### 9-68

485H 

### 9.2.4.3. Phoenix, Arizona Urban Visibility Preference Study ___________________ 1743H 

### 9-69

486H 

### 9.2.4.4. British Columbia, Canada Urban Visibility Preference Study_____________ 1743H 

### 9-69

487H 

### 9.2.4.5. Washington, DC Urban Visibility Preference Studies___________________ 17435H 

### 9-69

48H 

### 9.2.4.6. Urban Visibility Valuation Studies__________________________________ 17436H 

### 9-71

489H 

### 9.2.5. Summary of Effects on Visibility __________________________________________ 1743H 

### 9-72

490H 

### 9.3. Effects on Climate ___________________________________________________________ 17438H 

### 9-74

491H 

### 9.3.1. The Climate Effects of Aerosols __________________________________________ 17439H 

### 9-74

492H 

### 9.3.2. Overview of Aerosol Me asurement Capabilities ______________________________ 1740H 

### 9-82

493H 

### 9.3.2.1. Satellite Remote Sensing ________________________________________ 174H 

### 9-82

49H 

### 9.3.2.2. Focused Field Campaigns _______________________________________ 1742H 

### 9-88

495H 

### 9.3.2.3. Ground-Based In Situ Measurement Networks _______________________ 1743H 

### 9-89

496H 

### 9.3.2.4. In Situ Aerosol Profiling Programs _________________________________ 174H 

### 9-91

497H 

### 9.3.2.5. Ground-Based Remote Sensing Measurement Networks _______________ 1745H 

### 9-95

498H 

### 9.3.2.6. Synergy of Measurements and Model Simulations ____________________ 1746H 

### 9-96

49H 

### 9.3.3. Assessments of Aerosol Characterization and Climate Forcing __________________ 174H 

### 9-99

50H 

### 9.3.3.1. The Use of Measured Aerosol Properties to Improve Models ___________ 1748H 

### 9-100

501H 

### 9.3.3.2. Intercomparisons of Satellite Measurements and Model Simulation of Aerosol Optical Depth _________________________________________ 1749H 

### 9-103

502H 

### 9.3.3.3. Satellite-Based Estimates of Aerosol Direct Radiative Forcing __________ 17450H 

### 9-105

503H 

### 9.3.3.4. Satellite-Based Estimates of Anthropogenic Component of Aerosol Direct Radiative Forcing ________________________________________ 1745H 

### 9-111

504H 

### 9.3.3.5. Aerosol-Cloud Interactions and Indirect Forcing _____________________ 17452H 

### 9-112

50H 

### 9.3.3.6. Remote Sensing of Aerosol-Cloud Interactions and Indirect Forcing _____ 17453H 

### 9-113

506H 

### 9.3.3.7. In Situ Studies of Aerosol-Cloud Interactions________________________ 1745H 

### 9-116

507H 

### 9.3.4. Outstanding Issues ___________________________________________________ 1745H 

### 9-117

508H 

### 9.3.5. Concluding Remarks __________________________________________________ 17456H 

### 9-120

509H 

### 9.3.6. Modeling the Effect of Aerosols on Climate_________________________________ 1745H 

### 9-121

510H 

### 9.3.6.1. Introduction__________________________________________________ 17458H 

### 9-121

51H 

### 9.3.6.2. Modeling of Atmospheric Aerosols________________________________ 17459H 

### 9-124

512H 

### 9.3.6.3. Calculating Aerosol Direct Radiative Forcing________________________ 17460H 

### 9-129

513H 

### 9.3.6.4. Calculating Aerosol Indirect Forcing_______________________________ 1746H 

### 9-137

514H 

### 9.3.6.5. Aerosol in the Climate Model s ___________________________________ 17462H 

### 9-145

51H 

### 9.3.6.6. Impacts of Aerosols on Climate Model Simulations ___________________ 17463H 

### 9-153

516H 

### 9.3.6.7. Outstanding Issues____________________________________________ 1746H 

### 9-157

517H 

### 9.3.6.8. Conclusions _________________________________________________ 17465H 

### 9-158

518H 

### 9.3.7. Fire as a Special Source of PM Welfare Effects _____________________________ 1746H 

### 9-159

519H 

### 9.3.8. Radiative Effects of Volcanic Aerosols ____________________________________ 1746H 

### 9-160

520H 

### 9.3.8.1. Explosive Volcanic Activity ______________________________________ 17468H 

### 9-160

521H 

### 9.3.9. Other Special Sources and Effects _______________________________________ 17469H 

### 9-164

52H 

### 9.3.9.1. Glaciers and Snowpack ________________________________________ 1740H 

### 9-167

523H 

### 9.3.9.2. Radiative Forcing by Anthropogenic Surface Albedo Change: BC in Snow and Ice ________________________________________________ 174H 

### 9-169

524H 

### 9.3.9.3. Effects on Local and Regional Climate ____________________________ 1742H 

### 9-170December 2009 xiv 52H 

### 9.3.10. Summary of Effects on Climate__________________________________________ 1743H 

### 9-171

526H 

### 9.4. Ecological Effects of PM______________________________________________________ 174H 

### 9-172

527H 

### 9.4.1. Introduction _________________________________________________________ 1745H 

### 9-172

528H 

### 9.4.1.1. Ecosystem Scale, Function, and Structure _________________________ 1746H 

### 9-173

529H 

### 9.4.1.2. Ecosystem Services ___________________________________________ 174H 

### 9-174

530H 

### 9.4.2. Deposition of PM _____________________________________________________ 1748H 

### 9-174

531H 

### 9.4.2.1. Forms of Deposition ___________________________________________ 1749H 

### 9-175

532H 

### 9.4.2.2. Components of PM Depos ition___________________________________ 17480H 

### 9-175

53H 

### 9.4.2.3. Magnitude of Dr y Deposit ion ____________________________________ 1748H 

### 9-179

534H 

### 9.4.3. Direct Effects of PM on Vegetation _______________________________________ 17482H 

### 9-182

53H 

### 9.4.3.1. Effects of Coarse-mode Particles_________________________________ 17483H 

### 9-182

536H 

### 9.4.4. PM and Altered Radiative Flux __________________________________________ 1748H 

### 9-183

537H 

### 9.4.5. Effects of Trace Metals on Ecosystems____________________________________ 17485H 

### 9-183

538H 

### 9.4.5.1. Effects on Soil Chemistry _______________________________________ 17486H 

### 9-185

539H 

### 9.4.5.2. Effects on Soil Microbes and Plant Uptake via Soil ___________________ 1748H 

### 9-186

540H 

### 9.4.5.3. Plant Response to Metals ______________________________________ 1748H 

### 9-189

541H 

### 9.4.5.4. Effects on Aquatic Ecosystems __________________________________ 17489H 

### 9-192

542H 

### 9.4.5.5. Effects on Animals ____________________________________________ 17490H 

### 9-192

543H 

### 9.4.5.6. Biomagnification acro ss Trophic Levels____________________________ 1749H 

### 9-193

54H 

### 9.4.5.7. Effects near Smelters and Roadsides _____________________________ 17492H 

### 9-194

54H 

### 9.4.5.8. Toxicity to Mosses and Lichens __________________________________ 17493H 

### 9-196

546H 

### 9.4.6. Organic Compounds __________________________________________________ 1749H 

### 9-196

547H 

### 9.4.7. Summary of Ecological Effects of PM _____________________________________ 17495H 

### 9-199

548H 

### 9.5. Effects on Materials _________________________________________________________ 17496H 

### 9-201

549H 

### 9.5.1. Effects on Paint ______________________________________________________ 1749H 

### 9-202

50H 

### 9.5.2. Effects on Metal Surfaces ______________________________________________ 17498H 

### 9-202

51H 

### 9.5.3. Effects on Stone _____________________________________________________ 1749H 

### 9-203

52H 

### 9.5.4. Summary of Effects on Materials_________________________________________ 1750H 

### 9-203

53H 

### Chapter 9 References ___________________________________________________________ 1750H 

### 9-204

54H 

## A NNEX 

## A. A TMOSPHERIC 

## S CIENCE 

## __________________________________________ 17502H 

## A-1

5H 

### Annex A References ___________________________________________________________ 17503H 

### A-353

56H 

## A NNEX 

## B. D OSIMETRY 

## ___________________________________________________ 17504H 

## B-1

57H 

### Annex B References _____________________________________________________________ 1750H 

### B-7

58H 

## A NNEX 

## C. C ONTROLLED 

## H UMAN 

## E XPOSURE 

## S TUDIES 

## ____________________________ 17506H 

## C-1

59H 

### Annex C References ____________________________________________________________ 1750H 

### C-14

560H 

## A NNEX 

## D. T OXICOLOGICAL 

## S TUDIES 

## _________________________________________ 17508H 

## D-1

561H 

### Annex D References ___________________________________________________________ 17509H 

### D-172

562H 

## A NNEX 

## E. E PIDEMIOLOGIC 

## S TUDIES 

## _________________________________________ 1750H 

## E-1

563H 

### Annex E References ___________________________________________________________ 175H 

### E-524

564H 

## A NNEX 

## F. S OURCE 

## A PPORTIONMENT 

## S TUDIES 

## __________________________________ 1752H 

## F-1

56H 

### Annex F References _____________________________________________________________ 1753H 

### F-11December 2009 xv 

# List of Tables

56H Table 1-1. Summary of NAAQS promulgated for PM, 1971-2006. ___________________________________________ 1754H 1-6

567H Table 1-2. Aspects to aid in judging caus ality.__________________________________________________________ 175H 1-20

568H Table 1-3. Weight of evidence for causal deter mination.__________________________________________________ 1756H 1-22

569H Table 2-1. Summary of causal determinations for short-term exposure to PM 2.5 . ________________________________ 175H 2-9

570H Table 2-2. Summary of causal determinations for long-term exposure to PM 2.5 . _______________________________ 1758H 2-11

571H Table 2-3. Summary of causal determinations for short-term exposure to PM 10-2.5 . _____________________________ 1759H 2-18

572H Table 2-4. Summary of causal determinations for short-term exposure to UFPs._______________________________ 17520H 2-21

573H Table 2-5. Summary of causality determinat ion for welfare effects. _________________________________________ 1752H 2-27

574H Table 2-6. Summary of PM causal determinations by exposure durati on and health outcome. ____________________ 1752H 2-32

57H Table 2-7. Summary of PM causal determina tions for welfare effects _______________________________________ 17523H 2-33

576H Table 3-1. Characteristics of ambient fine (ultrafine pl us accumulation-mode) and coarse particles._________________ 17524H 3-4

57H Table 3-2. Constituents of atmospheric part icles and their major sources._____________________________________ 1752H 3-7

578H Table 3-3. Proximity to PM 2.5 and PM 10 monitors for total population a by city.__________________________________ 17526H 3-35

579H Table 3-4. Proximity to PM 2.5 and PM 10 monitors for children age 0-4 yr, children age 5-17 yr, and adults age 65 yr and older. a _______________________________________________________________ 1752H 3-37

580H Table 3-5. Proximity to PM 2.5 and PM 10 monitors for adults age 65 yr and older a by city. _________________________ 17528H 3-38

581H Table 3-6. Proximity to PM 2.5 and PM 10 monitors based on the population i dentified as white, black, Hispanic, or non-Hispanic a . _______________________________________________________________ 17529H 3-39

582H Table 3-7. Proximity to PM 2.5 and PM 10 monitors based on the population below or above the poverty line, population over age 25 with less than hi gh school education, popul ation over 25 with high school education, and population over 25 with college education or more a . ______________________ 17530H 3-40

583H Table 3-8. PM 2.5 distributions derived from AQS data (concentration in μg/m 3 ).________________________________ 1753H 3-44

584H Table 3-9. PM 10-2.5 distributions derived from AQS data (concentration in μg/m 3 ). ______________________________ 17532H 3-47

58H Table 3-10. PM 10 distributions derived from AQS data (concentration in μg/m 3 ). ________________________________ 1753H 3-49

586H Table 3-11. Inter-sampler comparison statistics for each pair of 24-h PM 2.5 monitors reporting to AQS for Boston, MA._________________________________________________________________________ 17534H 3-63

587H Table 3-12. Inter-sampler comparison statistics for each pair of 24-h PM 2.5 monitors reporting to AQS for Pittsbu rgh, PA. ______________________________________________________________________ 1753H 3-67

58H Table 3-13. Inter-sampler comparison statistics for each pair of 24-h PM 2.5 monitors reporting to AQS for Los Angeles, CA._____________________________________________________________________ 17536H 3-69

589H Table 3-14. Inter-sampler comparison statistics for each pair of 24-h PM 10 monitors reporting to AQS for Boston, MA. ___________________________________________________________________________ 1753H 3-75

590H Table 3-15. Inter-sampler comparison statistics for each pair of 24-h PM 10 monitors reporting to AQS for Pittsburgh, PA. _________________________________________________________________________ 17538H 3-78

591H Table 3-16. Inter-sampler comparison statistics for each pair of 24-h PM 10 monitors reporting to AQS for Los Angeles, CA. _______________________________________________________________________ 17539H 3-81December 2009 xvi 592H Table 3-17. Example of emissions factors (ng/km) for trace elements under variable speed and steady speed driving conditions for PM emi tted by diesel and gasoline engines. ___________________________ 17540H 3-106

593H Table 3-18. Estimates of annual average natural background concentrations of PM 2.5 and PM 10 (μg/m 3 ) from Trijonis et al. (1990, 157058)._________________________________________________________ 1754H 3-140

594H Table 3-19. Annual and quarterly mean PM 2.5 concentrations ( μ g/m 3 ) measured at IMPROVE sites in 2004.________________________________________________________________________________ 17542H 3-142

59H Table 3-20. Annual and quarterly mean PM 2.5 concentrations ( μ g/m 3 ) for the CMAQ “base case” at IMPROVE sites in 2004._________________________________________________________________ 17543H 3-150

596H Table 3-21. Annual and quarterly mean PM 2.5 concentrations ( μ g/m 3 ) for the CMAQ PRB simulations at IMPROVE sites in 2004._________________________________________________________________ 1754H 3-151

597H Table 3-22. Annual and quarterly mean of the CMAQ-predicted base case PM 2.5 concentrations ( μ g/m 3 ) in the U.S. EPA CONUS regions in 2004. ___________________________________________________ 1754H 3-151

598H Table 3-23. Annual and quarterly mean of the CMAQ-predicted PRB PM 2.5 concentrations ( μ g/m 3 ) in the U.S. EPA CONUS regions in 2004. _____________________________________________________ 17546H 3-152

59H Table 3-24. Examples of studies comparing near-road pers onal exposures with fixed site ambient concentrations. ________________________________________________________________________ 1754H 3-164

60H Table 4-1. Breathing patterns with activi ty level in adult human male. _______________________________________ 17548H 4-12

601H Table 6-1. Characteristics of epidemiologic studies in vestigating associations between PM and changes in HRV._________________________________________________________________________ 17549H 6-6

602H Table 6-2. Epidemiologic studies of ventri cular arrhythmia and ambient PM concentration, in patients with implantable cardiove rter defibrillators. ___________________________________________________ 1750H 6-15

603H Table 6-3. PM Concentrations reported in epidemiol ogic studies ECG changes suggestive of ischemia. _____________________________________________________________________________ 175H 6-22

604H Table 6-4. PM concentrations reported in epidemi ologic studies of va somotor function._________________________ 1752H 6-26

605H Table 6-5. Mean PM concentrations reported in epidemi ologic studies of blood pressure. _______________________ 1753H 6-35

60H Table 6-6. PM concentrations reported in epidemiol ogic studies of inflammation, hemostasis, thrombosis, coagulation factor s and oxidativ e stress. ___________________________________________ 1754H 6-42

607H Table 6-7. Description of ICD-9 and ICD-10 codes for diseases of the circulatory system. _______________________ 175H 6-57

608H Table 6-8. Characterization of ambient PM concentrati ons in epidemiologic st udies of hospital admission and ED visits for ca rdiovascular diseases. ___________________________________________ 1756H 6-63

609H Table 6-9. PM concentrations reported in studies of out-of-hospital cardiac arrest. _____________________________ 175H 6-77

610H Table 6-10. Characterization of ambient PM concentrations from epidemiol ogic studies of respiratory morbidity and short-term exposures in asthmatic children and adults. ______________________________ 1758H 6-87

61H Table 6-11. PAMCHAR PM 10-2.5 inflammation results with ambient PM.______________________________________ 1759H 6-119

612H Table 6-12. Other ambient PM – in vivo PM 10-2.5 studies – BALF results, 18-24 h post-IT exposure.________________ 17560H 6-119

613H Table 6-13. Description of ICD-9 and ICD-10 codes for diseases of the respiratory system. ______________________ 1756H 6-133

614H Table 6-14. PM concentrations in epidemiologic st udies of respiratory diseases. ______________________________ 17562H 6-146

615H Table 6-15. Overview of U.S. and Canadian multicity PM studies of mortality analyzed in the 2004 PM AQCD and the PM ISA b ._________________________________________________________________ 17563H 6-159

61H Table 6-16. NMMAPS national and regional percentage increase in all-cause, cardio-respiratory, and other-cause mortality associated with a 10 μg/m 3 increase in PM 10 at lag 1 day for the periods 1987-1994, 1995-2000, and 1987-2000. ______________________________________________ 17564H 6-163December 2009 xvii 617H Table 6-17. Effect modification of composit ion on the estimated percent increase in mortality with a 10 μ g/m 3 increase in PM 2.5 . _________________________________________________________________ 1756H 6-194

618H Table 6-18. Study-specific PM 2.5 factor/source categories associated with health effects. ________________________ 1756H 6-207

619H Table 7-1. Characterization of ambient PM concentrations from studies of subclinical measures of cardiovascular diseases and long-term exposure. ______________________________________________ 1756H 7-11

620H Table 7-2. Characterization of ambient PM concentrations from studies of clinical cardiovascular diseases and long -term exposure. __________________________________________________________ 17568H 7-14

621H Table 7-3. Characterization of ambient PM concentrations from studies of respiratory symptoms/disease and l ong-term exposures. _________________________________________________ 17569H 7-22

62H Table 7-4. Characterization of ambient PM concentrations from studies of FEV 1 and long-term exposures. ____________________________________________________________________________ 1750H 7-27

623H Table 7-5. Characterization of ambient PM concentra tions from studies of reproductive, developmental, prenatal and neonatal outcomes and l ong-term exposure.___________________________ 175H 7-46

624H Table 7-6. Characterization of ambient PM concentrations from recent st udies of cancer and long-term exposures to PM. _______________________________________________________________________ 1752H 7-70

625H Table 7-7. Associations* between ambient PM concentrati ons from select studies of lung cancer mortality and incidence. __________________________________________________________________ 1753H 7-72

62H Table 7-8. Characterization of ambient PM concentrations from studies of mortality and long-term exposures to PM. _______________________________________________________________________ 1754H 7-83

627H Table 7-9. Comparison of results from ACS intra-urban analysis of Los Angeles and New York City using kriging or land use regr ession to estimate exposure. _______________________________________ 175H 7-91

628H Table 7-10. Distribution of the effect of a hypothetical reduction of 10 μg/m 3 PM 10 in 2000 on all-cause mortality 2000-2009 in Switzerland. _________________________________________________________ 1756H 7-94

629H Table 8-1. Definitions of susceptible and vu lnerable in the PM literature.______________________________________ 175H 8-2

630H Table 8-2. Susceptibility fa ctors evaluated. _____________________________________________________________ 1758H 8-3

631H Table 8-3. Percent of the U.S. population with respirat ory diseases, cardiovascular diseases, and diabetes. ______________________________________________________________________________ 1759H 8-11

632H Table 9-1. Regional Planning Organization websites with visib ility characterization and source attribution assess ment information. _________________________________________________________ 17580H 9-17

63H Table 9-2. Summary of urban visibilit y preference studies.________________________________________________ 1758H 9-67

634H Table 9-3. Top-of-atmosphere, cloud-free, instantaneous direct aerosol radi ative forcing dependence on aerosol and surface properties.__________________________________________________________ 17582H 9-81

635H Table 9-4. Summary of major satellite measurements current ly available for the tropospheric aerosol characterization and radiat ive forcing research.________________________________________________ 17583H 9-83

63H Table 9-5. List of major intensive field ex periments that are relevant to aerosol research in a variety of aerosol regimes around the globe c onducted in the past two decades. _____________________________ 17584H 9-93

637H Table 9-6. Summary of major U.S. surface in situ and remote sensing networks for the tropospheric aerosol characterization and r adiative forci ng research. _________________________________________ 1758H 9-94

638H Table 9-7. Summary of approaches to estimating the aeros ol direct radiative forcing in three categories: (1) satellite retr ievals; (2) satellite-model integrations; and (3) model simulati ons.___________________________________________________________________________ 17586H 9-106

639H Table 9-8. Summary of seasonal and annual average clear-sky DRF (W/m 2 ) at the TOA and the surface (SFC) over global OCEAN der ived with different methods and data. ________________________ 1758H 9-109December 2009 xviii 640H Table 9-9. Summary of seasonal and annual average clear-sky DRF (W/m 2 ) at the TOA and the surface (SFC) over global LAND derived with different methods and data.__________________________ 1758H 9-110

