Contains Errata Sheet created on 3/4/2009 
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. 

National Center for Environmental Assessment - RTP 

Office of Research and Development 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 
Table of Contents 

Table of Contents______________________________________________________________________________________ i

List of Tables _________________________________________________________________________________________ v

List of Figures _______________________________________________________________________________________ vii

Acronyms and Abbreviations____________________________________________________________________________ x

Authors and Contributors_____________________________________________________________________________ xxii

SOX Project Team___________________________________________________________________________________ xxvi

Clean Air Scientific Advisory Committee for Sulfur Oxides Primary NAAQS Review Panel _____________________ xxviii

Preface ____________________________________________________________________________________________ xxx

Chapter 1. Introduction _______________________________________________________________________________ 1-1

1.1. Document Development ____________________________________________________________________________________ 1-2

1.2. Document Organization_____________________________________________________________________________________ 1-2

1.3. EPA Framework for Causal Determination ______________________________________________________________________ 1-2

1.3.1. Scientific Evidence Used in Establishing Causality ___________________________________________________________ 1-3

1.3.2. Association and Causation _____________________________________________________________________________ 1-4

1.3.3. Evidence for Going beyond Association to Causation_________________________________________________________ 1-4

1.3.4. Multifactorial Causation ________________________________________________________________________________ 1-6

1.3.5. Uncertainty__________________________________________________________________________________________ 1-7

1.3.5.1. Types of Uncertainty ____________________________________________________________________________ 1-7

1.3.5.2. Approaches to Characterizing Uncertainty ___________________________________________________________ 1-8

1.3.6. Application of Framework for Causal Determination __________________________________________________________ 1-9

1.3.7. First Step—Determination of Causality ___________________________________________________________________ 1-11

1.3.8. Second Step—Evaluation of Population Response _________________________________________________________ 1-12

1.3.9. Concepts in Evaluating Adversity of Health Effects__________________________________________________________ 1-12

1.4. Conclusions _____________________________________________________________________________________________ 1-13

Chapter 2. Source to Dose ____________________________________________________________________________ 2-1

2.1. Sources of Sulfur Oxides____________________________________________________________________________________ 2-1

2.2. Atmospheric Chemistry _____________________________________________________________________________________ 2-3

2.3. Measurement Methods and Associated Issues___________________________________________________________________ 2-5

2.3.1. Sources of Positive Interference _________________________________________________________________________ 2-5

2.3.2. Sources of Negative Interference ________________________________________________________________________ 2-6

2.3.3. Other Techniques for Measuring SO2 _____________________________________________________________________ 2-6

2.4. Monitoring Site Characteristics _______________________________________________________________________________ 2-7

2.4.1. Design Criteria for the NAAQS SO2 Monitoring Networks______________________________________________________ 2-7

2.4.1.1. Horizontal and Vertical Placement _________________________________________________________________ 2-7

2.4.1.2. Spacing from Minor Sources______________________________________________________________________ 2-7

2.4.1.3. Spacing from Obstructions _______________________________________________________________________ 2-8

2.4.1.4. Spacing from Trees_____________________________________________________________________________ 2-8

2.4.2. Locations of SO2 Monitors in Selected Metropolitan Areas_____________________________________________________ 2-9

2.4.3. Ambient SO2 Concentrations in Relation to SO2 Sources_____________________________________________________ 2-23

2.5. Environmental Concentrations of SOX ________________________________________________________________________ 2-32

2.5.1. Spatial and Temporal Variability of Ambient SO2 Concentrations_______________________________________________ 2-32

2.5.2. Five-Minute Sample Data in the Monitoring Network ________________________________________________________ 2-41

2.5.3. Policy Relevant Background Contributions to SO2 Concentrations______________________________________________ 2-46

2.6. Issues Associated with Evaluating SO2 Exposure________________________________________________________________ 2-50

2.6.1. General Considerations for Personal Exposure ____________________________________________________________ 2-50

2.6.2. Methods Used for Monitoring Personal Exposure ___________________________________________________________ 2-53

2.6.3. Relationship between Personal Exposure and Ambient Concentration __________________________________________ 2-53

2.6.3.1. Indoor Versus Outdoor SO2 Concentrations_________________________________________________________ 2-54

2.6.3.2. Relationship of Personal Exposure to Ambient Concentration ___________________________________________ 2-56

2.6.4. Exposure Errors in Epidemiologic Studies ________________________________________________________________ 2-60

 i 
2.6.4.1. Community Time-Series Studies__________________________________________________________________ 2-61

2.6.4.2. Short-Term Panel Studies_______________________________________________________________________ 2-63

2.6.4.3. Long-Term Cohort Studies ______________________________________________________________________ 2-63

2.6.4.4. Summary of Evaluation of Exposure Error in Epidemiologic Studies ______________________________________ 2-63

2.7. Dosimetry of Inhaled Sulfur Oxides___________________________________________________________________________ 2-64

2.7.1. Respiratory Gas Deposition____________________________________________________________________________ 2-64

2.7.2. Particles and Sulfur Oxide Mixtures______________________________________________________________________ 2-66

2.7.3. Distribution and Elimination of SOX ______________________________________________________________________ 2-67

Chapter 3. Integrated Health Effects ____________________________________________________________________ 3-1

3.1. Respiratory Morbidity Associated with Short-Term Exposure________________________________________________________ 3-2

3.1.1. Summary of Findings from the Previous Review_____________________________________________________________ 3-2

3.1.2. Potential Mode of Action for Respiratory Health Effects _______________________________________________________ 3-3

3.1.3. Respiratory Effects Associated with Peak (5-10 min) Exposure _________________________________________________ 3-4

3.1.3.1. Respiratory Symptoms __________________________________________________________________________ 3-5

3.1.3.2. Lung Function _________________________________________________________________________________ 3-5

3.1.3.3. Airway Inflammation ____________________________________________________________________________ 3-8

3.1.3.4. Mixtures and Interactive Effects ___________________________________________________________________ 3-8

3.1.3.5. Summary of Evidence on the Effect of Peak Exposure on Respiratory Health _______________________________ 3-9

3.1.4. Respiratory Effects Associated with Short-Term (≥ 1 h) Exposure______________________________________________ 3-11

3.1.4.1. Respiratory Symptoms _________________________________________________________________________ 3-11

3.1.4.2. Lung Function ________________________________________________________________________________ 3-17

3.1.4.3. Airway Inflammation ___________________________________________________________________________ 3-19

3.1.4.4. Airway Hyperresponsiveness and Allergic Sensitization _______________________________________________ 3-19

3.1.4.5. Respiratory Illness-Related Absences _____________________________________________________________ 3-21

3.1.4.6. Emergency Department Visits and Hospitalizations for Respiratory Diseases_______________________________ 3-21

3.1.4.7. SO2-PM Interactions and Other Mixture Effects ______________________________________________________ 3-30

3.1.4.8. Summary of Evidence on the Effect of Short-Term (≥ 1 h) Exposure on Respiratory Health ___________________ 3-30

3.1.5. Evidence of the Effects of SO2 on Respiratory Morbidity from Intervention Studies_________________________________ 3-32

3.1.6. Summary of Evidence of the Effect of Short-Term SO2 Exposure on Respiratory Health ____________________________ 3-33

3.2. Systemic Morbidity Associated with Short-Term SO2 Exposure _____________________________________________________ 3-34

3.2.1. Summary of Findings from the Previous Review____________________________________________________________ 3-34

3.2.2. Cardiovascular Effects Associated with Short-Term Exposure _________________________________________________ 3-34

3.2.2.1. Heart Rate and Heart Rate Variability______________________________________________________________ 3-35

3.2.2.2. Repolarization Changes ________________________________________________________________________ 3-36

3.2.2.3. Cardiac Arrhythmias ___________________________________________________________________________ 3-37

3.2.2.4. Blood Pressure _______________________________________________________________________________ 3-37

3.2.2.5. Blood Markers of Cardiovascular Risk _____________________________________________________________ 3-38

3.2.2.6. Acute Myocardial Infarction______________________________________________________________________ 3-39

3.2.2.7. Emergency Department Visits and Hospitalizations for Cardiovascular Diseases ____________________________ 3-39

3.2.2.8. Summary of Evidence on the Effects of Short-Term SO2 Exposure on Cardiovascular Health __________________ 3-42

3.2.3. Other Effects Associated with Short-Term SO2 Exposure_____________________________________________________ 3-42

3.3. Mortality Associated with Short-Term SO2 Exposure _____________________________________________________________ 3-43

3.3.1. Summary of Findings from the Previous Review____________________________________________________________ 3-43

3.3.2. Mortality and Short-Term SO2 Exposure in Multicity Studies and Meta-Analyses __________________________________ 3-43

3.3.2.1. Multicity Studies ______________________________________________________________________________ 3-44

3.3.2.2. Meta-Analyses of Air Pollution-Related Mortality Studies_______________________________________________ 3-47

3.3.3. Evidence of the Effect of SO2 on Mortality from an Intervention Study ___________________________________________ 3-48

3.3.4. Summary of Evidence on the Effects of Short-Term SO2 Exposure on Mortality ___________________________________ 3-49

3.4. Morbidity Associated with Long-Term SO2 Exposure _____________________________________________________________ 3-52

3.4.1. Summary of Findings from the Previous Review____________________________________________________________ 3-52

3.4.2. Respiratory Effects Associated with Long-Term Exposure to SO2 ______________________________________________ 3-53

3.4.2.1. Asthma, Bronchitis, and Respiratory Symptoms______________________________________________________ 3-53

3.4.2.2. Lung Function ________________________________________________________________________________ 3-55

3.4.2.3. Morphological Effects __________________________________________________________________________ 3-56

3.4.2.4. Lung Host Defense ____________________________________________________________________________ 3-56

3.4.2.5. Summary of Evidence on the Effects of Long-Term Exposure on Respiratory Health_________________________ 3-57

3.4.3. Carcinogenic Effects Associated with Long-Term Exposure___________________________________________________ 3-57

3.4.4. Cardiovascular Effects Associated with Long-Term Exposure _________________________________________________ 3-59

3.4.5. Prenatal and Neonatal Outcomes Associated with Long-Term Exposure ________________________________________ 3-60

3.4.6. Other Organ System Effects Associated with Long-Term Exposure_____________________________________________ 3-63

3.5. Mortality Associated with Long-Term SO2 Exposure _____________________________________________________________ 3-63

 ii 
3.5.1. Summary of Findings from the Previous Review____________________________________________________________ 3-63

3.5.2. Associations of Mortality and Long-Term Exposure in Key Studies _____________________________________________ 3-64

3.5.2.1. U.S. Cohort Studies____________________________________________________________________________ 3-64

3.5.2.2. European Cohort Studies _______________________________________________________________________ 3-67

3.5.2.3. Cross-Sectional Analysis Using Small Geographic Scale ______________________________________________ 3-67

3.5.3. Summary of Evidence on the Effect of Long-Term Exposure on Mortality ________________________________________ 3-68

Chapter 4. Public Health Impact________________________________________________________________________ 4-1

4.1. Assessment of Concentration-Response Function and Potential Thresholds ___________________________________________ 4-1

4.1.1. Evidence from Human Clinical Studies ____________________________________________________________________ 4-1

4.1.2. Evidence from Epidemiologic Studies _____________________________________________________________________ 4-4

4.1.3. Summary of Evidence on Concentration-Response Functions and Thresholds_____________________________________ 4-7

4.2. Susceptible and Vulnerable Populations________________________________________________________________________ 4-7

4.2.1. Pre-existing Disease __________________________________________________________________________________ 4-8

4.2.1.1. Pre-existing Respiratory Diseases _________________________________________________________________ 4-8

4.2.1.2. Pre-existing Cardiovascular Diseases ______________________________________________________________ 4-9

4.2.2. Genetic Factors for Oxidant and Inflammatory Damage from Air Pollutants_______________________________________ 4-10

4.2.3. Age-Related Susceptibility_____________________________________________________________________________ 4-12

4.2.4. Other Potentially Susceptible Populations_________________________________________________________________ 4-14

4.2.5. Factors that Potentially Increase Vulnerability to SO2 ________________________________________________________ 4-14

4.2.6. Summary of Potentially Susceptible and Vulnerable Populations_______________________________________________ 4-15

Chapter 5. Summary and Conclusions __________________________________________________________________ 5-1

5.1. Emissions and Ambient Concentrations of SO2 __________________________________________________________________ 5-1

5.2. Health Effects of SO2_______________________________________________________________________________________ 5-2

5.3. Integration of the Evidence __________________________________________________________________________________ 5-8

5.4. Susceptible and Vulnerable Populations_______________________________________________________________________ 5-10

5.5. Conclusions _____________________________________________________________________________________________ 5-10

ANNEXES

Annex A. Literature Selection__________________________________________________________________________ A-1

A.1. Literature Search and Retrieval ______________________________________________________________________________ A-1

A.2. General Criteria for Study Selection ___________________________________________________________________________ A-1

A.2.1. Criteria for Selecting Epidemiologic Studies ________________________________________________________________ A-1

A.2.2. Criteria for Selecting Animal and Human Toxicological Studies _________________________________________________ A-2

A.3. Other Approaches to the Causal Determination __________________________________________________________________ A-4

A.3.1. Surgeon General’s Report: The Health Consequences of Smoking______________________________________________ A-4

A.3.2. EPA: Guidelines for Carcinogen Risk Assessment___________________________________________________________ A-6

A.3.3. Improving the NAS/IOM Presumptive Disability Decision-Making Process for Veterans Report ________________________ A-9

A.3.4. National Acid Precipitation Assessment Program Guidelines__________________________________________________ A-14

A.3.5. IARC Guidelines for Scientific Review and Evaluation Categories______________________________________________ A-16

A.3.6. NTP: Report on Carcinogens __________________________________________________________________________ A-22

Annex B. Additional Information on the Atmospheric Chemistry of SOX ______________________________________ B-1

B.1. Introduction ______________________________________________________________________________________________ B-1

B.1.1. Multiphase Chemical Processes Involving SOX and Halogens__________________________________________________ B-2

B.1.2. Mechanisms for the Aqueous Phase Formation of Sulfate_____________________________________________________ B-4

B.1.3. Multiphase Chemical Processes Involving SOX and NH3 ______________________________________________________ B-5

B.2. Transport of SOX in the Atmosphere___________________________________________________________________________ B-5

B.3. Emissions of SO2 _________________________________________________________________________________________ B-6

B.4. Methods Used to Calculate SOx and Chemical Interactions in the Atmosphere _________________________________________ B-8

B.5. Chemical-transport Models __________________________________________________________________________________ B-9

B.5.1. Regional Scale Chemical-Transport Models________________________________________________________________ B-9

B.5.2. Intra-urban Scale Dispersion Modeling ___________________________________________________________________ B-13

B.5.3. Global-scale CTMs __________________________________________________________________________________ B-13

B.5.4. Modeling the Effects of Convection______________________________________________________________________ B-14

B.5.5. CTM Evaluation_____________________________________________________________________________________ B-15

B.6. Sampling and Analysis of SOX ______________________________________________________________________________ B-15

B.6.1. Sampling and Analysis for SO2 _________________________________________________________________________ B-15

B.6.1.1. Other Techniques for Measuring SO2______________________________________________________________ B-16

B.6.2. Sampling and Analysis for SO4

2-, NO3, and NH4+ ___________________________________________________________ B-16

 iii 
Annex C. Modeling Human Exposure ___________________________________________________________________ C-1

C.1. Introduction ______________________________________________________________________________________________ C-1

C.2. Population Exposure Models: Their Evolution and Current Status____________________________________________________ C-5

C.3. Ambient Concentrations of SO2 and Related Air Pollutants _________________________________________________________ C-7

C.4. Characterization of Microenvironmental Concentrations ___________________________________________________________ C-8

C.4.1. Characterization of Activity Events _______________________________________________________________________ C-9

C.4.2. Characterization of Inhalation Intake and Uptake ____________________________________________________________ C-9

Annex D. Controlled Human Exposure __________________________________________________________________ D-1

Annex E. Toxicological Studies ________________________________________________________________________ E-3

Annex F. Epidemiologic Studies _______________________________________________________________________ F-1

References

 iv 
List of Tables 

Table 1-1. Aspects to aid in judging causality. _________________________________________________________________________ 1-9

Table 1-2. Weight of evidence for causal determination. ________________________________________________________________ 1-11

Table 2-1. Proximity to SO2 monitors for the total population by city. Percentages are given with respect to the total population in 

each city.____________________________________________________________________________________________ 2-15

Table 2-2. Proximity to SO2 monitors for children aged 0-4 yr by city. Percentages are given with respect to the total population in 

the age group in each city. ______________________________________________________________________________ 2-15

Table 2-3. Proximity to SO2 monitors for children aged 5-17 yr by city. Percentages are given with respect to the total population in 

the age group in each city. ______________________________________________________________________________ 2-16

Table 2-4. Proximity to SO2 monitors for adults aged 65 yr and over by city. Percentages are given with respect to the total 

population in the age group in each city. ___________________________________________________________________ 2-16

Table 2-5. Monitor counts for California and San Diego County, 2005._____________________________________________________ 2-23

Table 2-6. Monitor counts for Ohio and Cuyahoga County, 2005. _________________________________________________________ 2-23

Table 2-7. Mean ambient concentrations of SO2 and SO4

2− in different regions of the U.S. averaged over 2003-2005. _______________ 2-32

Table 2-8. Concentration distributions of SO2 inside and outside CMSAs from 2003-2005. _____________________________________ 2-33

Table 2-9. Range of mean annual SO2 concentrations and Pearson correlation coefficients in urban areas having at least four 

regulatory monitors, 2003–2005. _________________________________________________________________________ 2-35

Table 2-10. Locations, counts, sampling periods and statistics for monitors reporting hourly maximum 5-min SO2 values, 1997-

2007. _______________________________________________________________________________________________ 2-41

Table 2-11. Locations, counts, sampling periods and statistics for monitors reporting all twelve 5-min SO2 values, 1997-2007.__________ 2-42

Table 2-12. Pearson correlation coefficient between maximum 5-min and 1-h avg SO2 concentrations at the 16 sites reporting all 

twelve 5-min SO2 values. _______________________________________________________________________________ 2-43

Table 2-13. Relationships of indoor to outdoor SO2 concentrations. ________________________________________________________ 2-55

Table 2-14. Association between personal exposure concentration and ambient concentration (longitudinal correlation coefficients)._____ 2-57

Table 2-15. Association between personal exposure concentration and ambient concentration (pooled correlation coefficients). ________ 2-58

Table 3-1. Percentage of asthmatic adults in controlled human exposures experiencing SO2 induced decrements in lung function. _____ 3-10

Table 4-1. Factors Potentially Contributing to Susceptibility or Vulnerability to Air Pollution ______________________________________ 4-8

Table 5-1. Key health effects of short-term exposure to SO2 observed in human clinical studies. _________________________________ 5-3

Table 5-2. Key respiratory health effects of exposure to SO2 in animal toxicological studies._____________________________________ 5-4

Table 5-3. Key findings on the health effects of SO2 exposure ___________________________________________________________ 5-11

Table 5-4. Effects of short-term exposure to SO2 on respiratory symptoms among children. ____________________________________ 5-14

Table 5-5. Effects of short-term SO2 exposure on emergency department visits and hospital admissions for respiratory outcomes. _____ 5-16

Table B-1. Atmospheric lifetimes of SO2 and reduced sulfur species with respect to reaction with OH, NO3, and Cl radicals.____________ B-1

Table B-2. Relative contributions of various reactions to the total S(IV) oxidation rate within a sunlit cloud, 10 min after cloud 

formation. ____________________________________________________________________________________________ B-3

Table B-3 Emissions of NOX, NH3, and SO2 in the U.S. by source and category, 2002. ________________________________________ B-6

 v 
Table C-1. The Essential Attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A ___________________________________ C-6

Table D-1. Effects of medications on SO2-induced changes in lung function among human subjects. ______________________________ D-1

Table D-2. Summary of new studies of controlled human exposure to SO2. __________________________________________________ D-2

Table E-1. Respiratory System – Effects of SO2 on lung function.__________________________________________________________ E-3

Table E-2. Respiratory System – Inflammatory responses following SO2 exposure.____________________________________________ E-4

Table E-3. Respiratory System – Effects of SO2 exposure on airway responsiveness and allergic sensitization. _____________________ E-5

Table E-4. Respiratory System – Effects of SO2 layered on metallic or carbonaceous particles. __________________________________ E-6

Table E-5. Respiratory System – Effects of mixtures containing SO2 and O3. _________________________________________________ E-9

Table E-6. Respiratory System – Effects of SO2 and sulfate mixtures. _____________________________________________________ E-10

Table E-7. Respiratory System – Effects of actual or simulated air pollution mixtures. _________________________________________ E-11

Table E-8. Effects of meteorological conditions on SO2 effects.___________________________________________________________ E-12

Table E-9. Cardiovascular effects of SO2 and metabolites. ______________________________________________________________ E-13

Table E-10. Hematological effects of SO2. ____________________________________________________________________________ E-14

Table E-11. Carcinogenic effects of SO2. _____________________________________________________________________________ E-15

Table E-12. Nervous system effects of SO2 and metabolites. _____________________________________________________________ E-16

Table E-13. Reproductive and developmental effects of SO2. _____________________________________________________________ E-19

Table E-14. Endocrine system effects of SO2. _________________________________________________________________________ E-20

Table E-15. Liver and gastrointestinal effects of SO2. ___________________________________________________________________ E-20

Table E-16. Renal effects of SO2.___________________________________________________________________________________ E-22

Table E-17. Respiratory System – Effect of SO2 on morphology.___________________________________________________________ E-22

Table E-18. Respiratory System – Effects of SO2 exposure on host lung defenses. ____________________________________________ E-23

Table E-19. Genotoxic effects of SO2 and metabolites. __________________________________________________________________ E-24

Table E-20. Respiratory System – Effects of SO2 and metabolites on biochemistry.____________________________________________ E-26

Table E-21. Lymphatic system effects of SO2 and SO2 mixtures. __________________________________________________________ E-28

Table F-1. Short-term exposure to SO2 and respiratory morbidity in field/panel studies. ________________________________________ F-1

Table F-2. Short-term exposure to SO2 and emergency department visits and hospital admissions for respiratory diseases.___________ F-20

Table F-3. Short-term exposure to SO2 and cardiovascular morbidity in field/panel studies. ____________________________________ F-59

Table F-4. Short-term exposure to SO2 and emergency department visits and hospital admissions for cardiovascular diseases. _______ F-65

Table F-5. Short-term exposure to SO2 and mortality. __________________________________________________________________ F-78

Table F-6. Long-term exposure to SO2 and respiratory morbidity._________________________________________________________ F-90

Table F-7. Long-term exposure to SO2 and lung cancer incidence and mortality.____________________________________________ F-102

Table F-8. Long-term exposure to SO2 and prenatal and neonatal outcomes. ______________________________________________ F-104

Table F-9. Long-term exposure to SO2 and mortality. _________________________________________________________________ F-111

 vi 
List of Figures 

Figure 1-1. Potential relationships of SOX with adverse health effects._______________________________________________________ 1-6

Figure 2-1. 2001 County-level SO2 emissions densities (tons per square mile) from off-road mobile and other transportation 

sources.______________________________________________________________________________________________ 2-2

Figure 2-2. Location of SO2 monitors with respect to population density in the Atlanta, GA MSA.__________________________________ 2-9

Figure 2-3. Location of SO2 monitors with respect to population density in the Cincinnati, OH MSA. ______________________________ 2-10

Figure 2-4. Location of SO2 monitors with respect to population density in the Cleveland, OH MSA. ______________________________ 2-11

Figure 2-5. Location of SO2 monitors with respect to population density in the Los Angeles/Riverside, CA MSA. ____________________ 2-12

Figure 2-6. Location of SO2 monitors with respect to population density in the New York City, NY/Philadelphia, PA MSA. _____________ 2-13

Figure 2-7. Location of SO2 monitors with respect to population density in the St. Louis, MO MSA. _______________________________ 2-14

Figure 2-8. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), California, 2005. _______________________________ 2-17

Figure 2-9. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Ohio, 2005.___________________________________ 2-18

Figure 2-10. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Arizona, 2005. ________________________________ 2-19

Figure 2-11. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Pennsylvania, 2005.____________________________ 2-20

Figure 2-12. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), New York, 2005._______________________________ 2-21

Figure 2-13. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Massachusetts, 2005. __________________________ 2-22

Figure 2-14. Location of SO2 monitors within a 15 km buffer zone with respect to combustion sources and highways in the Atlanta, 

GA MSA. ____________________________________________________________________________________________ 2-24

Figure 2-15. Location of SO2 monitors within a 15 km buffer zone with respect to combustion sources and highways in the 

Cincinnati, OH MSA. ___________________________________________________________________________________ 2-25

Figure 2-16. Location of SO2 monitors within a 15 km buffer zone with respect to combustion sources and highways in the 

Cleveland, OH MSA.___________________________________________________________________________________ 2-26

Figure 2-17. Location of SO2 monitors within a 15 km buffer zone with respect to combustion sources and highways in the Los 

Angeles/Riverside, CA MSA. ____________________________________________________________________________ 2-27

Figure 2-18. Location of SO2 monitors within a 15 km buffer zone with respect to combustion sources and highways in the New York 

City, NY/Philadelphia, PA MSA. __________________________________________________________________________ 2-28

Figure 2-19 Location of SO2 monitors within a 15 km buffer zone with respect to combustion sources and highways in the St. Louis, 

MO MSA.____________________________________________________________________________________________ 2-29

Figure 2-20. State-level SO2 emissions, 1990-2005._____________________________________________________________________ 2-30

Figure 2-21. Annual mean ambient SO2 concentration, 1989 through 1991 (A), and 2003 through 2005 (B)._________________________ 2-30

Figure 2-23. Annual mean ambient SO2 emissions for Acid Rain Program cooperating facilities, 2006. _____________________________ 2-31

Figure 2-24. Diel variation in SO2 concentration across all monitoring sites reporting into AQS for 2005.____________________________ 2-34

Figure 2-25. Steubenville, OH, 2003–2005. ___________________________________________________________________________ 2-36

Figure 2-26. Philadelphia, 2003–2005. _______________________________________________________________________________ 2-37

Figure 2-27. Los Angeles, 2003–2005. _______________________________________________________________________________ 2-38

 vii 
Figure 2-28. Riverside, CA, 2003–2005. ______________________________________________________________________________ 2-39

Figure 2-29. Phoenix, 2003–2005. __________________________________________________________________________________ 2-40

Figure 2-30. SO2 monitors reporting maximum or continuous 5-min avg values for any period, 1997–2007. _________________________ 2-42

Figure 2-31. Time series and frequency distributions of voluntarily reported maximum 5-min SO2 concentrations from 6 monitors 

located in Iowa, Missouri, Pennsylvania and West Virginia._____________________________________________________ 2-44

Figure 2-32. Time series of hourly maximum 5-min SO2 data showing a 24 h (upper panels) and 1 week (lower panels) time window 

centered on the peak value for the two sites with the lowest (IA) and highest (PA) maximum values in the preceding 

figure. ______________________________________________________________________________________________ 2-45

Figure 2-33. Annual mean model-predicted concentrations of SO2 (ppb). ____________________________________________________ 2-47

Figure 2-34. 15-min avg ambient SO2 concentrations measured at 1 Hawaii Volcanoes National Park monitoring site (Jaggar 

Museum), March 12, 13, and 15, 2007. ____________________________________________________________________ 2-48

Figure 2-35. 15-min avg ambient SO2 concentrations measured at 2 Hawaii Volcanos National Park monitoring sites on September 

29, 2007. ____________________________________________________________________________________________ 2-49

Figure 2-36. Percentage of time spent in various environments in the U.S. ___________________________________________________ 2-50

Figure 2-37. Average annual indoor and outdoor SO2 concentrations for each of the six cities included in the Harvard six-cities study 

analysis. ____________________________________________________________________________________________ 2-54

Figure 3-1. Distribution of individual airway sensitivity to SO2. _____________________________________________________________ 3-7

Figure 3-2. Odds ratios (95% CI) for incidence of morning asthma symptoms of 846 asthmatic children from the National 

Cooperative Inner-City Asthma Study. _____________________________________________________________________ 3-11

Figure 3-3. Odds ratios (95% CI) for daily asthma symptoms of 990 asthmatic children from the Childhood Asthma Management 

Program Study. _______________________________________________________________________________________ 3-12

Figure 3-4. Odds ratios (95% CI) for incidence of cough among children, grouped by season. ___________________________________ 3-14

Figure 3-5. Odds ratios (95% CI) for the incidence of lower respiratory tract or asthma symptoms among children, grouped by 

season. _____________________________________________________________________________________________ 3-15

Figure 3-6. Relative risks (95% CI) of SO2-associated emergency department visits and hospitalizations for all respiratory causes 

among all ages and separated by age group. _______________________________________________________________ 3-23

Figure 3-7. Relative risks (95% CI) of SO2-associated emergency department visits and hospitalizations for asthma among all ages 

and age-specific groups.________________________________________________________________________________ 3-26

Figure 3-8. Relative risks (95% CI) of SO2-associated emergency department visits (*) and hospitalizations for all respiratory 

causes and asthma, with and without copollutant adjustment.___________________________________________________ 3-29

Figure 3-9. Relative risks (95% CI) of SO2-associated emergency department visits (*) and hospitalizations for all cardiovascular 

causes, arranged by age group. __________________________________________________________________________ 3-40

Figure 3-10. All cause mortality excess risk estimates for SO2 from the National Morbidity, Mortality, and Air Pollution Study. ___________ 3-44

Figure 3-11. Relative risks (95% CI) of SO2-associated all-cause (nonaccidental) mortality, with and without copollutant adjustment, 

from multicity and meta-analysis studies. ___________________________________________________________________ 3-50

Figure 3-12. Relative risks (95% CI) of SO2-associated mortality for all (nonaccidental), respiratory, and cardiovascular causes from 

multicity studies. ______________________________________________________________________________________ 3-51

Figure 3-13. Relative risks (95% CI) for low birth weight, grouped by trimester of SO2 exposure. __________________________________ 3-61

Figure 3-14. Relative risks (95% CI) of SO2-associated all-cause (nonaccidental) mortality, with and without adjustment for sulfate, 

from longitudinal cohort studies. __________________________________________________________________________ 3-69

 viii 
Figure 4-1. Percent of mild and moderate asthmatics ( V ̇

E = 40-50 L/min) experiencing an SO2-induced increase in (a) sRaw of 

≥ 100% or a decrease in (b) FEV1 of ≥ 15%, adjusted for effects of moderate to heavy exercise in clean air._______________ 4-2

Figure 4-2. SO2-induced increase in sRaw among mild and moderate asthmatics following 10 min exposures with moderate to 

heavy exercise (VE = 40-50 L/min). ________________________________________________________________________ 4-3

Figure 4-3. SO2-induced decrease in FEV1 among mild and moderate asthmatics following 10 min exposures with moderate to 

heavy exercise (VE = 40-50 L/min). ________________________________________________________________________ 4-4

Figure 4-4. Adjusted odds ratios of asthma hospitalizations by groupings of 24-h avg SO2 concentrations in Bronx County, New 

York. ________________________________________________________________________________________________ 4-5

Figure 4-5. Relative odds ratio of incidence of lower respiratory tract symptoms smoothed against 24-h avg SO2 concentrations on 

the previous day, controlling for temperature, city, and day of week._______________________________________________ 4-6

Figure 4-6. Relative risks (95% CI) of age-specific associations between short-term exposure to SO2 and respiratory ED visits* and 

hospitalizations._______________________________________________________________________________________ 4-11

Figure 4-7. Summary density curves of the relative risks of age-specific associations between short-term exposure to SO2 and ED 

visits and hospitalizations for all respiratory causes. __________________________________________________________ 4-13

Figure 4-8. Summary density curves of the relative risks of age-specific associations between short-term exposure to SO2 and ED 

visits and hospitalizations for asthma. _____________________________________________________________________ 4-13

Figure 5-1. Odds ratios (95% CI) for the association between short-term exposures to ambient SO2 and respiratory symptoms in 

children.______________________________________________________________________________________________ 5-6

Figure 5-2. Relative risks (95% CI)for the association between short-term exposures to ambient SO2 and emergency department 

(ED) visits/hospitalizations for all respiratory diseases and asthma in children. ______________________________________ 5-7

Figure A-1. Selection process for studies included in the ISA. _____________________________________________________________ A-3

Figure A-2. Focusing on unmeasured confounders/covariates, or other sources of spurious association from bias.___________________ A-12

Figure A-3. Example posterior distribution for the determination of Sufficient. ________________________________________________ A-12

Figure A-4. Example posterior distribution for the determination of Equipoise and Above._______________________________________ A-13

Figure A-5. Example posterior distribution for the determination of Against.__________________________________________________ A-14

Figure B-1. Transformations of sulfur compounds in the atmosphere. _______________________________________________________ B-2

Figure B-2. Comparison of aqueous-phase oxidation paths._______________________________________________________________ B-4

Figure B-3. Sulfate wet deposition (mg S/m2/yr) of the mean model versus measurements for the National Atmospheric Deposition 

Program (NADP) network. ______________________________________________________________________________ B-14

Figure C-1.