641H Table 9-10. Estimates of anthropogenic components of aerosol optical depth (T ant ) and clear-sky DRF at the TOA from model simulations. ________________________________________________________ 17589H 9-112

642H Table 9-11. Anthropogenic emissions of aerosol s and precursors for 2000 and 1750. __________________________ 17590H 9-126

643H Table 9-12. Summary of statistics of AeroCom Exper iment A results from 16 global models. _____________________ 1759H 9-127

64H Table 9-13. SO 4 2– mass loading, MEE and AOD at 550 nm, shortwav e radiative forcing at the top of the atmosphere, and normalized forcing with respect to AOD and mass. ______________________________ 17592H 9-131

645H Table 9-14. Particulate organic matter (POM) and BC mass loading, AOD at 550 nm, shortwave radiative forcing at the top of the atmosphere, and normalized forcing with respect to AOD and mass. ____________________________________________________________________________ 17593H 9-132

64H Table 9-15. Differences in present day and pre-i ndustrial outgoing solar radiation (W/m 2 ) in the different experiments.__________________________________________________________________________ 17594H 9-141

647H Table 9-16. Forcings used in IPCC AR4 simulati ons of 20th century climate change. ___________________________ 1759H 9-145

648H Table 9-17. Climate forcings (1880-2003) used to driv e GISS climate simulat ions, along with the surface air temperature changes obtained for several periods. ___________________________________ 17596H 9-154

649H Table 9-18. Overview of the different aerosol indirect effects and their sign of the net radiative flux change at the top of the atmosphere (TOA). _________________________________________________ 1759H 9-166

650H Table 9-19. Overview of the different aerosol indirect e ffects and their implications for the global mean net shortwave radiation of the surface Fsfc (columns 2-4) and for precipitation (columns 5-7). ________________________________________________________________________________ 17598H 9-166

651H Table 9-20. Recent studies highlighting POP occurrence and fate in the major arctic compartments._______________ 1759H 9-168

652H Table 9-21. Factors potentially important in estimating Hg exposure.________________________________________ 1760H 9-177

653H Table A-1. Summary of integrated and continuous sampl ers included in the field comparison. ____________________ 1760H A-1

654H Table A-2. Summary of PM 2.5 and PM 10 FRM and FEM samplers. __________________________________________ 17602H A-5

65H Table A-3. Measurement and analytical specific ations for filter analysis of mass, elements, ions, and carbon. _______________________________________________________________________________ 17603H A-7

65H Table A-4. Measurement and analytical specif ications for filter analysis of organi c species. ______________________ 17604H A-9

657H Table A-5. Measurement and analytical specifications for continuous mass and mass surrogate instruments. __________________________________________________________________________ 17605H A-11

658H Table A-6. Measurement and analytical specificat ions for continuous elements. ______________________________ 1760H A-14

659H Table A-7. Measurement and analytical specif ications for continuous NO 3 -. _________________________________ 1760H A-15

60H Table A-8. Measurement and analytical specif ications for continuous SO 4 2– . _________________________________ 17608H A-17

61H Table A-9. Measurement and analytical specifications for ions other than NO 3 – and SO 4 2– .______________________ 17609H A-19

62H Table A-10. Measurement and analytical specificat ions for continuous carbon. ________________________________ 1760H A-20

63H Table A-11. Summary of mass measurement comparisons. _______________________________________________ 176H A-23

64H Table A-12. Summary of element and liquid water content measurement comparisons. _________________________ 1762H A-31

65H Table A-13. Summary of PM 2.5 NO 3 - measurement comparisons.___________________________________________ 1763H A-32

6H Table A-14. Summary of PM 2.5 SO 4 2– measurement comparisons __________________________________________ 1764H A-38

67H Table A-15. Summary of PM 2.5 carbon measurement comparisons. _________________________________________ 1765H A-41December 2009 xix 68H Table A-16. Summary of particle mass spectro meter measurement comparisons.______________________________ 176H A-48

69H Table A-17. Summary of key parameters for TD-GC/MS and pyrolysis-GC/MS. _______________________________ 176H A-52

670H Table A-18. Relevant Spatial Scales for PM 10 , PM 2.5 , and PM 10-2.5 Measurement_______________________________ 1768H A-54

671H Table A-19. Major routine operati ng air monitoring networks a ______________________________________________ 1769H A-56

672H Table A-20. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Atlanta, GA. __________________________________________________________________________ 17620H A-98

673H Table A-21. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Birmingham, AL. ______________________________________________________________________ 1762H A-102

674H Table A-22. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Boston, MA. _________________________________________________________________________ 1762H A-106

675H Table A-23. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Chicago, IL. _________________________________________________________________________ 17623H A-111

67H Table A-24. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Denver, CO. _________________________________________________________________________ 17624H A-116

67H Table A-25. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Detroit, MI. __________________________________________________________________________ 17625H A-120

678H Table A-26. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Houston, TX._________________________________________________________________________ 1762H A-123

679H Table A-27. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Los Angeles, CA._________________________________________________________________________ 1762H A-127

680H Table A-28. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for New York, NY. ___________________________________________________________________________ 17628H A-131

681H Table A-29. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Philadelphi a, PA. _____________________________________________________________________ 17629H A-136

682H Table A-30. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Phoenix, AZ._________________________________________________________________________ 17630H A-140

683H Table A-31. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Pittsburgh, PA. _______________________________________________________________________ 1763H A-144

684H Table A-32. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Riverside, CA.________________________________________________________________________ 17632H A-148

685H Table A-33. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for Seattle, WA. _________________________________________________________________________ 1763H A-152

68H Table A-34. Inter-sampler correlation statistics for each pair of PM 2.5 monitors reporting to AQS for St. Louis, MO. __________________________________________________________________________ 17634H A-155

687H Table A-35. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Atlanta, GA. _________________________________________________________________________ 17635H A-159

68H Table A-36. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Birmingham, AL. ______________________________________________________________________ 1763H A-163

689H Table A-37. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Boston, MA. _________________________________________________________________________ 1763H A-166

690H Table A-38. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Chicago, IL. _________________________________________________________________________ 17638H A-170December 2009 xx 691H Table A-39. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Denver, CO. _________________________________________________________________________ 17639H A-174

692H Table A-40. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Detroit, MI. __________________________________________________________________________ 17640H A-178

693H Table A-41. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Houston, TX._________________________________________________________________________ 1764H A-181

694H Table A-42. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Los Angeles, CA._________________________________________________________________________ 17642H A-184

695H Table A-43. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for New York, NY. ___________________________________________________________________________ 17643H A-188

69H Table A-44. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Philadelphi a, PA. _____________________________________________________________________ 1764H A-191

697H Table A-45. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Phoenix, AZ._________________________________________________________________________ 17645H A-195

698H Table A-46. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Pittsburgh, PA. _______________________________________________________________________ 1764H A-200

69H Table A-47. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Riverside, CA.________________________________________________________________________ 1764H A-204

70H Table A-48. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for Seattle, WA. _________________________________________________________________________ 17648H A-207

701H Table A-49. Inter-sampler correlation statistics for each pair of PM 10 monitors reporting to AQS for St. Louis, MO. __________________________________________________________________________ 17649H A-211

702H Table A-50. Correlation coefficients of hourly and da ily average particle number, surface and volume concentrations in select ed particle size ranges.______________________________________________ 17650H A-212

703H Table A-51. Different receptor models used in the Super site source apportionment studies: chemical mass balance. _______________________________________________________________________ 1765H A-273

704H Table A-52. Different receptor models used in the Supersites source apportionment studies: factor analysi s. ____________________________________________________________________________ 17652H A-274

705H Table A-53. Different receptor models used in the Supersites source apportionment studies: tracerbased methods. ______________________________________________________________________ 17653H A-278

706H Table A-54. Different receptor models used in the Supersites source apportionment studies: meteorology-based methods.____________________________________________________________ 17654H A-280

70H Table A-55. Source Profiles: Part I__________________________________________________________________ 1765H A-286

708H Table A-56. PM 2.5 receptor model results ( μ g/m 3 ) ______________________________________________________ 1765H A-289

709H Table A-57. PM 10 receptor model resu lts (mass percent) ________________________________________________ 1765H A-290

710H Table A-58. Exposure Assessment Study Summaries __________________________________________________ 17658H A-291

71H Table A-59. Examples of studies showing developments with UFP sampling methods since the 2004 PM AQCD. __________________________________________________________________________ 17659H A-317

712H Table A-60. Summary of in-vehicle studies of exposure assessment._______________________________________ 1760H A-318

713H Table A-61. Summary of personal PM exposure studies with no indoor source during 2002-2008. ________________ 176H A-320

714H Table A-62. Summary of PM specie s exposure studies. _________________________________________________ 1762H A-324

715H Table A-63. Summary of personal PM exposur e source apportionment studies. ______________________________ 1763H A-337December 2009 xxi 716H Table A-64. Summary of PM inf iltration studies. _______________________________________________________ 1764H A-339

71H Table A-65. Summary of PM – copollut ant exposure studies. _____________________________________________ 1765H A-350

718H Table B-1. Ultrafine disposition in humans. ____________________________________________________________ 176H B-1

719H Table B- 2. Ultrafine disposit ion in animals. ____________________________________________________________ 176H B-2

720H Table B- 3. In vitro studies of ul trafine dis position. _______________________________________________________ 1768H B-3

721H Table B-4. Olfactory particl e translocation. ____________________________________________________________ 1769H B-3

72H Table B-5. Studies of respiratory tract mucosal and macrophage clearance as a function of age. __________________ 1760H B-5

723H Table C-1. Cardiovascula r effects.___________________________________________________________________ 176H C-1

724H Table C-2. Respiratory effects.______________________________________________________________________ 1762H C-9

725H Table C- 3. Central nervous system effe cts. ___________________________________________________________ 1763H C-13

726H Table D-1. Cardiovascula r effects.___________________________________________________________________ 1764H D-1

72H Table D-2. Respiratory effects: in vitro studies. ________________________________________________________ 1765H D-31

728H Table D-3. Respiratory effects: in vivo studies. ________________________________________________________ 176H D-81

729H Table D-4. Effects related to immunity and allergy. ____________________________________________________ 176H D-116

730H Table D-5. Effects of the cent ral nervous system. _____________________________________________________ 1768H D-155

731H Table D-6. Reproductive and devel opmental effects. __________________________________________________ 1769H D-157

732H Table D-7. Mutagenic/genotoxic effects in bacterial cultures. ____________________________________________ 17680H D-163

73H Table D-8. Mutagenicity and genotoxicity data summar y: In vitro and in vivo.________________________________ 1768H D-166

734H Table E-1 Short-term exposure – cardiovascular morbidity outcomes: PM 10 __________________________________ 17682H E-1

735H Table E-2. Short-term exposure - cardiovascular morbidity studies: PM 10-2.5 .__________________________________ 17683H E-19

736H Table E-3. Short-term exposure - cardiovascular morbidity studies: PM 2.5 (including PM components/sources). __________________________________________________________________ 17684H E-21

73H Table E-4. Short-term exposure-cardiovascular morbi dity studies: Other size fractions. ________________________ 17685H E-79

738H Table E-5. Short-term exposure-cardiovascular: ED/HA PM 10 ____________________________________________ 1768H E-85

739H Table E-6. Short-term exposure-cardiovascular-ED/HA - PM 10-2.5 . ________________________________________ 1768H E-104

740H Table E-7. Short-term exposure – cardiovascular: ED/HA PM 2.5 (including PM components/sources)_____________ 1768H E-107

741H Table E-8. Short-term exposure-cardiovascula r-ED/HA-other size fractions. ________________________________ 17689H E-130

742H Table E-9. Short-term exposure-respiratory morbidity outcomes -PM 10 . ____________________________________ 17690H E-134

743H Table E-10. Short-term exposure - respiratory morbidity outcomes - PM 10-2.5 . _________________________________ 1769H E-169

74H Table E-11. Short-term exposure - respiratory morbidity outcomes - PM 2.5 (including components/sources). _________________________________________________________________ 17692H E-173

745H Table E-12. Short-term exposure-respiratory-ED/HA-PM 10 ._______________________________________________ 17693H E-230

746H Table E-13. Short-term exposure-respiratory-ED/HA-PM 10-2.5 . _____________________________________________ 17694H E-256

74H Table E-14. Short-term exposure-respiratory-ED/HA-PM 2.5 (including PM components/sources). _________________ 17695H E-262

748H Table E-15. Short-term exposure-respiratory-ED/HA-Other Size Fractions. __________________________________ 1769H E-284

749H Table E-16. Short-term exposure-mortality - PM 10 . _____________________________________________________ 1769H E-293

750H Table E-17. Short-term exposure-mortality - PM 10-2.5 .____________________________________________________ 17698H E-343December 2009 xxii 751H Table E-18. Short-term exposure-mortality - PM 2.5 (including PM c omponents/sources).________________________ 1769H E-346

752H Table E-19. Short-term exposure-mortality - other PM size fractions. _______________________________________ 170H E-360

753H Table E-20. Long-term exposure - cardiovascular morbidity outcomes - PM 10 .________________________________ 170H E-363

754H Table E-21. Long-term effects-cardiovascular- PM 2.5 (including PM c omponents/sources). ______________________ 1702H E-372

75H Table E-22. Long-term exposure - respiratory morbidity outcomes - PM 10 . ___________________________________ 1703H E-383

756H Table E-23. Long-term exposure - respiratory morbidity outcomes - PM 10-2.5 . _________________________________ 1704H E-407

75H Table E-24. Long-term exposure - respiratory morbidity outcomes - PM 2.5 (including PM components/sources). _________________________________________________________________ 1705H E-412

758H Table E-25. Long-term exposure - respiratory morbidity outcomes - other PM size fractions. ____________________ 1706H E-432

759H Table E-26. Long-term exposure - cancer outcomes - PM 10 . ______________________________________________ 170H E-434

760H Table E-27. Long-term exposure - cancer outcomes - PM 2.5 (including PM components/sources). ________________ 1708H E-437

761H Table E-28. Long-term exposure - cancer outcomes - other PM size fractions. _______________________________ 1709H E-440

762H Table E-29. Long-term exposure - reproductive outcomes - PM 10 . _________________________________________ 170H E-441

763H Table E-30. Long-term exposure-mortality - PM 10 .______________________________________________________ 17H E-491

764H Table E-31. Long-term exposure-mortality - PM 10 .______________________________________________________ 172H E-498

765H Table E-32. Long-term exposure-mortality - PM 10-2.5 . ____________________________________________________ 173H E-499

76H Table E-33. Long-term exposure-mortality - PM 2.5 (including PM c omponents/sources). ________________________ 174H E-500

76H Table E-34. Long-term exposure - central ner vous system outc omes - PM.__________________________________ 175H E-521

768H Table F-1. Epidemiologic studies of ambient PM sources, fact ors, or constituents______________________________ 176H F-1

769H Table F-2. Human clinical studies of ambient PM s ources, factors, or constituents _____________________________ 17H F-6

70H Table F-3. Toxicological studies of ambient PM s ources, factors, or constituents ______________________________ 178H F-7December 2009 xxiii 

# List of Figures

71H Figure 1-1. Identification of studies fo r inclusion in the ISA. ________________________________________________ 179H 1-11

72H Figure 2-1. Summary of effect estimates (per 10 μg/m 3 ) by increasing concentration from U.S. studies examining the association between short-term exposure to PM 2.5 and cardiovascular and respiratory effect s, and mortality ___________________________________________________________ 1720H 2-14

73H Figure 2-2. Summary of effect estimates (per 10 μg/m 3 ) by increasing concentration from U.S. studies examining the association between long-term exposure to PM 2.5 and cardiovascular and respiratory effect s, and mortality ___________________________________________________________ 172H 2-15

74H Figure 2-3. Summary of U.S. studies examining the asso ciation between short-term exposure to PM 10-2.5 and cardiovascular morbidity/mortality and respiratory morbidity/mortality_____________________ 172H 2-20

75H Figure 3-1. Particle size distribut ions by number and volume. _______________________________________________ 1723H 3-2

76H Figure 3-2. X-ray spectra and scanning elec tron microscopy images of individual particles.________________________ 1724H 3-5

7H Figure 3-3. Detailed source categorization of anthropogenic emissions of primary PM 2.5 , PM 10 and gaseous precursor species SO 2 , NO X , NH 3 and VOCs for 2002 in units of million metric tons (MMT). ____________________________________________________________________________ 1725H 3-9

78H Figure 3-4. Primary emissions and formation of SOA through gas, cloud and condensed phase reactions. _____________________________________________________________________________ 1726H 3-12

79H Figure 3-5. Schematic of the resistance-in-serie s analogy for atmospheric deposition.___________________________ 172H 3-15

780H Figure 3-6. The relationship between particle diameter and V d for particles. ___________________________________ 1728H 3-19

781H Figure 3-7. PM 2.5 monitor distribution in comparison with population density, Boston CSA. _______________________ 1729H 3-33

782H Figure 3-8. PM 10 monitor distribution in comparison with population density, Boston CSA.________________________ 1730H 3-34

783H Figure 3-9. Three-yr avg 24-h PM 2.5 concentration by county deriv ed from FRM or FRM-like data, 2005-2007. ____________________________________________________________________________ 173H 3-43

784H Figure 3-10. Three-yr avg 24-h PM 10-2.5 concentration by county derived from co-located low volume FRM PM 10 and PM 2.5 monitors, 2005-2007. ___________________________________________________ 1732H 3-46

785H Figure 3-11. Three-yr avg 24-h PM 10 concentration by county deriv ed from FRM or FEM monitors, 2005-2007. ____________________________________________________________________________ 173H 3-48

786H Figure 3-12. Three-yr avg 24-h PM 2.5 OC concentrations measured at CSN sites across the U.S., 2005- 2007._________________________________________________________________________________ 1734H 3-52

78H Figure 3-13. Three-yr avg 24-h PM 2.5 EC concentrations measured at CSN sites across the U.S., 2005- 2007._________________________________________________________________________________ 1735H 3-53

78H Figure 3-14. Three-yr avg 24-h PM 2.5 SO 4 2– concentrations measured at CS N sites across the U.S., 2005-2007. ____________________________________________________________________________ 1736H 3-54

789H Figure 3-15. Three-yr avg 24-h PM 2.5 NO 3 – concentrations measured at CS N sites across the U.S., 2005-2007. ____________________________________________________________________________ 173H 3-55

790H Figure 3-16. Three-yr avg 24-h PM 2.5 NH 4 + concentrations measured at CS N sites across the U.S., 2005-2007. ____________________________________________________________________________ 1738H 3-56

791H Figure 3-18. Seasonally-stratified 3-yr avg PM 2.5 speciation estimates fo r 2005-2007 derived using the SANDWICH method. ____________________________________________________________________ 1739H 3-58

792H Figure 3-19. Locations of PM 2.5 monitors and major highw ays, Boston, MA. ____________________________________ 1740H 3-61December 2009 xxiv 793H Figure 3-20. Seasonal distributi on of 24-h avg PM 2.5 concentrations by site for Boston, MA, 2005-2007.______________ 174H 3-62

794H Figure 3-21. Locations of PM 2.5 monitors and major highw ays, Pitts burgh, PA.__________________________________ 1742H 3-65

795H Figure 3-22. Seasonal distributi on of 24-h avg PM 2.5 concentrations by site for Pittsburgh, PA, 2005- 2007._________________________________________________________________________________ 1743H 3-66

796H Figure 3-23. Locations of PM 2.5 monitors and major highwa ys, Los Angeles, CA.________________________________ 174H 3-68

79H Figure 3-24. Seasonal distributi on of 24-h avg PM 2.5 concentrations by site for Los Angeles, CA, 2005- 2007._________________________________________________________________________________ 1745H 3-69

798H Figure 3-25. Inter-sampler correlations for 24-h PM 2.5 as a function of dist ance between monitors in Boston, MA. ___________________________________________________________________________ 1746H 3-71

79H Figure 3-26. Inter-sampler correlations for 24-h PM 2.5 as a function of dist ance between monitors in Pittsburgh, PA. _________________________________________________________________________ 174H 3-71

80H Figure 3-27. Inter-sampler correlations for 24-h PM 2.5 as a function of distanc e between monitors in Los Angeles, CA.___________________________________________________________________________ 1748H 3-72

801H Figure 3-28. Seasonal distributi on of 24-h avg PM 10-2.5 concentrations by site __________________________________ 1749H 3-73

802H Figure 3-29. Locations of PM 10 monitors and major highw ays, Boston, MA.____________________________________ 1750H 3-74

803H Figure 3-30. Seasonal distributi on of 24-h avg PM 10 concentrations by site for Boston, MA, 2005-2007. ______________ 175H 3-75

804H Figure 3-31. Locations of PM 10 monitors and major highw ays, Pittsburgh, PA. __________________________________ 1752H 3-76

805H Figure 3-32. Seasonal distributi on of 24-h avg PM 10 concentrations by site for Pittsburgh, PA, 2005- 2007._________________________________________________________________________________ 1753H 3-77

806H Figure 3-33. Locations of PM 10 monitors and major highwa ys, Los Angel es, CA. ________________________________ 1754H 3-80

807H Figure 3-34. Seasonal distributi on of 24-h avg PM 10 concentrations by site for Los Angeles, CA, 2005- 2007._________________________________________________________________________________ 175H 3-81