__________________________________________________ C-4

Schematic description of a general framework identifying the processes (steps or components) involved in assessing 

inhalation exposures and doses for individuals and populations.

 ix 
Acronyms and Abbreviations 

α alpha 

β beta; the calculated health effect parameter 

γN2O5 reaction potential coefficient (gamma) for N2O5

Δ delta, difference; change 

τ tau; atmospheric lifetime 

μeq microequivalent 

8-OHdG 8-hydroxy-2Ν-deoxyguanosine 

ACCENT Atmospheric Composition Change: the European NeTwork of excellence (European 

Union Project) 

ACS American Cancer Society 

ADS annular denuder system 

AERMOD AMS/EPA Regulatory Model (steady-state plume model) 

AHH aryl hydrocarbon hydroxylase 

AHR airways hyper responsiveness 

AIRMoN Atmospheric Integrated Research Monitoring Network 

AirPEx Air Pollution Exposure (model) 

AirQUIS Air Quality Information System (model) 

AIRS Atmospheric Infrared Sounder (instrument) 

ALSC Adirondack Lake Survey Corporation 

ALT alanine-amino-transferase 

AM alveolar macrophages 

AMMN N-nitroso-acetoxymethylmethylamine 

AMS American Meteorological Society 

AP alkaline phosphatase 

APEX Air Pollution Exposure (model) 

APHEA Air Pollution on Health: a European Approach (study) 

APIMS atmospheric pressure ionization mass spectrometer 

AQCD Air Quality Criteria Document 

AQS Air Quality System (database) 

ARIC Atherosclerosis Risk in Communities (study) 

ARP Acid Rain Program 

ASG Atmospheric Studies Group of TRC 

ASI Acid Stress Index 

asl above sea level 

AST aspartate-amino-transferase 

atm atmosphere 

ATMOS Atmospheric Trace Molecule Spectroscopy 

ATTILA type of Lagrangian model 

B[a]P benzo[a]pyrene 

Ba barium 

BAL bronchoalveolar lavage 

BC black carbon 

BCS base-cation surplus 

BHPN N-bis(2-hydroxypropyl) nitrosamine 

BHR bronchial hyperresponsiveness 

 x 
BME Bayesian Maximum Entropy (framework) 

Br bromine 

Br− bromine ion 

Br2 molecular bromine 

BrCl bromine chloride 

BrO bromine oxide 

BS black smoke 

bw body weight 

C carbon or carbon black particles 

C2H6 ethane 

C5H8 isoprene 

Ca ambient air concentration 

Ca calcium 

CA chromosome aberrations 

Ca(NO3)2 calcium nitrate 

Ca(OH)2 calcium hydroxide 

Ca2+ calcium ion 

CaCl2 calcium chloride 

CaCO3 calcium carbonate 

CALPUFF Advanced non-steady-state meteorological and air quality modeling system developed 

by ASG scientists and distributed by TRC. Used by the EPA for assessing long range 

transport of pollutants. 

CAMP Childhood Asthma Management Program 

CAMX urban multi-scale grid based model 

CARB California Air Resources Board 

CASAC Clean Air Scientific Advisory Committee (CASAC) of EPA’s Science Advisory Board 

CASTNet Clean Air Status and Trends Network 

CAT catalase 

CB4 Carbon Bond 4 chemical mechanism model 

CDC Centers for Disease Control and Prevention 

CFD computational fluid dynamics (modeling) 

CFR Code of Federal Regulations 

CH2I2 diiodomethane 

CH2O formaldehyde 

(CH3)2SO dimethyl sulfoxide, DMSO 

CH3C(O) acetyl radical 

CH3C(O)OO acetyl peroxy radical 

CH3CHO acetaldehyde 

CH3Hg methylmercury, MeHg 

CH3OOH methyl hydroperoxide 

CH3-S-CH3 dimethylsulfide, DMS 

CH3-S-H methyl mercaptan 

CH3SO3H methanesulfonic acid 

CH3-S-S-CH3 dimethyl disulfide, DMDS 

CH4 methane 

CHAD Consolidated Human Activities Database 

CHF congestive heart failure 

chl a chlorophyll a

Chol cholesterol 

 xi 
CHS Children’s Health Study 

CI confidence interval 

Ci interstitial air concentration 

CIMS chemical ionization mass spectroscopy 

Cl chlorine 

CL critical load 

Cl− chlorine ion 

Cl2 molecular chlorine 

CLaMS type of Lagrangian model 

CMAQ Community Multiscale Air Quality (modeling system) 

CMD count median diameter 

CMSA consolidated metropolitan statistical area 

CO carbon monoxide 

CO2 carbon dioxide 

CO3

− carbonate ion 

CoH coefficient of haze 

CONUS contiguous United States 

COPD chronic obstructive pulmonary disease 

CS2 carbon disulfide 

CTM chemical transport model 

Cu copper 

CVD cardiovascular disease 

CYP cytochrome P450 

Dae aerodynamic diameter 

DEcCBP DEP extract coated carbon black particles 

DEN diethylnitrosamine 

DEP diesel exhaust particles 

DEP+C diesel exhaust particle extract adsorbed to C 

dG 2N-deoxyguanosine 

DMBA 7,12-dimethylbenzanthracene 

DMDS dimethyl disulfide, CH3-S-S-CH3

DMS dimethyl sulfide, CH3-S-CH3

DMSO dimethylsulfoxide 

DNS direct numerical simulation (approach) 

DOAS differential optical absorption spectroscopy 

DON dissolved organic nitrogen 

EC elemental carbon 

ECG electrocardiography; electrocardiogram 

ED emergency department 

EDXRF energy dispersive X-ray fluorescence 

EE energy expenditure (average EE rate) 

EIB exercise-induced bronchial reactivity 

ELF epithelial lining fluid 

EMAP Environmental Monitoring and Assessment Program 

EMECAM Spanish Multicentre Study on Air Pollution and Mortality 

EMEP Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission 

of Air Pollutants in Europe 

EOS Earth Observation System 

EPA U.S. Environmental Protection Agency 

 xii 
ESA European Space Agency 

ET extrathoracic 

Fe iron 

FEM(s) Federal Equivalent Method(s) 

FePO4 iron phosphate 

FeS iron sulfide 

FEV0.75 forced expiratory volume in 0.75 second 

FEV1 forced expiratory volume in 1 second 

F-factor fraction of the change in mineral acid anions that is neutralized by base cation release 

FHLC fetal hamster lung cells 

FLEXPART type of Lagrangian model 

FPD flame photometric detector 

FR Federal Register 

FRM Federal Reference Method 

FTIR Fourier Transform Infrared Spectroscopy 

FVC forced vital capacity 

G6PD glucose-6-phosphate dehydrogenase 

GAM Generalized Additive Model(s) 

GAW Global Atmospheric Watch (program) 

GCE Goddard Cumulus Ensemble (model) 

GCS γ-glutamylcysteine synthetase 

GEOS Goddard Earth Observing System 

GEOS-1DAS Goddard Earth Observing System Data Assimilation System 

GEOS-Chem Goddard Earth Observing System (with global chemical transport model) 

GFED Global Fire Emissions Database 

GHG greenhouse gas 

GIS Geographic Information System 

GLM Generalized Linear Model(s) 

GOES Geostationary Operational Environmental Satellites 

GOME Global Ozone Monitoring Experiment 

GPx glutathione peroxidase 

GRed glutathione reductase 

GSD geometric standard deviation 

GSH glutathione; reduced glutathione 

GSSG glutathione disulfide 

GSSO3H glutathione S-sulfonate 

GST glutathione-S-transferase 

GT γ-glutamyl transpeptidase 

h hour 

H hydrogen; hydrogen atom 

H+ hydrogen ion 

H2O water 

H2O2 hydrogen peroxide 

H2S hydrogen sulfide 

H2SO3 sulfurous acid 

H2SO4 sulfuric acid 

HAP(s) hazardous air pollutant(s) 

HAPEM Hazardous Air Pollutant Exposure Model 

 xiii 
HAPEM6 HAPEM, version 6 

HC hydrocarbon 

HCHO formaldehyde 

HCl hydrochloric acid 

HEADS Harvard-EPA Annular Denuder System 

HEI Health Effects Institute 

HF high frequency 

Hg mercury 

HNO2, HONO nitrous acid 

HNO3, HOONO nitric acid 

HNO4 pernitric acid 

HO2 hydroperoxyl; hydroperoxyl radical 

HO2NO2 peroxynitric acid 

HOBr hypobromous acid 

HOCl hypochlorous acid 

HOX hypohalous acid 

HP hydrolyzed protein 

HR heart rate 

HRV heart rate variability 

HSO3

 ̄ hydrogen sulfite, bisulfite 

HSO4

 ̄ bisulfate ion 

HSO4

 ̄ sulfuric acid ion 

hv solar ultraviolet photon with energy at wavelength v

HVA-ICa high-voltage activated calcium currents 

i microenvironment 

I iodine 

IA Integrated Assessment 

IARC International Agency for Research on Cancer (WHO) 

IBEM individual based exposure model(s) 

IC ion chromatography 

ICARTT International Consortium for Atmospheric Research on Transport and Transformation 

ICD9 International Classification of Diseases, Ninth Revision 

ICDs implanted cardioverter defibrillators 

Ig immunoglobulin (e.g., IgA, IgE, IgG) 

IgG immunoglobulin 

IHD ischemic heart disease 

IIASA International Institute for Applied Systems Analysis (an international research 

organization) 

IL interleukin (e.g., IL-4, IL-6, IL-8) 

IMPROVE Interagency Monitoring of Protected Visual Environments (network) 

IO iodine oxide 

IOM Institute of Medicine 

IPC International Cooperative Programme 

IPCC Intergovernmental Panel on Climate Change 

IPCC-AR4 IPCC 4th Assessment Report 

IQR interquartile range 

IR infrared 

ISA Integrated Science Assessment 

ISAAC International Study of Asthma and Allergies in Children 

 xiv 
ISC3 steady-state Gaussian plume dispersion model used to assess pollutant concentrations 

from industrial sources 

IUGR intrauterine growth retardation 

i.v. intravenous (injection route) 

JPL Jet Propulsion Laboratory 

K potassium 

K+ potassium ion 

Ka , Kb dissociation constant(s) 

KH Henry’s Law constant in M/atm 

KNO3 potassium nitrate 

Kw ion product of water 

LC0.01 lethal concentration at which 0.01% of exposed animals die 

LD33 lethal dose at which 33% of exposed animals die 

LDH lactate dehydrogenase, lactic acid dehydrogenase 

LES Large Eddy Simulation (approach) 

LF low frequency 

LIDAR Light Detection and Ranging (remote sensing system) 

LIF laser-induced fluorescence 

LIMS Limb Infrared Monitor of the Stratosphere 

LOD limit of detection 

LOEL lowest-observed-effect level 

LRD lower respiratory disease 

LRS lower respiratory symptoms 

LRTAP Long Range Transport of Air Pollution 

LTM Long-Term Monitoring (project) 

M molar 

MAD median aerodynamic diameter 

MAQSIP Multiscale Air Quality Simulation Platform (model) 

MAX-DOAS multiple axis differential optical absorption spectroscopy 

MBL marine boundary layer 

MCh methacholine 

ME microenvironmental (factors) 

MEF50% maximal midexpiratory flow at 50% of forced vital capacity 

MeHg methylmercury, CH3Hg 

MEM model ensemble mean 

MENTOR Modeling Environment for Total Risk 

MENTOR-1A MENTOR for One-Atmosphere (model) 

MET metabolic equivalent of tasks 

Mfg manufacturing 

Mg magnesium 

Mg2+ magnesium ion 

MgO magnesium oxide 

MI myocardial infarction 

MIMS membrane inlet mass spectrometry 

min minute 

MM5 Penn State/NCAR (National Center for Atmospheric Research) Mesoscale Model, 

version 5 

MMAD mass median aerodynamic density 

MMD mass median diameter 

 xv 
MMEF maximal midexpiratory flow 

Mn manganese 

MN micronuclei 

MNPCE micronucleated PCE 

Mo molybdenum 

MOBILE6 Highway Vehicle Emission Factor Model 

MODIS Moderate Resolution Imaging Spectroradiometer 

MONICA Monitoring Trend and Determinants in Cardiovascular Disease (registry) 

MOPITT Measurement of Pollution in the Troposphere 

MOZART Model for Ozone and Related Chemical Tracers 

MOZART-2 Model for Ozone and Related Chemical Tracers, version 2 

MPAN peroxymethacrylic nitrate 

MSA metropolitan statistical area 

Mt million tons 

N nitrogen 

N, n number of observations 

N2 molecular nitrogen; nonreactive nitrogen 

N2O nitrous oxide 

N2O5 dinitrogen pentoxide 

NA not available; insufficient data 

Na sodium 

Na+ sodium ion 

Na2MoO4 sodium molybdate 

Na2SO4 sodium sulfate 

NAAQS National Ambient Air Quality Standards 

NaCl sodium chloride 

NaCO3 sodium carbonate 

NADP National Atmospheric Deposition Program 

NAMS National Air Monitoring Stations 

NAPAP National Acid Precipitation Assessment Program 

NARSTO North American Regional Strategy for Atmospheric Ozone 

NAS National Academy of Sciences 

NASA National Aeronautics and Space Administration 

NATTS National Air Toxics Trends (network) 

NCAR National Center for Atmospheric Research 

NCICAS National Cooperative Inner-City Asthma Study 

NCore National Core Monitoring Network 

NDMA N-nitroso-dimethylamine 

NEI National Emissions Inventory 

NEM National Exposure Model 

NEM / pNEM NEM and pNEM 

NERL National Exposure Research Laboratory 

NH2 amino (chemical group) 

NH3 ammonia 

NH4

+ ammonium ion 

(NH4)2SO4 ammonium sulfate 

NH4Cl ammonium chloride 

NH4NO3 ammonium nitrate 

 xvi 
NHANES National Health and Nutrition Examination Survey 

NHAPS National Human Activity Pattern Survey 

NHX nitrogen category label for NH3 (ammonia) plus NH4

+(ammonium) 

NHY total reduced nitrogen (from ammonia and ammonium) 

Ni nickel 

NILU Norwegian Institute for Air Research 

nitro-PAH nitro-polycyclic aromatic hydrocarbon 

NMBzA N-nitrosomethylbenzylamine 

NMMAPS National Morbidity, Mortality, and Air Pollution Study 

NO nitric oxide 

NO2 nitrogen dioxide 

NO2

 ̄ nitrite ion 

NO3 nitrate, nitrate radical 

NO3

 ̄ nitrate, nitrate ion 

NOAA U.S. National Oceanic and Atmospheric Administration 

NOAA-ARL U.S. National Oceanic and Atmospheric Administration Air Resources Laboratory 

NOAEL no-observed-adverse-effect level 

NOEL no-observed-effect level 

NOX oxides of nitrogen; sum of NO and NO2

NOY sum of NOX and NOZ; odd nitrogen species; total oxidized nitrogen 

NOZ sum of all inorganic and organic reaction products of NOX (HONO, HNO3, HNO4, 

organic nitrates, particulate nitrate, nitro-PAHs, etc.) 

NPS National Park Service 

NR not reported 

Nr reactive nitrogen 

NRC National Research Council 

NS nonsignificant 

NSF National Science Foundation 

nss non-sea salt 

NSTC National Science and Technology Council 

NTN National Trends Network 

NTP National Toxicology Program 

16O2 isotope of oxygen 

O2 molecular oxygen 

O3 ozone 

OAQPS Office of Air Quality Planning and Standards (U.S. EPA) 

OC organic carbon 

OCS carbonyl sulfide 

OH hydroxyl radical 

OR odds ratio 

P phosphorus 

P, p probability value 

P1 1st percentile 

P5 5th percentile 

P95 95th percentile 

P99 99th percentile 

PAARC Air Pollution and Chronic Respiratory Diseases (study) 

PAH(s) polycyclic aromatic hydrocarbon(s) 

PAMS Photochemical Assessment Monitoring Stations 

 xvii 
PAN(s) peroxyacyl nitrate(s) (e.g. most common PAN: peroxyacetyl nitrate)

Pb lead 

PBEM population based exposure model(s) 

PBL planetary boundary layer 

PC(SO2) provocative concentration of SO2 that produces a 100% increase in specific airway 

resistance 

PCB(s) polychlorinated biphenyl compound(s) 

PCE polychromatic erythrocytes 

PD100 provocative dose that produces a 100% increase in sRAW 

PD20 provocative dose that produces a 20% decrease in FEV1 

PD20FEV1 20% decrease in forced expiratory volume in 1 second 

pdf bi-Gaussian (probability density function) 

PEACE Pollution Effects on Asthmatic Children in Europe (study) 

PEC pulmonary endocrine cells 

PEF peak expiratory flow 

PEMs personal exposure monitors 

PF pulsed fluorescence 

pH relative acidity 

PIXE proton induced X-ray emission 

PKA cyclic AMP-dependent protein kinase A 

pKa dissociation constant 

PKI synthetic peptide inhibitor of PKA 

PL phospholipids 

PM particulate matter 

PM10 particulate matter with a 50% upper cut point at 10 μm aerodynamic diameter and a 

collection efficiency curve as defined in the Code of Federal Regulations 

PM2.5 particulate matter with a 50% upper cut point at 2.5 μm aerodynamic diameter and a 

collection efficiency curve as defined in the Code of Federal Regulations; surrogate for 

fine PM 

PM10-2.5 particulate matter with a 50% upper cut point at 10 μm aerodynamic diameter, a 50% 

lower cut point at 2.5 μm aerodynamic diameter, and collection efficiency curves 

identical to those for PM10 and PM2.5; surrogate for thoracic coarse PM (does not 

include fine PM) 

PM13 particulate matter with a 50% upper cut point at 13 μm aerodynamic diameter 

PM-CAMX Comprehensive Air Quality Model with extensions and with particulate matter chemistry 

PMT photomultiplier tube 

PNC particle number concentration 

pNEM probabilistic National Exposure Model 

PnET Photosynthesis and EvapoTranspiration (model) 

PnET-BGC Photosynthesis and EvapoTranspiration-BioGeoChemical (model) 

PnET-CN Photosynthesis and EvapoTranspiration model of C, water, and N balances 

PnET-N-DNDC Photosynthesis and EvapoTranspiration-Denitrification-Decomposition (model) 

pNO3

− particulate nitrate 

PO4

−, PO43− phosphate 

POPs persistent organic pollutants 

ppb parts per billion 

ppbv parts per billion by volume 

ppm parts per million 

PPN peroxypropionyl nitrate 

ppt parts per trillion 

 xviii 
pptv parts per trillion by volume 

PRB policy relevant background 

pSO4

2− particulate sulfate 

PTFE Polytetrafluoroethylene Filters (Teflon filter for air sampling) 

Q flow rate; discharge 

Q10 temperature coefficient 

QAPP Quality Assurance Project Plan 

QT interval measure of the time interval between the start of the Q wave and the end of the T wave 

in the heart’s electrical cycle 

R- generic organic group attached to a molecule 

R, r correlation coefficient 

R2, r

2 coefficient of determination 

Ra aerodynamic resistance 

RACM Regional Atmospheric Chemistry Mechanism 

RADM Regional Acid Deposition Model 

RAMS Regional Atmospheric Modeling System 

RANS Reynolds Averaged Numerical Simulation (approach) 

RAPS Regional Air Pollution Study 

RAR rapidly activating receptor 

Raw airway resistance 

Rb boundary layer resistance 

RBC red blood cell or erythrocyte 

Rc internal resistance 

R-C(O)OO organic peroxy radical 

R-COO(s) strongly acidic organic anion(s) 

RDBMS relational database management system 

RDT Recovery Delay Time 

REMAP Regional Environmental Monitoring and Assessment Program 

RH relative humidity 

RMR resting metabolic rate 

RMSE root mean squared error 

r-MSSD root mean square of successive differences in R-R intervals. 

R-O2 organic peroxyl; organic peroxy 

R-O2NO2 peroxynitrate 

R-ONO2 organic nitrate 

RR risk ratio; relative risk 

RRX lognormal-transformed response ratio 

RuBisCO ribulose-1,5-bisphosphate carboxylase/oxygenase 

32S sulfur-32, stable isotope of sulfur 

34S sulfur-34, stable isotope of sulfur 

35S sulfur-35, radioactive isotope of sulfur 

86Sr strontium-86, stable isotope of strontium 

87Sr strontium-87, stable isotope of strontium 

S sulfur 

S2- sulfur radical 

S2* electronically excited sulfur molecule 

S2O disulfur monoxide 

SAB Science Advisory Board 

SAPALDIA Study of Air Pollution and Lung Diseases in Adults 

 xix 
SAPRAC Statewide Air Pollution Research Center 

SAPRC Stratospheric Processes and their Role in Climate 

SAVIAH Small-Area Variation in Air Pollution and Health (study) 

SBL Stable Boundary Layer 

SBUV Solar Backscatter Ultraviolet Spectrometer 

s.c. subcutaneous (route of injection) 

SC safe concentration 

SCAQS Southern California Air Quality Study 

SCE sister chromatid exchanges 

SCIAMACHY Scanning Imaging Absorption Spectrometer for Atmospheric Cartography 

SD standard deviation 

SDNN standard deviation of normal R-R intervals 

Se selenium; 

SE, se; SEM, sem standard error; standard error of mean 

SEARCH Southeastern Aerosol Research and Characterization Study (monitoring program) 

SEPs somatosensory-evoked potentials 

SES socioeconomic status 

SGV sub grid variability 

SHEDS Simulation of Human Exposure and Dose System 

Si silicon 

SIDS sudden infant death syndrome 

SIP State Implementation Plan 

SLAMS State and Local Air Monitoring Stations 

SMOKE Spare-Matrix Operator Kernel Emissions 

SNP single nucleotide polymorphism 

SO sulfur monoxide 

SO2 sulfur dioxide 

SO3 sulfur trioxide 

SO3

2- sulfite ion 

SO4

2− sulfate ion 

SOB shortness of breath 

SOD superoxide dismutase 

SOX sulfur oxides 

SPARROW SPAtially Referenced Regressions on Watershed Attributes (model) 

SPF specific pathogen free 

SPM suspended particulate matter 

SQCA squamous cell carcinoma 

Sr strontium 

sRaw specific airway resistance 

SRB sulfate-reducing bacteria 

SRP soluble reactive phosphorus 

SSO seabuckthorn seed oil 

SSWC Steady State Water Chemistry (model) 

STE stratospheric-tropospheric exchange 

STN Speciation Trends Network 

STRF Spatio-Temporal Random Field (theory) 

SUM06 seasonal sum of all hourly average concentrations ≥ 0.06 ppm 

SV40 simian virus 40 

 xx 
SVOC semivolatile organic compound 

T, t time; duration of exposure 

TAF Tracking and Analysis Framework (model) 

Tair air temperature 

TAR Third Assessment Report 

TBARS thiobarbituric acid reactive substances 

TC total carbon 

TDLAS Tunable Diode Laser Absorption Spectrometer 

TEA triethanolamine 

Tg teragram 

TIME Temporally Integrated Monitoring of Ecosystems (program) 

TNF tumor necrosis factor (e.g., TNF-α) 

TOC potassium channel transient outward currents 

TOR thermal-optical reflectance (method) 

TRACE-P Transport and Chemical Evolution over the Pacific 

TSP total suspended particles 

TSS total suspended solids 

TTX tetrodotoxin 

TTX-R tetrodotoxin-resistant 

TTX-S tetrodotoxin-sensitive 

Twater water temperature 

U.S. United States of America 

UMD-CTM University of Maryland Chemical Transport Model 

UNECE United Nations Economic Commission for Europe 

URI upper respiratory infections 

URS upper respiratory symptoms 

USDA U.S. Department of Agriculture 

USFS U.S. Forest Service 

USGS U.S. Geological Survey 

UV ultraviolet 

UV-A ultraviolet radiation of wavelengths from 320 to 400 nm 

UV-B ultraviolet radiation of wavelengths from 280 to 320 nm 

 ̇ VE minute ventilation 

Vd deposition rate 

VEPs visual-evoked potentials 

VOC volatile organic compound 

W tungsten 

WHO World Health Organization 

WMO World Meteorological Organization 

WRF Weather Research and Forecasting (model) 

wt % percent by weight 

XNO3 nitrate halogen-X salt 

XO halogen-X oxide 

XRF X-ray fluorescence 

yr year 

Zn zinc 

ZnO zinc oxide 

 xxi 
Authors and Contributors 

Authors 

Dr. Jee Young Kim (SOX Team Leader)—National Center for Environmental Assessment, 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jeffrey Arnold—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. James S. Brown—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Barbara Buckley—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Ila Cote—National Center for Environmental Assessment, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. Douglas Johns—National Center for Environmental 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 Environmental 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. Anu Mudipalli—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Joseph Pinto—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 

Dr. David Svendsgaard—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 

Dr. William Wilson—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

 xxii 
Dr. Brett Grover—National Exposure Research Laboratory, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. Douglas Bryant—Intrinsik Science, Mississauga, Ontario, Canada 

Dr. Arlene Fiore—Geophysical Fluid Dynamics Laboratory/National Oceanographic & Atmospheric 

Administration, Princeton, NJ 

Dr. Panos Georgopoulos—Computational Chemodynamics Laboratory, Environmental and Occupational 

Health Sciences Institute, Piscataway, NJ 

Dr. Vic Hasselblad—Duke University Medical Center, Durham, NC 

Dr. Larry Horowitz—Geophysical Fluid Dynamics Laboratory/National Oceanographic and Atmospheric 

Administration, Princeton University Forrestal Campus, Princeton, NJ 

Ms. Annette Ianucci—Sciences International, Alexandria, VA 

Dr. Kazuhiko Ito—Department of Environmental Medicine, New York University School of Medicine, 

Tuxedo, NY 

Dr. Jane Koenig— Department of Environmental and Occupational Health Sciences, University of 

Washington, Seattle, WA 

Dr. Therese Mar— Department of Environmental and Occupational Health Sciences, University of 

Washington, Seattle, WA 

Dr. James Riddle—Sciences International, Alexandria, VA 

Contributors 

Ms. Christina Cain—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Ms. Rebecca Daniels—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Steven J. Dutton—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Elizabeth Oesterling —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. Jennifer Richmond-Bryant—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. Dale Allen—Department of Atmospheric and Oceanic Sciences, University of Maryland, College 

Park, MD 

Ms. Louise Camalier—Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

 xxiii 
Dr. Russell Dickerson—Department of Atmospheric and Oceanic Science, University of Maryland, 

College Park, MD 

Dr. Tina Fan—Environmental and Occupational Health Sciences Institute, Piscataway, NJ 

Dr. William Keene—Department of Environmental Sciences, University of Virginia, Charlottesville, VA 

Dr. Randall Martin—Department of Physics and Atmospheric Science, Dalhousie University, Halifax, 

Nova Scotia, Canada 

Dr. Maria Morandi—Department of Environmental Sciences, School of Public Health, University of 

Texas – Houston Health Science Center, Houston, TX 

Dr. William Munger—Division of Engineering and Applied Sciences, Harvard University, Cambridge, 

MA 

Mr. Charles Piety—Department of Meteorology, University of Maryland, College Park, MD 

Dr. Sandy Sillman—Department of Atmospheric, Ocean, and Space Sciences, University of Michigan, 

Ann Arbor, MI 

Dr. Helen Suh—Department of Environmental Health, Harvard School of Public Health, Boston, MA 

Dr. Charles Wechsler—Environmental and Occupational Health Sciences Institute, Piscataway, NJ 

Dr. Clifford Weisel—Environmental and Occupational Health Sciences Institute, Piscataway, NJ 

Dr. Jim Zhang—Environmental and Occupational Health Sciences Institute, Piscataway, NJ 

Reviewers 

Dr. Tina Bahadori—American Chemistry Council, Arlington, VA 

Dr. Tim Benner—Office of Science Policy, U.S. Environmental Protection Agency, Washington, DC 

Dr. Daniel Costa—National Program Director for Air, U.S. Environmental Protection Agency, Research 

Triangle Park, NC 

Dr. Robert Devlin—National Health and Environmental Effects Research Laboratory, U.S. Environmental 

Protection Agency, Chapel Hill, NC 

Dr. Judy Graham—American Chemistry Council, Arlington, VA 

Dr. Stephen Graham—Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Ms. Beth Hassett-Sipple—Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Gary Hatch—National Health and Environmental Effects Research Laboratory, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Dr. Scott Jenkins—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. David Kryak—National Exposure Research Laboratory, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

 xxiv 
Mr. John Langstaff—Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Morton Lippmann—Department of Environmental Medicine, New York University School of 

Medicine, Tuxedo, NY 

Dr. Karen Martin—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. William McDonnell—William F. McDonnell Consulting, Chapel Hill, NC 

Dr. Dave McKee—Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Ms. Connie Meacham—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Lucas Neas—National Health and Environmental Effects Research Laboratory, U.S. Environmental 

Protection Agency, Chapel Hill, NC 

Dr. Russell Owen—National Health and Environmental Effects Research Laboratory, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Dr. Halûk Özkaynak—National Exposure Research Laboratory, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Dr. Jennifer Peel—Department of Environmental and Radiological Health Sciences, Colorado State 

University, Fort Collins, CO 

Mr. Harvey Richmond—Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Mr. Steven Silverman—Office of General Counsel, U.S. Environmental Protection Agency, Washington, 

DC 

Dr. Michael Stewart—Office of Air Quality Planning and Standards, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Ms. Susan Stone—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Ms. Chris Trent—Office of Air Quality Planning and Standards, Office of Air and Radiation, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Dr. John Vandenberg—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Alan Vette—National Exposure Research Laboratory, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

Ms. Debra Walsh—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Mr. Ron Williams—National Exposure Research Laboratory, U.S. Environmental Protection Agency, 

Research Triangle Park, NC 

 xxv 
SOX Project Team 

Executive Direction 

Dr. Ila Cote (Acting Director)—National Center for 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 for Environmental Assessment, U.S. Environmental 

Protection Agency, Research Triangle Park, NC 

Scientific Staff 

Dr. Jee Young Kim (SOx Team Leader)—National Center for Environmental Assessment, U.S. 

Environmental Protection Agency, Research Triangle Park, NC 

Dr. Jeffrey Arnold—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. James S. Brown—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Barbara Buckley—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Steven J. Dutton—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. Douglas Johns—National Center for Environmental 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 Environmental 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. Anu Mudipalli—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

 xxvi 
Dr. Elizabeth Oesterling —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. Joseph Pinto—National Center for Environmental 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 

Mr. Jason Sacks—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Dr. David Svendsgaard—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 

Dr. William Wilson—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Technical Support Staff 

Ms. Ellen Lorang—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Ms. Connie Meacham—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Ms. Deborah Wales—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

Mr. Richard Wilson—National Center for Environmental Assessment, U.S. Environmental Protection 

Agency, Research Triangle Park, NC 

 xxvii 
Clean Air Scientific Advisory Committee for 

Sulfur Oxides Primary NAAQS Review Panel 

Chairperson 

Dr. Rogene Henderson*, Senior Scientist Emeritus, Lovelace Respiratory Research Institute, 

Albuquerque, NM 

Members 

Mr. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine, University of Southern 

California, Los Angeles, CA 

Dr. John R. Balmes, Professor, Department of Medicine, Division of Occupational and Environmental 

Medicine, University of California, San Francisco, CA 

Dr. Ellis Cowling*, University Distinguished Professor At-Large Emeritus, Colleges of Natural 

Resources and Agriculture and Life Sciences, North Carolina State University, Raleigh, NC 

Dr. James D. Crapo*, Professor of Medicine, Department of Medicine, National Jewish Medical and 

Research Center, Denver, CO 

Dr. Douglas Crawford-Brown*, Professor and Director, Department of Environmental Sciences and 

Engineering, Carolina Environmental Program, University of North Carolina, Chapel Hill, NC 

Dr. Terry Gordon, Professor, Environmental Medicine, NYU School of Medicine, Tuxedo, NY 

Dr. Dale Hattis, Research Professor, Center for Technology, Environment, and Development, George 

Perkins Marsh Institute, Clark University, Worcester, MA 

Dr. Donna Kenski, Director of Data Analysis, Lake Michigan Air Directors Consortium, Rosemont, IL 

Dr. Patrick Kinney, Associate Professor, Department of Environmental Health Sciences, Mailman 

School of Public Health, Columbia University, New York, NY 

Mr. Steven Kleeberger, Professor and Lab Chief, Laboratory of Respiratory Biology, National Institute 

of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC 

Dr. Timothy Larson, Professor, Department of Civil and Environmental Engineering, University of 

Washington, Seattle, WA 

Dr. Kent Pinkerton, Professor, Regents of the University of California, Center for Health and the 

Environment, University of California, Davis, CA 

Mr. Richard L. Poirot*, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Dr. Edward Postlethwait, Professor and Chair, Department of Environmental Health Sciences, School of 

Public Health, University of Alabama, Birmingham, AL 

Dr. Armistead (Ted) Russell*, Professor, Department of Civil and Environmental Engineering, Georgia 

Institute of Technology, Atlanta, GA 

 xxviii 
Dr. Jonathan M Samet, Professor and Chair , Department of Epidemiology, Bloomberg School of Public 

Health, Johns Hopkins University, Baltimore, MD 

Dr. Richard Schlesinger, Associate Dean, Department of Biology, Dyson College, Pace University, New 

York, NY 

Dr. Christian Seigneur, Vice President, Atmospheric & Environmental Research, Inc., San Ramon, CA 

Dr. Elizabeth A. (Lianne) Sheppard, Research Professor, Biostatistics and Environmental & 

Occupational Health Sciences, Public Health and Community Medicine, University of Washington, 

Seattle, WA 

Dr. Frank Speizer*, Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical 

School, Boston, MA 

Dr. George Thurston, Professor, Environmental Medicine, NYU School of Medicine, New York 

University, Tuxedo, NY 

Dr. James Ultman, Professor, Chemical Engineering, Bioengineering Program, Pennsylvania State 

University, University Park, PA 

Dr. Ronald Wyzga, Technical Executive, Air Quality Health and Risk, Electric Power Research Institute, 

P.O. Box 10412, Palo Alto, CA 

Science Advisory Board Staff 

Dr. Holly Stallworth, Designated Federal Officer, 1200 Pennsylvania Avenue, NW 1400F, Washington, 

DC, 20460, Phone: 202-343-9981, Fax: 202-233-9867, (stallworth.holly@epa.gov) 

* Members of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the EPA Administrator 

 xxix 
Preface 

Legislative Requirements 

Section 109, Title 42 (U.S. Code, 2003) directs the U.S. Environmental Protection Agency (EPA) 

Administrator to propose and promulgate “primary” and “secondary” National Ambient Air Quality 

Standards (NAAQS) for pollutants listed under section 108. Section 109(b)(1) defines a primary standard 

as one “the attainment and maintenance of which in the judgment of the Administrator, based on such 

criteria and allowing an adequate margin of safety, are requisite to protect the public health.”1

 A 

secondary standard, as defined in section 109(b)(2), must “specify a level of air quality the attainment and 

maintenance of which, in the judgment of the EPA Administrator, based on such criteria, is required to 

protect the public welfare from any known or anticipated adverse effects associated with the presence of 

[the] pollutant in the ambient air.”2

 The requirement that primary standards include an adequate margin of 

safety was intended to address uncertainties associated with inconclusive scientific and technical 

information available at the time of standard setting. It was also intended to provide a reasonable degree 

of protection against hazards that research has not yet identified. See Lead Industries Association v. EPA, 

647 F.2d 1130, 1154 (D.C. Cir, 1980), cert. denied, 449 U.S. 1042 (1980); American Petroleum Institute v. 

Costle, 665 F.2d 1176, 1186 (D.C. Cir, 1981) cert. denied, 455 U.S. 1034 (1982). The aforementioned 

uncertainties are components of the risk associated with pollution at levels below those at which human 

health effects can be said to occur with reasonable scientific certainty. Thus, in selecting primary 

standards that include an adequate margin of safety, the Administrator is seeking not only to prevent 

pollution levels that have been demonstrated to be harmful but also to prevent lower pollutant levels that 

may pose an unacceptable risk of harm, even if the risk is not precisely identified as to nature or degree. 

In selecting a margin of safety, the EPA considers such factors as the nature and severity of the 

health effects involved, the size of sensitive or vulnerable population(s) at risk, and the kind and degree of 

the uncertainties that must be addressed. The selection of any particular approach to providing an 

adequate margin of safety is a policy choice left specifically to the Administrator’s judgment. See Lead 

Industries Association v. EPA, supra, 647 F.2d at 1161-62. 

In setting standards that are “requisite” to protect public health and welfare, as provided in section 

109(b), EPA’s task is to establish standards that are neither more nor less stringent than necessary for 

these purposes. In so doing, EPA may not consider the costs of implementing the standards. See Whitman 

v. American Trucking Associations, 531 U.S. 457, 465472, 475-76 (U.S. Supreme Court 2001). 

Section 109(d)(1) requires that “not later than December 31, 1980, and at 5-year intervals 

thereafter, the Administrator shall complete a thorough review of the criteria published under section 108 

and the national ambient air quality standards...and shall make such revisions in such criteria and 

standards and promulgate such new standards as may be appropriate...” Section 109(d)(2) requires that an 

independent scientific review committee “shall complete a review of the criteria...and the national 

primary and secondary ambient air quality standards...and shall recommend to the Administrator any 

new...standards and revisions of existing criteria and standards as may be appropriate...” Since the early 

1980s, this independent review function has been performed by the Clean Air Scientific Advisory 

Committee (CASAC) of EPA’s Science Advisory Board (SAB). 

 

1

 The legislative history of section 109 indicates that a primary standard is to be set at “the maximum permissible ambient air level . . which will 

protect the health of any [sensitive] group of the population,” and that for this purpose “reference should be made to a representative sample of 

persons comprising the sensitive group rather than to a single person in such a group” [S. Rep. No. 91-1196, 91st Cong., 2d Sess. 10 (1970)]. 

(Tsai et al., 2006) 

2

 Welfare effects as defined in section 302(h) [42 U.S.C. 7602(h)] include, but are not limited to, “effects on soils, water, crops, vegetation, manmade materials, animals, wildlife, weather, visibility and climate, damage to and deterioration of property, and hazards to transportation, as well 

as effects on economic values and on personal comfort and well-being.” 

 xxx 
History of Reviews of the Primary NAAQS for Sulfur Oxides 

On April 30, 1971, the EPA promulgated primary NAAQS for sulfur oxides (SOX). These primary 

standards, which were based on the findings outlined in the original 1969 Air Quality Criteria for Sulfur 

Oxides, were set at 0.14 parts per million (ppm) averaged (avg) over a 24-hour (h) period, not to be 

exceeded more than once per year, and 0.030 ppm annual arithmetic mean with sulfur dioxide (SO2) as 

the indicator. In 1982, EPA published the Air Quality Criteria for Particulate Matter and Sulfur Oxides 

(U.S. EPA, 1982) along with an addendum of newly published controlled human exposure studies, which 

updated the scientific criteria upon which the initial standards were based. In 1986, a second addendum 

was published presenting newly available evidence from epidemiologic and controlled human exposure 

studies (U.S. EPA, 1986a). In 1988, EPA published a proposed decision not to revise the existing 

standards (53 FR 14926). However, EPA specifically requested public comment on the alternative of 

revising the current standards and adding a new 1-h primary standard of 0.4 ppm. 

As a result of public comments on the 1988 proposal and other post-proposal developments, EPA 

published a second proposal in the Federal Register (FR) on November 15, 1994 (59 FR 58958). The 

1994 re-proposal was based in part on a supplement to the second addendum of the criteria document, 

which evaluated new findings on short-term SO2 exposures in asthmatics (U.S. EPA, 1994c). As in the 

1988 proposal, EPA proposed to retain the existing 24-h and annual standards. The EPA also solicited 

comment on three regulatory alternatives to further reduce the health risk posed by exposure to high 5-

minute (min) peaks of SO2 if additional protection were judged to be necessary. The three alternatives 

were: 1) revising the existing primary SO2 NAAQS by adding a new 5-min standard of 0.60 ppm SO2; 2) 

establishing a new regulatory program under section 303 of the Act to supplement protection provided by 

the existing NAAQS, with a trigger level of 0.60 ppm SO2, one expected exceedance; and 3) augmenting 

implementation of existing standards by focusing on those sources or source types likely to produce high 

5-min peak concentrations of SO2. On May 22, 1996, EPA’s final decision, that revisions of the NAAQS 

for SOX were not appropriate at that time, was announced in the Federal Register (61 FR 25566). 

 xxxi 
Chapter 1. Introduction 

The Integrated Science Assessment (ISA) is a concise review, synthesis, and evaluation of the most 

policy-relevant science, and communicates critical science judgments relevant to the NAAQS review. As 

such, the ISA forms the scientific foundation for the review of the primary (health-based) NAAQS for 

SOX.

1

 The primary NAAQS for SOX, with SO2 serving as the indicator, is set at 0.14 ppm, averaged over 

a 24-h period, not to be exceeded more than once per year, and 0.030 ppm annual arithmetic mean. The 

ISA accurately reflects “the latest scientific knowledge useful in indicating the kind and extent of 

identifiable effects on public health which may be expected from the presence of [a] pollutant in ambient 

air” (Clean Air Act, Section 108, 2003). Key information and judgments formerly contained in the Air 

Quality Criteria Document (AQCD) for SOX are incorporated in this assessment. Additional details of the 

pertinent scientific literature published since the last review, as well as selected older studies of particular 

interest, are included in a series of annexes. This ISA thus serves to update and revise the information 

available at the time of the previous review of the NAAQS for SOX in 1996. 

SO2 is the most important of the gas-phase sulfur oxides (SOX) for both atmospheric chemistry and 

health effects. SOX is usually defined to include sulfur trioxide (SO3) and gas-phase sulfuric acid (H2SO4) 

as well, but neither is present in the atmosphere in concentrations significant for human exposures. 

Descriptions of the atmospheric chemistry of SOX include both gaseous and particulate species; a 

meaningful analysis would not be possible otherwise. Most studies on the health effects of gaseous SOX

focus on SO2; effects of other gaseous species are considered as information is available. The health 

effects of particulate SOX are included in the review of the NAAQS for particulate matter (PM). In 

evaluating the health evidence, this ISA considers possible influences of other atmospheric pollutants, 

including interactions of SO2 with other co-occurring pollutants such as PM, nitrogen oxides (NOX), 

carbon monoxide (CO), and ozone (O3). 

The Integrated Plan for Review of the Primary NAAQS for SOX (U.S. EPA, 2007) identifies key 

policy-relevant questions that provide a framework for this review of the scientific evidence. These 

questions frame the entire review of the NAAQS, and thus are informed by both science and policy 

considerations. The ISA organizes and presents the scientific evidence such that, when considered along 

with findings from risk analyses and policy considerations, will help the EPA address these questions 

during the NAAQS review. 

 How has new information altered/substantiated the scientific support for the occurrence of 

health effects following short- and/or long-term exposure to levels of SOX found in the 

ambient air? 

 How does new information influence conclusions from the previous review regarding the 

effects of SOX on susceptible populations? 

 At what levels of SOX exposure do health effects of concern occur? 

 How has new information altered conclusions from previous reviews regarding the 

plausibility of adverse health effects caused by SOX exposure? 

 To what extent have important uncertainties identified in the last review been reduced? 

Have new uncertainties emerged? 

 What are the air quality relationships between short-term and long-term exposures to SOX? 

 

1

A review of the secondary SO2 NAAQS, in conjunction with a review of the secondary NAAQS for NOX, is underway independently, as is a 

review of the primary NAAQS for NOX and a review of the primary and secondary NAAQS for PM. 

 1-1 
1.1. Document Development 

EPA initiated the current formal review of the NAAQS for SOX on May 15, 2006 with a call for 

information from the public (FR, 2006). In addition to the call for information, publications are identified 

through an ongoing literature search process that includes extensive computer database mining on specific 

topics. Additional publications were identified by EPA scientists in a variety of disciplines by combing 

through relevant, peer-reviewed scientific literature obtained through these ongoing literature searches, 

reviewing previous EPA reports, and a review of reference lists from important publications. All relevant 

epidemiologic, human clinical, and animal toxicological studies, including those related to exposureresponse relationships, mechanism(s) of action, or susceptible subpopulations published since the last 

review were considered. Added to the body of research were EPA’s analyses of air quality and emissions 

data, studies on atmospheric chemistry, transport, and fate of these emissions, as well as issues related to 

exposure to SOX. Further information was acquired from consultation with content and area experts and 

the public. Annex A has more discussion of search strategies and criteria for study selection. 

1.2. Document Organization 

The ISA is composed of five chapters. This introductory chapter presents background information, 

and provides an overview of EPA’s framework for making causal judgments. Chapter 2 highlights key 

concepts or issues relevant to understanding the atmospheric chemistry, sources, exposure, and dosimetry 

of SOX, following a “source-to-dose” paradigm. Chapter 3 evaluates and integrates epidemiologic, human 

clinical, and animal toxicological information relevant to the review of the primary NAAQS for SOX. 

Chapter 4 has information related to the public health impact of ambient SOX exposure, with emphasis on 

potentially susceptible and vulnerable population groups. Finally, Chapter 5 presents key findings and 

conclusions from the atmospheric sciences, ambient air data analyses, exposure assessment, dosimetry, 

and health effects for consideration in the review of the NAAQS for SOX. 

A series of annexes supplement this ISA. The annexes provide additional details of the pertinent 

literature published since the last review, as well as selected older studies of particular interest. These 

annexes contain information on: 

 atmospheric chemistry of SOX as well as the sampling and analytic methods for 

measurement of SOX; 

 environmental concentrations and human exposure to SOX; 

 toxicological studies of health effects in laboratory animals; 

 human clinical studies of health effects related to peak (5-10 min) and short-term (1-h or 

longer) exposure to SOX; and 

 epidemiologic studies of health effects from short- and long-term exposure to SOX. 

Detailed information about methods and results of health studies is summarized in tabular format, and 

generally includes information about: concentrations of SOX and averaging times; study methods 

employed; results and comments; and quantitative results for relationships between effects and exposure 

to SOX. 

1.3. EPA Framework for Causal Determination 

It is important to have a consistent and transparent basis to evaluate the causal nature of air 

pollution-induced health effects. The framework described below establishes uniform language 

 1-2 
concerning causality and brings more specificity to the findings. It draws standardized language from 

across the federal government and wider scientific community, especially from the recent National 

Academy of Sciences (NAS) Institute of Medicine (IOM) document, Improving the Presumptive 

Disability Decision-Making Process for Veterans (IOM, 2007), the most recent comprehensive work on 

evaluating the causality of health effects. This section: 

 describes the kinds of scientific evidence used in establishing a general causal relationship 

between exposure and health effects; 

 defines cause, in contrast to statistical association; 

 discusses the sources of evidence necessary to reach a conclusion about the existence of a 

causal relationship; 

 highlights the issue of multifactorial causation; 

 identifies issues and approaches related to uncertainty; and 

 provides a framework for classifying and characterizing the weight of evidence in support 

of a general causal relationship. 

Approaches to assessing the separate and combined lines of evidence (e.g., epidemiologic, human 

clinical, and animal toxicological studies) have been formulated by a number of regulatory and science 

agencies, including the IOM of the National Academies of Science (IOM, 2008), International Agency for 

Research on Cancer (IARC, 2006), EPA Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005), 

Centers for Disease Control and Prevention (CDC, 2004), and National Acid Precipitation Assessment 

Program (NAPAP, 1991). Highlights or excerpts from the various decision framework documents are 

included in Annex A. 

These formalized approaches offer guidance for assessing causality. The frameworks are similar in 

nature, although adapted to different purposes, and have proven effective in providing a uniform structure 

and language for causal determinations. Moreover, these frameworks must support decision-making under 

conditions of uncertainty. 

1.3.1. Scientific Evidence Used in Establishing Causality 

Causality determinations are based on the evaluation and synthesis of evidence from across 

scientific disciplines; the type of evidence that is most important for such determinations will vary by 

pollutant or assessment. The most compelling evidence of a causal relationship between pollutant 

exposures and human health effects comes from human clinical studies. This type of study experimentally 

evaluates the health effects of administered exposures in humans under highly-controlled laboratory 

conditions. 

In epidemiologic or observational studies of humans, the investigator does not control exposures or 

intervene with the study population. Broadly, observational studies can describe associations between 

exposures and effects. These studies fall into several categories: cross-sectional, prospective cohort, and 

time-series studies. “Natural experiments” offer the opportunity to investigate changes in health with a 

change in exposure; these include comparisons of health effects before and after a change in population 

exposures, such as closure of a pollution source. 

Experimental animal data complement the clinical and observational data; these studies can help 

characterize effects of concern, exposure-response relationships, sensitive subpopulations and modes of 

action. In the absence of clinical or epidemiologic data, animal data alone may be sufficient to support a 

likely causal determination, assuming that humans respond similarly to the experimental species. 

 1-3 
1.3.2. Association and Causation 

“Cause” is a significant, effectual relationship between an agent and an associated disorder or 

disease. “Association” is the statistical dependence among events, characteristics, or other variables. An 

association is prima facie evidence for causation; alone, however, it is insufficient proof of a causal 

relationship between exposure and disease. Unlike an association, a causal claim supports the creation of 

counterfactual claims; that is, a claim about what the world would have been like under different or 

changed circumstances (IOM, 2007). Much of the newly available health information evaluated in this 

ISA comes from epidemiologic studies that report a statistical association between ambient exposure and 

health outcome. 

Many of the health outcomes reported in these studies have complex etiologies. The diseases, such 

as asthma, coronary heart disease or cancer are typically initiated by a web of multiple agents. Outcomes 

depend on a variety of factors, such as age, genetic susceptibility, nutritional status, immune competence, 

and social factors (Gee and Payne-Sturges, 2004; IOM, 2007). Further, exposure to a combination of 

agents could cause synergistic or antagonistic effects. Thus, the observed risk represents the net effect of 

many actions and counteractions. 

1.3.3. Evidence for Going beyond Association to Causation 

Moving from association to causation involves elimination of alternative explanations for the 

association. Human clinical studies are experiments conducted in a controlled laboratory setting using 

fixed concentrations of air pollutants under carefully regulated environmental conditions and subject 

activity levels. Results of human clinical studies may provide evidence of potential mechanisms for 

observed effects and a direct quantitative assessment of the exposure-health response relationship among 

individuals. In a randomized crossover study design, subjects in a population are exposed to a pollutant 

and a sham at different time points, and the responses to the two types of exposures are compared. This 

study design is effective at controlling for potential confounders, since the subject is serving as his/her 

own control. The results are assessed by rigorous comparison of changes in appropriate outcomes 

between the pollutant and sham exposures. By assigning exposure randomly, the study design attempts to 

remove the effect of any factor that might influence exposure. Done properly, and setting aside the role of 

chance, only a causal relationship between exposure and health outcome should produce observed 

associations in randomized clinical trials. 

A lack of observation of effects from human clinical studies does not necessarily mean that a causal 

relationship does not exist. Human clinical studies are often limited because the study population is 

generally small, which restricts the ability to discern statistically significant findings in the health 

outcomes of interest between exposure to varying concentrations of air pollutant and clean air and to 

precisely characterize the exposure-response relationship. In addition, the most susceptible individuals 

may be explicitly excluded (for ethical reasons), and other susceptible individuals or groups, such as those 

with preexisting health conditions, may not be included. Study subjects must either be healthy, or have a 

level of illness which does not preclude them from participating in the study. Asthmatics who are unable 

to withhold the use of bronchodilators for at least 6 hours prior to exposure and subjects with a recent 

history of upper respiratory tract infections are typically excluded from clinical studies of air pollution 

exposure. While human clinical studies provide important information on the biological plausibility of 

associations observed between SO2 exposure and health outcomes in epidemiologic studies, observed 

effects in these studies may underestimate the response in certain subpopulations for these reasons. 

Epidemiologic studies provide important information on the associations between health effects 

and exposure of human populations to ambient air pollution. These studies also help to identify 

susceptible or vulnerable subgroups and associated risk factors. However, associations observed between 

specific air pollutants and health outcomes in epidemiologic studies may be confounded by copollutants 

 1-4 
or meteorological conditions. Extensive discussion of these issues is provided in the 2004 AQCD for PM 

(U.S. EPA, 2004) and the 2006 AQCD for Ozone and Related Photochemical Oxidants (U.S. EPA, 

2006b), and therefore presented only briefly below. 

Inferring causation requires consideration of potential confounders. In confounding, the apparent 

effect of the exposure of interest is distorted because the effect of an extraneous factor is mistaken for or 

mixed with the actual exposure effect, which may be null (Rothman and Greenland, 1998). When 

associations are found in epidemiologic studies, one approach to remove spurious associations from 

possible confounders is to control for characteristics that may differ between exposed and unexposed 

persons; this is frequently termed “adjustment.” Multivariable regression models constitute one tool for 

estimating the association between exposure and outcome after adjusting for characteristics of participants 

that might confound the results. The use of multipollutant regression models has been the prevailing 

approach for controlling potential confounding by copollutants in air pollution health effects studies. 

Finding the likely causal pollutant from multipollutant regression models is made difficult by the 

possibility that one or more air pollutants may be acting as a surrogate for an unmeasured or poorlymeasured pollutant or for a particular mixture of pollutants. Further, the correlation between the air 

pollutant of interest and various copollutants may show temporal and spatial discongruities that can 

influence exposures and health effects. Thus, results of models that attempt to distinguish gaseous and 

particle effects must be interpreted with caution. Despite these limitations, the use of multipollutant 

models is still the prevailing approach employed in most air pollution epidemiologic studies, and may 

provide some insight into the potential for confounding or interaction among pollutants. 