80H Figure 3-35. Inter-sampler correlations for 24-h PM 10 as a function of dist ance between monitors in Boston, MA. ___________________________________________________________________________ 1756H 3-82

809H Figure 3-36. Inter-sampler correlations for 24-h PM 10 as a function of dist ance between monitors in Pittsburgh, PA. _________________________________________________________________________ 175H 3-83

810H Figure 3-37. Inter-sampler correlations for 24-h PM 10 as a function of distanc e between monitors in Los Angeles, CA.___________________________________________________________________________ 1758H 3-83

81H Figure 3-38. Bin-wise Spearman correlation coefficients in aerosol particle number concentrations between the Ift (urban background) and the Eis enbahn-strasse (city/urban center) sites in Leipzig, Ge rmany. ______________________________________________________________________ 1759H 3-84

812H Figure 3-39. Dimensionless concentration as a function of height at windward and leeward locations and street canyon aspec t ratios (H/W). __________________________________________________________ 1760H 3-86

813H Figure 3-40. Inter-sampler correlations for 24-h PM 2.5 and PM 10 as a function of distance between monitors for samplers located wi thin 4 km (neighborhood scale). __________________________________ 176H 3-87

814H Figure 3-41. Particle size distributions measured at va rious distances from the 710 freeway in Los Angeles, CA (top), and particle number concentration as a f unction of distance from the 710 freeway (bottom).____________________________________________________________________ 1762H 3-88

815H Figure 3-42. Mass distributions for BaP at a high traffic urban center (HTC), high traffic urban periphery (HTP), low traffic urban center (LTC), low traffic urban periphery (LTP), and low traffic industrial urban periphery (LTI P) in Sev ille, Spain. _____________________________________________ 1763H 3-90

816H Figure 3-43. Mass distributions for 16 PAHs at a high tr affic city center in Seville, Spain.__________________________ 1764H 3-91December 2009 xxv 817H Figure 3-44. Ambient 24-h PM 2.5 concentrations in the U.S., 1999-2007, showing A) ambient concentrations, B) number of trends sites above the 24-h NAAQS and C) trends by U.S. EPA Region.___________________________________________________________________________ 1765H 3-92

81H Figure 3-45. Ambient annual PM 2.5 concentrations in the U.S., 1999-2007, showing A) ambient concentrations, B) number of trends sites above the annual NAAQS and C) trends by U.S. EPA Region.___________________________________________________________________________ 176H 3-93

819H Figure 3-46. Ambient 24-h PM 10 concentrations in the U.S., 1988-2007, showing A) ambient concentrations, B) number of trends sites above the 24-h NAAQS and C) trends by U.S. EPA Region.___________________________________________________________________________ 176H 3-94

820H Figure 3-47. Regional and seasonal trends in annual PM 2.5 compostion from 2002 to 2007 derived using the SANDWICH method. _________________________________________________________________ 1768H 3-95

821H Figure 3-48. UFP size distribution at highway (site A) and background (site B) sites in Los Angeles, CA, during summer and winter seasons, with winter broken into day and ev ening distributions. ______________ 1769H 3-97

82H Figure 3-49. Diel plot generated from hourly FRM-like PM 2.5 data ( μ g/m 3 ) stratified by weekday (left) and weekend (right) for Pittsburgh, PA, and Seattle, WA, 2005-2007. __________________________________ 170H 3-98

823H Figure 3-50. Diel plots generated from hourly FEM PM 10 data ( μ g/m 3 ) stratified by weekday (left) and weekend (right) for Chicago, IL, and Phoenix, AZ, 2005-2007. ____________________________________ 17H 3-99

824H Figure 3-51. Average diel variation in total particle number (ToN) and total particle volume (ToV) on weekdays (left column) and Sundays (right column) from two sites in Denmark: one in a busy street canyon (Jagtv) and one measuring urban background (HCØ). __________________________ 172H 3-100

825H Figure 3-52. Distribution of correlations between 24-h avg PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily max 8-h avg O 3 for the U.S. stratified by season (2005-2007)._____________ 173H 3-101

826H Figure 3-53. Distribution of correlations between 24-h avg PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily max 8-h avg O 3 for the U.S. stratified by season (2005-2007)._____________ 174H 3-102

827H Figure 3-54. Schematic of organic composition of particulate emissions from gasoli ne-fueled vehicles. _____________ 175H 3-105

82H Figure 3-55. Source category contributions to PM 2.5 at a number of sites in the East derived using PMF. ____________ 176H 3-108

829H Figure 3-56. Pearson correlation coefficients for source category contributions to PM 2.5 between the 10 Regional Air Pollution Study /Regional Air Monitoring System (RAPS/RAMS) monitoring sites in St. Louis. ______________________________________________________________________ 17H 3-109

830H Figure 3-57. Pearson correlation coefficients for source contributions to PM 10-2.5 between the 10 Regional Air Pollution Study /Regional Air Monitoring System (RAPS/RAMS) monitoring sites in St. Louis. ______________________________________________________________________ 178H 3-109

831H Figure 3-58. Eight km southeast U.S. CMAQ-UCD domain zoomed over Tampa Bay, FL.________________________ 179H 3-115

832H Figure 3-59. Two km southeast U.S. CMAQ-UCD domain zoomed over Tampa Bay, FL. ________________________ 1780H 3-115

83H Figure 3-60. Hourly average CMAQ-UCD predictions and measured observations of NO (top), NO 2 (middle), and total NO X (bottom) concentrati ons for May 1-31, 2002. ______________________________ 178H 3-116

834H Figure 3-61. CMAQ-UCD predictions and measured observati ons of ethene concentrations at Sydney, FL for May 1-31, 2002. __________________________________________________________________ 1782H 3-117

835H Figure 3-62. CMAQ-UCD predictions and measured observati ons of isoprene concentrations at Sydney, FL for May 1-31, 2002. __________________________________________________________________ 1783H 3-118

836H Figure 3-63. CMAQ-UCD predictions and measured observations of PM 2.5 concentrations at Sidney, FL for May 1-31, 2002. ____________________________________________________________________ 1784H 3-119

837H Figure 3-64. CMAQ-UCD predictions of HNO 3 – concentrations and corresponding measured observations at Sydney, FL, for May 1-31, 2002.______________________________________________ 1785H 3-121December 2009 xxvi 83H Figure 3-65. CMAQ-UCD predictions of NH 3 concentrations and corres ponding measured observations at Sydney, FL, for May 1-31, 2002. ________________________________________________________ 1786H 3-122

839H Figure 3-66. CMAQ-UCD predictions of pNO 3 – concentrations and corresponding measured observations at Sydney, FL, for 1-31 May, 2002.______________________________________________ 178H 3-123

840H Figure 3-67. CMAQ-UCD predictions of the ratio of HNO 3 to total NO 3 and corresponding measured observations at Sydney, FL, for May 1-31, 2002.______________________________________________ 178H 3-124

841H Figure 3-68. CMAQ-UCD predicted size and chemical-form fractions of total NO 3 – for days in May 2002 with measured observations. _____________________________________________________________ 1789H 3-125

842H Figure 3-69. Scatter plot of total nitrate (HNO 3 plus pNO 3 – ) wet deposition (mg N/m 2 /yr) of the model mean versus measurements for the North American Deposition Program (NADP) network. ____________ 1790H 3-127

843H Figure 3-70. Scatter plot of total SO 4 2– wet deposition (mg S/m 2 /yr) of the model mean versus measurements for the National Atmospheric Deposition Program (NADP) network. __________________ 179H 3-127

84H Figure 3-71. CMAQ modeling domains for the OAQPS risk and exposure assessments: 36 km outer parent domain in black; 12 km western U.S. (WUS) domain in red; 12 km eastern U.S. (EUS) domain in blue. __________________________________________________________________ 1792H 3-128

845H Figure 3-72. 12-km EUS Summer sulfate PM. __________________________________________________________ 1793H 3-129

846H Figure 3-73. 12-km EUS Winter nitrate PM. ____________________________________________________________ 1794H 3-130

847H Figure 3-74. 12-km EUS Winter total nitrate (HNO 3 + total pNO 3 – ). __________________________________________ 1795H 3-131

84H Figure 3-75. 12-km EUS annual sulfat e wet deposition.___________________________________________________ 1796H 3-132

849H Figure 3-76. 12-km EUS annual nitrat e wet deposition. ___________________________________________________ 179H 3-133

850H Figure 3-77. CMAQ vs. measured air concentrations from east-coast sites in the IMPROVE, CSN (labeled STN), and CASTNet sites in the summer of 2002 for sulfate (left) and ammonium (right). _______________________________________________________________________________ 1798H 3-134

851H Figure 3-78. Comparison of CMAQ-predicted and NADP-measured NH 4 + wet deposition ________________________ 179H 3-134

852H Figure 3-79. CMAQ-predicted (red symbols and lines) and 12-h measured (blue symbols and lines) NH 3 and SO 4 2– surface concentrations at high and low c oncentration grid cells in North Carolina in July 2004. __________________________________________________________________________ 1780H 3-135

853H Figure 3-80. Surface grid cell (layer 1) analysis of the sensitivity of NH X deposition and transport to the change in NH 3 V d in CMAQ. ______________________________________________________________ 1780H 3-136

854H Figure 3-81. Total column analysis for NH 3 (left) and NH X (right) showing modeled NH 3 emissions, transformation, and transport throughout the mixed layer and up to the free troposphere. ______________ 17802H 3-136

85H Figure 3-82. Range of influence (where 50% of emitted NH 3 deposits) from the high concentration Sampson County grid cell in t he June 2002 CMAQ simulation of V d sensitiv ities._____________________ 17803H 3-137

856H Figure 3-83. Areal extent of the change in NH X range of influence as predicted by CMAQ for the Sampson County high concentration grid cell (c enter of range circles) in June 2002 using the base case and sensitivity case V d . ______________________________________________________ 17804H 3-138

857H Figure 3-84. IMPROVE monitoring site loca tions. _______________________________________________________ 17805H 3-141

85H Figure 3-85. 12-km EUS Summer SO 4 2– PM. ___________________________________________________________ 17806H 3-144

859H Figure 3-86 12-km EUS Winter NO 3 - PM. _____________________________________________________________ 1780H 3-145

860H Figure 3-87. 12-km EUS Winter total nitrate (HNO 3 + total particulate NO 3 – ). __________________________________ 1780H 3-145

861H Figure 3-88. Monthly average of PM 2.5 concentrations measured at IMPROVE sites in the East and Midwest for 2004. ______________________________________________________________________ 17809H 3-146December 2009 xxvii 862H Figure 3-89. Monthly average of PM 2.5 concentrations measured at IMPROVE sites in the West for 2004.________________________________________________________________________________ 1780H 3-147

863H Figure 3-90. Distribution of PM 2.5 concentrations measured at IMPROVE sites in the East and Midwest for 2004. _____________________________________________________________________________ 178H 3-148

864H Figure 3-91. Distribution of PM 2.5 concentrations measured at IMPRO VE sites in the West for 2004. _______________ 1782H 3-149

865H Figure 3-92. Model of total personal exposure to PM as a function of ambient and nonambient sources. ____________ 1783H 3-154

86H Figure 3-93. Distribution of time sample popul ation spends in various env ironments, from the National Human Activity Pa ttern Survey. ___________________________________________________________ 1784H 3-159

867H Figure 3-94. Total exposure to SO 4 2– as a function of measured ambient SO 4 2– concentration, from the Vancouver study. ______________________________________________________________________ 1785H 3-166

86H Figure 3-95. Estimated ambient exposure to PM 2.5 as a function of measured ambient PM 2.5 concentration, from t he Vancouver study____________________________________________________ 1786H 3-166

869H Figure 3-96. Total exposure to PM 2.5 as a function of measured ambient PM 2.5 concentration, from the Vancouver study. ______________________________________________________________________ 178H 3-167

870H Figure 3-97. F inf

as a function of particle size.___________________________________________________________ 178H 3-170

871H Figure 3-98. Apportionment of aliphatic carbon, carbonyl, and SO 4 2– components of outdoor, indoor, and personal PM 2.5 samples, for Los Angeles (top), Eliz abeth (center), and Houston (bottom). _____________ 1789H 3-172

872H Figure 3-99. Apportionment of infiltrated PM from mechanical generation (top), primary combustion (center), and secondary combustion (bottom).________________________________________________ 17820H 3-173

873H Figure 3-100. Results of the positive matrix factorizati on model showing differences in the mass of outdoor PM and PM that has infiltrat ed indoors based on source category. _________________________ 1782H 3-174

874H Figure 3-101. Grid resolution of the CMAQ model in Phil adelphia compared with dist ribution of census tracts in which exposure assessment is performed.____________________________________________ 1782H 3-178

875H Figure 4-1. Diagrammatic representation of resp iratory tract r egions in humans. ________________________________ 17823H 4-3

876H Figure 4-2. Structure of lower airways with progression from the large airways to the alveolus. _____________________ 17824H 4-4

87H Figure 4-3. Comparison of total and regional deposition results from the ICRP and MPPD models for a resting breathing pattern (V T = 625 mL, f = 12 min -1 ) and corrected for par ticle inhal ability. _______________ 17825H 4-7

87H Figure 4-4. Comparison of total and regional deposition results from the ICRP and MPPD models for a light exercise breathing pattern (V T = 1250 mL, f = 20 min -1 ) and corrected for particle inhalab ility. _____________________________________________________________________________ 17826H 4-8

879H Figure 4-5. Total lung deposition measured in healthy adults (UF, 11 M, 11 F, 31 ± 4 yr; fine and coarse, 11 M, 11 F, 25 ± 4 yr) during controlled breathi ng on a mouthpiece. __________________________ 1782H 4-9

80H Figure 4-6. Total deposition of hygroscopic sodium chlo ride and hydrophobic aluminosilicate aerosols during oral breathing (V T = 1.0 L; f = 15 min -1 )._________________________________________________ 1782H 4-16

81H Figure 4-7. Retention of poorly soluble particles (0.5-5 μm ) in the alveolar regi on of the lung over time in various mamma lian spec ies. ____________________________________________________________ 17829H 4-20

82H Figure 5-1. PM oxidativ e potential. ____________________________________________________________________ 17830H 5-2

83H Figure 5-2 PM stimulates pulmonary ce lls to produce ROS/RNS.____________________________________________ 1783H 5-3

84H Figure 5-3. PM activates cell signaling pathw ays leading to pulmonary inflammation. ____________________________ 17832H 5-5

85H Figure 5-4. Potential pathways for the effects of PM on the respiratory system. _________________________________ 1783H 5-7

86H Figure 5-5. Potential pathways for the effects of PM on the cardio vascular sy stem. _____________________________ 17834H 5-13December 2009 xxviii 87H Figure 6-1. Excess risk estimates per 10 μg/m 3 increase in 24-h avg PM 2.5, PM 10- 2.5, and PM 10 concentration for CVD ED visits and HAs. ____________________________________________________ 17835H 6-62

8H Figure 6-2. Excess risk estimates per 10 μg/m 3 increase in 24-h avg (unless otherwise noted) PM 2.5 , PM 10- 2.5 , and PM 10 concentration for MI and IHD ED visits and HAs. _______________________________ 17836H 6-66

89H Figure 6-3. Excess risk estimates per 10 μg/m 3 increase in 24-h avg PM 2.5 , PM 10- 2.5 , and PM 10 concentration for CHF ED visits and HAs. ____________________________________________________ 1783H 6-69

890H Figure 6-4. Excess risk estimates per 10 μ g/m 3 increase in 24-h avg PM 2.5 and PM 10 concentration for CBVD ED visits and HAs._________________________________________________________________ 1783H 6-72

891H Figure 6-5. Excess risk estimates per 10 μ g/m 3 increase in 24-h avg PM 2.5 , and PM 10-2.5 for cardiovascular disease ED visits or HAs, adjusted for co-pollutants.________________________________ 17839H 6-74

892H Figure 6-6. Combined random-effect estimate of the conc entration-response rela tionship between MI emergency hospital admissions and PM 10 , computed by fitting a pi ecewise linear spline, with slope changes at 20 μ g/m 3 and 50 μ g/m 3 . ________________________________________________ 17840H 6-76

893H Figure 6-7. Respiratory symptoms and/or medication us e among asthmatic children following acute exposure to PM. ________________________________________________________________________ 1784H 6-86

894H Figure 6-8. Respiratory symptoms and/or medication use among asthmatic adults following acute exposure to particles. ____________________________________________________________________ 17842H 6-92

895H Figure 6-9. Respiratory symptoms following acute expos ure to particles and additional criteria pollutants. _____________________________________________________________________________ 17843H 6-93

896H Figure 6-10. Excess risk estimates per 10 μ g/m 3 24-h avg PM 2.5 , PM 10- 2.5 , and PM 10 concentration for ED visits and HAs for respirat ory diseases in child ren. _________________________________________ 1784H 6-134

897H Figure 6-11. Excess risks estimates per 10 μ g/m 3 increase in 24-h avg PM 2.5 , PM 10- 2.5 , and PM 10 for ED visits and HAs for respirat ory diseases among adults.__________________________________________ 17845H 6-138

89H Figure 6-12. Excess risk estimates per 10 μ g/m 3 increase in 24-h avg PM 2.5 , PM 10- 2.5 , and PM 10 for asthma ED visits and HAs._______________________________________________________________ 17846H 6-140

89H Figure 6-13. Excess risks estimates per 10 μ g/m 3 increase in 24-h avg PM 2.5 , PM 10- 2.5 , and PM 10 for COPD ED visits and HAs among older adults ( 65+ yr, unless other age group is noted)._______________ 1784H 6-143

90H Figure 6-14. Excess risks estimates per 10 μ g/m 3 increase in 24-h avg PM 2.5 , PM 10- 2.5 , and PM 10 for respiratory infection ED visits* and HAs. ____________________________________________________ 1784H 6-145

901H Figure 6-15. Excess risk estimates per 10 μ g/m 3 increase in 24-h avg PM 2.5 , and PM 10-2.5 for respiratory disease ED visits or HAs, adjusted for co-pollutants.___________________________________________ 17849H 6-148

902H Figure 6-16. National and regional es timates of smooth seasonal effects for PM 10 at a 1-day lag and their sensitivity to the degrees of freedom a ssigned to the smooth function of time in the updated NMMAPS data 1987-2000.________________________________________________________ 17850H 6-162

903H Figure 6-17. Percent increase in the daily number of deaths, for all ages, associated with a 10- μ g/m 3 increase in PM 10 : lag 1 (A) and lags 0 and 1 (B) for all three centers. ______________________________ 1785H 6-166

904H Figure 6-18. Effect modification by city char acteristics in 20 U.S. cities. ______________________________________ 17852H 6-168

905H Figure 6-19. Percent excess risk in mortality (all-c ause [nonaccidental] and c ause-specific) per 10 μg/m 3 increase in PM 10 by individual-level characteristics. _____________________________________ 17853H 6-170

906H Figure 6-20. Percent excess risk in mortality (all-c ause [nonaccidental] and c ause-specific) per 10 μg/m 3 increase in PM 10 by location of deat h and by season. ___________________________________ 17854H 6-171

907H Figure 6-21. Percent increase in mortality (all-cause [ nonaccidental] and cause-specific) per 10 μg/m 3 increase in PM 10 by contributing causes of death. _____________________________________________ 1785H 6-172December 2009 xxix 908H Figure 6-22. Summary of percent increase in all-cause ( nonaccidental) mortality fr om recent multicity studies per 10 μg/m 3 increase in PM 10 . _____________________________________________________ 17856H 6-174

90H Figure 6-23. Percent increase in all-cause (nonaccident al) and cause-specific mortality per 10 μ g/m 3 increase in the average of 0- and 1-day lagged PM 2.5 , combined by cli matic regions. _________________ 1785H 6-177

910H Figure 6-24. Empirical Bayes-adjusted city-specific pe rcent increase in total (nonaccidental), cardiovascular, and respiratory mortality per 10 μ g/m 3 increase in the average of 0- and 1- day lagged PM 2.5 by decreasing mean 24-h avg PM 2.5 concentrations. _____________________________ 1785H 6-178

91H Figure 6-25. Summary of percent increase in all-c ause (nonaccidental) mortality per 10 μg/m 3 increase in PM 2.5 by various e ffect modi fiers. _______________________________________________________ 17859H 6-181

912H Figure 6-26. Summary of percent increase in all-cause ( nonaccidental) and cause-specific mortality per 10 μg/m 3 increase in PM 2.5 from recent multi city st udies.________________________________________ 17860H 6-183

913H Figure 6-28. Percent increase in all-cause (nonaccident al) and cause-specific mortality per 10 μ g/m 3 increase in the average of 0- and 1-day lagged PM 10-2.5 , combined by cli matic regions.________________ 1786H 6-186

914H Figure 6-29. Empirical Bayes-adjusted city-specific pe rcent increase in total (nonaccidental), cardiovascular, and respiratory mortality per 10 μ g/m 3 increase in the average of 0- and 1- day lagged PM 10-2.5 by decreasing 98th percentile of mean 24-h avg PM 10-2.5 concentrations. ___________ 17862H 6-187

915H Figure 6-30. Summary of percent increase in total (nonaccidental) and cause-specific mortality per 10 μg/m 3 increase in PM 10-2.5 for all U.S.-, Canadian-, and international-based studies. ________________ 17863H 6-190