Another way to adjust for potential confounding is through stratified analysis, i.e., examining the 

association within homogeneous groups with respect to the confounding variable. Stratified analysis can 

also be used to examine potential effect modification. The use of stratified analyses has an additional 

benefit: it allows examination of effect modification through comparison of the effect estimates across 

different groups. If investigators successfully measured characteristics that distort the results, adjustment 

of these factors help separate a spurious from a true causal association. Appropriate statistical adjustment 

for confounders requires identifying and measuring all reasonably expected confounders. Deciding which 

variables to control for in a statistical analysis of the association between exposure and disease depends 

on knowledge about possible mechanisms and the distributions of these factors in the population under 

study. Identifying these mechanisms makes it possible to control for potential sources that may result in a 

spurious association. 

Measurement error is another problem encountered when adjusting for spurious associations. 

Controlling for confounders, whether by adjustment or stratification, is only successful when the 

confounder is well-measured. Considered together, the effects of a well-measured covariate may be 

overestimated in contrast to a covariate measured with greater error. There are several components that 

contribute to exposure measurement error in these studies, including the difference between true and 

measured ambient concentrations, the difference between avg personal exposure to ambient pollutants 

and ambient concentrations at central monitoring sites, and the use of average population exposure rather 

than individual exposure estimates. 

Confidence that unmeasured confounders are not producing the findings is increased when multiple 

studies are conducted in various settings using different subjects or exposures; each of which might 

eliminate another source of confounding from consideration. Thus, multi-city studies which use a 

consistent method to analyze data from across locations with different levels of covariates can provide 

insight on potential confounding in associations. The number and degree of diversity of covariates, as 

well as their relevance to the potential confounders, remain matters of scientific judgment. Intervention 

studies, because of their experimental nature, can be particularly useful in characterizing causation. 

In addition to clinical and epidemiologic studies, the tools of experimental biology have been 

valuable for developing insights into human physiology and pathology. Laboratory tools have been 

extended to explore the effects of putative toxicants on human health, especially through the study of 

model systems in other species. Background knowledge of the biological mechanisms by which an 

exposure might or might not cause disease can prove crucial in establishing, or negating, a causal claim. 

 1-5 
At the same time, species can differ from each other in fundamental aspects of physiology and anatomy 

(e.g., metabolism, airway branching, hormonal regulation) that may limit extrapolation. Testable 

hypotheses about the causal nature of proposed mechanisms or modes of action are central to utilizing 

experimental data in causal determinations. 

Figure 1-1. Potential relationships of SOX with adverse health effects. 

1.3.4. Multifactorial Causation 

Scientific judgment is needed regarding likely sources and magnitude of confounding, together 

with judgment about how well the existing constellation of study designs, results, and analyses address 

this potential threat to inferential validity. One key consideration in this review is evaluation of the 

potential contribution of SOX to health effects, when it is a component of a complex air pollutant mixture. 

 1-6 
There are multiple ways by which SOX might cause or be associated with adverse health effects, as 

illustrated in Figure 1-1. First, the reported SOX effect estimates in epidemiologic studies may reflect 

independent SOX effects on respiratory health. Second, ambient SOX may be serving as an indicator of 

complex ambient air pollution mixtures that share the same source as SOX (i.e., combustion of sulfurcontaining fuels or metal smelting). Finally, copollutants may mediate the effects of SOX or SOX may 

influence the toxicity of copollutants. Epidemiologists use the term “interaction” or “effect modification” 

to denote the departure of the observed joint risk from what might be expected based on the separate 

effects of the factors. These possibilities are not necessarily exclusive. In addition, confounding can result 

in the production of an association between adverse health effects and SOX that is actually attributable to 

another factor that is associated with SOX in a particular study. Multivariate models are the most widely 

used strategy to address confounding in epidemiologic studies, but such models are not always easily 

interpreted when assessing effects of covarying pollutants such as PM, O3, and nitrogen dioxide (NO2). 

1.3.5. Uncertainty1

In estimating the causal influence of an exposure on health outcomes, it is recognized that scientific 

findings incorporate uncertainty. Uncertainty can be defined as a state of having limited knowledge where 

it is impossible to exactly describe an existing state or future outcome – the lack of knowledge about the 

correct value for a specific measure or estimate.2

 Uncertainty characterization and uncertainty assessment 

are two activities that lead to different degrees of sophistication in describing uncertainty. Uncertainty 

characterization generally involves a qualitative discussion of the thought processes that lead to the 

selection and rejection of specific data, estimates, scenarios, etc. The uncertainty assessment is more 

quantitative. The process begins with simpler measures (i.e., ranges) and simpler analytical techniques 

and progresses, to the extent needed to support the decision for which the assessment is conducted, to 

more complex measures and techniques. Data will not be available for all aspects of an assessment and 

those data that are available may be of questionable or unknown quality. In these situations, evaluation of 

uncertainty can include professional judgment or inferences based on analogy with similar situation. The 

net result is that the assessments will be based on a number of assumptions with varying degrees of 

uncertainty. Additionally, uncertain information from different sources of different quality must be 

combined. It is important that uncertainty be qualitatively and, to the extent possible, quantitatively 

described. 

1.3.5.1. Types of Uncertainty 

Uncertainty in assessment can be classified into three broad categories: uncertainty regarding 

missing or incomplete information needed to fully define the exposure and dose (scenario uncertainty); 

uncertainty regarding some parameters (parameter uncertainty); and uncertainty regarding gaps in 

scientific theory required to make predictions on the basis of causal inferences (model uncertainty). 

Identification of the sources of uncertainty is the first step toward eventually determining the type of 

action necessary to reduce that uncertainty. The following sections will discuss sources of uncertainty and 

approaches for analyzing uncertainty. 

 

1

 This discussion on uncertainty was adapted from EPA’s Guidelines for Exposure Assessment, May 29, 1992, Federal Register 57(104):22888-

22938, EPA/600/Z-92/001. 

2

 Variability is distinguished from uncertainty in that the former is an actual difference in some property of exposure or response; an intrinsic 

property that can be better understood with additional information but not reduced or eliminated. In contrast, uncertainty can be reduced or 

eliminated with additional information. 

 1-7 
Scenario Uncertainty 

The sources of scenario uncertainty include descriptive errors, aggregation errors, errors in 

professional judgment, and incomplete analysis. Descriptive errors include errors in information, such as 

misclassification of disease or pollutant exposure. Aggregation errors arise as a result of lumping 

approximations. Included among these are assumptions of homogeneous populations, and spatial and 

temporal approximations such as assumptions of steady-state conditions. Errors in professional judgment 

also are a source of uncertainty. A potentially serious source of uncertainty arises from incomplete 

analysis. For example, the lack of experimental data from clinical studies on severe asthmatics to 

pollutant exposures limits the qualitative and quantitative analyses of health effects. 

Parameter Uncertainty 

Sources of parameter uncertainty include measurement errors, sampling errors, variability, and use 

of generic or surrogate data. Measurement errors can be random or systematic. Random error results from 

imprecision in the measurement process. Systematic error is a bias or tendency away from the true value. 

Sampling errors concern sample representativeness. The purpose of sampling is to make an inference 

about the nature of the whole from a measurement of a subset of the total population. The inability to 

characterize the inherent variability in various parameters is a major source of uncertainty. The use of 

generic or surrogate data is common when site-specific data are not available, for example generalized 

descriptions of environmental settings. The approach to characterizing uncertainty in parameter values 

will vary. It can involve an order-of-magnitude bounding of the parameter range when uncertainty is high, 

or a description of the range for each of the parameters including the lower- and upper-bound and the best 

estimate values and justification for these based on available data or professional judgment. In some 

circumstances, characterization can take the form of a probabilistic description of the parameter range. 

The appropriate characterization will depend on several factors, including whether a sensitivity analysis 

indicates that the results are significantly affected by variations within the range. When the results are 

significantly affected by a particular parameter an attempt should be made to reduce the uncertainty by 

developing a description of the likely occurrence of particular values within the range. If enough data are 

available, standard statistical methods can be used to obtain a meaningful representation. 

Model Uncertainty 

Model uncertainty can be defined as gaps in scientific theory required to make predictions on the 

basis of causal inferences. Modeling errors are due to models being simplified representations of reality; 

hence, rationales for model selection should be discussed. Even after the most appropriate model has been 

selected for the purpose at hand, one is still faced with the question of how well the model represents the 

real situation. This question is compounded by the overlap between modeling uncertainties and other 

uncertainties, e.g., natural variability in inputs, representativeness of the modeling scenario, and 

aggregation errors. A dilemma is that many existing models (particularly the very complex ones) and the 

hypotheses contained within them cannot be fully tested, although certain components of the model may 

be tested. Even when a model has been validated under a particular set of conditions, uncertainty will 

exist in its application to situations beyond the test system. 

1.3.5.2. Approaches to Characterizing Uncertainty 

 Various approaches to characterizing uncertainty can be described, such as classical statistical 

methods, sensitivity analysis, or probabilistic uncertainty analysis, in order of increasing complexity and 

data requirements. 

 1-8 
Classical statistical methods are used to analyze uncertainty in measured values (Robinson, 1989). 

Given a data set of measured exposure values for a series of individuals, the population distribution may 

be estimated directly, provided that the sample design was developed properly to capture a representative 

sample. The measured exposure values also may be used to directly compute confidence interval 

estimates for percentiles of the distribution. When the distribution is estimated from measured exposures 

for a probability sample of population members, confidence interval estimates for percentiles of the 

exposure distribution are the primary uncertainty characterization. 

Sensitivity analysis is the process of changing one variable while leaving the others constant and 

determining the effect on the output. The procedure involves fixing each uncertain quantity, one at a time, 

at its credible lower-bound and then its upper-bound (holding all others at their medians), and then 

computing the outcomes for each combination of values. These results are useful to identify the variables 

that have the greatest effect on exposure and to help focus further information gathering. The results do 

not provide any information about the probability of a quantity’s value being at any level within the 

range; therefore, this approach is most useful at the screening level when deciding about the need and 

direction of further analyses. 

Probabilistic uncertainty analysis is generally considered the next level of complexity (Eggleston, 

1993). The most common example is the Monte Carlo technique where probability density functions are 

assigned to each parameter, then values from these distributions are randomly selected and inserted into 

the equation. After this process is completed many times, a distribution of predicted values results that 

reflects the overall uncertainty in the inputs to the calculation. By averaging over many different 

competing models, Bayesian Model Averaging incorporates model uncertainty into conclusions about 

parameters and prediction. 

1.3.6. Application of Framework for Causal Determination 

EPA uses a two-step approach to evaluate the scientific evidence on health effects of criteria 

pollutants. The first step determines the weight of evidence in support of causation and characterizes the 

strength of any resulting causal classification. The second step includes further evaluation of the 

quantitative evidence regarding the concentration-response relationships and the levels, duration and 

pattern of exposures at which effects are observed. 

To aid judgment, various “aspects”1

 of causality have been discussed by many philosophers and 

scientists. The most widely cited aspects of causality in epidemiology, and public health in general, were 

articulated by Sir Austin Bradford Hill in 1965 and have been widely used (CDC, 2004; U.S. EPA, 2005; 

IARC, 2006; IOM, 2007). These nine aspects (Hill, 1965) have been modified (below) for use in causal 

determinations specific to health and environmental effects and pollutant exposures.2

 

Table 1-1. Aspects to aid in judging causality. 

1. Consistency of the observed association. An inference of causality is strengthened when a 

pattern of elevated risks is observed across several independent studies. The reproducibility 

of findings constitutes one of the strongest arguments for causality. If there are discordant 

 

1

The “aspects” described by Hill (1965) have become, in the subsequent literature, more commonly described as “criteria.” The original term 

“aspects” is used here to avoid confusion with “criteria” as it is used, with different meaning, in the Clean Air Act. 

2

 The Hill apects were developed for use with epidemiology data. They have been modified here for use with a broader array of data, i.e., 

epidemiologic, controlled human exposure, and animal toxicological studies, as well as in vitro data, and to be more consistent with EPA’s 

Guidelines for Carcinogen Risk Assessment. 

 1-9 
results among investigations, possible reasons such as differences in exposure, 

confounding factors, and the power of the study are considered. 

2. Strength of the observed association. The finding of large, precise risks increases 

confidence that the association is not likely due to chance, bias, or other factors. A modest 

risk, however, does not preclude a causal association and may reflect a lower level of 

exposure, an agent of lower potency, or a common disease with a high background level. 

3. Specificity of the observed association. As originally intended, this refers to increased 

inference of causality if one cause is associated with a single effect or disease (Hill, 1965). 

Based on current understanding this is now considered one of the weaker guidelines for 

causality; for example, many agents cause respiratory disease and respiratory disease has 

multiple causes. The ability to demonstrate specificity under certain conditions remains, 

however, a powerful attribute of experimental studies. Thus, although the presence of 

specificity may support causality, its absence does not exclude it. 

4. Temporal relationship of the observed association. A causal interpretation is strengthened 

when exposure is known to precede development of the disease. 

5. Biological gradient (exposure-response relationship). A clear exposure-response 

relationship (e.g., increasing effects associated with greater exposure) strongly suggests 

cause and effect, especially when such relationships are also observed for duration of 

exposure (e.g., increasing effects observed following longer exposure times). There are, 

however, many possible reasons that a study may fail to detect an exposure-response 

relationship. Thus, although the presence of a biologic gradient may support causality, the 

absence of an exposure-response relationship does not exclude a causal relationship. 

6. Biological plausibility. An inference of causality tends to be strengthened by consistency 

with data from experimental studies or other sources demonstrating plausible biological 

mechanisms. A lack of biologic understanding, however, is not a reason to reject causality. 

7. Coherence. An inference of causality may be strengthened by other lines of evidence (e.g., 

clinical and animal studies) that support a cause-and-effect interpretation of the association. 

The absence of other lines of evidence, however, is not a reason to reject causality. 

8. Experimental evidence (from human populations). The strongest evidence for causality 

can be provided when a change in exposure brings about a change in adverse health effect 

or disease frequency in either clinical or observational studies. 

9. Analogy. Structure-activity relationships and information on the agent’s structural analogs 

can provide insight into whether an association is causal. Similarly, information on mode 

of action for a chemical, as one of many structural analogs, can inform decisions regarding 

likely causality. 

While these aspects provide a framework for assessing the evidence, they do not lend themselves to 

being considered in terms of simple formulas or fixed rules of evidence leading to conclusions about 

causality (Hill, 1965). For example, one cannot simply count the number of studies reporting statistically 

significant results or statistically nonsignificant results for health effects and reach credible conclusions 

about the relative weight of the evidence and the likelihood of causality. Rather, these important 

considerations are taken into account with the goal of producing an objective appraisal of the evidence, 

informed by peer and public comment and advice, which includes weighing alternative views on 

controversial issues. Additionally, it is important to note that the principles in Table 1-1 cannot be used as 

a strict checklist, but rather to determine the weight of the evidence for inferring causality. In particular, 

not meeting one or more of the principles does not automatically exclude a study from consideration (e.g., 

see discussion in U.S. Surgeon General’s Report, CDC, 2004). 

 1-10 
1.3.7. First Step—Determination of Causality 

In the ISA, EPA assesses results of recent relevant publications, in light of evidence available 

during the previous NAAQS review, to draw conclusions on the causal relationships between relevant 

pollutant exposures and health outcomes. This ISA uses a five-level hierarchy that classifies the weight of 

evidence for causation, not just association1

; that is, whether the weight of scientific evidence makes 

causation at least as likely as not, in the judgment of the reviewing group. In developing this hierarchy, 

EPA has drawn on the work of previous evaluations, most prominently the IOM’s Improving the 

Presumptive Disability Decision-Making Process for Veterans (IOM, 2007), EPA’s Guidelines for 

Carcinogen Risk Assessment (U.S. EPA, 2005), and the U.S. Surgeon General’s smoking reports (CDC, 

2004). Excerpts from these reports are presented in Annex A. In the ISA, EPA uses a series of five 

descriptors to characterize the weight of evidence for causality. This weight of evidence evaluation is 

based on various lines of evidence from epidemiologic, controlled human exposure and animal studies, or 

other mechanistic, toxicological, or biological sources. These separate judgments are integrated into a 

qualitative statement about the overall weight of the evidence and causality. The five descriptors for 

causal determination are described in Table 1-2. 

Table 1-2. Weight of evidence for causal determination. 

Relationship Description 

Causal relationship Evidence is sufficient to conclude that there is a causal relationship between relevant pollutant exposures and the health 

outcome. That is, a positive association has been observed between the pollutant and the outcome in studies in which 

chance, bias, and confounding could be ruled out with reasonable confidence. Evidence includes, for example, controlled 

human exposure studies; or observational studies that cannot be explain by plausible alternatives or are supported by 

other lines of evidence (e.g. animal studies or mechanism of action information). Evidence includes replicated and 

consistent high-quality studies by multiple investigators. 

Likely to be a causal relationship Evidence is sufficient to conclude that a causal relationship is likely to exist between relevant pollutant exposures and the 

health outcome but important uncertainties remain. That is, a positive association has been observed between the 

pollutant and the outcome in studies in which chance and bias can be ruled out with reasonable confidence but potential 

issues remain. For example: a) observational studies show positive associations but copollutant exposures are difficult to 

address and/or other lines of evidence (controlled human exposure, animal, or mechanism of action information) are 

limited or inconsistent; or b) animal evidence from multiple studies, sex, or species is positive but limited or no human 

data are available. Evidence generally includes replicated and high-quality studies by multiple investigators. 

Suggestive of a causal relationship Evidence is suggestive of a causal relationship between relevant pollutant exposures and the health outcome, but is 

limited because chance, bias and confounding cannot be ruled out. For example, at least one high-quality study shows a 

positive association but the results of other studies are inconsistent. 

Inadequate to infer a causal 

relationship 

Evidence is inadequate to determine that a causal relationship exists between relevant pollutant exposures and the 

health outcome. The available studies are of insufficient quantity, quality, consistency or statistical power to permit a 

conclusion regarding the presence or absence of an association between relevant pollutant exposure and the outcome. 

Suggestive of no causal relationship Evidence is suggestive of no causal relationship between relevant pollutant exposures and the health outcome Several 

adequate studies, covering the full range of levels of exposure that human beings are known to encounter and 

considering sensitive subpopulations, are mutually consistent in not showing a positive association between exposure 

and the outcome at any level of exposure. The possibility of a very small elevation in risk at the levels of exposure 

studied can never be excluded. 

 

 

1

 It should be noted that the CDC and IOM frameworks use a four-category hierarchy for the strength of the evidence. A five-level hierarchy is 

used here to be consistent with the EPA Guidelines for Carcinogen Risk Assessment and to provide a more nuanced set of categories. 

 1-11 
1.3.8. Second Step—Evaluation of Population Response 

Beyond judgments regarding causality are questions relevant to quantifying risks to populations. 

The fundamental issue is to understand the relationships between pollutant exposures and health effects in 

the human population. In addressing this issue several important questions must be considered: 

 What is the concentration-response or dose-response relationship in the human population? 

 What is the interrelationship between incidence and severity of effect? 

 What exposure conditions (dose or exposure, duration and pattern) are important? 

 What subpopulations appear to be differentially affected i.e., more susceptible to effects? 

To address these questions the second step of the framework evaluates the entirety of policyrelevant quantitative evidence regarding the concentration-response relationships including levels of 

pollutant and exposure durations at which effects are observed, and subpopulations that differ from the 

general population. This integration of evidence results in identification of a study or set of studies that 

best approximates the concentration-response relationship for the U.S. population, given the current state 

of knowledge and the uncertainties that surround these estimates. 

To accomplish this integration, evidence from multiple and diverse types of studies is considered. 

Response is evaluated over a range of observations which is determined by the type of study and methods 

of exposure or dose and response measurements. Results from different protocols are compared and 

contrasted. Extensive human concentration-response data exist for all criteria pollutants, unlike most other 

environmental pollutants. Animal data also inform evaluation of concentration-response, particularly 

relative to dosimetry, mechanisms of action, and characteristics of susceptible subpopulations. For some 

health outcomes, the probability and severity of health effects and associated uncertainties can be 

characterized. Chapter 5 presents the integrated findings informative for evaluation of population risks. 

An important consideration in characterizing the public health impacts associated with exposure to 

a pollutant is whether the concentration-response relationship is linear across the full concentration range 

encountered, or if nonlinear relationships exist along any part of this range. Of particular interest is the 

shape of the concentration-response curve at and below the level of the current standards. The shape of 

the concentration-response curve varies, depending on the type of health outcome, underlying biological 

mechanisms and dose. At the human population level, however, various sources of variability and 

uncertainty tend to smooth and “linearize” the concentration-response function (such as the low data 

density in the lower concentration range, possible influence of measurement error, and individual 

differences in susceptibility to air pollution health effects). Additionally, many chemicals and agents may 

act by perturbing naturally occurring background processes that lead to disease, which also linearizes 

population concentration-response relationships (Clewell and Crump, 2005; Crump et al., 1976; Hoel, 

1980). These attributes of population dose-response may explain why the available human data at ambient 

concentrations for some environmental pollutants (e.g., O3, lead [Pb], PM, secondhand tobacco smoke, 

radiation) do not exhibit evident thresholds for cancer or noncancer health effects, even though likely 

mechanisms of action include nonlinear processes for some key events. These attributes of human 

population dose-response relationships have been extensively discussed in the broader epidemiologic 

literature (e.g., Rothman and Greenland, 1998). 

1.3.9. Concepts in Evaluating Adversity of Health Effects 

In evaluating the health evidence, a number of factors can be considered in determining the extent 

to which health effects are “adverse” for health outcomes such as changes in lung function. What 

constitutes an adverse health effect may vary between populations. Some changes in healthy individuals 

may not be considered adverse while those of a similar type and magnitude are potentially adverse in 

more susceptible individuals. 

 1-12 
The American Thoracic Society (ATS) published an official statement titled “What Constitutes an 

Adverse Health Effect of Air Pollution?” (ATS, 2000). This statement updated the guidance for defining 

adverse respiratory health effects that had been published 15 years earlier (ATS, 1985), taking into 

account new investigative approaches used to identify the effects of air pollution and reflecting concern 

for impacts of air pollution on specific susceptible groups. In the 2000 update, there was an increased 

focus on quality of life measures as indicators of adversity and a more specific consideration of 

population risk. Exposure to air pollution that increases the risk of an adverse effect to the entire 

population is viewed as adverse, even though it may not increase the risk of any identifiable individual to 

an unacceptable level. For example, a population of asthmatics could have a distribution of lung function 

such that no identifiable single individual has a level associated with significant impairment. Exposure to 

air pollution could shift the distribution such that no one individual experiences clinically relevant effects; 

however, this shift toward decreased lung function would be considered adverse because individuals 

within the population would have diminished reserve function and, therefore, would be at increased risk if 

affected by another agent. 

1.4. Conclusions 

The ISA is a concise review, synthesis, and evaluation of the most policy-relevant science, and 

communicates critical science judgments relevant to the NAAQS review. It reviews the most policy 

relevant evidence from epidemiologic, human clinical, and animal toxicological studies, including 

mechanistic evidence from basic biological science. Annexes to the ISA provide additional details of the 

literature published since the last review. A framework for making critical judgments concerning causality 

is presented in this chapter. It relies on a widely accepted set of principles and standardized language to 

express evaluation of the evidence. This approach can bring rigor and clarity to the current and future 

assessments. This ISA should assist EPA and others, now and in the future, to represent accurately what is 

presently known—and what remains unknown—concerning the effects of SOX on human health. 

 1-13 
Chapter 2. Source to Dose 

This chapter contains basic information about concepts and findings in atmospheric sciences, 

human exposure assessment, and human dosimetry. It is meant to serve as a prologue for the detailed 

discussions of health effects data in Chapters 3 and 4. Section 2.1 gives an overview of the sources of 

SO2. Atmospheric chemistry processes involved in the oxidation of SO2 and those involved in the 

production of SO2 from reduced sulfur gases in the atmosphere are discussed in Section 2.2. A description 

of SO2 measurement methods and monitor siting are presented in Sections 2.3 and 2.4. Data for ambient 

SO2 concentrations are characterized in Section 2.5. Policy relevant background (PRB) concentrations of 

SO2, i.e., those concentrations defined to result from uncontrollable emissions, are also presented in 

Section 2.5. Factors related to personal exposure to SO2 are discussed in Section 2.6. Finally, Section 2.7 

covers the dosimetry of SO2 in the respiratory tract. This organization generally follows that given in the 

National Research Council (NRC) paradigm for integrating air pollutant research (NRC, 1998). 

2.1. Sources of Sulfur Oxides 

Anthropogenic emissions of SO2 in the U.S. are mainly due to combustion of fossil fuels by 

electrical utilities (~66 %) and industry (~29%). Transportation-related sources contribute only ~5% 

based on 2002 statistics (U.S. EPA, 2006c). Thus, most SO2 emissions originate from point sources. 

Annex B has a detailed breakdown of emissions by source category. Almost all of the S in fuel is released 

as volatile components (SO2 or SO3) during combustion. Hence, based on S content in fuel stocks, sulfur 

emissions can be calculated to a higher degree of accuracy than other pollutants such as NOX or primary 

PM. However, these estimates are national averages and may not accurately reflect the contribution of 

specific local sources for determining individual exposure to SO2 at a particular location and time. 

An additional source of SO2 emissions of concern in particular locations not immediately obvious 

from national-scale averages and totals are transit and in-port activities in areas with substantial shipping 

traffic (Wang et al., 2007). Because of the importance of these SO2 emissions, the ports of Long Beach 

and Los Angeles, CA, for example, are part of a Sulfur Emissions Control Area in which S contents of 

fuels are not to exceed 1.5%. Figure 2-1 shows SO2 emissions densities combined for all non- or off-road 

transportation-related emitters in which coastal areas with ports, and shipping routes, such as the 

Mississippi River, are easily discerned. In Los Angeles County, CA, for example, off-road transportation 

including shipping and port traffic contributed 1.4 of the total 4.1 tons of SO2 per square mile in 2001; in 

King County (including the city of Seattle), WA, the off-road transportation fraction was 42% of the total 

SO2 emissions density, or 1.2 of the total 2.8 tons per square mile. Emissions data more specific to the 

ports are not available in the routine emissions inventories. Increased uncertainty accompanies estimates 

of the actual SO2 loads from these sources. Modeling studies by Vutukuru and Dabdub (2008) for 

southern California ports, for example, have shown that ships contribute >1 ppb to the 24-h avg SO2

concentration at the coast, in Long Beach, CA, with < 10% of that (<0.1 ppb) farther inland. 

The largest natural sources of SO2 are volcanoes and wildfires. Although SO2 constitutes a 

relatively minor fraction (0.005% by volume) of total volcanic emissions (Holland, 1978), concentrations 

in volcanic plumes can be in the range of several to tens of ppm. Volcanic sources of SO2 in the U.S. are 

limited to the Pacific Northwest, Alaska, and Hawaii. Sulfur is a component of amino acids in vegetation 

and is released during combustion. Gaseous S emissions from this source are mainly in the form of SO2. 

In addition to its role as an emitted primary pollutant, SO2 is also produced by the photochemical 

oxidation of reduced S compounds such as dimethyl sulfide (CH3-S-CH3, or DMS), hydrogen sulfide 

(H2S), carbon disulfide (CS2), carbonyl sulfide (OCS), methyl mercaptan (CH3-S-H), and dimethyl 

disulfide (CH3-S-S-CH3). The sources for these compounds are mainly biogenic (see Annex Table B-3). 

 2-1 
Emissions of reduced S species are associated typically with marine organisms living either in pelagic or 

coastal zones, and with anaerobic bacteria in marshes and estuaries. Emissions of DMS from marine 

plankton represent the largest single atmospheric source of reduced sulfur species (Berresheim et al., 

1995). OCS is lost mainly by photolysis (e-folding lifetime1

, [τ] ~6 months). Other reduced S species are 

lost mainly by reaction with hydroxyl radical (OH) and NO3 radicals, and are relatively short-lived; 

lifetimes range from a few hours to a few days (see Annex Table B-1). Reaction with NO3 radicals at 

night most likely represents the major loss process for DMS and CH3-S-H. Although the mechanisms for 

the oxidation of DMS are not completely understood, excess sulfate in marine aerosol appears related 

mainly to the production of SO2 from the oxidation of DMS. Emissions of S from natural sources are 

small compared to industrial emissions within the U.S. However, important exceptions occur locally as 

the result of volcanic activity, wildfires and in certain coastal zones as described above. 

Source: EPA, 2006 

Figure 2-1. 2001 County-level SO2 emissions densities (tons per square mile) from off-road 

mobile and other transportation sources. 