916H Figure 6-31. Percent increase in PM 10 risk estimates (point estimates and 95% CIs) associated with a 5th-95th percentile increase in PM 2.5 and PM 2.5 chemical components. ____________________________ 17864H 6-192

917H Figure 6-32. Sensitivity of the percent increase in PM 10 risk estimates (point estimates and 95% CIs) associated with an interquar tile increase in Ni. _______________________________________________ 17865H 6-193

918H Figure 6-33. Percent excess risk (CI) of total (nonaccident al) mortality per IQR of concentrations. _________________ 1786H 6-196

91H Figure 6-34. Relative risk and CI of cardiovascular mortality associated with estimated PM 2.5 source contributions. _________________________________________________________________________ 1786H 6-197

920H Figure 6-35. Concentration-response curves (s pline model) for all-cause, cardiovascular, respiratory and other cause mortality from the 20 NMMAPS cities. ____________________________________________ 1786H 6-198

921H Figure 6-36. Percent increase in the risk of death on days with PM 10 concentrations in the ranges of 15-24, 25-34, 35-44, and 45 μg/m 3 and greater, compared to a reference of days when concentrations were below 15 μg/m 3 ._______________________________________________________ 17869H 6-199

92H Figure 6-37. Combined concentration-response curves (spli ne model) for all-cause, cardiovascular, and respiratory mortality fr om the 22 APHEA cities. _______________________________________________ 1780H 6-200

923H Figure 7-1. Risk estimates for the associat ions of clinical outcomes with long-term exposure to ambient PM 2.5 and PM 10 . ________________________________________________________________________ 178H 7-15

924H Figure 7-2. Adjusted ORs and 95% Cls of symptoms and re spiratory diseases associated with a decline of 10 μg/m 3 PM 10 levels in Swiss Surveillance Program of Childhood Allergy and Respiratory Symptoms. __________________________________________________________________ 1782H 7-23

925H Figure 7-3. Effect of PM 2.5 on the association of l ung function with asthma. ___________________________________ 1783H 7-24

926H Figure 7-4. Proportion of 18-yr olds with an FEV 1 below 80% of the predict ed value plotted against the average levels of pollutants from 1994 through 2000 in the 12 southern California communities of the Child ren’s Health Study. __________________________________________________ 1784H 7-28

927H Figure 7-5. Percent increase in postneonatal mortality per 10 μg/m 3 in PM 10 , comparing risk for total and respirator y morta lity. _________________________________________________________________ 1785H 7-58December 2009 xxx 928H Figure 7-6. Mortality risk estimates associated with long-term exposure to PM 2.5 from the Harvard Six Cities Study (SCS) and the American Cancer Society Study (ACS).________________________________ 1786H 7-85

92H Figure 7-7. Mortality risk estimates, long-term exposure to PM 2.5 in recent cohort studies. ________________________ 178H 7-86

930H Figure 7-8. Plots of the relative risk of death from card iovascular disease from the Women’s Health Initiative study displaying the between-city and within-city contribut ions to the overall association between PM 2.5 and cardiovascular mortality windows of exposure-effects. _________________ 178H 7-92

931H Figure 7-9. The model-averaged estimated effect of a 10-μg/m 3 increase in PM 2.5 on all-cause mortality at different lags (in year s) between exposure and death._________________________________________ 1789H 7-93

932H Figure 7-10. Time course of relative risk of death a fter a sudden decrease in air pollution exposure during the year 2000, assuming a steady state model (solid line) and a dynamic model (bold dashed line). ______________________________________________________________________ 1780H 7-94

93H Figure 7-11. Experts’ mean effect estimates and uncer tainty distributions for the PM 2.5 mortality concentration-response c oefficient for a 1 μg/m 3 change in annual average PM 2.5 . ____________________ 178H 7-96

934H Figure 9-1. Important factors involved in se eing a scenic vist a are outlined. ____________________________________ 1782H 9-3

935H Figure 9-2. Schematic of remote-area (top ) and urban (bottom) nighttime sky visibility showing the effects of PM and light po llution._____________________________________________________________ 1783H 9-4

936H Figure 9-3. Effect of relative humidity on light sca ttering by mixtures of ammonium nitrate and ammonium sulfate. _______________________________________________________________________ 1784H 9-6

937H Figure 9-4. Estimated fractions of total particulate ni trate during each fiel d campaign comprised of ammonium nitrate, reacted sea sa lt nitrate (shown as NaNO 3 ), and reacted soil dust nitrate (shown as Ca(NO 3 ) 2 ).____________________________________________________________________ 1785H 9-12

938H Figure 9-5. A scatter plot of the original IMPROVE algor ithm estimated particle li ght scattering versus measured particle light scattering. __________________________________________________________ 1786H 9-15

93H Figure 9-6. Scatter plot of the revised algorithm esti mates of light scattering versus measured light scattering._____________________________________________________________________________ 178H 9-15

940H Figure 9-7. IMPROVE network PM species estimated light extinction for 2000 (left) and for 2004 (right). ____________ 178H 9-18

941H Figure 9-8. Mean estimated light extinction from PM speciation measur ements for the first (top left), second (top right), third (bottom left), and fourth (bottom right) calendar quarters of 2004._______________ 1789H 9-18

942H Figure 9-9. Percent contributions of ammonium nitrate (left column) and ammonium sulfate (right column) to particulate light extinction for eac h calendar quarter of 2004 (first through fourth quarter arranged from top to bottom). _______________________________________________________ 17890H 9-20

943H Figure 9-10. Percent contributions of organic mass (left co lumn) and EC (right column) to particulate light extinction for each calendar quarter of 2004 (first through fourth quarter arranged from top to bottom). _________________________________________________________________________ 1789H 9-21

94H Figure 9-11. Percent contributions of coarse mass (left col umn) and fine soil (right column) to particulate light extinction for each calendar quarter of 2004 (first through fourth quarter arranged from top to bottom). _________________________________________________________________________ 17892H 9-22

945H Figure 9-12. IMPROVE Mean PM 2.5 mass concentration determined by summing the major components for the 2000-2004. ______________________________________________________________________ 17893H 9-24

946H Figure 9-13. IMPROVE and CSN (STN) mean PM 2.5 mass concentration determined by summing the major components for 2000-2004. __________________________________________________________ 17894H 9-24

947H Figure 9-14. IMPROVE mean ammonium nitrate c oncentrations for 2000-2004. ________________________________ 17895H 9-25

948H Figure 9-15. IMPROVE and CSN (STN) mean ammonium nitr ate concentrations for 2000-2004. ___________________ 17896H 9-25December 2009 xxxi 94H Figure 9-16. IMPROVE mean ammonium sulfate c oncentrations for 2000-2004. ________________________________ 1789H 9-26

950H Figure 9-17. IMPROVE and CSN (STN) mean ammonium sulf ate concentrations for 2000-2004. ___________________ 1789H 9-26

951H Figure 9-18. IMPROVE monitored mean organic mass concentrations for 2000-2004.____________________________ 1789H 9-28

952H Figure 9-19. IMPROVE and CSN (STN) mean organic mass concentrations for 2000-2004. _______________________ 1790H 9-28

953H Figure 9-20. IMPROVE mean EC concent rations for 2000-2004. ____________________________________________ 1790H 9-29

954H Figure 9-21. IMPROVE and CSN (STN) mean EC concentrations for 2000-2004. _______________________________ 17902H 9-29

95H Figure 9-22. IMPROVE mean fine soil conc entrations for 2000-2004._________________________________________ 17903H 9-30

956H Figure 9-23. IMPROVE and CSN (STN) fine soil concentra tions, 2000-2004.___________________________________ 17904H 9-30

957H Figure 9-24. Regional and local contributions to annual average PM 2.5 by particulate SO 4 2– , nitrate and total carbon (i.e., organic plus EC) for select urban areas based on paired IMPROVE and CSN monitori ng sites.____________________________________________________________________ 17905H 9-32

958H Figure 9-25. IMPROVE mean coarse mass co ncentrations for 2000-2004._____________________________________ 17906H 9-33

95H Figure 9-26. Ten-year (1995-2004) haze trends for the mean of the 20% best annual haze conditions._______________ 1790H 9-34

960H Figure 9-27. Ten-year (1995-2004) haze trends for the mean of the 20% worst annual haze conditions.______________ 17908H 9-35

961H Figure 9-28. Ten-year trends in the 80th percentile particulate SO 4 2– concentration based on IMPROVE and CASTNet monitoring and net SO 2 emissions from the Na tional Emissions Trends (NET) data base by region of the U.S. _______________________________________________________ 1790H 9-36

962H Figure 9-29. Map of 10-yr trends (1994-2003) in haze by parti culate nitrate contribution to haze for the worst 20% annual haze periods. ___________________________________________________________ 1790H 9-37

963H Figure 9-30. Contributions of the Pacific C oast area to the ammonium sulfate ( μ g/m 3 ) at 84 remote-area monitoring sites in western U.S. based on traj ectory regression for all sample periods from 2000-2002 ____________________________________________________________________________ 179H 9-39

964H Figure 9-31. Shows the IMPROVE monitoring sites in the WRAP region with at least three years of valid data and identifies the six sites select ed to demonstrate the apportionment tools. _____________________ 1792H 9-41

965H Figure 9-32. Particulate SO 4 2– (a) and nitrate (b) source attribut ion by region using CAMx modeling for six western remote ar ea monitoring sites_____________________________________________________ 1793H 9-43

96H Figure 9-33. Monthly averaged model predicted organic ma ss concentration apportioned into primary PM and anthropogenic and biogenic secondary PM categories for the Olympic NP (top) and San Gorgonio W (bottom) monitoring sites. _______________________________________________ 1794H 9-44

967H Figure 9-34. Monthly averaged model predicted organic ma ss concentration apportioned into primary PM and anthropogenic and biogenic secondary PM categories for the Yellowstone NP (top) and Grand Canyon (Hopi Point) (bottom) monitoring sites. _______________________________________ 1795H 9-45

968H Figure 9-35. Monthly averaged model predicted organic ma ss concentration apportioned into primary PM and anthropogenic and biogenic secondary PM categories for the Badland NP (top) and Salt Creek W (bott om) monitoring sites. __________________________________________________ 1796H 9-46

96H Figure 9-36. Comparison of carbon concentrations between Seattle (Puget Sound site) and Mt. Rainer (left) and between Phoenix and Tonto (right) showing the background site concentration (gray) and the urban excess concentration (black) for total, fossil and contemporary carbon during the summer and winter studies._______________________________________________________ 179H 9-47

970H Figure 9-37. Average contemporary fraction of PM 2.5 carbon for the summer (top) and winter (bottom), estimated from IMPROVE monitoring data (June 2004-February 2006) based on EC/TC ratios. ________________________________________________________________________________ 1798H 9-48December 2009 xxxii 971H Figure 9-38. Results of the weighted emissions potential t ool applied to primary OC emissions (top) and EC emissions (bottom) for the baseline and projected 2018 emissions inventories for Olympic NP. ___________________________________________________________________________ 179H 9-50

972H Figure 9-39. Results of the weighted emissions potential t ool applied to primary OC emissions (top) and EC emissions (bottom) for the baseline and projected 2018 emi ssions inventories for San Gorgonio W. ___________________________________________________________________________ 17920H 9-51

973H Figure 9-40. Results of the weighted emissions potential t ool applied to primary OC emissions (top) and EC emissions (bottom) for the baseline and projected 2018 emissions inventories for Yellowstone NP. ________________________________________________________________________ 1792H 9-53

974H Figure 9-41. Results of the weighted emissions potential t ool applied to primary OC emissions (top) and EC emissions (bottom) for the baseline and projected 2018 emi ssions inventories for Grand Canyon NP. ___________________________________________________________________________ 1792H 9-54

975H Figure 9-42. Results of the weighted emissions potential t ool applied to primary OC emissions (top) and EC emissions (bottom) for the baseline and projected 2018 emissions inventories for Badlands NP. __________________________________________________________________________ 17923H 9-55

976H Figure 9-43. Results of the weighted emissions potential t ool applied to primary OC emissions (top) and EC emissions (bottom) for the baseline and projected 2018 emi ssions inventories for Salt Creek W.______________________________________________________________________________ 17924H 9-56

97H Figure 9-44. BRAVO study haze contributions for Big Bend NP, TX during a 4-mo period in 1999. __________________ 17925H 9-58

978H Figure 9-45. Maps of spatial patterns for average annual par ticulate nitrate measurements (top), and for ammonia emissions for April 2002 from the WR AP emissions invent ory (bottom). _____________________ 17926H 9-59

97H Figure 9-46. Maps of spatial patterns of annual NO (left) and NO 2 (right) emissions for 2002 from the WRAP emissions inventory. _______________________________________________________________ 1792H 9-60

980H Figure 9-47. Midwest ammonia monitoring network. ______________________________________________________ 17928H 9-61

981H Figure 9-48. Upwind transport probability fields associated with high particu late nitrate concentrations measured at Toronto, Canada; Boundary Water Canoe Area, MN; Shenandoah NP, VA; Lye Brook, VT; and Great S moky Mountains NP, TN. ___________________________________________ 1792H 9-62

982H Figure 9-49. Trajectory probability fields for periods with high particulate SO 4 2– measured at Underhill, VT and Brigantine, NJ (shown as white stars) associated with oil-burning trace components (left) and with coal-burni ng trace components (right). ___________________________________________ 17930H 9-63

983H Figure 9-50. Scatter plots of particulate SO 4 2– (left) and particulate SO 4 2– and organic mass (right) versus nephelometer measured particle li ght scattering for Acadia NP, ME. _________________________ 1793H 9-64

984H Figure 9-51. CMAQ air quality modeling projections of visi bility responses on t he 20% worst haze days at Great Smoky Mountains NP, NC (top) and Swanquarter W, NC (bottom) to 30% reductions. ____________________________________________________________________________ 17932H 9-65

985H Figure 9-52. Aerosol radiat ive forcing. _________________________________________________________________ 1793H 9-76

986H Figure 9-53. Global average radiative forcing (RF) estimates and uncertainty ranges in 2005, relative to the pre-industrial climate. _________________________________________________________________ 17934H 9-78

987H Figure 9-54. Probability distribution functions (PDFs) for anthropogenic aer osol and GHG RFs. ____________________ 17935H 9-79

98H Figure 9-55. The clear-sky forcing efficiency E τ , defined as the diurnally averaged aerosol direct radiative effect (W/m 2 ) per unit AOD at 550 nm, calculated at both TOA and the surface, for typical aerosol types over different geographi cal regions. ________________________________________ 17936H 9-80

98H Figure 9-56. A composite of MODIS/Terra observed aerosol optical depth (at 550 nm, green light near the peak of human vision) and fine-mode frac tion that shows spatial and seasonal variations of aerosol types.________________________________________________________________ 1793H 9-87December 2009 xxxiii 90H Figure 9-57. Oregon fire on September 4, 2003, as observed by MISR: (a) MISR nadir view of the fire plume, with five patch locations numbered and wind-vectors superposed in yellow; (b) MISR aerosol optical depth at 558 nm; and (c) MISR stereo height without wind correction for the same region. _____________________________________________________________________ 17938H 9-88

91H Figure 9-58. Global maps at 18 km resolution showing monthly average (a) AOD at 865 nm and (b) Ångström exponent of AOD over water surfaces only for June, 1997, derived from radiance measurements by the POLDER. ___________________________________________________________ 1793H 9-89

92H Figure 9-59. A dust event that originated in the S ahara desert on 17 August 2007 and was transported to the Gulf of Mexico. ____________________________________________________________________ 17940H 9-91

93H Figure 9-60. A constellation of five spacecraft that overfl y the Equatorat about 1:30 p.m., the so-called A-Train, carries sensor s having complementary capab ilities, offering unprecedented opportunities to study aerosols from space in multi ple dimens ions._________________________________ 1794H 9-92

94H Figure 9-61. Geographical coverage of acti ve AERONET sites in 2006. _______________________________________ 17942H 9-95

95H Figure 9-62. Comparison of the mean concentration ( μ g/m 3 ) and standard deviation of the modeled (STEM) aerosol chemical components with shipboard measurements during INDOEX, ACE-Asia, and ICARTT.__________________________________________________________________ 17943H 9-98

96H Figure 9-63. Correlations between one-hour PM 2.5 surface measurements in the U.S. and southern Canada reported to AIRNOW and MODIS satellit e AOD values for the period between 4 July and 1 September 2009._______________________________________________________________ 1794H 9-99

97H Figure 9-64. Location of aerosol chemical composition measurements with aerosol mass spectrometers. ___________ 17945H 9-101

98H Figure 9-65. Scatterplots of the submicrometer POM meas ured during NEAQS versus A) acetylene and B) iso-propy l nitrate. ____________________________________________________________________ 17946H 9-102

9H Figure 9-66. Comparison of annual mean aerosol optical depth (AOD). ______________________________________ 1794H 9-104

10H Figure 9-67. Percentage contributions of i ndividual aerosol components. _____________________________________ 17948H 9-105

10H Figure 9-68. Geographical patterns of seasonally (MAM) aver aged aerosol optical depth at 550 nm (left panel) and the diurnally averaged clear -sky aerosol direct radiativ e (solar spectrum) forcing (W/m 2 ) at the TOA (right panel) derived from satellite (Terra) retrievals. ____________________________ 1794H 9-107

102H Figure 9-69. Summary of observation- and model-based ( denoted as OBS and MOD, respectively) estimates of clear-sky, annual average DR F at the TOA and at the surface. ________________________ 17950H 9-108

103H Figure 9-70. Scatter plots showing mean cloud drop effective radius (re) versus aerosol extinction coefficient (unit: km-1) for various liquid water path (LWP) bands on April 3, 1998 at ARM SGP site. ____________________________________________________________________________ 1795H 9-115

104H Figure 9-71. Sampling the Arctic Haze. Pollution and smok e aerosols can travel long distances, from mid-latitudes to the Arctic , causing “Arc tic Haze.” _____________________________________________ 17952H 9-123

105H Figure 9-72. Global annual averaged AOD (upper panel) and aer osol mass loading (lower panel) with their components simulated by 15 models in AeroCom- A (excluding one model which only reported mass). _______________________________________________________________________ 17953H 9-130

106H Figure 9-73. Aerosol direct radiative forcing in various climate and aerosol models._____________________________ 17954H 9-134

107H Figure 9-74. Aerosol optical thickness and anthropogenic shortwave all-sky radiative forcing from the AeroCom study. _______________________________________________________________________ 1795H 9-135

108H Figure 9-75. Radiative forcing from the cloud albedo effect (1 st aerosol indirect effect) in the global climate models used from IPCC (2007, 092765), Chapt er 2, Figure 2.14, of the IPCC AR4. ____________ 17956H 9-136December 2009 xxxiv 109H Figure 9-76. Anthropogenic impact on cloud cover, planetary albedo, radiative flux at the surface (while holding sea surface temperatures and sea ic e fixed) and surface air temperature change from the direct aerosol forcing (top row), the first indirect effect (second row) and the second indirect effe ct (third row). __________________________________________________________ 1795H 9-139

10H Figure 9-77. Global average present- day short wave cloud forcing at TOA (top) and change in whole sky net outgoing shortwave radiation (bottom) between the present-day and pre-industrial simulations for each model in each experiment. ______________________________________________ 17958H 9-140

10H Figure 9-78. Direct radiative forcing by anthropogenic aer osols in the GISS model (including sulfates, BC, OC and nitrates). ___________________________________________________________________ 1795H 9-148

102H Figure 9-79. Percentage of aerosol optical depth in the GISS, left, based on Liu et al. (2006, 190422), provided by A. Lacis, GISS, and GFDL, right, from Ginoux et al. (2006, 190582). ____________________ 17960H 9-151

103H Figure 9-80. Most probable aerosol altitude (in pressure, hP a) from the GISS model in January (top) and July (bottom). _________________________________________________________________________ 1796H 9-153

104H Figure 9-81. Time dependence of aerosol optical th ickness (left) and climate forc ing (right). Note that as specified, the aerosol trends are all “flat” from 1990-2000. ______________________________________ 17962H 9-155

105H Figure 9-82. Change in global mean ocean temperature (left axis) and ocean heat content (right axis) for the top 3000 m due to different forcings in the GFDL model._____________________________________ 17963H 9-156

106H Figure 9-83. Visible (wavelength 0.55 μ m) optical depth estimates of stratospheric SO 4 2– aerosols formed in the aftermath of explosive volc anic eruptions that occurred between 1860 and 2000.________________________________________________________________________________ 17964H 9-163

107H Figure 9-84. The transfer of POPs between the major abiotic compartments of the Arctic.________________________ 17965H 9-169

108H Figure 9-85. Relationship of plant nutrients and trace metals with vegetation.__________________________________ 1796H 9-185

109H Figure A-1. PM 2.5 monitor distribution in comparison wi th population density, Atlanta, GA. _______________________ 1796H A-59

102H Figure A-2. PM 10 monitor distribution in comparison wi th population density, Atlanta, GA. _______________________ 17968H A-60

102H Figure A-3. PM 2.5 monitor distribution in comparison wi th population density, Birmingham, AL. ___________________ 1796H A-61

102H Figure A-4. PM 10 monitor distribution in comparison wi th population density, Birmingham, AL.____________________ 1790H A-62

1023H Figure A-5. PM 2.5 monitor distribution in comparison wi th population density, Boston, MA. ______________________ 179H A-63

1024H Figure A-6. PM 10 monitor distribution in comparison wi th population density, Boston, MA. _______________________ 1792H A-64

1025H Figure A-7. PM 2.5 monitor distribution in comparison wi th population density, Chicago, IL. _______________________ 1793H A-65

1026H Figure A-8. PM 10 monitor distribution in comparison wi th population density, Chicago, IL. _______________________ 1794H A-66

1027H Figure A-9. PM 2.5 monitor distribution in comparison wi th population density, Denver, CO._______________________ 1795H A-67

1028H Figure A-10. PM 10 monitor distribution in comparison wi th population density, Denver, CO. _______________________ 1796H A-68

1029H Figure A-11. PM 2.5 monitor distribution in comparison wi th population density, Detroit, MI. ________________________ 179H A-69

103H Figure A-12. PM 10 monitor distribution in comparison wi th population density, Detroit, MI. ________________________ 1798H A-70

103H Figure A-13. PM 2.5 monitor distribution in comparison wi th population density, Houston, TX. ______________________ 179H A-71

1032H Figure A-14. PM 10 monitor distribution in comparison wi th population density, Houston, TX. ______________________ 17980H A-72

103H Figure A-15. PM 2.5 monitor distribution in comparison with population density, Los Angeles, CA.___________________ 1798H A-73

1034H Figure A-16. PM 10 monitor distribution in comparison with population density, Los Angeles, CA. ___________________ 17982H A-74

1035H Figure A-17. PM 2.5 monitor distribution in comparison wi th population density, New York, NY. _____________________ 17983H A-75

1036H Figure A-18. PM 10 monitor distribution in comparison wi th population density, New York, NY. _____________________ 17984H A-76December 2009 xxxv 1037H Figure A-19. PM 2.5 monitor distribution in comparison with population density, Philadelphia, PA. ___________________ 17985H A-77

1038H Figure A-20. PM 10 monitor distribution in comparison wi th population density, Philadelphia, PA. ___________________ 17986H A-78

1039H Figure A-21. PM 2.5 monitor distribution in comparison wi th population density, Phoenix, AZ. ______________________ 1798H A-79

104H Figure A-22. PM 10 monitor distribution in comparison wi th population density, Phoenix, AZ._______________________ 1798H A-80

104H Figure A-23. PM 2.5 monitor distribution in comparison wi th population density, Pittsburgh, PA._____________________ 1798H A-81

1042H Figure A-24. PM 10 monitor distribution in comparison wi th population density, Pittsburgh, PA. _____________________ 1790H A-82

1043H Figure A-25. PM 2.5 monitor distribution in comparison with population density, Riverside, CA. _____________________ 179H A-83

104H Figure A-26. PM 10 monitor distribution in comparison with population density, Riverside, CA. _____________________ 1792H A-84

1045H Figure A-27. PM 2.5 monitor distribution in comparison wi th population density, Seattle, WA._______________________ 1793H A-85

1046H Figure A-28. PM 10 monitor distribution in comparison wi th population density, Seattle, WA. _______________________ 1794H A-86

1047H Figure A-29. PM 2.5 monitor distribution in comparison wi th population density, St. Louis, MO. _____________________ 1795H A-87

1048H Figure A-30. PM 10 monitor distribution in comparison with population density, St. Louis, MO.______________________ 1796H A-88

1049H Figure A-31. Three-yr avg of 24-h PM 2.5 Cu concentrations measured at CSN sites across the U.S., 2005-2007. ___________________________________________________________________________ 179H A-89

105H Figure A-32. Three-yr avg of 24-h PM 2.5 Fe concentrations measured at CSN sites across the U.S., 2005-2007 ___________________________________________________________________________ 1798H A-90

105H Figure A-33. Three-yr avg of 24-h PM 2.5 Ni concentrations measured at CSN sites across the U.S., 2005-2007 ___________________________________________________________________________ 179H A-91

1052H Figure A-34. Three-yr avg of 24-h PM 2.5 Pb concentrations measured at CSN sites across the U.S., 2005-2007 ___________________________________________________________________________ 180H A-92

1053H Figure A-35. Three-yr avg of 24-h PM 2.5 Se concentrations measured at CSN sites across the U.S., 2005-2007 ___________________________________________________________________________ 180H A-93

1054H Figure A-36. Three-yr avg of 24-h PM 2.5 V concentrations measured at CSN sites across the U.S., 2005- 2007 ________________________________________________________________________________ 1802H A-94

105H Figure A-37. PM 2.5 monitor distribution and major highways, Atlanta, GA. _____________________________________ 1803H A-96

1056H Figure A-38. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Atlanta, GA. _________________________________________________________________________________ 1804H A-97

1057H Figure A-39. PM 2.5 inter-sampler correlations as a function of distance between monitors for Atlanta, GA. ___________ 1805H A-99

1058H Figure A-40. PM 2.5 monitor distribution and major highways, Birmingham, AL. ________________________________ 1806H A-100

1059H Figure A-41. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Birmingham, AL. ______________________________________________________________________ 1807H A-101

106H Figure A-42. PM 2.5 inter-sampler correlations as a f unction of distance between monitors for Birmingham, AL. ______________________________________________________________________ 180H A-103

106H Figure A-43. PM 2.5 monitor distribution and major highways, Boston, MA.____________________________________ 1809H A-104

1062H Figure A-44. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Boston, MA. ________________________________________________________________________________ 180H A-105

1063H Figure A-45. PM 2.5 inter-sampler correlations as a function of distance between monitors for Boston, MA. __________ 180H A-107

1064H Figure A-46. PM 2.5 monitor distribution and major highways, Chicago, IL. ____________________________________ 1802H A-108

1065H Figure A-47. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Chicago, IL. _________________________________________________________________________________ 1803H A-110December 2009 xxxvi 106H Figure A-48. PM 2.5 inter-sampler correlations as a function of distance between monitors for Chicago, IL.___________ 1804H A-113

1067H Figure A-49. PM 2.5 monitor distribution and major highways, Denver, CO.____________________________________ 1805H A-114

1068H Figure A-50. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Denver, CO. ________________________________________________________________________________ 1806H A-115

1069H Figure A-51. PM 2.5 inter-sampler correlations as a function of distance between monitors for Denver, CO. __________ 1807H A-117

107H Figure A-52. PM 2.5 monitor distribution and major highways, Detroit, MI._____________________________________ 180H A-118

107H Figure A-53. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Detroit, MI._________________________________________________________________________________ 1809H A-119

1072H Figure A-54. PM 2.5 inter-sampler correlations as a function of distance between monitors for Detroit, MI. ___________ 1802H A-121

1073H Figure A-55. PM 2.5 monitor distribution and major highways, Houston, TX. ___________________________________ 1802H A-122

1074H Figure A-56. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Houston, TX. ________________________________________________________________________________ 1802H A-123

1075H Figure A-57. PM 2.5 inter-sampler correlations as a function of distance between monitors for Houston, TX. ________________________________________________________________________________ 18023H A-124

1076H Figure A-58. PM 2.5 monitor distribution and major highways, Los Angeles, CA.________________________________ 18024H A-125

107H Figure A-59. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Los Angeles, CA._________________________________________________________________________ 18025H A-126

1078H Figure A-60. PM 2.5 inter-sampler correlations as a func tion of distance between monitors for Los Angeles, CA._________________________________________________________________________ 18026H A-127

1079H Figure A-61. PM 2.5 monitor distribution and major highways, New York City, NY. ______________________________ 18027H A-128

108H Figure A-62. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for New York, NY. ___________________________________________________________________________ 1802H A-130

108H Figure A-63 PM 2.5 inter-sampler correlations as a function of distance between monitors for New York, NY. ________________________________________________________________________________ 18029H A-133

1082H Figure A-64. PM 2.5 monitor distribution and major highways, Philadelphia, PA. ________________________________ 1803H A-134

1083H Figure A-65. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Philadelphi a, PA. _____________________________________________________________________ 1803H A-135

1084H Figure A-66. PM 2.5 inter-sampler correlations as a f unction of distance between monitors for Philadelphi a, PA. _____________________________________________________________________ 18032H A-137

1085H Figure A-67. PM 2.5 monitor distribution and major highways, Phoenix, AZ. ___________________________________ 1803H A-138

1086H Figure A-68. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Phoenix, AZ. ________________________________________________________________________________ 18034H A-139

1087H Figure A-69. PM 2.5 inter-sampler correlations as a functi on of distance between monitors for Phoenix, AZ. ________________________________________________________________________________ 18035H A-141

108H Figure A-70. PM 2.5 monitor distribution and major highways, Pittsburgh, PA.__________________________________ 18036H A-142

1089H Figure A-71. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Pitsburgh, PA.________________________________________________________________________ 18037H A-143

109H Figure A-72. PM 2.5 inter-sampler correlations as a function of distance between monitors for Pittsburgh, PA. ________________________________________________________________________________ 1803H A-145

109H Figure A-73. PM 2.5 monitor distribution and major highways, Riverside, CA. __________________________________ 18039H A-146December 2009 xxxvii 1092H Figure A-74. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Riverside, CA.________________________________________________________________________ 1804H A-147

1093H Figure A-75. PM 2.5 inter-sampler correlations as a function of distance between monitors for Riverside CA. ________________________________________________________________________________ 1804H A-149

1094H Figure A-76. PM 2.5 monitor distribution and major highways, Seattle, WA.____________________________________ 18042H A-150

1095H Figure A-77. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for Seattle, WA.________________________________________________________________________________ 18043H A-151

1096H Figure A-78. PM 2.5 inter-sampler correlations as a function of distance between monitors for Seattle, WA. __________ 1804H A-152

1097H Figure A-79. PM 2.5 monitor distribution and major highways, St. Louis, MO. __________________________________ 18045H A-153

1098H Figure A-80. Box plots illustrating the seas onal distribution of 24-h avg PM 2.5 concentrations for St. Louis, MO. __________________________________________________________________________ 18046H A-154

109H Figure A-81 PM 2.5 inter-sampler correlations as a function of distance between monitors for St. Louis, MO.________________________________________________________________________________ 18047H A-156

10H Figure A-82. PM 10 monitor distribution and major highways, Atla nta, GA. ____________________________________ 1804H A-157

10H Figure A-83. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Atlanta, GA. ________________________________________________________________________________ 18049H A-158

102H Figure A-84. PM 10 inter-sampler correlations as a function of distance between monitors for Atlanta, GA. ___________ 1805H A-160

103H Figure A-85. PM 10 monitor distribution and major highways, Birmingham, AL._________________________________ 1805H A-161

104H Figure A-86. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Birmingham, AL. ______________________________________________________________________ 18052H A-162

105H Figure A-87 PM 10 inter-sampler correlations as a function of distance between monitors for Birmingham, AL. ________________________________________________________________________________ 18053H A-164

106H Figure A-88. PM 10 monitor distribution and major highways, Boston, MA. ____________________________________ 18054H A-165

107H Figure A-89. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Boston, MA. ________________________________________________________________________________ 1805H A-166

108H Figure A-90 PM 10 inter-sampler correlations as a function of distance between monitors for Boston, MA.___________ 18056H A-167

109H Figure A-91. PM 10 monitor distribution and major highways, Chicago, IL. ____________________________________ 18057H A-168

10H Figure A-92. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Chicago, IL. _________________________________________________________________________________ 1805H A-169

1H Figure A-93. PM 10 inter-sampler correlations as a function of distance between monitors for Chicago, IL. ___________ 18059H A-171

12H Figure A-94. PM 10 monitor distribution and major highways, Denver, CO. ____________________________________ 1806H A-172

13H Figure A-95. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Denver, CO. ________________________________________________________________________________ 1806H A-173

14H Figure A-96. PM 10 inter-sampler correlations as a function of distance between monitors for Denver, CO.___________ 18062H A-175

15H Figure A-97. PM 10 monitor distribution and major highways, Detroit, MI. _____________________________________ 18063H A-176

16H Figure A-98. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Detroit, MI._________________________________________________________________________________ 18064H A-177

17H Figure A-99. PM 10 inter-sampler correlations as a function of distance between monitors for Detroit, MI.____________ 18065H A-178

18H Figure A-100. PM 10 monitor distribution and major highways, Houston, TX. ___________________________________ 1806H A-179December 2009 xxxviii 19H Figure A-101. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Houston, TX. ________________________________________________________________________________ 18067H A-180

120H Figure A-102. PM 10 inter-sampler correlations as a function of distance between monitors for Houston, TX. __________ 1806H A-182

12H Figure A-103. PM 10 monitor distribution and major highways, Los Angeles, CA. ________________________________ 18069H A-183

12H Figure A-104. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Los Angeles, CA._________________________________________________________________________ 1807H A-184

123H Figure A-105. PM 10 inter-sampler correlations as a function of distance between monito rs for Los Angeles, CA. ________________________________________________________________________________ 1807H A-185

124H Figure A-106. PM 10 monitor distribution and major highways, New York, NY. __________________________________ 18072H A-186

125H Figure A-107. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for New York, NY. ___________________________________________________________________________ 18073H A-187

126H Figure A-108. PM 10 inter-sampler correlations as a function of distance between monitors for New York, NY. ________________________________________________________________________________ 18074H A-188

127H Figure A-109. PM 10 monitor distribution and major highways, Philadelphia, PA. ________________________________ 18075H A-189

128H Figure A-110. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Philadelphi a, PA. _____________________________________________________________________ 18076H A-190

129H Figure A-111. PM 10 inter-sampler correlations as a function of distance between monitors for Philadelphia, PA. ________________________________________________________________________________ 1807H A-191

130H Figure A-112. PM 10 monitor distribution and major highways, Phoe nix, AZ.____________________________________ 1807H A-192

13H Figure A-113. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Phoenix, AZ. ________________________________________________________________________________ 18079H A-194

132H Figure A-114. PM 10 inter-sampler correlations as a function of distance between monitors for Phoenix, AZ. __________ 180H A-197

13H Figure A-115. PM 10 monitor distribution and major highways, Pittsburgh, PA. __________________________________ 180H A-198

134H Figure A-116. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Pittsburgh, PA. _______________________________________________________________________ 1802H A-199

135H Figure A-117. PM 10 inter-sampler correlations as a function of distance between monitors for Pittsburgh, PA. ________________________________________________________________________________ 1803H A-201

136H Figure A-118. PM 10 monitor distribution and major highways, Rive rside, CA. __________________________________ 1804H A-202

137H Figure A-119. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Riverside, CA.________________________________________________________________________ 1805H A-203

138H Figure A-120. PM 10 inter-sampler correlations as a function of distance between monitors for Riverside, CA. ________________________________________________________________________________ 1806H A-205

139H Figure A-121. PM 10 monitor distribution and major highways, Seattle, WA. ____________________________________ 1807H A-206

140H Figure A-122. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for Seattle, WA.________________________________________________________________________________ 180H A-207

14H Figure A-123. PM 10 inter-sampler correlations as a function of distance between monitors for Seattle, WA.___________ 1809H A-208

142H Figure A-124. PM 10 monitor distribution and major highways, St. Louis, MO. __________________________________ 1809H A-209

143H Figure A-125. Box plots illustrating the seas onal distribution of 24-h avg PM 10 concentrations for St. Louis, MO.________________________________________________________________________________ 1809H A-210

14H Figure A-126. PM 10 inter-sampler correlations as a function of distance between monitors for St. Louis, MO.________________________________________________________________________________ 18092H A-212December 2009 xxxix 145H Figure A-127. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Atlanta, GA. _______________ 18093H A-213

146H Figure A-128. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived us ing the SANDWICH method in Birmingham, AL.____________ 18094H A-214

147H Figure A-129. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Boston, MA. _______________ 18095H A-215

148H Figure A-130. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Chicago, IL. _______________ 18096H A-216

149H Figure A-131. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter, deriv ed using the SANDWICH method in Denver, CO. ______________ 18097H A-217

150H Figure A-132. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Detroit, MI. ________________ 1809H A-218

15H Figure A-133. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Houston, TX. ______________ 1809H A-219

152H Figure A-134. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived us ing the SANDWICH method in Los Angeles, CA. ___________ 180H A-220

153H Figure A-135. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in New York, NY. _____________ 180H A-221

154H Figure A-136. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Philadelphia, PA. ___________ 1802H A-222

15H Figure A-137. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Phoenix, AZ._______________ 1803H A-223

156H Figure A-138. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Pittsburgh, PA. _____________ 1804H A-224

157H Figure A-139. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived us ing the SANDWICH method in Riverside, CA. _____________ 1805H A-225

158H Figure A-140. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in Seattle, WA. _______________ 1806H A-226

159H Figure A-141. Seasonally averaged PM 2.5 speciation data for 2005-2007 for a) annual, b) spring, c) summer, d) fall and e) winter derived using the SANDWICH method in St. Louis, MO. _____________ 1807H A-227

160H Figure A-142. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Atlanta, GA, 2005-2007.________________________________________________________________ 180H A-228

16H Figure A-143. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Birmingham, AL, 2005-2007. ____________________________________________________________ 1809H A-228

162H Figure A-144. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Boston, MA, 2005-2007.________________________________________________________________ 180H A-229

163H Figure A-145. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Chicago, IL, 2005-2007. ________________________________________________________________ 18H A-229

164H Figure A-146. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Denver, CO, 2005-2007. _______________________________________________________________ 182H A-230

165H Figure A-147. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Detroit, MI, 2005-2007._________________________________________________________________ 183H A-230December 2009 xl 16H Figure A-148. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Houston, TX, 2005-2007. _______________________________________________________________ 184H A-231

167H Figure A-149. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Los Angeles, CA, 2005-2007.____________________________________________________________ 185H A-231

168H Figure A-150. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for New York, NY, 2005-2007.______________________________________________________________ 186H A-232

169H Figure A-151. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Philadelphia, PA, 2005-2007.____________________________________________________________ 187H A-232

170H Figure A-152. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Phoenix, AZ, 2005-2007. _______________________________________________________________ 18H A-233

17H Figure A-153. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Pittsburgh, PA, 2005-2007. _____________________________________________________________ 189H A-233

172H Figure A-154. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Riverside, CA, 2005-2007. ______________________________________________________________ 1820H A-234

173H Figure A-155. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for Seattle, WA, 2005-2007. _______________________________________________________________ 182H A-234

174H Figure A-156. Seasonal patterns in PM 2.5 chemical composition from city-w ide monthly average values for St. Louis, MO, 2005-2007. ______________________________________________________________ 182H A-235

175H Figure A-157. Diel plots generated from all available hourly FRM-like PM 2.5 data, stratified by weekday (left) and weekend (right ), in Atlanta, GA. __________________________________________________ 1823H A-235

176H Figure A-158. Diel plots generated from all available hourly FRM-like PM 2.5 data, stratified by weekday (left) and weekend (right ), in Chicago, IL.___________________________________________________ 1824H A-236

17H Figure A-159. Diel plots generated from all available hourly FRM-like PM 2.5 data, stratified by weekday (left) and weekend (right), in Houston, TX.__________________________________________________ 1825H A-236

178H Figure A-160. Diel plots generated from all available hourly FRM-like PM 2.5 data, stratified by weekday (left) and weekend (right), in New York, NY. ________________________________________________ 1826H A-237

179H Figure A-161. Diel plots generated from all available hourly FRM-like PM 2.5 data, stratified by weekday (left) and weekend (right ), in Pittsburgh, PA. ________________________________________________ 1827H A-237

180H Figure A-162. Diel plots generated from all available hourly FRM-like PM 2.5 data, stratified by weekday (left) and weekend (right), in Seattle, WA. __________________________________________________ 182H A-238

18H Figure A-163. Diel plots generated from all available hourly FRM-like PM 2.5 data, stratified by weekday (left) and weekend (right), in St. Louis, MO._________________________________________________ 1829H A-238

182H Figure A-164. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right ), in Atlanta, GA. __________________________________________________ 1830H A-239

183H Figure A-165. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right ), in Chicago, IL.___________________________________________________ 183H A-239

184H Figure A-166. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right), in Denver, CO. __________________________________________________ 1832H A-240

185H Figure A-167. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right ), in Detroit, MI. ___________________________________________________ 183H A-240

186H Figure A-168. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right), in Los Angeles, CA. ______________________________________________ 1834H A-241December 2009 xli 187H Figure A-169. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right), in Philadelphia, PA. ______________________________________________ 1835H A-241

18H Figure A-170. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right ), in Phoenix, AZ. __________________________________________________ 1836H A-242

189H Figure A-171. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right ), in Pittsburgh, PA. ________________________________________________ 1837H A-242

190H Figure A-172. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right), in Riverside, CA._________________________________________________ 183H A-243

19H Figure A-173. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right), in Seattle, WA. __________________________________________________ 1839H A-243

192H Figure A-174. Diel plot generated from all available hourly FRM/FEM PM 10 data, stratified by weekday (left) and weekend (right), in St. Louis, MO._________________________________________________ 1840H A-244

193H Figure A-175. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Atlanta, GA, strati fied by season (2005-2007). ___________________ 184H A-245

194H Figure A-176. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Birmingham, AL, strati fied by season (2005-2007). _______________ 1842H A-246