Because OCS is relatively long-lived, it can survive oxidation in the troposphere and be transported 

upward into the stratosphere. Crutzen (1976) proposed that its oxidation to sulfate in the stratosphere 

serves as the major source of the stratospheric aerosol layer. However, Myhre et al. (2004) proposed that 

SO2 transported upward from the troposphere by deep convection is the most likely source, since the flux 

of OCS is too small. In addition, in situ measurements of the isotopic composition of S in stratospheric 

 

1

 The e-folding lifetime is the time needed to reduce the concentration to i/e of its initial value. 

 2-2 
sulfate do not match those of OCS (Leung et al., 2002). Thus, anthropogenic SO2 emissions could be 

important precursors to the formation of the stratospheric aerosol layer. 

2.2. Atmospheric Chemistry 

The only gaseous forms of SOX of interest in tropospheric chemistry are SO2 and SO3. SO3 can be 

emitted from the stacks of power plants and factories; however, it reacts extremely rapidly with water 

(H2O) in the stacks or immediately after release into the atmosphere to form H2SO4, which mainly 

condenses onto existing particles when particle loadings are high; it can nucleate to form new particles 

under lower concentration conditions. Thus, only SO2 is present in the tropospheric boundary layer at 

concentrations of concern for human exposures. The gas phase oxidation of SO2 is initiated by the 

reaction 

 Reaction 2-1 

where M is an atmospheric constituent such as N2 and O2 that helps stabilize the reaction product. 

Reaction 2-1 is followed by 

 Reaction 2-2 

Reaction 2-3 

Because the saturation vapor pressure of H2SO4 is extremely low, it will be removed rapidly from the gas 

phase by transfer to aerosol particles and cloud drops. Depending on atmospheric conditions and 

concentrations of ambient particles and gaseous species that can participate in new particle formation, it 

can also nucleate to form new particles. Rate coefficients for the reactions of SO2 with either the 

hydroperoxyl radical (HO2) or NO3 are too low to be significant (Jet Propulsion Laboratory, 2003). Note: 

for the subsequent discussion that sulfates such as H2SO4 are referenced using the S(VI) notation and 

intermediate sulfites such as SO3

2- are denoted by S(IV). 

S(VI) species, including the bisulfate ion (HSO4

-

) and sulfate (SO4

2-) are the dominant S species in 

clouds. Intermediate S(IV) products, such as hydrogen sulfite (HSO3

-

) and sulfite (SO3

2-) are also present 

in clouds following dissolution of SO2 in water but preceding transformation to S(VI) products. The chief 

species capable of oxidizing S(IV) to S(VI) in cloud water are O3, peroxides (either hydrogen peroxide 

(H2O2) or organic peroxides), hydroxyl (OH) radicals, and ions of transition metals such as iron (Fe), 

manganese (Mn) and copper (Cu) that can catalyze the oxidation of S(IV) to S(VI) by O2. The basic 

mechanism of the aqueous phase oxidation of SO2 has long been studied and can be found in numerous 

texts on atmospheric chemistry, e.g., Seinfeld and Pandis (1998), Finlayson-Pitts and Pitts (1999), Jacob 

(1999), and Jacobson (2002). Following Jacobson (2002), the steps involved in the aqueous phase 

oxidation of SO2 can be summarized as: 

 2-3 
Dissolution of SO2 

 Reaction 2-4 

The formation and dissociation of H2SO3

 Reaction 2-5 

In the pH range commonly found in rainwater (pH 2 to 6), the most important reaction converting 

S(IV) to S(VI) is 

Reaction 2-6 

because SO3

2−

 is much less abundant than HSO3

−

. 

For pH up to about 5.3, H2O2 is the dominant oxidant. At pH > 5.3, O3 becomes dominant, 

followed by Fe(III), according to Seinfeld and Pandis (1998). However, differences in concentrations of 

oxidants result in differences in the pH at which this transition occurs. It should also be noted that the 

oxidation of SO2 by O3 and O2 tends to be self-limiting: as sulfate is formed, the pH decreases and the 

rates of these reactions decrease. Higher pH levels are expected to be found mainly in marine aerosols. 

However, in marine aerosols, the chloride-catalyzed oxidation of S(IV) may be more important (Hoppel 

and Caffrey, 2005; Zhang and Millero, 1991). Because the ammonium ion (NH4

+

) is so effective in 

neutralizing acidity, it affects the rate of oxidation of S(IV) to S(VI) and the rate of dissolution of SO2 in 

particles and cloud drops. 

A comparison of the relative rates of oxidation by gas and aqueous phase reactions by Warneck 

(1999) indicates that on average only about 20% of SO2 is oxidized by gas phase reactions; the remainder 

is oxidized by aqueous phase reactions. In areas away from strong pollution sources, the SO2 τ is ~7 days 

with respect to gas phase oxidation, based on measurements of the rate constant for Reaction 2-1 (Jet 

Propulsion Laboratory, 2003) and a nominal concentration for the OH radical of 106

/cm3. However, the 

mechanism of SO2 oxidation at a particular location depends on local environmental conditions. For 

example, oxidants such as OH radicals are depleted near stacks. Under this condition, almost no SO2 is 

oxidized in the gas phase. Further downwind, as the plume is diluted with background air, the gas phase 

oxidation of SO2 increases in importance. Conditions in the plume can become more oxidizing than in 

background air as distance from the stack increases. This can cause the SO2 oxidation rate to exceed that 

in background air. SO2 in the planetary boundary layer is also removed from the atmosphere by dry 

deposition to moist surfaces, resulting in an atmospheric τ with respect to dry deposition of approximately 

1 day to 1 week. Wet deposition of S naturally depends on the variable nature of rainfall, but in general 

results in τ ~7 days for SO2. Oxidation and deposition together lead to an overall lifetime of SO2 in the 

atmosphere of < 1 to 4 days, depending on altitude and location and meteorological conditions. Smaller 

values are expected near the surface. 

 2-4 
2.3. Measurement Methods and Associated Issues 

Currently, ambient SO2 is measured using instruments based on pulsed ultraviolet (UV) 

fluorescence. The UV fluorescence monitoring method for atmospheric SO2 was developed as an 

improvement to the flame photometric detection (FPD) method, which in turn had replaced the 

pararosaniline wet chemical method. This latter method is still the EPA’s Federal Reference Method 

(FRM) for atmospheric SO2, but it is rarely used because it is complex and has slow response even in its 

automated forms. Both the UV fluorescence and FPD methods are designated as Federal Equivalent 

Methods (FEMs) by EPA, but UV fluorescence has largely supplanted the FPD approach because the UV 

method is inherently linear and the FPD method needs consumable hydrogen gas. 

In the UV fluorescence method, SO2 molecules absorb UV light at one wavelength and emit UV 

light at longer wavelengths through excitation of the SO2 molecule to a higher energy (singlet) electronic 

state. Once excited, the molecule decays nonradiatively to a lower-energy electronic state from which it 

then decays to the original electronic state by emitting a photon of light at a longer wavelength (i.e., a 

lower-energy photon) than the original, incident photon. The intensity of the emitted light is thus 

proportional to the number of SO2 molecules in the sample gas. 

In commercial analyzers, light from a high-intensity UV lamp passes through a bandwidth filter to 

allow only photons with wavelengths around the SO2 absorption peak (near 214 nanometers [nm]) to 

enter the optical chamber. The light passing through the source bandwidth filter is collimated using a UV 

lens and passes through the optical chamber, where it is detected on the opposite side of the chamber by 

the reference detector. A photomultiplier tube (PMT) is offset from and placed perpendicular to the light 

path to detect the SO2 fluorescence. Since the SO2 fluorescence at 330 nm is different from its excitation 

wavelength, an optical bandwidth filter is placed in front of the PMT to filter out any stray light from the 

UV lamp. A lens is located between the filter and the PMT to focus the fluorescence onto the active area 

of the detector and optimize the fluorescence signal. The limit of detection (LOD) for a non-trace level 

SO2 analyzer is required to be 10 ppb (40 CFR 53.23.c). However, most commercial analyzers have 

detection limits of about 3 ppb; many monitors might have lower effective detection limits. The EPA, 

through its National Core (NCore) initiative (U.S. EPA, 2005b) is in the process of supporting state, local, 

tribal, and federal networks in the implementation of newer trace-level SO2 instrumentation. These new 

trace-level instruments have detection limits of 0.1 ppb or lower. More information related to SO2

sampling and analysis is in Annex Section B.6. 

2.3.1. Sources of Positive Interference 

The most common sources of interference to the UV fluorescence method for SO2 are other gases 

that fluoresce in a similar fashion when exposed to UV radiation. The most significant of these are 

polycyclic aromatic hydrocarbons (PAHs), of which naphthalene is a prominent example. Xylene is 

another common hydrocarbon that can cause fluorescent interference. Consequently, any such aromatic 

hydrocarbons in the optical chamber can act as positive interference. To remove this source of 

interference, high-sensitivity SO2 analyzers, such as those to be used in the NCore network (U.S. EPA, 

2005b), have hydrocarbon scrubbers to remove these compounds from the sample stream before the 

sample air enters the optical chamber. 

Luke (1997) reported positive artifacts of a modified pulsed fluorescence detector generated by the 

coexistence of nitric oxide (NO), CS2, and a number of highly fluorescent aromatic hydrocarbons such as 

benzene, toluene, o-xylene, m-xylene, p-xylene, m-ethyltoluene, ethylbenzene, and 1,2,4-

trimethylbenzene. The positive artifacts could be reduced by using a hydrocarbon “kicker” membrane. At 

a flow rate of 300 standard cc/min and a pressure drop of 645 torr across the membrane, the interference 

from ppm levels of many aromatic hydrocarbons was eliminated. NO fluoresces in a spectral region close 

to that of SO2. However, in high-sensitivity SO2 analyzers, the bandpass filter in front of the PMT is 

 2-5 
designed to prevent NO fluorescence from being detected at the PMT. Care must be exercised when using 

multicomponent calibration gases containing both NO and SO2, so that the NO rejection ratio of the SO2

analyzer is sufficient to prevent NO interference. 

The most common source of positive bias (as contrasted with positive spectral interference) in 

high-sensitivity SO2 monitoring is stray light in the optical chamber. Since SO2 can be electronically 

excited by a broad range of UV wavelengths, any stray light with an appropriate wavelength that enters 

the optical chamber can excite SO2 in the sample and increase the fluorescence signal. Furthermore, stray 

light at the wavelength of the SO2 fluorescence that enters the optical chamber may impinge on the PMT 

and increase the fluorescence signal. Several design features minimize stray light, including the use of 

light filters, dark surfaces, and opaque tubing. 

Nicks and Benner (2001) reported a sensitive SO2 chemiluminescence detector based on a 

differential measurement: response from ambient SO2 is determined by the difference between air 

containing SO2 and air scrubbed of SO2 when both air samples contain other detectable sulfur species. 

Assuming monotonic efficiency of the sulfur scrubber, all positive artifacts should also be reduced with 

this technique. 

2.3.2. Sources of Negative Interference 

Nonradiative deactivation (quenching) of excited SO2 molecules can occur from collisions with 

common molecules in air, including nitrogen, oxygen, and water. During collisional quenching, the 

excited SO2 molecule transfers energy, kinetically allowing the SO2 molecule to return to the original 

lower energy state without emitting a photon. Collisional quenching results in a decrease in the SO2

fluorescence and, hence, an underestimation of SO2 concentration in the air sample. Of particular concern 

is the variable water vapor content of air. Luke (1997) reported that the response of the detector could be 

reduced by an amount of ~7 to 15% at water vapor mixing ratios of 1 to 1.5 mole percent (relative 

humidity [RH] = 35 to 50% at 20 to 25°C and 1 atmosphere [atm] for a modified pulsed fluorescence 

detector [Thermo Environmental Instruments, Model 43s]). Condensation of water vapor in sampling 

lines must be avoided, as water on the inlet surfaces can absorb SO2 from the sample air. The simplest 

approach to avoid condensation is to heat sampling lines to a temperature above the expected dew point 

and to within a few degrees of the controlled optical bench temperature. At very high SO2 concentrations, 

reactions between electronically excited SO2 and ground state SO2 might occur, forming SO3 and SO 

(Calvert et al., 1978). However, the possibility that this artifact might be affecting measurements at very 

high SO2 levels has not been examined. 

2.3.3. Other Techniques for Measuring SO2

More sensitive techniques for measuring SO2 are available, but most of these systems are too 

complex and expensive for routine monitoring applications. However, techniques such as those described 

by Luke (1997) can be used to improve the sensitivity of ambient SO2 monitors by eliminating sources of 

common interference. See descriptions in Annex B. 

 2-6 
2.4. Monitoring Site Characteristics 

2.4.1. Design Criteria for the NAAQS SO2 Monitoring Networks1

Trace level SO2 monitoring is currently required at the approximately 75 proposed NCore sites, as 

noted in CFR 40 Part 58 Appendices C and D. Continued operation of existing State and Local Air 

Monitoring Sites (SLAMS) for SO2 using Federal Reference Methods (FRM) or Federal Equivalent 

Methods (FEM) is required until discontinuation is approved by the EPA Regional Administrator. Where 

SLAMS SO2 monitoring is required, at least one of the sites must be a maximum concentration site for 

that specific area. In 2007, there were ~500 SO2 monitors reporting values to the EPA Air Quality System 

database (AQS). The AQS contains measurements of air pollutant concentrations in the 50 states, plus the 

District of Columbia, Puerto Rico, and the Virgin Islands, for the 6 criteria air pollutants and hazardous 

air pollutants. 

The appropriate spatial scales for SO2 SLAMS monitoring are the microscale, middle, and possibly 

neighborhood scales. 

 Micro (~5 - 100 meters(m) and middle scale (~100 - 500 m)—Some data uses associated 

with microscale and middle scale measurements for SO2 include assessing the effects of 

control strategies to reduce concentrations (especially for the 3-h and 24-h averaging 

times), and monitoring air pollution episodes. 

 Neighborhood scale (~500 m – 4 km)—This scale applies where there is a need to collect 

air quality data as part of an ongoing SO2 stationary source impact investigation. Typical 

locations might include suburban areas adjacent to SO2 stationary sources, for example, or 

for determining background concentrations as part of studies of population responses to 

SO2 exposure. 

2.4.1.1. Horizontal and Vertical Placement 

The probe, or at least 80 percent of the monitoring path, must be located between 2 and 15 m above 

ground level for all SO2 monitoring sites. The probe, or at least 90 percent of the monitoring path, must be 

positioned at least 1 m vertically or horizontally from any supporting structure, walls, parapets, 

penthouses, etc., and away from dusty or dirty areas. If the probe, or a significant portion of the 

monitoring path, is located near the side of a building, it should be located on the windward side relative 

to the prevailing wind direction during the season of highest concentration potential for the pollutant 

being measured. 

2.4.1.2. Spacing from Minor Sources 

Local minor sources of a primary pollutant such as SO2 can affect concentrations of that particular 

pollutant at a monitoring site. If the objective for that site is to investigate these local primary pollutant 

emissions, then the site should be located where the spatial and temporal variability in these emissions 

can be captured. This type of monitoring site would likely be the microscale type. If a monitoring site is 

to be used to determine air quality over a much larger area, such as a neighborhood or city, a monitoring 

 

1

 This section is adapted from Code of Federal Regulations 40 CFR Parts 53 and 58 and Appendix E to Part 58, as revised: Vol. 71, No. 200 / 17 

October 2006 

 2-7 
agency should avoid placing a monitor probe, path, or inlet near local, minor sources. The plume from the 

local minor sources should not be allowed to inappropriately influence the air quality data collected. 

To minimize these potential interferences, the probe, or at least 90 percent of the monitoring path, 

must be placed away from the furnace, incineration flues, or other minor sources of SO2. The separation 

distance should take into account the heights of the flues, type of waste or fuel burned, and the S content 

of the fuel. 

2.4.1.3. Spacing from Obstructions 

Buildings and other obstacles may possibly scavenge SO2 and can act to restrict airflow for any 

pollutant. To avoid this interference, the probe, inlet, or at least 90 percent of the monitoring path must 

have unrestricted airflow and be located away from obstacles. The distance from the obstacle to the probe, 

inlet, or monitoring path must be at least twice the height of the obstruction’s protrusion. An exception 

can be made for measurements taken in street canyons or at source-oriented sites where buildings and 

other structures are unavoidable. Generally, a probe or monitoring path located near or along a vertical 

wall is undesirable, because air moving along the wall may be subject to possible removal mechanisms. A 

probe, inlet, or monitoring path must have unrestricted airflow in an arc of at least 180 degrees. This arc 

must include the predominant wind direction for the season of greatest pollutant concentration potential. 

Special consideration must be devoted to the use of open path analyzers, due to their inherent 

potential sensitivity to certain types of interferences, or optical obstructions. A monitoring path must be 

clear of all trees, brush, buildings, plumes, dust, or other optical obstructions, including potential 

obstructions that may move due to wind, human activity, growth of vegetation, etc. Temporary optical 

obstructions, such as rain, particles, fog, or snow, should be considered when locating an open path 

analyzer. Any temporary obstructions that are of sufficient density to obscure the light beam will affect 

the ability of the open path analyzer to measure pollutant concentrations continuously. Transient, but 

significant obscuration of especially longer measurement paths could occur because certain 

meteorological conditions (e.g., heavy fog, rain, snow) and/or aerosol levels are of sufficient density to 

prevent the analyzer’s light transmission. If certain compensating measures are not otherwise 

implemented at the onset of monitoring (e.g., shorter path lengths, higher light source intensity), data 

recovery during periods of greatest primary pollutant potential could be compromised. For instance, if 

heavy fog or high particulate levels are coincident with periods of projected NAAQS-threatening 

pollutant potential, the resulting data may not be representative for reflecting maximum pollutant 

concentrations, despite the fact that the site may otherwise exhibit an acceptable, even exceedingly high 

overall valid data capture rate. 

2.4.1.4. Spacing from Trees 

Trees can provide surfaces for SO2 adsorption or reactions and surfaces for particle deposition. 

Trees can also act as obstructions in cases where they are located between the air pollutant sources or 

source areas and the monitoring site, and where the trees are of sufficient height and leaf canopy density 

to interfere with normal airflow around the probe, inlet, or monitoring path. To reduce possible 

interference, the probe, inlet, or at least 90 percent of the monitoring path must be at least 10 meters or 

further from the drip line of trees. 

For microscale sites, no trees or shrubs should be located between the probe and the source under 

investigation, such as a roadway or a stationary source. 

 2-8 
2.4.2. Locations of SO2 Monitors in Selected Metropolitan Areas 

Figures 2-2 through 2-7 display SO2 monitor density with respect to population density for the six 

metropolitan regions analyzed. The locations of SO2 monitors in selected areas where air pollution-health 

outcome studies have been conducted are characterized in this section. The studies are described in later 

chapters of the ISA. The study areas included six regions comprising eight metropolitan statistical areas 

(MSAs), as defined by the U.S. Census Bureau (http://www.census.gov/): Atlanta, Cincinnati, Cleveland, 

Los Angeles/Riverside, New York City/Philadelphia, and St. Louis. SO2 monitor location data (i.e., 

latitude/longitude) for 2004 were obtained from EPA’s AirData website (http://www.epa.gov/air/data/). 

Monitors were mapped for a particular region if they were contained by the MSA or if they were within 

15 km of its boundary. The total population and populations of three sensitive subgroups (under age 5, 

age 5 to 17, and those over age 65) were calculated for those areas using the population data contained 

within the census block maps. 

Figure 2-2. Location of SO2 monitors with respect to population density in the Atlanta, GA MSA. 

 2-9 
Figure 2-3. Location of SO2 monitors with respect to population density in the Cincinnati, OH 

MSA. 

 2-10 
Figure 2-4. Location of SO2 monitors with respect to population density in the Cleveland, OH 

MSA. 

 2-11 
Figure 2-5. Location of SO2 monitors with respect to population density in the Los 

Angeles/Riverside, CA MSA. 

 2-12 
Figure 2-6. Location of SO2 monitors with respect to population density in the New York City, 

NY/Philadelphia, PA MSA. 

 2-13 
Figure 2-7. Location of SO2 monitors with respect to population density in the St. Louis, MO MSA. 

Tables 2-1 through Table 2-4 break down the population density around SO2 monitors for the total 

population and for sub-populations of children aged 0-4 yr and 5-17 yr, and adults aged 65 yr and over, 

for each region. Variation in percentage within a certain radius of the monitor was fairly low for each city 

across the three age groups. However, between-city disparities in population density were larger. The 

New York City/Philadelphia region had the highest population density in all three age groups and overall 

 2-14 
with the highest or near highest proportion, ~72-73%, of each population within 15 km of the monitors. 

The mid-western cities of Cincinnati, Cleveland, and St. Louis also had a large proportion, ~66-75%, of 

their population within 15 km of a monitor. This suggests that these sub-populations, as well as the total 

populace, are well represented by the monitoring networks in these regions. Los Angeles and Atlanta had 

lower proportions of their populations within 15 km of an SO2 monitor, with Atlanta having only ~22% 

for the total population within 15 km, with similar proportions of children and a slightly higher (~27%) of 

the elder population represented. These latter figures likely reflect the lower density of local SO2 sources 

in these regions. 

Table 2-1. Proximity to SO2 monitors for the total population by city. Percentages are given with 

respect to the total population in each city. 

Proximity Region 

to SO2

Monitor 

(km) 

Atlanta Cincinnati Cleveland Los Angeles New York/ 

Philadelphia 

St. Louis 

n % n % n % n % n % n % 

0-1 13,389 0.3 14,745 0.7 29,733 1.4 72,465 0.4 432,621 1.7 42,211 1.5 

0-5 214,238 4.3 225,274 10.8 382,849 17.8 1,663,990 9.9 6,985,553 27.8 799,941 28.5 

0-10 610,742 12.3 733,054 35.2 1,006,787 46.8 6,030,847 35.8 13,727,129 54.7 1,561,159 55.6 

0-15 1,080,472 21.7 1,367,658 65.6 1,553,286 72.3 9,694,760 57.6 18,304,364 72.9 1,897,492 67.6 

Total MSA 4,980,447 100.0 2,085,092 100.0 2,149,472 100.0 16,839,035 100.0 25,094,739 100.0 2,807,659 100.0 

Note that population in proximity to the monitor is cumulative (i.e. those within 5 km includes those within 1 km) and the total MSA population includes those living 

in the region beyond the 15 km radius of the monitor. 

Table 2-2. Proximity to SO2 monitors for children aged 0-4 yr by city. Percentages are given with 

respect to the total population in the age group in each city. 

Region Proximity to 

SO2 Monitor 

(km) Atlanta Cincinnati Cleveland Los Angeles New York/ 

Philadelphia 

St. Louis 

n % n % n % n % n % n % 

0-1 425 0.1 1,057 0.8 2,409 1.7 4,548 0.4 28,421 1.7 2,485 1.4 

0-5 14,216 4.5 15,606 11.0 30,903 21.9 126,764 10.4 453,267 27.7 50,937 28.5 

0-10 37,440 11.8 48,942 34.6 71,852 50.8 451,417 37.1 890,406 54.4 100,856 56.4 

0-15 68,231 21.5 94,851 67.0 105,809 74.8 721,458 59.2 1,198,850 73.3 121,685 68.0 

Total MSA 317,949 100.0 141,537 100.0 141,425 100.0 1,218,227 100.0 1,635,831 100.0 178.868 100.0 

Note that population in proximity to the monitor is cumulative (i.e. those within 5 km includes those within 1 km) and the total MSA population includes those living 

in the region beyond the 15 km radius of the monitor. 

 2-15 
Table 2-3. Proximity to SO2 monitors for children aged 5-17 yr by city. Percentages are given with 

respect to the total population in the age group in each city. 

Region 

Proximity to 

SO2 Monitor (km) Atlanta Cincinnati Cleveland Los Angeles New York/ 

Philadelphia 

St. Louis 

n % n % n % n % n % n % 

0-1 833 0.1 2,629 0.7 5,639 1.4 11,611 0.4 70,589 1.6 8,512 1.6 

0=5 33,552 4.1 41,083 10.5 77,974 19.3 321,665 9.9 1,185,810 26.9 151,317 28.7 

0-10 92,715 11.4 133,500 34.2 194,495 48.2 1,146,231 35.4 2,375,339 53.9 295,508 56.0 

0-15 168,430 20.5 258,819 66.2 291,335 72.2 1,853,488 57.2 3,212,239 72.9 356,514 67.6 

Total USA 813,107 100.0 390,704 100.0 403,465 100.0 3,238,473 100.0 4,406,226 100.0 527,773 100.0 

Note that population in proximity to the monitor is cumulative (i.e. those within 5 km includes those within 1 km) and the total MSA population includes those living 

in the region beyond the 15 km radius of the monitor. 

Table 2-4. Proximity to SO2 monitors for adults aged 65 yr and over by city. Percentages are 

given with respect to the total population in the age group in each city. 

Region 

Proximity to 

SO2 Monitor (km) Atlanta Cincinnati Cleveland Los Angeles New York/ 

Philadelphia 

St. Louis 

n % n % n % n % n % n % 

0-1 279 0.1 1,826 0.8 3,425 1.1 7,197 0.5 52,315 1.7 6,929 2.0 

0-5 17,237 5.3 29,363 12.5 46,067 14.8 138,838 7.0 805,226 25.9 123,954 35.4 

0-10 53,503 16.4 90,437 38.5 144,465 46.4 511,431 33.0 1,668,273 53.6 219,972 62.9 

0-15 88,867 27.2 164,229 69.9 233,210 74.9 852,535 55.0 2,243,853 72.1 257,891 73.7 

Total MSA 326,858 100.0 235,116 100.0 311,579 100.0 1,549,138 100.0 3,113,768 100.0 349,881 100.0 

Note that population in proximity to the monitor is cumulative (i.e. those within 5 km includes those within 1 km) and the total MSA population includes those living 

in the region beyond the 15 km radius of the monitor. 

Figures 2-8 through 2-13 illustrate the 2005 geospatial locations of monitors for SO2, NO2, CO, 

particulate matter ≤ 10 μm (PM10), particulate matter ≤ 2.5 μm (PM2.5), and O3. These locations, sited in 

several cities in six states, were selected as relevant for SO2 health effects studies presented in Chapter 3; 

see the discussion of intracity SO2 correlations that follows. For each state, Map A shows locations of 

each monitor for all six pollutants; and Map B shows only the SO2 monitor locations. Totals for each 

monitor type are included. These figures demonstrate the important point that not all SO2 monitors in any 

Consolidated Metropolitan Statistical Area (CMSA) are co-located with monitors for other pollutants. 

 2-16 
Figure 2-8. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), California, 2005. 

Shaded counties have at least one monitor. 

 2-17 
Figure 2-9. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Ohio, 2005. 

Shaded counties have at least one monitor. 

 2-18 
Figure 2-10. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Arizona, 2005. 

Shaded counties have at least one monitor. 

 2-19 
Figure 2-11. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Pennsylvania, 

2005. Shaded counties have at least one monitor. 

 2-20 
Figure 2-12. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), New York, 2005. 

Shaded counties have at least one monitor. 

 2-21 
Figure 2-13. Criteria pollutant monitor locations (A) and SO2 monitor locations (B), Massachusetts, 

2005. Shaded counties have at least one monitor. 

Table 2-5 lists the totals for all criteria air pollutant monitors (except Pb) in California, as well as 

the subset of these monitors in San Diego County. At each of the four sites where SO2 was measured in 

San Diego county, NO2, CO, PM10, PM2.5, and O3 were also measured, with the exception of PM2.5 at one 

site (AQS ID 060732007) in Otay Mesa, CA. Table 2-6 lists the totals for all criteria air pollutant 

monitors (except Pb) in Ohio, as well as the subset of monitors in Cuyahoga County. In Cuyahoga 

County, PM10 and PM2.5 were measured at all four sites where SO2 was also measured in 2005, but O3 and 

CO were not measured at any of those four sites; NO2 was only measured at one site (AQS ID 39050060) 

near Cleveland’s city center and ~0.5 km from the intersection of Interstate Highways 77 and 90. 

 2-22 
Table 2-5. Monitor counts for California and San Diego County, 2005. 

SO2 NO2 O3 CO PM10 PM2.5

California (all) 35 105 176 86 177 97 

San Diego County 4 9 10 6 7 7 

Table 2-6. Monitor counts for Ohio and Cuyahoga County, 2005. 

SO2 NO2 O3 CO PM10 PM2.5

Ohio (all) 31 4 49 15 49 49 

Cuyahoga County 4 2 3 4 6 7 

2.4.3. Ambient SO2 Concentrations in Relation to SO2 Sources 

Figures 2-14 through 2-19 show the locations of SO2 monitors in relation to SO2 point sources from 

electricity generation for the Atlanta, Cincinnati, Cleveland, Los Angeles/Riverside, New York 

City/Philadelphia, and St. Louis MSAs. Information on point sources (i.e., fossil fueled electricity 

generating units and industrial point sources) for 2002 was obtained from the National Emissions 

Inventory (NEI) (EPA, 2006). Point sources were mapped for a particular region if they were contained 

by the MSA or if they were within 30 km of its boundary, and SO2 monitors were mapped with buffer 

zones of 1, 5, 10, and 15 km constructed around the monitor locations. The figures show that the SO2

monitors are placed in relative proximity to the majority of point sources of SO2 with the exception of 

Atlanta, where most point sources are found outside the city center. In all cases, at least 1 monitor is in the 

vicinity of the most populated urban center and at least one is sited for background SO2 concentrations. 