195H Figure A-177. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Boston, MA, stratified by season (2005-2007).___________________ 1843H A-247

196H Figure A-178. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Chicago, IL, stratifi ed by season (2005-2007). ___________________ 184H A-248

197H Figure A-179. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Denver, CO, stratified by season (2005-2007).___________________ 1845H A-249

198H Figure A-180. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Detroit, MI, stratifi ed by season (2005-2007). ____________________ 1846H A-250

19H Figure A-181. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Houston, TX, stratified by season (2005-2007). __________________ 1847H A-251

120H Figure A-182. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Los Angeles, CA, stra tified by season (2005-2007)._______________ 184H A-252

120H Figure A-183. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for New York, NY, stratified by season (2005-2007). _________________ 1849H A-253

120H Figure A-184. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Philadelphia, PA, stra tified by season (2005-2007). _______________ 1850H A-254

1203H Figure A-185. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Phoenix, AZ, stratified by season (2005-2007). __________________ 185H A-255

1204H Figure A-186. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Pittsburgh, PA, stratified by season (2005-2007)._________________ 1852H A-256

1205H Figure A-187. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Riverside, CA, stra tified by season (2005-2007). _________________ 1853H A-257

1206H Figure A-188. Correlations between 24-h PM 2.5 and co-located 24-h avg PM 10 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for St. Louis, MO, stratified by season (2005-2007). _________________ 1854H A-258

1207H Figure A-189. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Atlanta, GA, strati fied by season (2005-2007). ___________________ 185H A-259December 2009 xlii 1208H Figure A-190. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Birmingham, AL, strati fied by season (2005-2007). _______________ 1856H A-260

1209H Figure A-191. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Boston, MA, stratified by season (2005-2007).___________________ 1857H A-261

120H Figure A-192. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Chicago, IL, stratifi ed by season (2005-2007). ___________________ 185H A-262

12H Figure A-193. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Denver, CO, stratified by season (2005-2007).___________________ 1859H A-263

12H Figure A-194. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Detroit, MI, strati fied by season (2005-2007).____________________ 1860H A-264

123H Figure A-195. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Houston, TX, stratified by season (2005-2007). __________________ 186H A-265

124H Figure A-196. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Los Angeles, CA, stra tified by season (2005-2007)._______________ 1862H A-266

125H Figure A-197. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for New York, NY, stratified by season (2005-2007)._________________ 1863H A-267

126H Figure A-198. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Philadelphia, PA, stra tified by season (2005-2007). _______________ 1864H A-268

127H Figure A-199. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Phoenix, AZ, stratified by season (2005-2007). __________________ 1865H A-269

128H Figure A-200. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Pittsburgh, PA, stratified by season (2005-2007)._________________ 186H A-270

129H Figure A-201. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for Riverside, CA, stra tified by season (2005-2007). _________________ 1867H A-271

120H Figure A-202. Correlations between 24-h PM 10 and co-located 24-h avg PM 2.5 , PM 10-2.5 , SO 2 , NO 2 and CO and daily maximum 8-h avg O 3 for St. Louis, MO, stratified by season (2005-2007). _________________ 186H A-272December 2009 xliii 

# PM ISA Project Team Executive Direction Dr. John Vandenberg (Director)—National Center fo r Environmental Assessment-RTP Division, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Debra Walsh (Deputy Director)—National Center for Environmental Assessment-RTP Division, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Mary Ross (Branch Chief)—National Center fo r Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Scientific Staff Dr. Lindsay Wichers Stanek (PM Team Leader)— National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jeffrey Arnold— National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC (now at Ins titute for Water Resources, U.S. Army Corps of Engineers, Washington, D.C.) Dr. Christal Bowman—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. James S. Brown—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Barbara Buckley—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Allen Davis—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jean-Jacques Dubois—National Center for Envi ronmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Steven J. Dutton—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Erin Hines—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Douglas Johns—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Ellen Kirrane—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Dennis Kotchmar—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Thomas Long—National Center for Environmenta l Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NCDecember 2009 xliv 

### Dr. Thomas Luben—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Qingyu Meng—Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Kristopher Novak—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Joseph Pinto—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Mary Ross—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Jason Sacks—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. David Svendsgaard—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Lisa Vinikoor—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. William Wilson—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Lori White—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC (now at National Ins titute for Environmental He alth Sciences, Research Triangle Park, NC) Technical Support Staff Mattie Arnold—Senior Environmental Employee Program, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Laeda Baston—Senior Environmental Employee Pr ogram, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Kimberly Branch—Student Services Contractor, Nati onal Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Ken Breito—Senior Environmental Employee Program, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Eleanor Jamison—Senior Environmental Employ ee Program, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Ryan Jones—Oak Ridge Institute for Science and Ed ucation, at National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Erica Lee—Oak Ridge Institute for Science and E ducation, at National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Barbara Liljequist—Senior Environmental Employee Program, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NCDecember 2009 xlv 

### Ellen Lorang—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Kelsey Matson—Student Services Contractor, Nationa l Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Sandy Pham—Student Services Contractor, Nationa l Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Olivia Phillpott—Senior Environmental Employ ee Program, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Deborah Wales—National Center for Environmental A ssessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Erica Wilson—Oak Ridge Institute for Science and Education, at National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Richard Wilson—National Center for Environmental A ssessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Barbara Wright—Senior Environmental Employee Program, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NCDecember 2009 xlvi 

# Authors, Contributors, Reviewers AUTHORS Dr. Lindsay Wichers Stanek (PM Team Leader)— National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jeffrey Arnold—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC (now at Ins titute for Water Resources, U.S. Army Corps of Engineers, Washington, D.C) Dr. Christal Bowman—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. James S. Brown—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Barbara Buckley—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Allen Davis—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jean-Jacques Dubois—National Center for Envi ronmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Steven J. Dutton—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Tara Greaver—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Erin Hines—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Douglas Johns—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Ellen Kirrane—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Dennis Kotchmar—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Thomas Long—National Center for Environmenta l Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Thomas Luben—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Qingyu Meng—Oak Ridge Institute for Science and Education, Postdoctoral Research Fellow to National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Kristopher Novak—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Joseph Pinto—National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NCDecember 2009 xlvii 

### Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Mary Ross—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Jason Sacks—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Timothy J. Sullivan—E&S Environmen tal Chemistry, Inc., Corvallis, OR Dr. David Svendsgaard—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Lisa Vinikoor—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. William Wilson—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Lori White— National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC (now at National Ins titute for Environmental He alth Sciences, Research Triangle Park, NC) Dr. Christy Avery—University of North Carolina, Chapel Hill, NC Dr. Kathleen Belanger —Center for Perinatal, Pe diatric and Environmental Epidemiology, Yale University, New Haven, CT Dr. Michelle Bell—School of Forestry & Environmen tal Studies, Yale University, New Haven, CT Dr. William D. Bennett—Center for Environmental Medicine, Asthma and Lung Biology, University of North Carolina, Chapel Hill, NC Dr. Matthew J. Campen—Lovelace Respirator y Research Institute, Albuquerque, NM Dr. Leland B. Deck— Stratus Consulting, Inc., Washington, DC Dr. Janneane F. Gent—Center for Perinatal, Pediatri c and Environmental Epidemiology, Yale University, New Haven, CT Dr. Yuh-Chin Tony Huang—Department of Medicine, Division of Pulmonary Medicine, Duke University Medical Center, Durham, NC Dr. Kazuhiko Ito—Nelson Institute of Environmenta l Medicine, NYU School of Medicine, Tuxedo, NY Mr. Marc Jackson—Integrated Laboratory Systems, Inc., Research Triangle Park, NC Dr. Michael Kleinman—Department of Community and Environmental Medicine, University of California, Irvine Dr. Sergey Napelenok—National Exposure Research La boratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Marc Pitchford—National Oceanic and Atmospheric Administration, Las Vegas, NV Dr. Les Recio—Genetic Toxicology Division, Integr ated Laboratory Systems, Inc., Research Triangle Park, NC Dr. David Quincy Rich—Department of Epidemiology , University of Medicine and Dentistry of New Jersey, Piscataway, NJ Dr. Timothy Sullivan— E&S Environmental Chemistry, Inc., Corvallis, OR Dr. George Thurston—Department of E nvironmental Medicine, NYU, Tuxedo, NYDecember 2009 xlviii 

### Dr. Gregory Wellenius—Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess Medical Center, Boston, MA Dr. Eric Whitsel—Departments of Epidemiology and Medicine, University of North Carolina, Chapel Hill, NC CONTRIBUTORS Dr. Philip Bromberg—Department of Medicine, Univ ersity of North Carolina, Chapel Hill, NC Mr. Michael Burr—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Turhan Carroll—Student Services Contractor, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Rosana Datti—Student Services Contractor, Nationa l Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Neil Frank—Office of Air Quality Planning and St andards, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Jonathan Krug—Student Services Contractor, Nati onal Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Katie Lane—Student Services Contractor, Nati onal Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Phil Lorang—Office of Air Quality Planning and St andards, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Christina Miller—National Center for Environm ental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Irina Mordukhovich—Oak Ridge Institute for Sc ience and Education, at National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Elizabeth Oesterling Owens—National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Adam Reff—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Victoria Sandiford— Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Mark Schmidt—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Angelina Schultz—Student Services Contractor, Na tional Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Kirsten Simmons—Student Services Contractor , National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Genee Smith—Oak Ridge Institute for Science and Education, at National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Kurt Susdorf—Student Services Contractor, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Barbara Turpin—Department of Environmental Sciences, Rutgers University, New Brunswick, NJDecember 2009 xlix 

### Ms. Lauren Tuttle—Student Services Contractor, Nati onal Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Rebecca Yang—Student Services Contractor, Nati onal Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC PEER REVIEWERS Dr. Sara Dubowsky Adar, Department of Epidemiology, University of Washington, Seattle, WA Mr. Chad Bailey, Office of Transportation and Air Quality, Ann Arbor, MI Mr. Richard Baldauf, Office of Transportation and Air Quality, Ann Arbor, MI Dr. Prakash Bhave, National Exposure Research La boratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. George Bowker, Office of Atmospheric Progr ams, U.S. Environmental Protection Agency, Washington, D.C. Dr. Judith Chow, Division of Atmospheric Sc iences, Desert Research Institute, Reno, NV Dr. Dan Costa, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Ila Cote, National Center for Environmental As sessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Robert Devlin, National Health and Environmenta l Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. David DeMarini, National Health and Envi ronmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Neil Donahue, Department of Chemical Engineer ing, Carnegie Mellon University, Pittsburgh, PA Dr. Aimen Farraj, National Health and Environmenta l Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Mark Frampton, Department of Environmental Me dicine, University of Rochester Medical Center, Rochester, NY Mr. Neil Frank, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Tyler Fox, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jim Gauderman, Department of Environmenta l Medicine, Department of Preventive Medicine, University of Southern California, Los Angeles, CA Dr. Barbara Glenn, National Center for Environmenta l Research, U.S. Environmental Protection Agency, Washington, D.C. Dr. Terry Gordon, School of Medicine, New York University, Tuxedo, NY Mr. Tim Hanley, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jack Harkema, Department of Pathobiology and Di agnostic Investigation, Michigan State University, East Lansing, MI Ms. Beth Hassett-Sipple, Office of Air Quality Pla nning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NCDecember 2009 l 

### Dr. Amy Herring, Department of Biostatistics, Un iversity of North Carolina, Chapel Hill, NC Dr. Israel Jirak, Department of Meteorology, Embry-Riddle Aeronautical University, Prescott, AZ Dr. Mike Kleeman, Department of Civil and Environmen tal Engineering, University of California, Davis, CA Dr. Petros Koutrakis, Exposure, Epidemiology a nd Risk Program, Harvard School of Public Health, Boston, MA Dr. Sagar Krupa, Department of Plant Patholog y, University of Minnesota, St. Paul, MN Mr. John Langstaff, Office of Air Quality Planning an d Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Meredith Lassiter, Office of Air Quality Pla nning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Phil Lorang, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Karen Martin, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Connie Meacham, National Center for Environmen tal Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Tom Pace, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jennifer Peel, Department of Environmental and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO Dr. Zackary Pekar, Office of Air Quality Planning an d Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Rob Pinder, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Norm Possiel, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Sanjay Rajagopalan, Division of Cardiovascula r Medicine, Ohio State University, Columbus, OH Dr. Pradeep Rajan, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Venkatesh Rao, Office of Air Quality Planning an d Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Joann Rice, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Harvey Richmond, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Victoria Sandiford, Office of Air Quality Planni ng and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Stefanie Sarnat, Department of Environmental and Occupational Health, Emory University, Atlanta, GA Dr. Frances Silverman, Gage Occupational and Environm ental Health, University of Toronto, Toronto, ONDecember 2009 li 

### Mr. Steven Silverman, Office of General Council, U.S. Environmental Protection Agency, Washington, D.C. Dr. Barbara Turpin, Department of Environmental Sciences, Rutgers University, New Brunswick, NJ Dr. Robert Vanderpool, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. John Vandenberg (Director)—National Center fo r Environmental Assessment-RTP Division, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Alan Vette, National Exposure Research Laborator y, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Debra Walsh (Deputy Director)—National Center for Environmental Assessment-RTP Division, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Tim Watkins, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Christopher Weaver, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Mr. Lewis Weinstock, Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Ms. Karen Wesson, Office of Air Quality Planning an d Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Jason West, Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC Mr. Ronald Williams, National Exposure Research La boratory, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. George Woodall, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC Dr. Antonella Zanobetti, Department of Environmental Health, Harvard School of Public Health, Boston, MADecember 2009 lii 

# Clean Air Scientific Advisory Committee for Particulate Matter NAAQS CHAIRPERSON Dr. Jonathan Samet, Department of Preventive Me dicine, Keck School of Medicine, University of Southern California, Los Angeles, CA MEMBERS Dr. Lowell Ashbaugh, Crocker Nuclear Lab, University of California, Davis, CA Dr. Ed Avol, Department of Preventive Medicine, Ke ck School of Medicine, University of Southern California, Los Angeles, CA Dr. Joseph Brain*, Department of Environmental Health, Harvard School of Public Health, Harvard University, Boston, MA Dr. Wayne Cascio, Brody School of Medicine, East Carolina University, Greenville, NC Dr. Ellis B. Cowling*, Colleges of Natural Resource s and Agriculture and Life Sciences, North Carolina State University, Raleigh, NC Dr. James Crapo*, Department of Medicine, Nationa l Jewish Medical and Research Center, Denver, CO Dr. Douglas Crawford-Brown, Department of Environm ental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC Dr. H. Christopher Frey*, Department of Civil, Cons truction and Environmental Engineering, College of Engineering, North Carolina State University, Raleigh, NC Dr. David Grantz, Botany and Plant Sciences and Ai r Pollution Research Center, Riverside Campus and Kearney Agricultural Center, University of California, Parlier, CA Dr. Joseph Helble, Thayer School of Engineering, Dartmouth College, Hanover, NH Dr. Rogene Henderson**, Lovelace Respirat ory Research Institute, Albuquerque, NM Dr. Philip Hopke, Department of Chemical Engineering, Clarkson University, Potsdam, NY Dr. Donna Kenski*, Lake Michigan Air Directors Consortium, Rosemont, IL Dr. Morton Lippmann, Nelson Institute of Environmen tal Medicine, New York University School of Medicine, Tuxedo, NY Dr. Helen Suh MacIntosh, Environmental Health, School of Public Health, Harvard University, Boston, MA Dr. William Malm, National Park Service Air Resources Division, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO Mr. Charles Thomas (Tom) Moore, Jr., Western Regional Air Partnership, Western Governors’ Association, Fort Collins, CODecember 2009 liii 

### Dr. Robert F. Phalen, Center for Occupation & Enviro nment Health, College of Medicine, Department of Community and Environmental Medicine, Air Pollution Health Effects Laboratory, University of California Irvine, Irvine, CA Dr. Kent Pinkerton, Center for Health and the Environment, University of California, Davis, CA Mr. Richard L. Poirot, Air Pollution Control Division, Department of Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT Dr. Armistead (Ted) Russell*, Department of Civil an d Environmental Engineering, Georgia Institute of Technology, Atlanta, GA Dr. Frank Speizer, Channing Laboratory, Harvard Medical School, Boston, MA Dr. Sverre Vedal, Department of Environmental a nd Occupational Health Scie nces, School of Public Health and Community Medicine, University of Washington, Seattle, WA *Members of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the EPA Administrator. **As immediate past CASAC Chair, Dr. Henderson is i nvited to participate in CASAC advisory activities for FY 2009. SCIENCE ADVISORY BOARD STAFF Dr. Holly Stallworth, Economist and Designated Fe deral Officer, Clean Air Scientific Advisory Committee, Environmental Economics Advisory Committee, Washington, D.C.December 2009 liv 

# Acronyms and Abbreviations α alpha, ambient exposure factor α -HCH alpha-hexachlorocyclohexane Å Ångström A surface albedo AAC abdominal aortic calcium AAS atomic absorption spectrophotometry AB Alcian Blue stain ABC Asian Brown Cloud ABI ankle-arm or resting blood pressure index AC air conditioning Ace acenaphthene ACE-1 angiotensin converting enzyme-1 ACEAsia (Asian Pacific Regional) Ae rosol Characterization Experiment ACGIH American Conference of G overnmental Industrial Hygienists ACh acetylcholine Acl acenaphthylene ACP accumulation mode particle ACS American Cancer Society Ad4BP adrenal-4-binding protein ADEOS-1 Advanced Earth Observing Satellite-1 A-DEP automobile diesel exhaust particles ADM angular distribution model(s), angular dependence model ADMA asymmetric dimethylarginine AD-Net Asian Dust Network Ae AERONET AeroCom Aerosol Comparisons between Observations and Models AERONET NASA AERosol RObotic NETwork AF atrial fibrillation AGA appropriate for gestational age AGE advanced glycation end product AHR airway hyperresponsiveness, airway hyperreactivity AhR arylhydrocarbon receptorDecember 2009 lv AHSMOG California Seventh Day Adventist study AI aerosol index AIC Akaike’s information criterion AIM ambient ion monitor AIOP 2003 Aerosol Intensive Operating Period AIRS Aerometric Information Retrieval System Al aluminum ALI air liquid interface AM alveolar macrophage(s) AM, AMF arbuscular mycorrhizal AMAP Arctic Monitoring and Assessment Programme AMDP annual maximum of daily precipitation AMI acute myocardial infarction AMS aerosol mass spectrometry Ang II angiotensin II ANOVA analysis of variance ANP atrial natriuretic peptide ANS autonomic nervous system Ant anthracene AOD aerosol optical depth AP-1 activator protein 1 APC antigen presenting cell(s) APCS Absolute Principal Components Scores APEX Air Pollutants Exposure Model APHEA Air pollution and Health: a European Approach APO apocynin ApoE apolipoprotein E APS aerodynamic particle sizer, aerosol polarimetry sensor aPTT activated partial thromboplastin time AQCD Air Quality Criteria Document AQI Air Quality Index AQM air quality model AQS U.S. EPA Air Quality System database Aqua NASA satellite AR4 Fourth Assessment Report (AR4) from the IPCCDecember 2009 lvi ARCTAS Arctic Research of the Composition of the Troposphere from Aircraft and Satellites ARD Air Resources Division ARDS adult respiratory distress syndrome ARI acute respiratory infection ARIC Atherosclerosis Risk in Communities study ARIES Aerosol Research and Inhalation Epidemiology Study ARM Atmospheric Radiation Measurement program ARQM Air Quality Research Branch (Meteorological Service of Canada Toronto) ARS Air Resource Specialists As arsenic ASDNN5 mean of the standard deviati on in all 5-min segments of a EKG 24 h recording ASOS Automated Surf ace Observing System ATOFMS aerosol time-of-f light mass spectrometry ATP adenosine triphosphate A-Train a group of 5 afternoon overpass satellites (Aura, PARASOL, CALIPSO, CloudSat, Aqua) ATS American Thoracic Society AURA NASA satellite avg average AVHRR Advanced Very High Resolution Radiometer ß beta, beta coefficient, slope β -HCH beta-hexachlorocyclohexane(s) 3 β HSD 3 β -hydroxysteroid dehydrogenase β TGF β transforming growth factor b ag absorption by gases coefficient b ap absorption by particles coefficient b ext light extinction coefficient b sg scattering by gases coefficient b sp sum of light scattering by (aerosol) particles coefficient Ba barium BaA benz[a]anthracene BAD bronchial artery diameter BAL bronchoalveolar lavage BALB/c albino inbred mouse strainDecember 2009 lvii BALF bronchoalveolar lavage fluid BALT bronchus-associated lymphoid tissues BAM beta attenuation monitor BaP benzo[a]pyrene BASIC Brain Attack Surveillance in Corpus Christi BASE-A Burning Airborne and Spaceborne Experiment - Amazon and Brazil BbF benzo[b]fluoranthene BC black carbon BCC-CMI Beijing Climate Center – Carbon Mitigation Initiative BCCR Bjerknes Centre for Climate Research BeP benz[e]pyrene BghiP, BpPe benzo[g,h,i]perylene BGT beta-gauge technique BH 4 tetrahydrobiopterin bhp brake horsepower BkF benzo[k]fluoranthene BMI body mass index BMP bone morphogenetic protein BN/BR Brown Norway rat strain BNP brain natriuretic peptide, B-type natriuretic peptide BOSS BYU Organic Sampling System BP blood pressure BPM blowing PM 2.5 BpPe benzo[ghi]perylene BPQ benz(ayrene (BaP)-quinone Br bromine BRAVO Big Bend Regional Aerosol and Visibility Observational (Study) BrdU bromodeoxyuridine BS black smoke BUC bucillamine (N-[2-mercapto -2-methylpropionyl]-L-cysteine) BVAIT B-Vitamin Atherosclerosis Intervention Trial BYU Brigham Young University C carbon C 4 Center of Clouds, Chemistry and Climate 12 C carbon-12December 2009 lviii 13 C carbon-13 14 C carbon-14 C 60 (OH) 24 water-soluble fullerene Ca calcium CAA Clean Air Act CAAA 1977 Clean Air Act Amendments CAAM continuous ambient mass monitor CAC coronary artery calcification CaCO 3 calcium carbonate CAD coronary artery disease CALINE California Line Source Dispersion Model CALIOP Cloud and Aerosol Lidar with Orthogonal Polarization CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations CAM Community Atmosphere Model CAMM continuous ambient mass monitor CAMP Childhood Asthma Management Program CAMx comprehensive air quality model with extensions Ca(NO 3 ) 2 calcium nitrate CAP concentrated ambient particle CAPMoN Canadian Air and Precipitation Monitoring Network CASAC Clean Air Scientific Advisory Committee CaSO 4 calcium sulfate CASTNet Clean Air Status and Trends Network CATS cumulative air toxics surface CB carbon black, chronic bronchitis CB-Fe carbon black particles artificially coated with Fe(II) salt. CB(P) carbon black (particles) CBSA Core-Based Statistical Area CB-V carbon black particles artificially coated with a targeted concentration of Vanadium (IV) salt CBVD cerebrovascular disease CC16 Clara cell protein, Clara cell 16 protein CCCma Canadian Centre for Climate Modeling and Analysis CCM3 Community Climate Model CCN cloud condensation nuclei CCPM continuous coarse particle monitorDecember 2009 lix CCSM3 Community climate system model, version 3 CCSP Climate Change Science Program Cd cadmium CDC Centers for Disease Control and Prevention CDE conjugated diene CDNC cloud droplet number concentration CDPHE Colorado Department of Public Health and Environment Ce cerium CEN European Committee for Standardization CenRAP Central Regional Air Planning Association CERES Clouds and the Earth’s Radiant Energy System CERFACS European Centre for Research and Advanced Training in Scientific Computation CF coronary flow, cystic fibrosis CFA coal fly ash CFD cystic fibrosis disease CFR Code of Federal Regulations CGCM3.1 Coupled global climate model cGMP cyclic guanosine monophosphate CH 2 Cl 2 methylene chloride CH 2 O formaldehyde CH 4 methane CHAD Consolidated Human Activity Database CHD chronic heart disease CHF congestive heart failure CHL crown heel length CHO Chinese hamster ovary cells Chr chrysene CHS Children’s Health Study CI confidence interval CIF carbon-impregnate d charcoal filter CIIT Chemical Industry Institute of Toxicology CIMT carotid intimal-medial thickness Cl chlorine CL chemiluminescence CLAMS Chesapeake Lighthouse and Aircraft Measurements for SatellitesDecember 2009 lx CM conditioned medium, cell culture medium CMAQ Community Multi-scale Air Quality modeling system CMAR CSIRO Marine and Atmospheric Research CMB chemical mass balance CMD count median diameter CNES Centre National d''Etudes Spatiales CNP carbon nano particle CNRM Centre National de Recherches Meteorologiques CNRM Center National Weather Research CNS central nervous system Co cobalt CO carbon monoxide CO 2 carbon dioxide COD coefficient of divergence COH, CoH coefficient of haze CONUS continental United States COO - carboxyl group COPD chronic obstructive pulmonary disease CoPP cobalt protoporphyrin COX-2 cyclooxygenase 2 enzyme CPC condensation particle counter CPZ capsazepine Cr chromium C-R concentration-response (relationship) CRP C-reactive protein Cs cesium 137 Cs cesium-137 CS cigarette smoke CSA Combined Statistical Area CSC cigarette smoke condensates CSE cigarette smoke extract cSHMT cytosolic serine hydroxymethyltransferase CSIRO Commonwealth Scientific and Industrial Research Organization CSN Chemical Speciation Network CTM chemistry-tran sport model, chemi cal transport modelDecember 2009 lxi Cu copper CuSO 4 copper sulfate Cu/Zn SOD Cu/Zn superoxide dismutase CUP Current Use Pesticide CV cardiovascular, coefficient of variation CVD cardiovascular disease(s) CVM contingent valuation method CYP cytochrome P450 CYP 1A1 cytochrome P450 1A1 Δ delta, change, difference Δ FEV 1 change in forced expiratory volume in one second d 50 50 percent cut point or 50 percent diameter d ae aerodynamic diameter of a particle D diameter D a Dalton DAAC Distributed Active Archive Center DAASS Dry Ambient Aero sol Size Spectrometer DABEX Dust and Biomass-burning Experiment (in West Africa) DAR denuded aortic ring DAX-1 x-chromosome gene-1 DBA dibenzo(a,hnthracene DBP diastolic blood pressure DC dendritic cell DC diesel exhaust particles + cigarette smoke condensates DC8 Douglas aircraft DCF direct climate forcing, 2',7'-dichlorofluorescin DDT dichlorodiphenyltrichloroethane DE diesel exhaust DEE diesel exhaust extract DEP diesel exhaust particle DEPAL diesel exhaust particles aliphatic (extract) DEPAR diesel exhaust particles aromatic (extract) DEPE diesel exhaust particles extract DEPM diesel exhaust particles methanol (extract) DEPME diesel exhaust particles methylene chloride extractDecember 2009 lxii DEPPO diesel exhaust particles polar (extract) Dex dexamethasone d

Fld change fold, unit change in property DFO desferrioxamine (Desferral) an iron chelator DFX deferasirox (Exjade) an oral iron chelator DHR dihydrorhodamine 123 DLCO carbon monoxide diffusing capacity DMEM Dulbecco’s modified Eagl e’s medium (culture medium) DMSO dimethyl sulfoxide DMT1 divalent metal transporter-1 protein DMTU dimethylthiourea DNA deoxyribonucleic acid DOE U.S. Department of Energy dpc days post conception DPC dodecylphosphocholine DPCC 1,2-dipalmitoyl-S N-glycero-3-phosphocholine DPI diphenyleneiodonium DPM diesel particulate matter DPPC dipalmitoylphosphatidylcholine DRE direct radiative effects DRF direct radiative forcing DRUM Davis Rotating Uniform size-cut Monitor DS diffusion screens DSP daily sperm production DTMA Dynamic mechanical thermal analysis DTPA diethylene triamine pentaacetic acid DU dust dv deciview(s) DVT deep vein thrombosis EAD electrical aerosol detector EANET Acid Deposition Monitoring Network in East Asia EARLINET European Aerosol Research Lidar Network EarthCARE Earth Clouds, Aerosols and Radiation Explorer EAST-AIRE East Asian Study for Tropospheric Aerosols EBCT electron beam computed tomographyDecember 2009 lxiii EC elemental carbon ECE-1 endothelin converting enzyme-1 ECG, EKG electrocardiogram ECHAM5 European Centre Hamburg with Hamburg Aerosol Module ECHO-G (ECHAM4 + HOPE-G): ECRHS European Community Respiratory Health Survey EC/TC ratio of elemental carbon to total carbon ED emergency room, emergency department EDGAR Emissions Database for Global Atmospheric Research EDTA ethylenediaminetetraacetic acid ED-XRF energy dispersive X-ray fluorescence EGM electrogram EGU electricity-generating unit EHC-93 Ottawa dust; urban air particulate matter PM 10 , collected in 1993 in Ottawa, Canada EKG, ECG electrocardiogram ELISA enzyme-linked immunosorbent assay EMECAS Spanish Multi-centric Study on the Relation between Air Pollution and Health EMEP European Monitoring and Evaluation Programme eNO exhaled nitric oxide eNOS endothelial nitric oxide synthase EOS Earth Observing System EPA U.S. Environmental Protection Agency ER estrogen receptor ERBS Earth Radiation Budget Satellite ERK1/2 ERK-1 (MAPK p42) and ERK-2 (MAPK p44) ESRL Earth System Research Laboratory ESTR expanded simple tandem repeat E T forcing efficiency ET extrathoracic region ET endothelin ET A endothelin A receptor subtype ET B endothelin B receptor subtype ETS environmental tobacco smoke EU endotoxin unitsDecember 2009 lxiv F breathing frequency f the ratio of ambient aerosol ma ss (wet) to dry aerosol mass M. f σ sp(RH) total light scattering coefficient at given relative humidity(RH) values faf anthropogenic fraction of fine-mode fraction ff fine mode fraction f(RH) the unitless water growth term that depends on relative humidity F fine particles F344 Fisher 344 strain of rats F a adjusted forcings FA filtered air FAC ferric ammonium citrate FBI Federal Bureau of Investigation FBS fetal bovine serum FCS fetal calf serum FDMS Filter Dynamics Measurement System FDMS-TEOM Filter Dynamics Measurement System - Tapered Element Oscillating Microbalance F e effective (F e ) forcings Fe iron Fe 2 (SO 4 ) 3 ferric sulfate FeCl 3 ferric chloride FEF forced expiratory flow FEF 25-75 mean forced expiratory flow over the middle half of the forced vital capacity FEF 50% mid-expiratory flow FEM Federal Equivalent Method FeNO fractional exhaled nitric oxide FERA Fire and Environmental Research Applications (Team) FEV 1 forced expiratory volume in one second FGA one fibrinogen alpha chain FGB one fibrinogen beta chain FGOALS-g1.0 Flexible Global Ocean-Atmosphere-Land System Model F

i instantaneous forcing FID flame ionization detection FIMS fast integrated mobility scanners F

inf infiltration factorsDecember 2009 lxv FKHR Proapoptotic Factor FOXO1 Fle fluorine Flu fluoranthene FMD flow-mediated dilation FPG formamidopyrimidine-DNA glycosylase f-PM, FPM fine particulate matter FR Federal Register FRM Federal Reference Method FROSTFIRE The landscape-scal e prescribed research burn in the boreal forest of interior Alaska, July 1999; conducted by FERA. Fs SST forcing(s), forcing driven by sea surface temperature (SST) Fsfc mean net solar flux at the (Earth) surface FT free troposphere FTIR Fourier transform infrared spectrometry F/UFP mix of fine and ultrafine particles, all < 2.5 μm FVC forced vital capacity γ GCS gamma glutamylcysteine sythetase Ga gallium GAM generalized additive model GATOR Gas, Aerosol, Transport, and Radiation model GATORG Gas, Aerosol, Transport, Radiation, and General circulation model GAW Global Atmospheric Watch network GBS group B streptococcus GC gas chromatography GCM(s) general circulation model(s), global climate model GCMOM General Circulation, Mesoscale and Ocean Model GC/MS gas chromatography/mass spectrometry GCS gamma glutamylcysteine sythetase GD gestational day GDF growth differentiation factor (e.g., GDF-9) GEE generalized estimating equations, gasoline engine exhaust GEIA Global Emissions Inventory Activity GEM gaseous elemental mercury GEOS-Chem Goddard Earth Observing System-CHEMistry GFAAS graphite furnace atomic absorption spectrometry GFAP glial fibrillatory acidic proteinDecember 2009 lxvi GFDL Geophysical Fluid Dynamics Laboratory GFDL-CM2.x GFDL Climate Models GFED Global Fire Emission Database GGT gamma-glutamyltranspeptidase GHG greenhouse gas GIS Geographic Information System GISS Goddard Institute for Space Studies GISS-AOM GISS Atmosphere-Ocean Model climate prediction model GISS-EH GISS AOM for sea ice model GISS-ER GISS AOM for liquid sea model GLAS Geoscience Lase r Altimeter System GLM generalized linear models GM geometric mean GM-CSF granulocyte macrophage colony-stimulating factor GMD Global Monitoring Division GMS Greater Mekong Subregion GOCART Goddard Chemistry Aerosol Radiation and Transport GOES Geostationary Operational Environmental Satellite GoMACCS Gulf of Mexico Atmospheric Composition and Climate Study GPS Global Positioning System GSD geometric standard deviation GSFC NASA Goddard Space Flight Center GSH glutathione GSH:GSSG ratio of reduced glutathione to glutathione disulfide (oxidized glutathione) GSO, GSNO S-Nitrosoglutathione GSSG glutathione disulfide; oxidized glutathione GST glutathione-S-transferase GSTM1 glutathione S-transferase polymorphism M1 GSTP1 glutathione-S-tran sferase polymorphism P1 GSTT1 glutathione-S-tran sferase polymorphism T1 GWP global warming potential h hour H atomic hydrogen, hydrogen radical, height, heart rate, high dose, high exposure H + hydrogen ionDecember 2009 lxvii HR heart rate H 2 molecular hydrogen H 2 CO formaldehyde H 2 O water H 2 O 2 hydrogen peroxide H 2 S hydrogen sulfide H 2 SO 4 sulfuric acid H9c2 rat embryonic cardiomyocytes cell line HA hospital admission HAEC Human Aortic Endothelial Cell HAPC Harvard ambient particle concentrator HBE, HBEC Human Bronchial Epithelial cells HC hydrocarbon(s); head circumference HCB hexachlorobenzene HCH hexachlorocyclohexane(s) (e.g. α -HCH, β -HCH) HDL high density lipoprotein HEAPSS Health Effects of Air Pollution among Susceptible Subpopulations study HEI Health Effects Institute HEPA high efficiency particle air (filter) HERO Health and Environmental Research Online, NCEA Database System HF heart failure, high frequency (HRV parameter), high (dose/exposure) filtered HFCD High-Fat Chow Diet HFE HFE gene, HFE protein Hg mercury Hg(0) gaseous elemental mercury Hg(II) gaseous divalent (oxidized) mercury HH hereditary hemochromatosis HNRS Hans Nixdorf Recall Study HO-1 heme oxygenase-1 hOGG1 8-hydroxyguanine DNA-glycosylase HOPE-G Hamburg Atmosphere-Ocean Coupled Circulation Model hPA hectopascal hPAEC human pulmonary artery endothelial cells hPBMC human peripheral blood mononuclear cellsDecember 2009 lxviii HPLC high pressure liquid chromatography HPMF high particulate matter filtered hPMVEC human pulmonary microvascular endothelial cells HR heart rate, hazard ratio, high level DE HRV heart rate variability HSD 17 β -hydroxysteroid dehydrogenase HSP-70 heat shock protein HSPH Harvard School of Public Health HSRL High Spectral Resolution Lidar HUVEC human umbilical vein endothelial cells h