SO2 data collected from the State and Local Monitoring System (SLAMS) and National Air 

Monitoring System (NAMS) networks, like those illustrated in Figure 2-2 through Figure 2-7 show that 

the decline in SO2 emissions from electric generating utilities has substantially improved air quality. No 

monitored exceedance of the SO2 annual ambient air quality standard in the lower 48 States of the U.S. 

has been recorded between 2000 and 2005, according to the EPA Acid Rain Program (ARP) 2005 

Progress Report (EPA, 2006b). EPA’s trends data (http://www.epa.gov/airtrends/) reveal that the national 

composite avg SO2 annual mean ambient concentration decreased by 48% from 1990 to 2005; the largest 

single-year reduction was 1994-95, the ARP’s first operating year (EPA, 2006b). Figure 2-20 depicts data 

for SO2 emissions in the contiguous United States (CONUS) during those years, with state-level totals. 

These emissions data trends are consistent with the trends in the observed ambient concentrations 

from the Clean Air Status and Trends Network (CASTNet). Following implementation of the Phase I 

controls on ARP sources between 1995 and 2000, significant reductions in SO2 and ambient SO4

2−

concentrations were observed at CASTNet sites throughout the eastern U.S.. The mean annual 

concentrations of SO2 and SO4

2−

 from CASTNet’s long-term monitoring sites can be compared using two 

3-year periods, 1989–1991 and 2003-2005, shown in Figure 2-21 for SO2 and Figure 2-22 for SO4

2−

. 

 2-23 
Figure 2-14. Location of SO2 monitors within a 15 km buffer zone with respect to combustion 

sources and highways in the Atlanta, GA MSA. 

 2-24 
Figure 2-15. Location of SO2 monitors within a 15 km buffer zone with respect to combustion 

sources and highways in the Cincinnati, OH MSA. 

 2-25 
Figure 2-16. Location of SO2 monitors within a 15 km buffer zone with respect to combustion 

sources and highways in the Cleveland, OH MSA. 

 2-26 
Figure 2-17. Location of SO2 monitors within a 15 km buffer zone with respect to combustion 

sources and highways in the Los Angeles/Riverside, CA MSA. 

 2-27 
Figure 2-18. Location of SO2 monitors within a 15 km buffer zone with respect to combustion 

sources and highways in the New York City, NY/Philadelphia, PA MSA. 

 2-28 
Figure 2-19 Location of SO2 monitors within a 15 km buffer zone with respect to combustion 

sources and highways in the St. Louis, MO MSA. 

 2-29 
Source: Environmental Protection Agency Clean Air Markets Division http://www.epa.gov/airmarkets/index.html

Figure 2-20. State-level SO2 emissions, 1990-2005. 

Source: U.S. EPA CASTNet 

Figure 2-21. Annual mean ambient SO2 concentration, 1989 through 1991 (A), and 2003 through 

2005 (B). 

 2-30 
Source: U.S. EPA CASTNet 

Figure 2-22. Annual mean ambient SO4

2− concentration, 1989 through 1991 (A), and 2003 through 

2005 (B). 

Source: Environmental Protection Agency, Clean Air Markets Division http://www.epa.gov/airmarkets/index.html 

Figure 2-23. Annual mean ambient SO2 emissions for Acid Rain Program cooperating facilities, 

2006. Dots represent monitoring sites. Lack of shading for Southern Florida indicates 

lack of monitoring coverage. 

 2-31 
From 1989 through 1991— the years prior to implementation of the ARP Phase I— the highest 

ambient mean concentrations of SO2 and SO4

2−

 were observed in western Pennsylvania and along the 

Ohio River Valley: > 20 μg/m3

 (~8 ppb) SO2 and > 15 μg/m3 SO4

2−

. These reductions are shown in Figure 

2-21 and Figure 2-22, respectively. In the years since the ARP controls were enacted, both the magnitude 

of SO4

2− concentrations and their areal extent have been significantly reduced, with the largest decreases 

again along the Ohio River Valley. 

Figure 2-23 depicts the magnitude and spatial distribution of SO2 emissions in 2006 from sources 

in the ARP for the CONUS. This depiction clearly shows the continuing predominance of SO2 sources in 

the U.S. east of the Mississippi River with even stronger magnitude in the central Ohio River Valley, as 

evident in the smoothed concentration plots in Figure 2-21. As shown in Table 2-7, regional distributions 

of SO2 and SO4

2−

 concentrations averaged for 2003–2005 reflect this geospatial emissions source 

difference as well. 

2.5. Environmental Concentrations of SOX 

2.5.1. Spatial and Temporal Variability of Ambient SO2 Concentrations 

SO2 concentrations have been falling throughout all regions of the CONUS, as demonstrated by the 

CASTNet data reviewed above. In and around most individual CMSAs, the trends are also toward lower 

SO2 levels. Table 2-7 shows that many annual and 1-h mean concentrations for the years 2003 through 

2005 were consistently at or below the operating LOD of ~3 ppb for the standard sensitivity UV 

fluorescence SO2 monitors deployed in the regulatory networks. Table 2-8 shows that the 1-h avg, 24-h 

avg, and aggregate mean value for the years 2003-2005 for inside and outside (all CMSAs) were 

operating just above the limit of detection (LOD) of ~ 3 ppb. 

Table 2-7. Mean ambient concentrations of SO2 and SO4

2− in different regions of the U.S. 

averaged over 2003-2005. 

Concentration 

Region 

SO2 (ppb) SO4

2− (μg/m3) 

Mid-Atlantic 3.3 4.5 

Midwest 2.3 3.8 

Northeast 1.2 2.5 

Southeast 1.3 4.1 

 2-32 
Table 2-8. Concentration distributions of SO2 inside and outside CMSAs from 2003-2005. Values 

shown are in ppb. 

Percentiles Averaging Time 

Monitor Locations 

Number of 

Samples Mean 

1 5 10 25 30 50 70 75 90 95 99 

Max 

1-H MAX CONCENTRATION 

Inside CMSAs 332405 13 1 1 1 3 4 7 13 16 30 45 92 714 

Outside CMSAs 53417 13 1 1 1 1 2 5 10 13 31 51 116 636 

1-H AVG CONCENTRATION 

Inside CMSAs 7408145 4 1 1 1 1 1 2 4 5 10 15 34 714 

Outside CMSAs 1197179 4 1 1 1 1 1 2 3 3 7 13 36 636 

24-H AVG CONCENTRATION 

Inside CMSAs 327918 4 1 1 1 1 2 3 5 6 10 13 23 148 

Outside CMSAs 52871 4 1 1 1 1 1 2 3 4 8 12 25 123 

ANNUAL AVG CONCENTRATION 

Inside CMSAs 898 4 1 1 1 1 2 4 5 6 8 10 12 15 

Outside CMSAs 143 4 1 1 1 1 2 3 4 5 8 9 13 14 

AGGREGATE 3-YR AVG CONCENTRATION, 2003-2005 

Inside CMSAs 283 4 1 1 1 2 3 3 5 5 8 10 12 14 

Outside CMSAs 42 4 1 1 1 2 2 3 4 5 8 9 13 13 

Figure 2-24 shows the composite diel variation in hourly SO2 concentrations for the mean and 

selected percentile values from all monitors reporting SO2 data into AQS. The AQS contains 

measurements of air pollutant concentrations in the 50 states plus the District of Columbia, Puerto Rico, 

and the Virgin Islands for the six criteria air pollutants and hazardous air pollutants. 

Figure 2-24 shows hourly mean concentrations and the 50th, 75th, 95th and 99th percentile values 

from all monitors reporting data into AQS. All of these metrics show clear daytime maxima and nighttime 

minima. The magnitude of the day-night difference increases with increasing concentrations. Note: 

concentrations given in AQS are rounded to the nearest ppb, and thereby flatten the variability at lower 

concentrations, as shown in the lower panel of Figure 2-24. The day-to-night variability likely reflects the 

entrainment of SO2 emitted by elevated point sources into the mixed layer that is growing by convection 

during the morning and early afternoon. The effect of higher concentration values on the means is shown 

by the 1 to 3 ppb difference between mean and 50th percentile values during the day. 

The strong west-to-east increasing gradient in SO2 emissions described above is well-replicated in 

the observed concentrations in the AQS data set. For example, Table 2-9 shows the mean annual 

concentrations from 2003–2005 for the 12 CMSAs with four or more SO2 regulatory monitors. Values 

ranged from a reported low of ~1 ppb in Riverside, CA and San Francisco, CA to a high of ~12 ppb in 

Pittsburgh, PA and 14 pbb in Steubenville, OH, in the highest SO2 source region. 

 2-33 
Source: EPA AQS 

Figure 2-24. Diel variation in SO2 concentration across all monitoring sites reporting into AQS for 

2005. The upper panel shows 95th, and 99th percentile values and the lower panel 

shows mean, 50th, and 75th percentile values. 

 2-34 
Table 2-9. Range of mean annual SO2 concentrations and Pearson correlation coefficients in 

urban areas having at least four regulatory monitors, 2003–2005. 

CMSA (# Monitors) Mean SO2 Concentration (ppb) Pearson Correlation Coefficient 

Philadelphia, PA (10) 3.6 – 5.9 0.37 – 0.84 

Washington, DC (5) 3.2 – 6.5 0.30 – 0.68 

Jacksonville, FL (5) 1.7 – 3.4 −0.03 – 0.51 

Tampa, FL (8) 2.0 – 4.6 −0.02 – 0.18 

Pittsburgh, PA (10) 6.8 – 12 0.07 – 0.77 

Steubenville, OH (13) 8.6 – 14 0.11 – 0.88 

Chicago, IL (9) 2.4 – 6.7 0.04 – 0.45 

Salt Lake City, UT (5) 2.2 – 4.1 0.01 – 0.25 

Phoenix, AZ (4) 1.6 – 2.8 −0.01 – 0.48 

San Francisco, CA (7) 1.4 – 2.8 −0.03 – 0.60 

Riverside, CA (4) 1.3 – 3.2 −0.06 – 0.15 

Los Angeles, CA (5) 1.4 – 4.9 −0.16 – 0.31 

The Pearson correlation coefficients (r) for concentration data from multiple monitors taken as 

pairs in these CMSAs were generally very low for all cities, especially at the lower end of the observed 

concentration range. Some negative correlation coefficients were observed on the West Coast and Florida 

(see Table 2-9). This reflects strong heterogeneity in SO2 ambient concentrations within a given CMSA 

and therefore indicates possibly different exposures of spatially distinct subgroups in these CMSAs. At 

higher concentrations, the r values were also higher. In some CMSAs, this heterogeneity may result from 

meteorological effects, whereby a generally well-mixed subsiding air mass containing one or more SO2

plumes with relatively high concentration would be more uniformly spread than faster-moving plumes 

with lower concentrations. However, instrument error may also play a role because the highest r values, 

i.e., those >0.7, correspond to the highest SO2 concentrations, i.e., >6 and > 10 ppb. Since the lowest SO2

concentrations are at or below the operating LOD and demonstrate the lowest correlation across monitors 

that share at least some air mass characteristics most of the year, the unbiased instrument error in this 

range may be confounding interpretation of any possible correlation. This could be because the same 

actual ambient concentration would be reported differently by different monitors (with different error 

profiles) in the CMSA for this lowest concentration range. 

To improve characterization of the extent and spatiotemporal variance of SO2 concentrations within 

each of the CMSAs having four or more SO2 monitors, the means, minima, and maxima were computed 

from daily mean data across all available monitors for each month for the years 2003 through 2005. 

Because many of these CMSAs with SO2 monitors also reported SO4

2−

, it is possible to compute the 

degree of correlation between SO2, the emitted species, and SO4

2−

, the most prominent oxidized product 

from SO2. Although SO4

2−

 values are averaged over all available data at each site, they are generally 

available at their monitoring sites on a schedule of only 1 in 3 days or 1 in 6 days. Furthermore, SO2 and 

SO4

2−

 monitors are not all co-located throughout the CMSAs. For each of the five example CMSAs in 

Figure 2-25 through Figure 2-29, monthly aggregated values are depicted for: (a) the monthly mean, 

minimum, and maximum SO2 concentrations; (b) the monthly mean, minimum and maximum SO4

2−

concentrations; and (c) a scatterplot of SO2 versus SO4

2−

 concentrations. 

 2-35 
In Steubenville, OH (Figure 2-25), the area of highest SO2 concentrations of all 12 CMSAs with 

more than four monitors, all monthly mean SO2 concentrations were substantially < 30 ppb, although max 

daily means in some months were often > 60 ppb, or even > 90 ppb. SO4

2−

 data at Steubenville were 

insufficient to make meaningful comparisons, although the 12 months of available SO4

2−

 data suggest no 

correlation with SO2. 

Figure 2-25. Steubenville, OH, 2003–2005. (a) Monthly mean, minimum, and maximum SO2

concentrations. (b) Monthly mean, minimum, and maximum SO4

2− concentrations. (c) 

Monthly mean SO4

2− concentrations as a function of SO2 concentrations. 

 2-36 
Figure 2-26. Philadelphia, 2003–2005. (a) Monthly mean, minimum, and maximum SO2

concentrations. (b) Monthly mean, minimum, and maximum SO4

2− concentrations. (c) 

Monthly mean SO4

2− concentrations as a function of SO2 concentrations. 

SO2 and SO4

2- trends differ substantially in Philadelphia, PA (Figure 2-26) from those in 

Steubenville, OH. SO2 in Philadelphia, PA (Figure 2-26). SO2 is present at roughly one-half the monthly 

mean concentrations in Steubenville, OH, and demonstrates a strong seasonality with SO2 concentrations 

peaking in winter. By contrast, SO4

2−

 concentrations in Philadelphia (Figure 2-26) peak in the three 

summer months, with pronounced wintertime minima. This seasonal anticorrelation still contains 

considerable monthly scatter. 

Los Angeles, CA (Figure 2-27), presents a special case since its size, power requirements, and role 

as a port city place a larger number of SO2 emitters in or near the city than would otherwise be expected 

on the West Coast. Concentrations of SO2 demonstrate weak seasonality in these 3 years, with 

 2-37 
summertime means of ~3 to 4 ppb, and maxima generally higher than wintertime ones, although the 

highest means and maxima occur during the winter of 2004–2005. SO4

2−

 at Los Angeles shows stronger 

seasonality, most likely because the longer summer days of sunny weather allow for additional oxidation 

of SO2 to SO4

2−

 than could happen in winter. Weak seasonal effects in SO2 likely explain the complete 

lack of correlation between SO2 and SO4

2−

 here. 

Figure 2-27. Los Angeles, 2003–2005. (a) Monthly mean, minimum, and maximum SO2

concentrations. (b) Monthly mean, minimum, and maximum SO4

2− concentrations. (c) 

Monthly mean SO4

2− concentrations as a function of SO2 concentrations. 

 2-38 
Figure 2-28. Riverside, CA, 2003–2005. (a). Monthly mean, minimum, and maximum SO2

concentrations. (b) Monthly mean, minimum, and maximum SO4

2− concentrations. (c) 

Monthly mean SO4

2− concentrations as a function of SO2 concentrations. 

Riverside, CA, CMSA (Figure 2-28) presents the strongest example among the 12 areas examined 

for this study of correlation between SO2 and SO4

2−

, though even here the coefficient of determination 

(R2

) value is merely 0.3. Seasonal peaks are obvious in summertime for SO2 and SO4

2−

, both at roughly 

one-half the ambient concentrations seen in Los Angeles. This is very likely due to Riverside’s geographic 

location just downwind of the regionally large electric generating utility sources near Los Angeles and the 

prevailing westerly winds in summer. Again, as with Los Angeles, the summertime peaks in SO4

2−

 are 

most likely due to the combination of peaking SO2 and favorable meteorological conditions allowing 

more complete oxidation. 

 2-39 
Phoenix, AZ was the CMSA with the lowest monthly mean SO2 and SO4

2−

 concentrations 

examined here (Figure 2-29). In Phoenix, nearly all monthly mean SO2 values were at or below the 

regulatory monitors’ operating LOD of ~3 ppb. SO4

2−

 concentrations were equivalently low, roughly onehalf the concentrations seen in Riverside, CA. The monthly mean data show strong summertime peaks for 

even these very low-level SO4

2−

 observations, which at ~1 to 3 μg/m3, were generally one-half of those in 

Philadelphia. This suggests some seasonality in SO2, though anticorrelated with SO4

2−

; however, the trend 

is very weak, as the correlation scatterplot shows. 

Figure 2-29. Phoenix, 2003–2005. (a) Monthly mean, minimum, and maximum SO2 concentrations. 

(b) Monthly mean, minimum, and maximum SO4

2− concentrations. (c) Monthly mean 

SO4

2− concentrations as a function of SO2 concentrations. 

 2-40 
2.5.2. Five-Minute Sample Data in the Monitoring Network 

Although the number of monitors across the CONUS varies somewhat from year to year, in 2006 

there were ~500 SO2 monitors in the NAAQS monitoring network (http://www.epa.gov/air/data/). The 

state and local agencies responsible for these monitors are required to report 1-h avg concentrations to the 

EPA AQS. However, a small number of sites, only 98 total from 1997 to 2007, and not the same sites in 

all years—voluntarily reported 5-min block avg data to AQS. Of these, only 16 reported all twelve 5-min 

avg in each hour at least part of the time between 1997 and 2007. The remainder reported only the 

maximum 5-min avg in each hour. See Table 2-10 and Table 2-11 for a breakdown of these monitoring 

locations and sampling periods, and Figure 2-30 for the distribution of these sites across the CONUS. 

Table 2-10. Locations, counts, sampling periods and statistics for monitors reporting hourly 

maximum 5-min SO2 values, 1997-2007. 

State Number of Counties Number of Monitors Number of Years Years Operating Mean GM1 GSD2 503 953 993 Max

AR 2 3 11 1997 - 2007 4 3 2 3 10 37 659 

CO 1 1 10 1997 - 2006 8 4 3 4 29 57 216 

DE 1 1 2 1997 - 1998 17 6 4 5 97 184 381 

DC 1 1 6 2000 - 2007 9 6 2 6 23 42 482 

FL 1 1 4 2002 - 2005 8 2 3 1 40 106 473 

IA 6 9 5 2001 - 2005 4 2 3 1 12 45 307 

LA 1 1 4 1997 - 2000 12 5 3 5 41 131 857 

MO 7 14 11 1997 - 2007 9 3 3 2 32 146 4367

MT 1 7 10 1997 - 2006 8 3 4 2 35 77 843 

NC 2 2 8 1997 - 2004 9 3 4 4 34 108 805 

ND 11 19 11 1997 - 2007 3 1 2 1 11 54 499 

PA 8 23 11 1997 - 2007 14 8 3 8 48 105 1099

SC 7 10 3 2000 - 2002 3 2 2 2 10 28 277 

UT 1 1 2 1997 - 1998 3 2 2 1 9 21 209 

WV 2 5 7 2001 - 2007 12 7 3 7 38 80 856 

1 Geometric mean 

2 Geometric standard deviation 

3 Percentile 

 2-41 
Table 2-11. Locations, counts, sampling periods and statistics for monitors reporting all twelve 

5-min SO2 values, 1997-2007. 

State Number of Counties Number of Monitors Number of Years Years Operating Mean GM1 GSD2 503 953 993 Max

DC 1 1 1 2007 5 4 2 4 12 19 400 

FL 1 1 4 2002 - 2005 4 2 3 1 18 62 473 

MO 1 2 5 2003 - 2007 3 2 2 1 11 43 259 

MT 1 4 1 2002 3 2 2 1 12 34 843 

NC 1 1 4 1999 - 2002 5 2 3 1 22 77 805 

PA 2 5 6 2002 - 2007 11 5 4 5 39 83 921 

WV 2 2 5 2001 - 2005 9 5 3 5 29 58 508 

1 Geometric mean 

2 Geometric standard deviation 

3 Percentile 

Figure 2-30. SO2 monitors reporting maximum or continuous 5-min avg values for any period, 

1997–2007. 

EPA guidance on quality assurance practices for SO2 monitoring by state/local monitoring agencies 

is aimed at ensuring adequate quality of 1-h SO2 concentration data. Measurement of 5-min avg 

concentrations may involve quality assurance challenges that are not addressed by current EPA guidance. 

This possibility has not been specifically investigated to date, so the information presented here should be 

considered to be of uncharacterized uncertainty at this time. Furthermore, the voluntary nature of this 

 2-42 
reporting results in irregular coverage in both space and time. For example, some sites reported data for 

some years while others did not. Because the 5-min data were reported voluntarily by the cooperating 

states from a subset of monitors in the national data network, no information is available to judge their 

degree of representativeness relative to the national network. 

When maximum 5-min concentrations were reported, the absolute highest concentration over the 

ten-year period exceeded 4 ppm, but the 99th percentile of the maximum 5-min concentrations were all 

below 200 ppb. Medians from reported data ranged from 1 ppb to 8 ppb, and the avg for each maximum 

5-min level ranged from 3 ppb to 17 ppb. Delaware, Pennsylvania, Louisiana, and West Virginia had 

mean values for maximum 5-min data exceeding 10 ppb. Among aggregated within-state data for the 16 

monitors from which all 5-min avg data were reported, the median values ranged from 1 ppb to 5 ppb, 

and the means ranged from 3 ppb to 11 ppb. The highest reported concentration was 921 ppb, but the 99th 

percentile values for aggregated within-state data were all below 90 ppb. It should be emphasized that 

monitoring was not continual during the ten-year reporting period, and monitoring was not performed 

simultaneously among the sites. For these reasons, caution must be taken when comparing the 

distributions among the various sites. 

Despite these limitations, distributions of the available 5-min data and comparisons with their 

respective 1-h avg can provide some insight into the temporal behavior of short-duration SO2

concentrations at the monitoring stations where these data are available. Table 2-12 summarizes 

correlations between the maximum 5-min avg and the corresponding 1-h avg computed from the 5-min 

data for the monitors reporting all twelve 5-min avg. The correlations are high with only one monitor 

observing a correlation coefficient under 0.9. 

Table 2-12. Pearson correlation coefficient between maximum 5-min and 1-h avg SO2

concentrations at the 16 sites reporting all twelve 5-min SO2 values. 

State Site ID Correlation Coefficient 

DC 110010041 0.87 

FL 120890005 0.92 

MO 290770026 0.92 

MO 290770037 0.93 

MT 301110066 0.95 

MT 301110079 0.93 

MT 301110082 0.93 

MT 301110083 0.92 

NC 371290006 0.91 

PA 420030021 0.94 

PA 420030064 0.95 

PA 420030116 0.96 

PA 420033003 0.95 

PA 420070005 0.93 

WV 540990002 0.95 

WV 541071002 0.93 

 2-43 
Figure 2-31. Time series and frequency distributions of voluntarily reported maximum 5-min SO2

concentrations from 6 monitors located in Iowa, Missouri, Pennsylvania and West 

Virginia. Frequency distributions are shown in terms of the log of the concentration 

on the x-axis. 

Figure 2-31 shows the time series and distribution of maximum 5-min SO2 data per hour for six 

monitors located in Iowa (site ID = 190330018), Missouri (290770026, 290770037 and 290990004), 

Pennsylvania (420070005) and West Virginia (541071002). These sites were selected to represent the 

available 5-min data. The highest observed 5-min values for these six monitors range from 166 ppb at the 

Iowa monitor (Mason City, IA) to 1099 ppb at the Pennsylvania monitor (Beaver Co, PA). In general, 

 2-44 
Figure 2-31 demonstrates that the higher range of mean values compared with the medians reflects a few 

high concentration events that skew the means upward with the mean concentration between 2 and 7 

times greater than the median of these data. 

Figure 2-32. Time series of hourly maximum 5-min SO2 data showing a 24 h (upper panels) and 1 

week (lower panels) time window centered on the peak value for the two sites with the 

lowest (IA) and highest (PA) maximum values in the preceding figure. 

Excursions of high 5-min concentrations should not be expected to be confined to one 5-min 

interval in any given hour as time scales for the meteorological conditions responsible for the excursions 

are much longer. During transport of emissions from their source to a receptor at the surface, turbulent 

 2-45 
mixing and dilution of the plume occur, further extending the area at the surface that is affected. Figure 

2-32 shows the time series of 5 min data centered on time of occurrence of the value (lower panel) for the 

two sites with the highest maximum value (PA) and lowest maximum value (IA) in the preceding figure. 

As can be seen for these two sites at least, high levels of SO2 can be sustained at the surface for a few 

hours and the time for these excursions to affect the surface can differ from site to site and during 

different periods at the same site. Note also that these events do not occur in isolation as can be seen at the 

PA site, at which several excursions above the 200 ppb level occurred within a week. Further analysis 

would be required to determine characteristic time scales for the durations of these excursions across the 

U.S., using emissions and meteorological data. 

2.5.3. Policy Relevant Background Contributions to SO2 

Concentrations 

Background concentrations used for purposes of informing decisions about the NAAQS are 

referred to as PRB concentrations; those concentrations that would occur in the U.S. in the absence of 

anthropogenic emissions in continental North America (defined here as the U.S., Canada, and Mexico). 

PRB concentrations include contributions from natural sources everywhere in the world, and from 

anthropogenic sources outside these three countries. Background levels so defined facilitate separation of 

cases where pollution levels can be controlled by U.S. regulations (or through international agreements 

with neighboring countries), from cases where pollution is generally uncontrollable by the U.S.. EPA 

assesses risks to human health and environmental effects from SO2 levels in excess of PRB 

concentrations. 

Contributions to PRB concentrations include natural emissions of SO2 and photochemical reactions 

involving reduced sulfur compounds of natural origin, as well as their long-range transport from outside 

of North America from any source. As an example, transport of SO2 from Eurasia across the Pacific 

Ocean or the Arctic Ocean would carry PRB SO2 into the U.S. Annex B contains a schematic diagram 

showing the major photochemical processes involved in the sulfur cycle, including natural sources of 

reduced sulfur species from anaerobic microbial activity in wetlands and volcanic activity. Volcanoes and 

wildfires are the major natural source of SO2. Biogenic emissions from agricultural activities are not 

considered in the formation of PRB concentrations. Discussions of the sources and estimates of emissions 

are given in Annex Section B.6. 

The MOZART-2 global model of tropospheric chemistry (Horowitz et al., 2003) is used to estimate 

the PRB contribution to SO2 concentrations. The model setup for the present-day simulation, i.e., 

including all sources in the U.S., Canada, and Mexico, was published in a series of papers from a recent 

model inter-comparison (Dentener et al., 2006a; van Noije et al., 2006). MOZART-2 is driven by the 

National Oceanic and Atmospheric Administration’s National Center for Environmental Prediction 

(NOAA/NCEP) meteorological fields and the International Institute for Applied Systems Analysis 

(IIASA) 2000 emissions at a resolution of 1.9° × 1.9° with 28 σ (sigma) levels in the vertical and includes 

gas- and aerosol-phase chemistry. Results shown in Figure 2-33 are for the meteorological year 2001. An 

additional PRB simulation was conducted in which continental North American anthropogenic emissions 

were set to zero. 

 2-46 
Source: NOAA Geophysical Fluid Dynamics Laboratory. 

Figure 2-33. Annual mean model-predicted concentrations of SO2 (ppb). 

 2-47 
The role of PRB in contributing to SO2 concentrations in surface air is examined first. Figure 2-33 

shows the annual mean predicted SO2 concentrations in surface air in the simulation including all sources, 

or the “base case” (top panel); the PRB simulation (middle panel); and the percentage contribution of the 

background to the total base case SO2 (bottom panel). Maximum concentrations in the base case 

simulation, > 5 ppb, occur along the Ohio River Valley (upper panel). Background SO2 concentrations are 

orders of magnitude smaller, below 10 parts per trillion (ppt) over much of the U.S. (middle panel). 

Maximum PRB concentrations of SO2 are 30 ppt. In the Northwest where there are geothermal sources of 

SO2, the contribution of PRB to total SO2 is 70 to 80%, however absolute SO2 concentrations are still of 

the order of ~2 ppb or less. With the exception of the West Coast where volcanic SO2 emissions cause 

high PRB concentrations, PRB contributes < 1% to present-day SO2 concentrations in surface air (bottom 

panel). 

Source: National Park Service 

Figure 2-34. 15-min avg ambient SO2 concentrations measured at 1 Hawaii Volcanoes National 

Park monitoring site (Jaggar Museum), March 12, 13, and 15, 2007. 

When estimating background concentrations, it is instructive to consider measurements of SO2 at 

relatively remote monitoring sites, i.e., sites located in sparsely populated areas not subject to obvious 

local sources of pollution. Berresheim et al. (1993) used a type of atmospheric pressure ionization mass 

spectrometer (APIMS) at Cheeka Peak, WA (48.30ΕN 124.62ΕW, 480 m asl) in April 1991 during a field 

study for DMS oxidation products. SO2 concentrations ranged between 20 and 40 ppt. Thornton et al. 

(2002) have also used an APIMS with an isotopically labeled internal standard to determine background 

 2-48 
SO2 levels. SO2 concentrations of 25 to 40 ppt were observed in northwestern Nebraska in October, 1999 

at 150 m above ground using the National Center for Atmospheric Research (NCAR)’s C-130 research 

aircraft. These data are comparable to remote central South Pacific convective boundary layer SO2 data 

(Thornton, 1999). 

As noted earlier, volcanic sources of SO2 in the U.S. are found in the Pacific Northwest, Alaska, 

and Hawaii. The greatest potential domestic effects from volcanic SO2 occurs on the island of Hawaii. 

Nearly continuous venting of SO2 from Mauna Loa and Kilauea produces SO2 in high concentrations (see 

Figure 2-34 and Figure 2-35) at two National Park sites near the Kilauea caldera and the nearby east rift 

zone. The latter emits several times as much SO2 as the Kilauea caldera. The two measurement sites 

within the National Park are < 3 km from the summit emission source and ~10 km from the east rift 

source and are affected by the two sources during southerly and easterly winds. A number of communities 

and population centers are within the same distance from the east rift gas source that affects these two 

monitoring sites. When the normal trade wind flows are disrupted, emissions from the sources can be 

brought directly to these various communities. Since these communities are located at a similar distance 

from the large east rift emission source as the National Park monitoring stations, it is probable that these 

communities experience SO2 concentrations as high as those measured within Hawaii Volcanoes National 

Park. 

Since 1980, the Mount St. Helens volcano (46.20°N, 122.18°W, summit 2549 m asl) in the 

Washington Cascade range has been a variable source of SO2. Its major effects came in the explosive 

eruptions of 1980, which primarily affected the northwestern U.S. The Augustine volcano near the mouth 

of the Cook Inlet in southwestern Alaska (59.363°N, 153.43°W, summit 1252 m asl) has emitted variable 

quantities of SO2 since its last major eruptions in 1986. Volcanoes in the Kamchatka peninsula in far 

eastern Siberia do not particularly affect the surface concentrations in northwestern North America. 

Overall, the background contribution to SO2 over the U.S. is relatively small, with a max PRB of 0.030 

ppb SO2, except for areas with volcanic activity. 

Source: National Park Service 

Figure 2-35. 15-min avg ambient SO2 concentrations measured at 2 Hawaii Volcanos National Park 

monitoring sites on September 29, 2007. 

 2-49 
2.6. Issues Associated with Evaluating SO2 Exposure 

2.6.1. General Considerations for Personal Exposure 

Human exposure to an airborne pollutant consists of contact between the human and the pollutant 

at a specific concentration for a specified period of time. People spend various amounts of time in 

different microenvironments characterized by different pollutant concentrations. The integrated exposure 

of a person to a given pollutant is the sum of the exposures over all time intervals for all 

microenvironments. Figure 2-36 represents a composite average of activity patterns across all age groups 

in the U.S., based on data collected in the National Human Activity Pattern Survey (NHAPS) (Klepeis et 

al., 2001). The demographic distribution of the respondents was designed to be similar to that of overall 

U.S. Census data. Different cohorts, e.g., the elderly, young and middle-aged working adults, and children 

exhibit different activity patterns. 

Source: Klepeis et al. (2001) 

Figure 2-36. Percentage of time spent in various environments in the U.S.1

 

1

 For example, the cohort of working adults between the ages of 18 and 65 yr represents ~50% of the population. Of this total, about 60% work 

outside the home, spending ~24% (40 h/168 h) of their time in factory/office environments. Thus, this cohort is likely to spend considerably 

more time in offices and factories than shown in the figure (5.4%), which reflects the entire population, and is also likely to spend less time in a 

residence than small children or the elderly. 

 2-50 
In a given microenvironment, the ambient component of a person’s microenvironmental exposure 

to a pollutant is determined by the following physical factors: 

 ambient concentration Ca

 air exchange rate ai

 pollutant specific penetration coefficient Pi

 pollutant specific decay rate ki

 fraction of time an individual spends in the microenvironment yi

These factors are in turn affected by the following exposure factors: 

 environmental conditions, such as weather and season 

 dwelling conditions such as: proximity to sources; the amount of natural ventilation (e.g., 

open windows and doors, and the “draftiness” of the dwelling); and the ventilation system 

 personal activities (e.g., the time spent cooking, commuting, or exercising [See 

Section 2.7.1]) 

 indoor sources and sinks of a pollutant 

Microenvironmental exposures can also be influenced by the individual-specific factors such as 

age, gender, health or socioeconomic status (SES). 

A person’s exposure to a pollutant, such as SO2, can be represented by: 

Equation 2-1 

where ET is an individual’s total personal exposure for a specific time period, n is the total number of 

microenvironments encountered, Ci is the average concentration, and ti is the time spent in the ith

microenvironment. A person’s exposure can be characterized as: an instantaneous exposure, a peak 

exposure such as might occur during a short-term event such as cooking, an average exposure, or an 

integrated exposure over all environments encountered. These distinctions are important because health 

effects caused by long-term low-level exposures may differ from those caused by short-term peak 

exposures. 

An individual’s total exposure (ET) can also be represented by: 

 Equation 2-2 

subject to the constraint 

Equation 2-3 

where Ea is the ambient component of personal exposure, Ena is the nonambient component of personal 

exposure, yo is the fraction of time spent outdoors, and yi is the fraction of time spent in microenvironment 

i. , Pi, ai, and ki are the infiltration factor, penetration coefficient, air exchange rate, and decay rate, 

respectively for microenvironment i. In the case where an exposure occurs mainly in one 

microenvironment, Equation 2-2 may be approximated by Equation 2-4 where y is the fraction of time 

spent outdoors, and α is the ratio of personal exposure from a pollutant of ambient origin to the pollutant’s 

ambient concentration (or the ambient exposure factor). Other symbols have the same definitions as in 

Equations 2-2 and 2-3. 

 2-51 
Equation 2-4 

If concentrations in a single microenvironment are considered, then Equation 2-4 can be reduced to 

Equation 2-5 

where Cme is the concentration in a microenvironment, Ca and Cna are the contributions to Cme from 

ambient and nonambient sources, S is the microenvironmental source strength, and V is the volume of the 

microenvironment. (Bracketed symbols are same as Equation 2-2.) In this equation, it is assumed that 

microenvironments do not exchange air with each other but only with the ambient environment. 

Microenvironments in which people are exposed to air pollutants such as SO2 typically include 

residential indoor environments, other indoor locations, near-traffic outdoor environments, other outdoor 

locations, and in vehicles, as shown in Figure 2-36. Indoor combustion sources such as gas stoves and 

space heaters need to be considered when evaluating exposures to SO2. However, in the U.S., the only 

important indoor sources of SO2 are kerosene heaters, which are not widely used. Exposure 

characterization is improved when microenvironment-level exposures are considered to estimate the 

ambient component of personal exposure and to describe the relationship between ambient air pollution 

exposures and health outcomes. 

Time-activity diaries, completed by study participants, are used to compile activity patterns for 

input to exposure models and assessments. The EPA’s National Exposure Research Laboratory (NERL) 

has consolidated the majority of the most significant human activity databases into one comprehensive 

database called the Consolidated Human Activity Database (CHAD). Eleven different human activity 

pattern studies were evaluated to obtain over 22,000 person-days of 24-h human activities in CHAD 

(McCurdy et al., 2000). These data can be useful in assembling population cohorts to be used in exposure 

modeling and analysis. 

In general, the relationship between personal exposures and ambient concentrations is modified by 

pollutant behavior in microenvironments. During infiltration, ambient pollutants can be lost through 

chemical and physical processes. Therefore, the ambient component of a pollutant’s microenvironmental 

concentration is less than its ambient concentration, and can be represented as the product of the 

ambient concentration and the infiltration factor (Finf or α [if people spend 100% of their time indoors]). 

In addition, exposure to nonambient, microenvironmental sources modifies the relationship between 

personal exposures and ambient concentrations. 

In practice, it is extremely difficult to characterize community exposures by measurements of each 

individual’s personal exposures. Instead, the distribution of personal exposures in a community, or the 

population exposure, is simulated by extrapolating measurements of personal exposure using various 

techniques or by stochastic, deterministic or hybrid exposure modeling approaches such as APEX, 

SHEDS, and MENTOR (see Annex Section C.2 for a description of modeling methods). Variations in 

community-level personal exposures are determined by cross-community variations in ambient pollutant 

concentrations and the physical and exposure factors mentioned above. These factors also determine the 

strength of the association between population exposure to SO2 of ambient origin and ambient SO2

concentrations. 

Of major concern is the ability of SO2 measured by ambient monitors to serve as a reliable 

indicator of personal exposure to SO2 of ambient origin. The key question is what errors are associated 

with using SO2 measured by ambient monitors as a surrogate for personal exposure to ambient SO2 and/or 

its oxidation products in epidemiologic studies. There are three aspects to this issue: (1) ambient and 

personal sampling issues; (2) the spatial variability of ambient SO2 concentrations as related to exposures; 

and (3) the associations between ambient concentrations and personal exposures as influenced by 

 2-52 
exposure factors, e.g., indoor sources and time spent indoors and outdoors. Items (1) and (3) are discussed 

individually in the following sections; item (2) was discussed previously in Section 2.4.2. 

2.6.2. Methods Used for Monitoring Personal Exposure 

Three basic methods of analysis have been used as personal exposure monitors (PEMs) to measure 

personal exposure to SO2. The Harvard-EPA annular denuder system (HEADS) was initially developed to 

measure particles and acid gases simultaneously (Brauer et al., 1999; Koutrakis et al., 1988b). The aerosol 

is initially sampled at 10 L/min through an impactor that is attached to an annular denuder to remove 

particles. Subsequently, the aerosol is sampled through an annular denuder coated with sodium carbonate 

(Na2CO3). This denuder is used to trap SO2, nitric acid (HNO3), and nitrous acid (HNO2). Following 

sampling, the denuder is extracted with ultrapure water and analyzed by ion chromatography. Collection 

efficiencies of SO2 in the denuder are typically around 0.993, which compares well with predicted values. 

For a study conducted in Baltimore, MD, Chang et al. (2000) developed and employed a personal 

roll-around system (RAS), which is an active sampling system designed to measure short-term personal 

exposure concentrations of several atmospherically relevant species, including SO2. For the measurement 

of SO2, the RAS employed an NO2/SO2 sorbent denuder worn on a vest by the study participant. The 

hollow glass denuder, encased in an aluminum jacket, is coated with triethanolamine (TEA) for the 

collection of SO2 and NO2, and aerosol is sampled through the denuder at 100 cc/min. Following 

sampling, the denuder can be extracted and analyzed for SO2 concentrations by ion chromatography. The 

detection limit for 1-h sampling of SO2 was reported to be 62 ppb, which resulted in many of the 1-h 

samples being below the LOD. 

The most commonly employed SO2 PEM method for personal exposure studies is the passive 

badge sampler. A personal multipollutant sampler has been developed to measure particulate and gaseous 

pollutants simultaneously (Demokritou et al., 2001). A single elutriator, operating at 5.2 L/min, is 

employed to sample particulate pollutants. A passive SO2 badge is attached diametrically to the elutriator, 

which has been coated with Teflon to minimize reactive gas losses. The passive badge sample is coated 

with TEA for the collection of SO2 and NO2. Because wind speed can affect the collection rate of the 

passive badge sampler, this system employs a constant face velocity across the passive badge sampler. For 

24-h sampling times, the estimated limit of detection (LOD) for SO2 is 5 ppb. 

Currently, limits exist for using PEM systems to measure personal exposure to SO2. Because SO2

concentrations have been declining annually in the U.S., little focus has been placed on improving the 

methods of analysis. LODs for SO2 PEMs (~5-10 ppb for 24-h sampling) are often greater than the 

concentrations of SO2 that are typically observed in urban ambient environments. However, much lower 

detection limits can be achieved by extending the sampling time (Kasper-Giebl et al., 1999). Personal 

exposure monitoring studies often suffer from having many of the daily SO2 samples (e.g., 30 to 70%) 

collected below the sampler’s LOD (see Table 2-14 and Table 2-15). Because of these issues, current 

methods cannot characterize hourly or shorter exposures and their relationship to ambient concentrations 

unless these values are in the range of several tens to hundreds of ppb. 

2.6.3. Relationship between Personal Exposure and Ambient 

Concentration 

Because SO2 concentrations have declined markedly over the past few decades, relatively few 

recent personal exposure studies have focused on SO2. Another consideration is that current indoor and 

outdoor levels in many areas are often beneath detection limits for passive personal SO2 monitors. 

 2-53 
2.6.3.1. Indoor Versus Outdoor SO2 Concentrations 

Several studies in the U.S., Canada, Europe, and Asia have examined the relationships of indoor, 

outdoor, and personal concentrations of SO2 to ambient SO2 concentrations. Perhaps the most 

comprehensive set of indoor-outdoor data was obtained by Spengler et al. (1979) during the Harvard Six 

Cities Study. These data are shown in Figure 2-37. 24-h ambient and indoor SO2 concentrations were 

measured every sixth day for 1 yr in a minimum of 10 homes or public facilities for each of the cities 

studied. 

Source: Adapted from Spengler et al. (1979) 

Figure 2-37. Average annual indoor and outdoor SO2 concentrations for each of the six cities 

included in the Harvard six-cities study analysis. PORT = Portage, WI / TOPE = 

Topeka, KS / KING = Kingston, TN / WAT = Watertown, MA / STL = St. Louis, MO / STEU 

= Steubenville, OH. 

As can be seen from Table 2-13, a wide range is found in the ratio of indoor to outdoor 

concentrations among the different studies. These differences among studies could be due in part to 

differences in building characteristics (e.g., forced ventilation, building age, and building function such as 

residences, schools, or other public buildings), in activities affecting air exchange rates, and in analytical 

capabilities. In several studies, high values for R2

 were found, suggesting that indoor levels were largely 

driven by outdoor levels. A few studies found higher levels of SO2 indoors than outdoors in some 

samples. This situation could have arisen if there were indoor sources or as a result of analytical 

measurement issues. One would expect to find lower concentrations indoors than outdoors because SO2 is 

consumed by reactions on indoor surfaces, especially those that are moist. Chao (2001) acknowledged 

this point but could not account for the findings of this study. It was noted that two samples had unusually 

high indoor to outdoor ratios and that the mean ratios would have been much lower otherwise. Wintersummer differences in the indoor:outdoor ratio are consistent with seasonal differences in air exchange 

rates, as noted by Brauer et al. (1991). 

 2-54 
Table 2-13. Relationships of indoor to outdoor SO2 concentrations. 

Study Location Indoor to Outdoor Ratio 

(# samples) Notes 

Spengler et al. (1979) Portage, WI 

Topeka, KS 

Kingston, TN 

Watertown, MA 

St. Louis. MO 

Steubenville, OH 

0.67 (349) 

0.50 (389) 

0.08 (425) 

0.33 (486) 

0.31 (543) 

0.39 (499) 

One year during Harvard Six Cities Study. West-Gaeke 

method. 

Stock et al. (1985) Houston, TX 0.54 (2425) May to October, continuous FRM for indoor and 

outdoor. 

Meranger and Brule (1987) Antigonish, NS, Canada 0.84 (8) Early spring, 1 wk avg in 1 house with oil furnace, 

FPD-TA 

Brauer et al. (1989) Boston, MA 0.23 (24) Summer, HEADS 

Li and Harrison (1990) Essex, UK 0.22 Summer 

Brauer et al. (1991) Boston, MA 0.39 (geom. mean) (29), R2 = 0.89 

0.05 (geom. mean) (23), R2 = 0.73 

Summer, HEADS 

Winter, HEADS 

Chan et al. (1994) Taipei, Taiwan 0.24 (15) 

0.23 (37) 

Summer, PS 

Winter, PS 

Lee et al. (1999b) Hong Kong 0.92, R2 = 0.56 Winter, PF 

Patterson and Eatough (2000) Lindon, UT 0.027 ± 0.0023, R2 = 0.73 Winter, ADS, all samples 

Kindzierski and Sembaluk 

(2001) 

Boyle, Alberta, Canada 

Sherwood Park, Alberta, 

Canada 

0.12 (12) 

0.14 (13) 

Late Fall, PS 

Chao (2001) Hong Kong 1.01 ± 0.78 (10) Summer. Windows mainly kept closed, PS 

Kindzierski and Ranganathan 

(2006) 

Fort McKay, Alberta, Canada 0.35 (30) Fall. All indoor levels < LOD and set =1/2 LOD, PS 

FPD-TA = Flame Photometric Detection-Thermal Analysis 

HEADS = Harvard-EPA Annular Denuder System 

PS = passive sampler 

PF = pulsed fluorescence 

FRM = Federal Reference Method 

ADS = Annular Denuder System 

Indoor, or nonambient, sources of SO2 could complicate the interpretation of associations between 

personal exposure to ambient SO2 in exposure studies. The source of indoor SO2 is combustion of sulfurcontaining fuels, and higher levels are expected when emissions are poorly vented. Brauer et al. (2002b) 

noted that only one study (Biersteker et al., 1965) conducted inferential analyses of potential determinants 

of exposure to indoor SO2 levels. In the Biersteker et al. study, conducted in the Netherlands, indoor 

levels increased with oil, coal, and gas heating, as well as smoking in homes and increased outdoor levels. 

Triche et al. (2005) measured SO2 levels in homes in which secondary heating sources (fireplaces, 

kerosene heaters, gas space heaters, and wood stoves) were used. They found elevated indoor levels of 

SO2 when kerosene heaters were in use. Median levels of SO2 when kerosene heaters were used (6.4 ppb) 

were much higher than when they were not in use (0.22 ppb). The maximum SO2 level associated with 

kerosene heater use was 90.5 ppb. They did not find elevated SO2 levels when the other secondary heating 

sources were in use.

 2-55 
2.6.3.2. Relationship of Personal Exposure to Ambient Concentration 

A few studies have evaluated the association of personal SO2 exposure to ambient concentrations 

(Brauer et al., 1989; Chang et al., 2000; Sarnat et al., 2000; 2001; 2005; 2006a). However, no studies have 

characterized the relationship between community avg exposures and ambient concentrations of SO2. 

Some of these personal exposure studies were conducted under the Health Effects Institute’s 

Characterization of Particulate and Gas Exposures of Sensitive Subpopulations Living in Baltimore and 

Boston research plan (Koutrakis et al., 2005). However, the focus of many of these studies has been 

exposure to particles with acid gases included to evaluate confounder or surrogate issues. 

Table 2-14 summarizes the longitudinal correlation coefficients between personal SO2 exposures 

and ambient concentrations of SO2, and Table 2-15 shows the pooled correlation coefficients. Most of the 

studies examined lack the ability to quantify 24-h avg personal SO2 exposures as a result of the low 

ambient SO2 concentrations and the limitations of passive sampling, except two studies conducted by 

Brauer et al. (1989) and Sarnat et al. (2006a) in which the sampling systems can adequately quantify 24-h 

personal exposures. 

Brauer et al. (1989) determined the slope of the regression line between personal and ambient 

concentrations to be 0.13 ± 0.02, R2

 = 0.43, based on 44 measurements made in Boston, MA during the 

summer of 1988. This study had the highest proportion of personal samples above the detection limit 

among the studies considered and the best regression fit between personal exposure and ambient 

concentrations. Most if not all of the data points obtained using the HEADS appeared to be above the 

working detection limits as defined by the authors in their publications (Brauer et al., 1989; Koutrakis et 

al., 1988b). Note that calculating detection limits in this way could result in lower apparent detection 

limits than if field blanks were used. The authors reported significance at the p < 0.001 level for the slope 

but not the intercept. Since the stationary monitoring site was located at an elevation of 250 m above 

street level, the use of data from this ambient monitoring site will overestimate personal exposure, as the 

concentration of SO2 increases with height because it is emitted mainly by elevated point sources. Indeed, 

the ambient concentrations are about a factor of two higher than the concentrations measured outside 

residences. Sarnat et al. (2006a) reported that ambient SO2 was observed to be significantly associated 

with personal SO2 exposure concentrations during the fall (slope = 0.08 for overall population) in a study 

in Steubenville, OH. The authors also observed the effect of ventilation on the association between 

personal exposure concentrations and ambient concentrations (slope = 0.07 for subjects in buildings with 

low ventilation rates, and 0.13 for subjects in buildings with high ventilation rates). 

The associations between personal exposure and ambient concentration cannot be examined in the 

other studies because almost all the personal exposure concentrations were beneath detection limits. For 

example, Chang et al. (2000) tested a new personal active sampling device (a RAS with a TEA-based 

denuder) on volunteer participants to measure hourly personal exposure to SO2. However, the method 

detection limit was too high for SO2 (62 ppb for 1-h sampling) to generate a robust SO2 exposure dataset 

to perform further analysis, and so the authors did not use the SO2 data. 

 2-56 
Table 2-14. Association between personal exposure concentration and ambient concentration 

(longitudinal correlation coefficients). 

Study Study Design Season Mean Conc.

(ppb) Statistics Comments 

Sarnat 

et al. 

(2000) 

Longitudinal, Baltimore, 20 senior, healthy, nonsmoking people (avg 

age 75), summer of 1998 and winter of 1999, 1 day averaged sample, 

for 12 consecutive days for each subject; four to six subjects were 

measured concurrently during each 12-day monitoring period. 

Winter Ambient: 

6.6 – 10.2 

Personal: 

-0.8 – 1.2 

Slope: NR 

Intercept: NR 

Correlation 

Coefficient (r): 

-0.75 to 0.65 with 

a median of 0.02 

(14 subjects) 

The LOD for 24-h sampling 

was 6.5 ppb. All personal 

samples were below LOD. 

Sarnat 

et al. 

(2001) 

Longitudinal, Baltimore, 56 seniors, schoolchildren, and people with 

COPD, summer of 1998 and winter of 1999, 14 of 56 subjects 

participated in both sampling seasons; all subjects were monitored for 

12 consecutive days (24-h avg samples) in each of the one or two 

seasons, with the exception of children who were measured for 8 

consecutive days during the summer. 

Winter Ambient: 

4 – 17 

Personal: 

-2 – 3 

Slope: 

-0.05 (N=487 with 

45 subjects) 

Intercept: 0.54* 

(N=487 with 45 

subjects) 

r = -0.75 to 0.6 

with a median of -

0.1 (45 subjects) 

1) Concentrations are 

estimated from Figure 1 in the 

paper. 

2) Correlation coefficients are 

estimated from Figure 2 in the 

paper. 

3) LOD was referred to Sarnat 

et al. (2000), which was 6.5 

ppb. Therefore, all personal 

samples were below LOD. 

Summer Ambient: 

2.8 – 4.5 

Personal: 

0.3 – 0.5 

Slope: 0.00 

(N-335) 

Intercept: NR 

r = -0.60 to 0.70 

with a median of 

0.00, 

(Sample size: NR) 

1) Correlation coefficients are 

estimated from Figure 1 in the 

paper. 

2) LOD was 2.3 ppb, and 

96.5% of personal samples 

were below LOD. 

Sarnat 

et al. 

(2005) 

Longitudinal, Boston, 43 seniors and schoolchildren, summer of 1999 

and winter of 2000, Similar study design as Sarnat et al. (2001). 

Winter Ambient: 

4.9 – 10.7 

Personal: - 

0.3 – 1.9 

Slope: -0.02 

(N = 299) 

Intercept: NR 

r = -0.55 to 0.60 

with a median of 

0.10 (Sample size 

NR) 

1) Correlation coefficients are 

estimated from Figure 1 in the 

paper. 

2) LOD was 3.2 ppb, and 

95.4% of personal samples 

were below LOD. 

NR: not reported 

* p ≤ 0.05 

In the context of determining the effects of ambient pollutants on human health, the association 

between the ambient component of personal exposures and ambient concentrations is more relevant than 

the association between personal total exposures (ambient component + nonambient component) and 

ambient concentrations. As described in Equations 2-2 and 2-4, personal total exposure can be 

decomposed into two parts: an ambient and a nonambient component. Usually, the ambient component of 

personal exposure is not directly measureable. However, it can be estimated by exposure models, or the 

personal total exposure can be regarded as the personal exposure of ambient origin if there are no indoor 

or nonambient sources. It is expected that the association between ambient concentrations and the 

ambient component of personal exposures would be stronger than the association between ambient 

concentrations and personal total exposures as long as the ambient and nonambient component of 

personal total exposure are independent. None of the studies examined indoor sources. However, indoor 

sources are not expected to be present for SO2. The correlation coefficients between personal ambient SO2

 2-57 
exposures and ambient SO2 concentrations in different types of exposure studies are relevant to different 

types of epidemiologic studies. 

Table 2-15. Association between personal exposure concentration and ambient concentration 

(pooled correlation coefficients). 

Study Study Design Season Mean Conc. 

(ppb) Statistics Comments 

Brauer et al. 

(1989) 

Pooled, Boston, study population was NR, the 

number of participants was estimated to be 48, 

July and August of 1988 for 24 days, 1 day 

averaged sample, two subjects were monitored 

each day. 

Summer Ambient: 

2.5 – 9.5 

Personal: 

0.4 – 1.8 

Slope: 0.13* 

(N=44) 

Intercept: Not 

significant 

R2: 0.43 

1) Concentrations estimated from 

Figure 2 in the paper. 

2) Central site monitor was 250 m 

above the ground level. 

3) LOD for personal samples was 

~0.19 ppb based on the method of 

determining the LOD for an active 

sampling system. 

Summer Ambient: 

2.7 ± 3.9 

Personal: 

1.5 ± 3.3 

Slope: 0.03 

(N=106) 

Intercept: NR 

R2: 0.00 

LOD was 5.5 ppb; 53.5% of 

personal samples were below LOD. 

Sarnat et al. 

(2006a) 

Steubenville, 15 senior subjects, summer and fall 

of 2000, two consecutive 24-h samples were 

collected for each subject for each wk, 23 wks 

total. Correlation coefficients were calculated in 

the pooled data set. 

Fall Ambient: 

5.4 ± 9.6 

Personal: 

0.7 ± 1.9 

Slope: 0.08* 

(N=152) 

Intercept: NR 

R2: 0.15 

LOD was 3.8 ppb, and 31.6% of 

personal samples were below LOD. 

* significant at α = 0.05 level 

There are three types of correlations generated from different study designs and ways to analyze 

the data from exposure studies: longitudinal, “pooled,” and daily-avg correlations (U.S. EPA, 2004). 

Longitudinal correlations1

 are calculated when data from a study includes measurements over multiple 

days for each subject (longitudinal study design). Longitudinal correlations describe the temporal 

relationship between daily personal SO2 exposure or microenvironment concentration and daily ambient 

SO2 concentration for the same subject. The longitudinal correlation coefficient can differ between 

subjects (i.e., each person may have a different correlation coefficient). The distribution of correlations for 

each subject across a population could be obtained with this type of data (e.g., Sarnat et al., 2000; 2001; 

2005). A longitudinal correlation coefficient between the ambient component of personal exposures and 

ambient concentrations is relevant to the panel epidemiologic study design. In Table 2-14, most 

longitudinal studies reported the association between personal total exposures and ambient concentrations 

for each subject. For some subjects the associations were strong and for some subjects the associations 

were weak. The weak personal and ambient associations do not necessarily mean that ambient 

 

1

( )( )

( ) ax

j

jiij

ax ssn

aaxx

r

i

i − 1

−−

=

∑

 where “r” is the longitudinal correlation coefficient between personal exposure and ambient concentration, “a” 

represents the ambient concentration, “x” represents exposure, “i” represents the ith subject, “j” represents the jth measurement (with the 

averaging time ranging from two days to two weeks for SO2 measurement), “s” represents the standard deviation, and “n” in the longitudinal 

studies is the number of measurements for each subject. The ambient concentration aj could be measured by one ambient monitor or the average 

of several ambient monitors. 

 2-58 
concentrations are not a good surrogate for personal exposures, because the weak associations could have 

resulted from the day-to-day variation in the nonambient component of total personal exposure. These 

types of correlations can have a substantial effect on the value of the resultant correlation coefficient. 

Mage et al. (1999) showed that very low correlations between personal exposure and ambient 

concentrations could be obtained when people with very different nonambient exposures are pooled, even 

though their individual longitudinal correlations are high. 

Pooled correlations1

 are calculated when a study involves one or only a few measurements per 

subject and when different subjects are studied on subsequent days. Pooled correlations combine 

individual-subject/individual-day data for the calculation of correlations. Pooled correlations describe the 

relationship between daily personal SO2 exposure and daily ambient SO2 concentration across all subjects 

in the study (e.g., Brauer et al., 1989; Sarnat et al., 2006a). 