ν photon HWS hardwood smoke Hz hertz IC ion chromatography ICAM-1 intercellula r adhesion molecule-1 ICARTT International Consortium for Atmospheric Research on Transport and Transformation ICAS Inner-City Asthma Study ICD implantable/implanted cardioverter defibrillator ICD-9 International Classification of Disease 9th revision ICD-10 International Classification of Disease 10th revision ICESat Ice, Cloud and land Elevation Satellite ICP-AES inductively coupled plasma-atomic emission spectroscopy ICP-MS inductively-coupled plasma-mass spectrometry ICR imprinting control region, mouse strain ICRP International Commission on Radiological Protection IDP indeno[1,2, 3-c,d]pyrene IFN- γ interferon-gamma IFS Integrated Forest Study Ig immunoglobulin (e.g., IgE) IGS International Genetic Standard IHD ischemic heart disease IIASA International Institute for Applied Systems Analysis IL interleukin iMDDC immature monocyte-derived dendritic cellsDecember 2009 lxix IMPACT Interactive Modeling Project for Atmospheric Chemistry and Transport IMPROVE Interagency Monitoring of Protected Visual Environment IN ice nuclei INAA instrumental neutron activation analysis INCA Interactions between Chemistry and Aerosol INDOEX Indian Ocean Experiment INGV-SXG Istituto Nazionale di Geofisica e Vulcanologia coupled to SINTEX-G INM-CM3.0 Institute of Numerical Mathematics climate model iNOS inducible nitric oxide synthase INTEX Intercontinental Chemical Transport Experiment I/O indoor-outdoor ratio IOM Institute of Medicine i.p. intraperitoneal IP inhalable particle IPCC Intergovernmental Panel on Climate Change IPSL-CM4 Institut Pierre Simon Laplace climate model IQR interquartile range Ir iridium IR incidence rate, infrared radiation IRE iron responsive element IRMS isotope ratio mass spectrometer ISA Integrated Science Assessment ISO International Standards Organization ISO isoprene, 2-methyl analog of 1,3-butadiene IT intratracheal, intratracheally IUGG International Union of Geodesy and Geophysics IUGR intrauterine growth restricti on, intrauterine growth retardation i.v. intravenous JNK c-jun N-terminal kinase κ B kappa B K potassium KC local neutrophil chemoattractant protein kHz kilohertz kJ kilojoules KLH keyhole limpet hemocyaninDecember 2009 lxx km kilometer km -1 inverse kilometer K ow octanol-water partition coefficient L, dL, mL, μ L Liter, deciLiter, milliLiter, microLiter L low La lanthanum LAC light-absorbing carbon LACE98 Lindenberg Aerosol Characterization Experiment 1998 LBA-SMOCC Large-Scale Atmosphere-Biosphere Experiment in Amazon LBW low birth weight LC lethal concentration LC 50 median lethal concentration LDH lactate dehydrogenase LDL low-density lipoprotein LDLR low-density lipoprotein receptor LDVP left developing ventricular pressure LES large eddy simulations model LF low frequency an HRV parameter LF/HF ratio of LF to HF an HRV parameter LIBS laser induced breakdown spectroscopy LIF leukemia inhibitory factor LITE Lidar In-space Technology Experiment LMD Laboratoire de Meteorologie Dynamique LMDz LMD with Zoom LMDZ-INCA LMDZ INteractive Chemistry and Aerosols model LMDZ-LOA LMDZ with Laboratoire d'Optique Atmosphérique model L-NAME arginine analog; N(G)-nitro-L- arginine methyl ester L-NMMA N(G)-mono-methyl-L-arginine LnRMSSD natural log of RMSSD; measure of HRV lnSDNN natural log of the standard deviation of NN intervals in an EKG LOA Laboratoire d’Optique Atmospherique LOESS locally weighted scatterplot smoothing LOSU level of scientific understanding Lpm liters per minute (L/min) LPMF low particulate matter filteredDecember 2009 lxxi LPO plasma lipid peroxides LPS lipopolysaccharide LRAT long range atmospheric transport LROT long range oceanic transport LSCE Laboratoire des Sciences du Climat et de l’Environnement LSDF low-sulfur diesel fuel LTB 4 leukotriene B 4 LTE 4 leukotriene E 4 LUA NRW The North Rhine-Westphalia State Environment Agency LUDEP LUng Dose Evaluation Program LUR land use regression LV left ventricle LVEDP left-ventricular end-diastolic pressure, LVSP left-ventricular systolic pressu re, left ventricular developed pressure L/W ratio of lumen to wall LWC liquid water content LWDE Low Whole Diesel Exhaust LWP liquid water path μg microgram μg/m 3 micrograms per cubic meter μm micrometer, micron m, cm, μm, nm meter(s), centimete r(s), micrometer(s), nanometer(s) M, mM, μ M, nM, pM Molar, milliMolar, microMolar, nanoMolar, picoMolar M dry aerosol mass, medium dose/exposure ma moving average MAM March-April-May MAN Maritime Aerosol Network MANE-VU Mid-Atlantic/Northeast Visibility Union MAP mitogen-activated protein, mean arterial pressure MAPK mitogen-activated protein kinase(s), MAP kinase MARAMA Mid Atlantic Regional Air Management Association MATCH Model of Atmospheric Transport and Chemistry max maximum MBP major basic protein MCAPS Medicare Air Pollution StudyDecember 2009 lxxii Mch methacholine MCN mixed carbon nanoparticle MCP-1 monocyte chemoattractant protein 1 MCV mean corpuscular volume MD mineral dust MDA malondialdehyde MDCT multidetector computed tomography ME Multilinear Engine MEE mass extinction efficiency MEF maximal expiratory flow MEF 50 maximum expiratory flow rate at 50% of vital capacity MeHg methyl mercury MENTOR Modeling Environment for Total Risk Studies MEP motorcycle exhaust particulate(s) MEPE motorcycle exhaust particulate extract (particle-free) MESA Multi-Ethnic Study of Atherosclerosis MFFSR multifilter rotating shadowband radiometer mg/m 3 milligrams per cubic meter Mg magnesium MI myocardial infarction MIROC3.x Model for Interdisci plinary Research on Climate MILAGRO Megacity Initiative: Local and Global Research Observations, study of air pollution in Mexico City min minute(s), minimum MINOS MPI Mediterranean INtensive Oxidant Study MIP-2 macrophage inflammatory protein-2 MIRAGE Megacities Impact on Regional and Global Environment program MIS mullerian inhibiting substance MISR Multi-angle Imaging SpectroRadiometer Mm megameter Mm -1 inverse megameter MM monocyte-derived macrophages MM5 mesoscale model MMAD mass median aerodynamic diameter MMD mass median diameter MMEF maximal mid-expiratory flowDecember 2009 lxxiii mmHg millimeters of mercury MMP mitochondria membrane potential MMP(2,9) matrix metalloproteinase (2, or 9) MMT million metric tons Mn manganese MN micronuclei MnSO 4 manganese sulfate MnSOD manganese superoxide dismutase MnTBAP manganese tetrakis (4-benzoic acid) porphyrin mo month MOA mode(s) of action MODIS MODerate resolution Imaging Spectroradiometer MOUDI Micro-Orifice Uniform Deposit Impactor MOZART MOdel for Ozone and Related chemical Tracers MP mid polar, myelopeptide MPC mean platelet component MPF median power frequency MPG N-(2-mercaptopropionyl) glycine MPI Max Planck Institute for Meteorology MPLNET Micro-Pulse Lidar Network MPO myeloperoxidase MPPD Multiple-Path Particle Dosimetry model MPV mean platelet volume MRI Meteorological Research Institute MRI-CGCM MRI coupled general circulation model mRNA messenger RNA MRPO Midwest Regional Planning Organization ms millisecond MSA metropolitan statistical area MSH melanocyte stimulating hormone MSHA Mount St. Helen ash MSU monosodium urate crystals MT metric ton MTHFR methylenetetrahydrofolate reductase MTT methyl thiazol tetrazoliumDecember 2009 lxxiv MV motor vehicle MWNT multiplewall nanotube M/Z mass-to-charge ratio N nitrogen N 2 O nitrous oxide Na sodium Na 2 SO 4 sodium sulfate NAAQS National Ambient Air Quality Standards NAC N-acetylcysteine, a thiol antioxidant NaCl sodium chloride NADPH reduced form of nicotinamide adenine dinucleotide phosphate NAG N-acetyl- β -D-glucosaminidase Na,K-ATPase sodium-potassi um adenosine triphosphatase NAMS National Ambient Monitoring Stations NaN 3 sodium azide NaNO 3 sodium nitrate nano-BAM low pressure-drop ultrafine particle impactor coupled with a Beta Attenuation Monitor NAPAP National Acid Precipitation Assessment Program NAPCA National Air Pollution Control Administration NAS National Academy of Sciences NASA U.S. National Aeronau tics and Space Administration NASDA National Space Development Agency, Japan NATA U.S. EPA’s National Air Toxics Assessment 2-NB 2-nitrobenzanthrone NC total (particle) number concentration NCAR National Center for Atmospheric Research NCC-MPSP negatively charged carboxylate-modified polystyrene particle(s) NCD Normal Chow Diet NCEA National Center for Environmental Assessment NCHS National Center for Health Statistics NCICAS National Cooperative Inner-City Asthma Study NCore National Core Nd drop number concentration Nd:YAG neodymium-doped yttrium aluminum garnet laser NDDN National Dry Deposition NetworkDecember 2009 lxxv NEAQS NOAA New England Air Quality Study NEI National Emissions Inventory NESCAUM Northeast States for Coordinated Air Use Management NET National Emissions Trends database NF κ B nuclear factor kappa-B NG neutrophil granulocytes NH northern hemisphere NH 3 ammonia NH 4 + ammonium ion NH 4 NO 3 ammonium nitrate (NH 4 ) 2 SO 4 ammonium sulfate NHANES National Health and Nutrition Examination Survey NHBE(C) normal human bronchial epithelial cells NHPAE normal human pulmonary artery endothelial cells NHS Nurses’ Health Study Ni nickel NIOSH National Institute for Occupational Safety and Health NIST National Institute of Standards and Technology NMHC non-methane volatile hydrocarbon NMMAPS U.S. National Morbidity, Mortality, and Air Pollution Study NO nitric oxide NO 2 , NO 2 · nitrogen dioxide, nitrogen dioxide radical NO 3 – nitrate NOAA National Oceanic and Atmospheric Administration NOAEL no observed adverse effect level NOS nitric oxide synthase NOS3 nitric oxide synthase 3 NOx nitrogen oxides, oxides of nitrogen (NO + NO 2 ) NP National Park NPM non-blowing PM 2.5 NPOESS National Polar-orbiting Operational Environment Satellite System NPS National Park Service, U.S. Department of the Interior NR not reported NR5A1 nuclear receptor subfamily 5, group A, member 1 NRC National Research CouncilDecember 2009 lxxvi NRPB National Radiological Protection Board NSA North Slope Alaska NT neurotrophin, nitrotyrosine NWS National Weather Service NYHA New York Heart Association O oxygen O 2 molecular oxygen O 3 ozone OAQPS Office of Air Quality Planning and Standards OC organic carbon OCM organic carbon mass OE organic extracts OGG1 8 oxo-guanine repair enzyme OH, OH• hydroxyl group, hydroxyl radical 8-OHdG 8-hydroxydeoxyguanosine OM organic matter OMI Ozone Monitoring Instrument OMM organic molecular marker OR odds ratio(s) OSM oncostatin M, a cytokine OSPM Operational Street Pollution Model OVA ovalbumin oxLDL oxidation of LDL, marker of oxidative stress 8-oxodG 8-oxo-7-hydr odeoxyguanosine ox-PAPC oxidized 1-palmitoyl-2-arachi donoyl-sn-glycero-3-phosphorylcholine P450 cytochrome P450 P450c17 cytochrome P450 17- α -hydroxylase P450scc cytochrome P450 cholesterol side chain cleavage enzyme P90 90th percentile; Printex 90 p probability value P phosphorus PA photoacoustic analyzer, physical activity, plasminogen activator, pulmonary arterial, alveolar pressure PAF platelet-activating factor PAH polycyclic aromatic hydrocarbon(s) PAI plasminogen activator inhibitor, (e.g. PAI-1)December 2009 lxxvii PALMS NOAA Particle Analysis by Laser Mass Spectrometry instrument PAMCHAR Chemical and Biological Ch aracterisation of Ambient Air Coarse, Fine, and Ultrafine Particles for Hu man Health Risk Assessment in Europe PAMS Photochemical Assessment Monitoring Stations network PAR photosynthetically active radiation PAR(s) Pulmonary Artery Rings PARASOL Polarization and Directionality of the Earth’s Reflectances, coupled with observations from a Lidar, a CNES satellite PARP poly(ADP-ribose) polymerase PAS Periodic Acid Schiff stain Pb lead 207 Pb lead-207 PBDE polybrominated diphenyl ether PBL planetary boundary layer PBMC peripheral blood mononuclear cell PBMM peripheral blood monocyte-derived macrophages PBP primary biological particle(s) PBS phosphate buffered saline PC synthetic carboxylate-modified particles PCA principal component analysis PCA-MPSP positively-charged amine modified polystyrene particle PCB polychlorinated biphenyl(s) PCDD polychlorinated dibenzo-p-dioxin PCIS Personal Cascade Impactor Sampler PCM NCAR Parallel Climate Model PCPSP positively charged polystyrene particle PCR polymerase chain reaction PDF probability distribution functions pDR personal DataRam PE post exposure, post exercise, phenylephrine PEACE Pollution Effects on Asthmatic Children in Europe study PEC particulate elemental carbon PECAM-1 platelet endothelial cell adhesion molecule 1 PEF peak expiratory flow (L/min) PEFR peak expiratory flow rateDecember 2009 lxxviii PEFT time to peak flow PEM personal exposure monitor PEM-West NASA Pacific Exploratory Missions in the western Pacific Penh enhanced pause Per perylene PESA particle elastic scattering analysis PFDE particle free diesel exhaust PGE 2 prostaglandin E 2 PGI 2 prostacyclin Phe phenanthrene PI post instillation, posterior interval, pulmonary inflammation PICT pollution-induced community tolerance PILS Particle Into Liquid Sampler PILS-IC Particle Into Liquid Sampler-Ion Chromatography PIXE Particle Induced X-ray Emission PKA protein kinase A PLS partial least squares, projection to latent structures PM particulate matter PMx particulate matter of a specific size range. X refers to the diameter at which the sampler collects 50% of the particles and rejects 50% of the particles. Collection efficiency in creases for particles with smaller diameters and decreases for partic les with larger diameters. The variation of collection efficiency with size is given by a collection efficiency curve. The definition of PMx is frequently abbreviated as “particles with a nominal mean aerodynamic diameter less than or equal to x μm. PMx-y particulate matter with a nominal mean diameter greater than x μm and less than y μm where x and y are the numeric mean aerodynamic or mobility diameters (μm). PM 0.1 particulate matter with a nominal mean mobility diameter less than or equal to 0.1 μm (referred to as ultrafine PM) PM 2.5 particulate matter with a nominal mean aerodynamic diameter less than or equal to 2.5 μ m (referred to as fine PM) PM 10 particulate matter with a nom inal mean aerodynamic diameter less than or equal to 10 μ m PM 10-2.5 particulate matter with a nomin al mean aerodynamic diameter greater than 2.5 μ m and less than or equal to 10 μ m (referred to as thoracic coarse particulate matter or the course fraction of PM 10 ) Concentration may be measured with a dichotomous sampler or calculated as the difference between measured PM 10 and measured PM 2.5 concentrations. PMA phorbol 12-myristate 13-acetate PMF particulate matter filtrat e, positive matrix factorizationDecember 2009 lxxix PM-HD particulate matter at high concentration PM-LD particulate matter at low concentration PMN polymorphonuclear leukocytes PN particle number PNC particle number concentration, particle number count PND, pnd post-natal day PNMD particle number median diameter PNN proportion of interval differences of successive normal-beat intervals in EKG pNN50 proportion of interval differences of successive normal-beat intervals greater than 50 ms in an EKG PNNL Pacific Northwest National Laboratory pNO 3 particulate nitrate POA primary organic aerosol POC particulate organic carbon POLDER POLarization and Directiona lity of the Earth's Reflectance POM particulate organic matter POP persistent organic pollutant P p particle density PP pulse pressure ppb parts per billion PPFL percent predicted lung function ppm parts per million ppt parts per trillion PRB policy-relevant background PRE AeroCom Experiment PRELC Primary Rat Epithelial Lung Cells PRIDE Puerto RIco Dust Experiment PS public school PSAS The French National Program on Air Pollution Health Effects PSO Public Service Company of Oklahoma pSO 4 particulate sulfate PSS physiologic saline solution PSU Pennsylvania State University PT prothrombin time PTT partial thomboplastin timeDecember 2009 lxxx PTV programmable temperature vaporization PVD peripheral vascular disease Pyr pyrene Q cardiac output Q coronary flow of the heart QAI QA interval QBQ backup quartz-fiber filter behind a quartz-fiber filter QEEG quantitative electroencephalography Qext the extinction coefficient (a function of particle size distribution and refractive index) r correlation coefficient R 2 coefficient of determination RAIN Regional Aerosol Intensive Network RAMS real-time total ambient mass sampler RANTES regulated upon activation, normal T cell expressed and secreted RAPS/RAMS Regional Air Pollution Study / Regional Air Monitoring Study RAR rapidly activating receptor(s) RASMC rat aortic smooth muscle cells RAW 264.7 mouse macrophage cell line RBC red blood cell RD respiratory disease REALM Regional East Atmospheric Lidar Mesonet RF radiative forcing(s) r eff particle effective radius RFL Fetal Lung Fibroblasts RH relative humidity RHMVE rat heart micro-vessel endothelial cell RHR Regional Haze Rule RLF rat lung fibroblasts RMC rat cardiomyocyte(s) RME rapeseed oil methyl ester RMSSD root mean squared differences of successive normal-beat to normalbeat (NN or RR) time intervals between each QRS complex in the EKG RMV respiratory minute volume RNA ribonucleic acidDecember 2009 lxxxi RNS reactive nitrogen species RO residual oil ROCK rho associated kinase ROFA residual oil fly ash (particles) ROFA-L residual oil fly ash leachate ROI reactive oxygen intermediates ROS reactive oxygen species RPO Regional Planning Organizations RR risk ratio, relative risk, normal- to-normal (NN or RR) time interval between each QRS complex in the EKG RS resuspended soil RSV respiratory syncytial virus RTI respiratory tract infection RTM Radiative Transfer Model RTP Research Triangle Park, North Carolina RV right ventricular RVCFB right ventricular cardio fibroblasts RVCM right ventricular cardiomyopathy, rat ventricular cardiomyocytes, reduced volume culture medium σ sigma, standard deviation 1 σ one sigma; one standard deviation σ g sigma-g; geometric standard deviation s second S sulfur SAB (EPA) Science Advisory Board SAFARI South African Fire-Atmosphere Research Initiative SAGE Stratospheric Aerosol and Gas Experiment SALIA German study on the Influence of Air Pollution on Lung Function, Inflammation, and Aging SAM Stratospheric Aerosol Measurement SAMUM Saharan Mineral Dust Experiment SAP2.3 Synthesis and Assessment Product 2.3 Sb antimony SB strand breaks SBL stable boundary layer SBP systolic blood pressureDecember 2009 lxxxii Sc scandium SC summer curbside particles SCAB California South Coast Air Basin SCAR Smoke/Sulfates, Clouds and Radiation SCARPOL Swiss Study on Childhood Allergy and Respiratory Symptoms with Respect to Air Pollution sCD40L soluble CD40 ligand SCE sister chromatid exchange SCS Harvard Six Cities Study SD standard deviation; Sprague-Dawley rat SDANN5 standard deviation of the average of normal to normal (N:N) intervals in all 5-min intervals in a 24-h period SDNN standard deviation normal-to-normal (NN or RR) time interval between each QRS complex in the EKG SDNN24HR standard deviation of the average of all normal to normal intervals in a 24-h period Se selenium se standard error SEARCH Southeastern Aerosol Research and Characterization sem standard error of mean SEM scanning electron microscopy SES socioeconomic status, sample equilibration system SF-1 steroidogenic factor -1 SF-UFID suspension, particle free ultrafine industrial exhaust SGA small for gestational age sGC soluble guanylate cyclase SGP Southern Great Plains -SH sulfhydryl group SH Mount Saint Helen’s ash SH, SHR spontaneously hypertensive disease model rat SHADE Saharan Dust Experiment SHEDS Stochastic Human Exposure and Dose Simulation model Si silicon sICAM-1 soluble intercellular adhesion molecule SIDS sudden infant death syndrome SiO 2 silicone dioxide SIPS State Implementation PlanDecember 2009 lxxxiii SJV San Joaquin Valley SLAMS State and Local Air Monitoring Stations SME soybean oil methyl ester SMOCC Smoke Aerosols, Clouds, Rainfall and Climate SMOKE Spare-Matrix Operator Kernel Emissions system SMPS scanning mobility particle sizer SMPS-APS scanning mobility particle sizer– aerodynamic particle sizer SMRA small mesenteric rat arteries SNP single-nucleotide polymorphi sm, sodium nitroprusside SNS sympathetic nervous system SO 2 sulfur dioxide SO 3 sulfur trioxide SO 4 2– sulfate SOA secondary organic aerosol SOC semi-volatile organic compound SOD superoxide dismutase SOPHIA Study of Particulates and Health in Atlanta SO X sulfur oxides, oxides of sulfur SP surfactant protein (e.g., SPA, SPD) SPA surfactant protein A SPD surfactant protein D SPEW Speciated Pollutant Emission Wizard SPG Southern Great Plains site SPM suspended particulate matter SPRINTARS Spectral Radiation-Tran sport Model for Aerosol Species SRM-154b NIST standard reference material 154b; (TiO 2 Titanium dioxide) SRM1648 NIST standard reference material 1648; (urban particulate matter) SRM-1649 NIST standard reference material 1649 (Washington, D.C. urban air particulate matter, urban dust) SRM-1650 NIST standard reference material 1650 (diesel exhaust particulate matter) SRM-1879 NIST standard reference material 1859; (silicone dioxide, respirable cristobalite [respirable crystalline silica]) SRM-2975 NIST standard reference material 2975 (diesel exhaust particulate matter) s-ROFA soluble portion of residual oil fly ash SS secondary sulfate, sea saltDecember 2009 lxxxiv SSA single-scattering albedo SSR standardized sex ratio SST sea surface temperature STEM Sulfur / Sulfate Transport Eulerian Model STN EPA Speciation Trend Network STP standard temperature and pressure STZ Streptozotocin SUB summer urban background particles SURFRAD NOAA GMD Surface Radiation network SVA supraventricular arrhythmia sVCAM-1 soluble vascular adhesion molecule 1 SVEB supraventricular ectopic beats SWNT singlewalled nanotube SXRF Synchroton X-ray fluorescence SZA solar zenith angle τ photochemical lifetime T body temperature TAR IPCC 3rd Assessment Report TARC thymus and activation-regulated chemokine TARFOX Tropospheric Aerosol Radiative Forcing Observational Experiment TAT thrombin-anti-thrombin complexes TB tracheobronchial TBA thiobarbituric acid TBAP tetrakis(4-benzoic acid) porphyrin TBARS thiobarbituric acid reactive substances TBQ backup quartz-fiber filter behind a Teflon-membrane filter 99m Tc Technetium-99m 99m Tc-DMTA 99M Tc Dynamic mechanical thermal analysis 99M Tc-DTPA 99M Tc-diethylenetriaminepentaacetic acid T co core temperature TD thermal desorption, tire debris extracted in methanol TD-GC/MS thermal desorption-ga s chromatography/mass spectrometry TEAC Trolox Equivalent Antioxidant Capacity assay TEOM Tapered Element Oscillating Microbalance TexAQS Texas Air Quality Field StudyDecember 2009 lxxxv TF tissue factor TFPI tissue factor pathway inhibitor Tg teragram TG terminal ganglion (neurons) TGF transforming growth factor TGF β β transforming growth factor Th thorium Th1 T helper cell type 1 Th2 T helper cell type 2 tHcy total homocysteine Ti titanium TIA transient ischemic attack TiFe iron-loaded fine titanium oxide TIMP-2 tissue inhibitor of MMP TiO 2 titanium dioxide TK thymidine kinase TM transition metals TM5 Thematic Mapper, a sensor on Landsat5 satellite TMTU tetramethylthiourea TNF- α tumor necrosis factor alpha TOA top of the atmosphere TOF-SIMS time-of-flight - secondary ion mass spectrometry TOMS Total Ozone Mapping Spectrometer TOT/GC thermal optical transmission analyzer coupled with gas chromatography TOVS TIROS-N Operational Vertical Sounder tPA, t-PA tissue plasminogen activator TRACE Transition Region and Coronal Explorer TRP transient receptor potential TRPV1 transient receptor potential vanilloid-1 receptor TR-XRF total reflection X-ray fluorescence TSA trichostatin A TSP total suspended particulate TSS WRAP Technical Support System website TVOC total VOC TWP Tropical West Pacific islandDecember 2009 lxxxvi TXB 2 thromboxane B-2 U uranium UACR urinary albumin / creatinine ratio UAE 2 United Arab Emirates Unified Aerosol Experiment UAP urban ambient particle UF ultrafine, uncertainty factor UFAA ultrafine ambient air UFC ultrafine carbon UfCB ultrafine carbon black UFDG ultrafine diesel engine exhaust UFID ultrafine industrial exhaust UFP ultrafine particle UFPM ultrafine particulate matter UFTiO 2 ultrafine titanium dioxide UIO University of Oslo U.K. United Kingdom UKMO United Kingdom Meteorological Office ULAQ University of lL’Aquila. ULTRA Exposure and Risk Assessment for Fine and Ultrafine Particles in Ambient Air UMI University of Michigan UNEP United Nations Environmental Programme UP urban particle UPM ultrafine particulate matter UPSP unmodified polystyrene particle(s) URI upper respiratory infection URS upper respiratory symptoms U.S. United States of America U.S.C. U.S. Code UV ultraviolet radiation V vanadium V, mV, μV volt, millivolt, microvolt VAQ visual air quality VCAM-1 vascular adhesion molecule 1 V d deposition velocity VEAPS Vitamin E Atherosclerosis Progression StudyDecember 2009 lxxxvii VEGF vascular endothelial growth factor VIEWS Visibility Information Exchange Web Site VISTAS Visibility Improvement State a nd Tribal Association of the Southeast VOC volatile organic compound VOSO 4 vanadyl sulfate VPB ventricular premature beat VR visual range VR1 vanilloid receptor 1 VSCC very sharp cut cyclone VSMC Vascular Smooth Muscle Cells V T tidal volume vWF von Willebrand factor W Wilderness WACAP Western Airborne Contaminates Assessment Project WBC white blood cell(s) WC winter curbside particles WHI Women’s Health Initiative WHI OS Women’s Health Initiative Observational Study wk week(s) WKY Wistar-Kyoto rat strain W/m 2 , W m -2 watts per square meter WMO World Meteorological Organization Wnt wingless gene family WRAP Western Regional Air Partnership WRF Weather Research and Forecasting model WS wood smoke WSOC water soluble organic carbon WUB winter urban background particles XAD polystyrene-divinyl benzene XPS X-ray photoelectron spectroscopy Y yttrium yr year Z radar reflectivity (measured in dBZ [decibels of Z, where Z represents the energy reflected back to the radar.]) Zn zinc ZnO zinc oxideDecember 2009 lxxxviii ZnS zinc sulfide ZnSO 4 zinc sulfate Zr zirconium