Daily-avg correlations2

 are calculated by averaging exposure across subjects for each day. Dailyavg correlations then describe the relationship between the daily avg exposure and daily ambient pollutant 

concentration. This type of correlation (i.e., the association between the ambient component of 

community avg exposures and ambient concentrations) is more directly relevant to community time-series 

studies, in which ambient concentrations are used as a surrogate for community avg exposure to 

pollutants of ambient origin. However, exposure of the population to SO2 of ambient origin has not been 

reported in any of the studies examined. 

Not only does the exposure study design determine the meaning of the correlation coefficients in 

the context of exposure assessment in epidemiologic studies, but the type of correlation calculation also 

affects the strength of the association between personal exposures and ambient concentrations. The 

strength of the association between personal exposures with ambient and/or outdoor concentrations for a 

population is determined by variations in several physical factors: indoor or other local sources, air 

exchange rate, penetration, decay rate of the pollutant in different microenvironments, and the time 

people spend in different microenvironments with different pollutant concentrations. For different types 

of correlation coefficients, the components of the variance of these physical factors are different, and 

therefore the strength of different types of correlation coefficients is different. Longitudinal correlation 

coefficients reflect the interpersonal variations of these physical factors. Pooled correlation coefficient 

reflect both inter- and intra- personal variations of these physical factors. For the association between 

community avg exposures and ambient concentrations, interpersonal variations of these physical factors 

are reduced by averaging personal exposures across a community. Therefore, the strength of the 

associations between personal exposures and ambient concentrations may not be directly comparable, 

although these associations are determined by the same set of physical factors (but affected in different 

ways). 

Since correlations are standardized quantities that depend on multiple features of the data not only 

is the linear “relatedness” (covariance) of the two quantities important in a correlation, but so is the 

variability of each. That variability can be affected by exposure factors in various ways. In the following 

assessments, the effects of these physical factors on the strength of correlation coefficients are primarily 

 

1

( )( )

( ) ax

ji

ij j

ax ssn

aaxx

r 1

,

−

−−

=

∑

 where “r” is the pooled correlation coefficient and “n” is the number of paired measurements of exposure and 

ambient concentration, and all other symbols are defined the same way as those in the longitudinal correlation coefficient. 

2

( )( )

( ) x a

j

j j

xa ssn

aaxx

r

j

−1

−−

=

∑

, where “r” is the daily-average correlation coefficient and “n” is the number of measurement period, during each of 

which the exposure for all subjects are measured, and all other symbols are defined the same way as those in the longitudinal correlation 

coefficient. 

 2-59 
examined within a study, and the purpose of the inter-study comparison is to examine the consistency of 

the effects across different types of studies. 

The strength of the associations between personal exposures and ambient concentrations could also 

be affected by the quality of the data collected during the exposure studies. There are at least six aspects 

associated with the quality of the data: method precision, method accuracy (compared with FRM), percent 

of data above method detection limits (based on field blanks), completeness of the data collection, sample 

size, and soundness of the quality assurance/quality control procedures. Unfortunately, not all studies 

reported the six aspects of the data quality issue. The fraction of data below the detection limit might be a 

concern for some studies (Sarnat et al., 2000; 2001; 2005). Correlation coefficients would be biased low if 

data used in their calculation are below detection limits. Sampling interferences associated with both 

ambient (see Section 2.3) and personal sampling (see Section 2.6.2) could also affect data quality. 

Therefore, caution must be exercised when interpreting the results in Table 2-14 and Table 2-15. Sarnat 

et al. (2001; 2005; 2006a) examined the associations between ambient SO2 concentrations and ambient or 

personal co-pollutant concentrations. Sarnat et al. (2001) reported that during the winter of 1999, ambient 

SO2 was significantly associated (p <0.05) with personal exposure to fine particulate matter (PM2.5) 

(slope = − 0.24), personal exposure to SO4

2−

 (slope = − 0.03), and personal exposure to PM2.5 of ambient 

origin (slope = − 0.16). However, it should be noted that all the slopes are negative perhaps as the result 

of measurement error. Sarnat et al. (2005) reported that significant associations between ambient SO2 and 

either personal exposures or ambient concentrations of other pollutants were found for personal SO4

2−

(winter, slope = 0.06); (summer, slope = 0.39); personal PM2.5 (summer, slope = 1.68), ambient SO4

2−

(winter, slope = 0.19); and ambient PM2.5 (winter, slope = 0.80). In Sarnat et al. (2006a), ambient SO2 was 

observed to be significantly associated with ambient PM2.5, ambient SO4

2−

, and ambient elemental carbon 

(EC) during the fall (R2

 = 0.22, 0.33, and 0.34 respectively). It was significantly associated with personal 

PM2.5 during the summer, and personal SO4

2−

 and personal EC during the fall (R2 = 0.07, 0.06, and 0.05 

respectively). 

Of significant concern is the ability of currently available techniques for monitoring either personal 

exposures or ambient concentrations to measure SO2 concentrations that are typically found in most urban 

environments. In some studies, most data might be beneath detection limits. This is especially true for 

personal exposure and indoor data. Indeed, in one study (Chang et al., 2000), the investigators had to 

discard data for SO2 because the values were mostly beneath detection limits. In the study of Kindzierski 

and Ranganathan (2006), all indoor concentration data were beneath detection limits. In Sarnat et al. 

(2000), ~70% of personal measurements were beneath detection limits, and ~33% of personal 

measurements returned apparent negative concentration values. In such situations, associations between 

ambient concentrations and personal exposure are inadequately characterized. When personal exposure 

concentrations are above detection limits, a reasonably strong association is observed between personal 

exposures and ambient concentrations. 

2.6.4. Exposure Errors in Epidemiologic Studies 

This assessment considered the errors that result from using the ambient concentration of an air 

pollutant as an exposure indicator rather than using the actual personal exposure to that air pollutant in the 

epidemiologic statistical analysis. Such errors change both the health effects estimate, expressed as the 

relative risk factor, β, and the standard error of β. There are many assumptions made in going from the 

available measurement of a pollution indicator to an estimate of the personal exposure. The importance of 

these assumptions and their effect on β depend on the type of epidemiologic study. 

The considerations of exposure error for SO2 are simplified compared to those for NO2 and PM. 

The only experimental measure available is the ambient concentration of SO2. In addition, indoor and 

other non-ambient sources of SO2 are not thought to be important in population studies, lessening 

concerns about the possible influence of exposures other than to ambient SO2. However, because SO2 is 

 2-60 
rapidly removed by interaction with surfaces (more slowly than O3 but more rapidly than NO2 or PM), the 

ratio of indoor concentrations to outdoor concentrations will be lower, and perhaps more variable than in 

the case of NO2, PM and CO (which is relatively un-reactive with surfaces). 

2.6.4.1. Community Time-Series Studies 

This section applies primarily to studies on the association of daily avg SO2 concentrations with 

daily measures of mortality or morbidity in a community. The following three exposure issues are of 

primary concern with respect to SO2 time-series epidemiologic analysis: (1) the relationship of the 

measured concentration of SO2 to the true concentration; (2) the relationship of day-to-day variations in 

the concentrations of SO2, as measured at a central monitoring site, with the corresponding variations in 

the avg concentration of SO2 over the geographic area from which the health measurements are drawn; 

and (3) the relationship of the community avg concentration of SO2 to the avg personal exposure to 

ambient SO2. These three issues are described below. 

Relationship of Measured SO2 to the True Concentration 

Since there is always a random component to instrumental measurement error, the correlation of 

the measured SO2 with the true SO2, on either a 24-h or 1-h basis, will be less than 1. Sheppard et al. 

(2005) indicate that instrument error in the individual or daily avg concentrations have “the effect of 

attenuating the estimate of α.” Zeger et al. (2000) suggest that instrument error has both Berkson and nonBerkson error components. However, the authors state that the “instrument error in the ambient levels is 

close to the Berkson type.” In order for this error to cause substantial bias in β, the error term (the 

difference between the true concentrations and the measured concentrations) must be strongly correlated 

with the measured concentrations. Zeger et al. (2000) suggest that, “further investigations of this 

correlation in cities with many monitors are warranted.” Averaging across multiple unbiased ambient 

monitors in a region should reduce the instrument measurement error (Sheppard et al., 2005; Wilson and 

Brauer, 2006; Zeger et al., 2000). There are concerns about the precision and accuracy of the ambient 

concentration measurements because SO2 concentrations are much lower now than when the SO2

standards were first promulgated. Typical ambient concentrations of SO2 in the contiguous U.S. are nearly 

all at or beneath the detection limit of the monitors currently used in the regulatory network. Thus, greater 

relative error is most often observed at the lower ambient concentrations compared to the less frequent 

higher concentration exposures, as might occur because of plume downwash near local point sources or 

entrainment of plumes downwind from large power plants or smelters. It is unclear how uncertainties in 

the true concentrations of SO2, i.e., instrument measurement error, will change β. 

Relationship of Day-to-day Variations in the Ambient Concentration of SO2 to Variations in 

the Community Average 

There has been little analysis of the spatial variation of SO2 across communities. SO2 emissions 

arise mainly from coal fired power plants (see Annex Table B-4). Newer power plants and smelters in the 

U.S. are no longer located within urban centers. However, some older power plants and industrial 

facilities are located in many urban areas, especially in the Midwest and Northeast. Downwash from the 

plumes emitted from these facilities can contribute to elevated levels of SO2 at the surface in these cities. 

However, it is anticipated that SO2 will behave largely as a regional pollutant in most areas. 

Site-to-site correlations of SO2 concentrations, as shown for several cities in Table 2-9 vary from 

very low to very high values. This suggests that the concentration of SO2, measured at any given 

monitoring site, may not be highly correlated with the avg community concentration in some areas. There 

are a number of possible reasons for these findings: local sources that cause the SO2 to be unevenly 

distributed spatially; a monitoring site being chosen to represent a nearby source; terrain features that 

 2-61 
divide the community into several sub-communities that differ in the spatial and temporal pattern of 

pollution; and errors in the measurement of the low concentrations of SO2 present at most sites. To the 

extent that the correlation of the ambient concentration with the community avg concentration is < 1, β

will be reduced. Similarly, β will be reduced if there are subareas of the community where the correlation 

between the local avg concentrations and the concentrations measured at the ambient monitoring site is 

< 1. If concentrations in an area of a community impacted by plumes from local SO2 sources might be 

higher than, and not well-correlated with, the concentrations at the ambient monitor, and if such high 

concentrations affect a sizable portion of the population exposed to emissions from a local source, that 

community might not be suitable for time-series epidemiologic analyses. On the other hand, if the plume 

impacts the ambient monitor, the high concentration of SO2 not accompanied by a corresponding high 

effect in the entire community will bias β toward the null. 

An additional complication will arise if location near a source is correlated with sensitivity. For 

example, if poverty causes sensitivity due to poor nutrition, and land prices decline the closer one gets to 

a major source, then exposure and sensitivity will be correlated. If the day to day variations in the 

concentrations due to the source are correlated with the day to day variations in the ambient 

concentrations used in the epidemiologic analysis, β will be increased above the β that would be found if 

the exposure and sensitivity were not correlated. However, it is more likely that the variations in the 

concentrations related to the source are not correlated with the ambient concentrations. In this case the 

health effects from the source SO2 would not contribute to β and β would be decreased compared to a 

uniform distribution of sensitivity. 

Relationship of Community Average Concentration of SO2 to Average Personal Exposure to 

Ambient SO2 

People spend much of their time indoors, and in the absence of indoor sources, indoor 

concentrations are lower than outdoor concentrations. This is very likely the case with SO2, since the only 

known significant indoor source of SO2 in the U.S. is the use of kerosene heaters, which is not thought to 

be widespread enough to influence population studies. Differences in infiltration factors among homes 

can also result in differences among individuals’ personal exposures. It is necessary to consider how this 

difference between the ambient concentration, which is used in epidemiologic analyses, and the personal 

ambient exposure (which includes exposure to the full outdoor concentration while outdoors and exposure 

of only a fraction of the outdoor concentrations while indoors) will affect the calculated β. Personal 

exposure to ambient SO2 is given by Ea = α • Ca where Ea is exposure to ambient SO2, α is the ambient 

exposure factor with values between 0 and 1, and Ca is the ambient SO2 concentration as measured at a 

community monitoring site. Zeger et al. (2000) noted that for community time-series epidemiology, which 

analyzes the association between health effects and potential causal factors at the community scale rather 

than the individual scale, it is the correlation of the daily avg ambient concentrations with the daily 

community average personal exposures that is important, not the correlation between the daily avg 

ambient concentrations and the individual personal exposures. Thus, as mentioned in Section 2.6.3, the 

low correlation between daily avg ambient concentrations and individual personal exposures, as 

frequently found in pooled panel exposure studies, is not relevant to community time-series 

epidemiologic analysis. Unfortunately, no studies provide adequate information about the community avg 

personal exposure to SO2. 

There has also been concern with the variation of α. Zeger et al. (2000) suggested (for PM) that 

variations in the individual daily values of α would be a Berkson error and would not change the point 

estimate of β, although such variations would increase the standard error of β. Sheppard et al. (2005) used 

simulations to confirm this for nonreactive pollutants. However, such variations increase the standard 

error. Day-to-day variations in the population avg fraction of ambient exposure will not change the point 

estimate of β unless the population avg fraction of ambient exposure is correlated with seasonal trends in 

ambient concentration, according to Sheppard et al. (2005). 

 2-62 
In the case of a correlation of location near a source with sensitivity, the health effects of the higher 

concentrations will be attributed to the avg concentration and β will be increased. A similar but lesser 

effect on β will occur anytime there are high concentrations resulting from localized sources that are not 

included in the long-term avg concentration and that vary from city to city. 

Both Zeger et al. (2000) and Sheppard et al. (2005) show that if βA

 is the health effect parameter 

that would be obtained with a time-series analysis using the ambient exposure and βC is the health effect 

parameter that would be obtained with a time-series analysis using the ambient concentration, then βC

 = 

α • βA

. Thus, time-series studies yield different parameters depending on whether they use concentration 

or exposure. However, the two parameters are related by α. 

2.6.4.2. Short-Term Panel Studies 

Panel epidemiology refers to studies that follow a relatively small number of subjects for a 

relatively short time. Panel studies typically examine the association between symptoms or health 

outcomes of individuals and either ambient concentrations or personal exposures. Personal exposures to 

SO2 usually are not measured. Rather, ambient concentrations are more often used in panel studies. 

Similar types of exposure error as discussed for community time series apply to panel studies. 

The ambient exposure factor (α) may differ for each person and each day leading to error in the 

exposure estimate. If a panel is composed of subjects who live in similar housing and have similar activity 

pattern, and the study is limited to a single season, the variation in α over time and individual subjects 

may be small. However, if the panels are composed of more diverse subjects or extend for more than one 

season, values of α may be quite variable. Such variability will affect the estimate of exposure for each 

subject. 

2.6.4.3. Long-Term Cohort Studies 

For long-term exposure epidemiologic studies, concentrations are integrated over time periods of a 

year or more and usually for spatial areas the size of a city, county, or metropolitan statistical area (MSA), 

although integration over smaller areas may be feasible. These studies focus on spatial variations in 

concentrations. Health effects are then regressed in a statistical model against the avg concentrations in 

the series of cities (or other areas). In time-series studies, a constant difference between the measured and 

the true concentration (instrument offset) will not affect β, nor will variations in the daily average α or the 

daily avg nonambient exposure, unless the variations are correlated with the daily variations in 

concentrations. However, in long-term exposure epidemiologic studies, if instrument measurement errors, 

long-term avg values of α, or long-term averages of nonambient exposure differ for different cities (or 

other areas used in the analysis), the city-to-city long-term ambient SO2 concentrations will not be 

perfectly correlated with the long-term avg exposure to either ambient or total SO2. This lack of 

correlation would be expected to bias the point estimate of β. 

2.6.4.4. Summary of Evaluation of Exposure Error in Epidemiologic Studies 

Exposure error caused by using ambient concentrations of SO2 as a surrogate for exposure to 

ambient SO2 affect β in different ways, depending upon the type of epidemiologic study. In community 

time-series and short-term panel epidemiologic studies, the nonambient source component of personal 

exposure and the variation in the ambient exposure factor caused by building ventilation practices and 

personal behaviors generally will not change the estimate of β. But, the spatial variation of SO2 or the 

representativeness of the ambient monitor might bias the estimate of β toward null. Therefore, β observed 

in SO2 community time-series or panel epidemiologic studies would be stronger and less uncertain if 

exposure errors had been adjusted and/or controlled. In long-term cohort epidemiologic studies, 

 2-63 
instrument measurement errors, factors that influence exposure to ambient SO2, or long-term averages of 

nonambient exposure may differ for different cities, which may bias the estimate of β, but the extent and 

direction of this bias is unclear. 

2.7. Dosimetry of Inhaled Sulfur Oxides 

This section is intended to present an overview of general concepts related to the dosimetry of SO2

in the respiratory tract. Dosimetry of SO2 refers to the measurement or estimation of the amount of SO2 or 

its reaction products reaching and persisting at specific respiratory tract and systemic sites after exposure. 

One of the principal effects of inhaled SO2 is that it stimulates bronchial epithelial irritant receptors and 

initiates a reflexive contraction of smooth muscles in the bronchial airways. The compound most directly 

responsible for health effects may be the inhaled SO2, and/or its chemical reaction products. Complete 

identification of the causative agents and their integration into SO2 dosimetry is a complex issue that has 

not been thoroughly evaluated. Few studies have investigated SO2 dosimetry in the interval since the 1982 

AQCD and the 1986 Second Addendum. 

2.7.1. Respiratory Gas Deposition 

The major factors affecting the transport and fate of gases and aerosols in the respiratory tract are: 

the morphology of the respiratory tract; the physiochemical properties of the mucus and surfactant layers; 

respiratory functional parameters such as tidal volume, flow rate, and route of breathing; physicochemical 

properties of the gas; and the physical processes that govern gas transport. Physicochemical properties of 

SO2 relevant to respiratory tract uptake include its solubility and diffusivity in epithelial lining fluid 

(ELF), as well as its reaction-rate with ELF constituents. Henry’s law relates the gas phase and liquid 

phase interfacial concentrations at equilibrium, and is a function of temperature and pressure. Henry’s law 

shows that the amount of SO2 in the aqueous phase is directly proportional to the partial pressure or 

concentration of SO2 in the gas phase. Although the solubility of most gases in mucus and surfactant is 

not known, the Henry’s law constant is known for many gases in water. Inversely proportional to 

solubility, the Henry’s law constant for SO2 is 0.048 (mole/liter) air / (mole/liter) water at 37° C and 1 

atm. For comparison, the value for O3 is 6.4 under the same conditions (Kimbell and Miller, 1999). In 

general, the more soluble a gas is in biological fluids, the more rapid and proximal its absorption will be 

in the respiratory tract. When the partial pressure of SO2 on mucosal surfaces exceeds that of the gas 

phase, such as during expiration, some desorption of SO2 from the ELF may be expected. 

Because SO2 is highly soluble in water, it is expected to be almost completely absorbed in the nasal 

passages of both humans and laboratory animals under resting conditions. The dosimetry of SO2 can be 

contrasted with the lower solubility gas, O3, for which the predicted tissue doses (O3 flux to liquid-tissue 

interface) are very low in the trachea and increase to a maximum in the terminal bronchioles or first 

airway generation in the pulmonary region (see Chapter 4, U.S. EPA, 2006b). Similar to O3, the nasal 

passages remove SO2 more efficiently than the oral pathway (Brain, 1970; Melville, 1970; Nodelman and 

Ultman, 1999). With exercise, the pattern of SO2 absorption shifts from the upper airways to the 

tracheobronchial airways in conjunction with a shift from nasal to oronasal breathing and increased 

ventilatory rates. As a result of its effect on delivery and uptake, mode of breathing is also recognized as 

an important determinant of the severity of SO2-induced bronchoconstriction. The greatest responses 

occur during oral breathing followed by oronasal breathing, and the smallest responses were observed 

during nasal breathing. 

Andersen et al. (1974) measured the nasal absorption of SO2 (25 ppm) in 7 volunteers during 

inspiration at an avg inspired flow of 23 L/min (i.e., eucapnic hyperpnea [presumably] to simulate light 

 2-64 
exertion). These investigators reported that the oropharyngeal SO2 concentration was below their limit of 

detection (0.25 ppm), implying that at least 99% of SO2 was absorbed in the nose of subjects during 

inspiration. Speizer and Frank (1966) also measured the absorption of SO2 (16.1 ppm) in 7 healthy 

subjects at an avg ventilation of 8.5 L/min (i.e., at rest). They reported that 14% of the inhaled SO2 was 

absorbed within the first 2 cm into the nose. The concentration of SO2 reaching the pharynx was below 

the limit of detection, suggesting that at least 99% was absorbed during inspiration. Melville (1970) 

measured the absorption of SO2 (1.5 to 3.4 ppm) during nasal and oral breathing in 12 healthy volunteers. 

Total respiratory tract absorption of SO2 was significantly greater (p < 0.01) during nasal than oral 

breathing (85 versus 70%, respectively) and was independent of the inspired concentration. Melville 

(1970) did not report respired flow rates, so the effect of flow on the SO2 absorption could not be 

discerned. However, it may be noted that the total respiratory absorption during nasal breathing reported 

by Melville (1970) was clearly less than the nasal absorption reported by both Andersen et al. (1974) and 

Speizer and Frank (1966). The disparity in nasal absorption between these studies is, in part, due to 

desorption of SO2 during expiration as discussed in Section 2.7.3. 

Frank et al. (1969) and Brain (1970) investigated the oral and nasal absorption of SO2 in the 

surgically isolated upper respiratory tract of anesthetized dogs. Radiolabeled SO2 (35SO2) at the 

concentrations of 1, 10, and 50 ppm was passed separately through the nose and mouth at the steady 

unidirectional flows of 3.5 and 35 L/min for 5 min. The nasal absorption of SO2 (1 ppm) was 99.9% at 3.5 

L/min and 96.8% at 35 L/min. The oral absorption of SO2 (1 ppm) was 99.56% at 3.5 L/min, but only 

34% at 35 L/min. The nasal absorption of SO2 at 3.5 L/min increased with concentration at 1, 10, and 50 

ppm and was reported to be 99.9, 99.99, and 99.999%, respectively. This increase in absorption with 

concentration was hypothesized to be due to increased mucus secretion and increased nasal resistance at 

the higher SO2 concentrations. The increased mucus was thought to provide a larger reservoir for SO2

uptake. The increased nasal resistance may increase turbulence in the airflow and, thereby, decrease the 

boundary layer between the gas and liquid phases. Dissimilar to the nose, SO2 absorption in the mouth 

decreased from 99.56 to 96.3% when the concentration was increased from 1 to 10 ppm at 3.5 L/min. 

Frank et al. (1969) noted that the aperture of the mouth may vary considerably, and that this variation may 

affect SO2 uptake in the mouth. Although SO2 absorption was dependent on inhaled concentration, the 

rate and route of flow had a greater effect on the magnitude of SO2 absorption in the upper airways. 

Strandberg (1964) studied the uptake of SO2 in the respiratory tract of rabbits. A tracheal cannula 

with two outlets was utilized to allow sampling of inspired and expired air, and SO2 absorption was 

observed to depend on inhaled concentration. The absorption during maximal inspiration was 95% at high 

concentrations (100 to 700 ppm), reflecting an increased SO2 removal in the extrathoracic (ET) region, 

whereas it was only 40% at low concentrations (0.05 to 0.1 ppm). On expiration, the total SO2 absorbed 

(i.e., inspiratory removal in the ET region plus removal in the lower airways) was 98% at high 

concentrations and only 80% at the lower concentrations. 

Amdur (1966) examined changes in airway resistance in guinea pigs due to SO2 exposure. Guinea 

pigs were exposed for 1-h to 0.1 to 800 ppm SO2 during natural unencumbered breathing or to 0.4 to 100 

ppm while breathing through a tracheal cannula. At concentrations of 0.4- to 0.5 ppm SO2, route of 

administration did not affect the airway resistance response, whereas at concentrations of > 2 ppm, the 

responses were greater in animals exposed by tracheal cannula. Based on the concentration-dependent 

absorption of SO2 in the ET region observed by Strandberg (1964), Amdur (1966) concluded that the 

airway resistance responses at low-exposure concentrations were independent of method of 

administration, because the lung received nearly the same concentration with or without the cannula as 

evidenced by minimal ET absorption. 

More recently, Ben-Jebria et al. (1990) investigated the absorption of SO2 in excised porcine 

tracheae. Absorption was monitored over a 30-min period following the introduction of SO2 (0.1 to 0.6 

ppm, inlet concentration) at a constant flow (2.7 to 11 L/min). The data were analyzed using diffusionreactor theory. An overall mass transfer coefficient (KSO2) was determined and separated into its 

contributions due to gas (convection and diffusion) and tissue phase (diffusivity, solubility, and reaction 

rates) resistances. SO2 in the liquid phase was assumed to form HSO3

−

 rapidly, in proportion with the gas 

 2-65 
phase SO2 concentration. HSO3

−

 then diffused down the concentration gradient into the tissues where it 

reacted irreversibly with biochemical substrates. Initially, KSO2 was limited only by gas phase resistance, 

but it decreased exponentially over the first 5 to 10 min of SO2 exposure to a smaller steady-state value 

that was due to tissue resistance to SO2 absorption. The initial and steady-state KSO2 values were found 

to be independent of inlet SO2 concentration, i.e., for a given flow, the fractional absorption of SO2 did 

not depend on SO2 concentration. An increased KSO2 (initial and steady-state) was observed with an 

increasing flow that was thought to be due to a decrease in the boundary layer near the walls of the 

trachea for radial SO2 transport. This is in agreement with Aharonson et al. (1974), who also reported that 

the transfer rate coefficient for SO2 increases with increasing flow. However, the initial molar flux of SO2

across the gas-tissue interface appears to increase purely as a function of the increase in mass transport 

occurring with increasing flow (see Figure 5 in Ben-Jebria et al., 1990). Given that the steady-state KSO2

remained stable during the 10 to 30 min of exposure and that no SO2 leakage through the tissue was 

identified, the authors concluded that there was an irreversible sink for SO2 within the tissue. 

Mathematical modeling specific to the regional respiratory uptake of SO2 is unavailable for humans 

and laboratory animals. More generally, the influence of age on gas dosimetry in humans during light 

activity (on average) was examined by Ginsberg et al. (2005) using the U.S. EPA reference concentration 

methodology (U.S. EPA, 1994a). For a highly soluble gas, such as SO2, they predicted that the majority of 

gas uptake would occur in the ET region and that the fractional uptake in these airways would be 

modestly greater in a 3-month-old infant than an adult. The rate of gas uptake per surface, however, in the 

ET region and large bronchial airways was not markedly different between infants and adults. The smaller 

bronchial airways of adults were predicted to receive a greater dose (i.e., uptake per unit time and surface 

area) relative to infants, although the majority of the inhaled SO2 would be removed proximal to these 

airways. 

In summary, inhaled SO2 is readily absorbed in the upper airways of both humans and laboratory 

animals. During nasal breathing, the majority of available data suggests 95% or greater SO2 absorption 

occurs in the nasal passages, even under ventilation levels comparable to exercise. Somewhat less SO2 is 

absorbed in the oral passage than in the nasal passages. The difference in SO2 absorption between the 

mouth and the nose is highly dependent on respired flow rates. With an increase in flow from 3.5 to 35 

L/min, nasal absorption is relatively unaffected, whereas oral absorption is reduced from 100 to 34%. 

Thus, the rate and route of breathing have a great effect on the magnitude of SO2 absorption in the upper 

airways and so the penetration of SO2 to the lower airways. Overall, the available data clearly show that 

the pattern of SO2 absorption that shifts from the upper airways to the tracheobronchial airways in 

conjunction with a shift from nasal to oronasal breathing and associated increased ventilatory rates in 

exercising humans. Mode of breathing is also recognized as an important determinant of the severity of 

SO2 induced bronchoconstriction, with the greatest responses occurring during oral breathing followed by 

oronasal breathing and the smallest responses observed during nasal breathing. 

2.7.2. Particles and Sulfur Oxide Mixtures 

As already discussed, inhaled SO2 is readily absorbed in the upper airways, particularly during 

nasal breathing. It has been suggested that SOX may become absorbed to particles and subsequently 

transported to more distal lung regions. Depending on atmospheric conditions, SO2 can be transformed to 

secondary sulfate particles and acid aerosols (H2SO4) and can adsorb onto particulate matter (see Section 

2.2). Jakab et al. (1996) observed that the conversion of SO2 to SO4

2−

 on the surface of carbon black 

aerosols was dependent on high relative humidity (≥ 85%) and SO2 concentration. These investigators 

suggested that fine carbon black particles can be an effective vector for delivery of SO4

2−

 to the peripheral 

lung. This is not believed to be a mechanism for bringing sulfite into the deep lung. Other studies 

investigating the effects of SO2 coated aerosols are briefly discussed in Section 3.1.4.7. 

 2-66 
Sulfate aerosols are hygroscopic and grow in the respiratory tract. The implications of hygroscopic 

growth on deposition have been reviewed extensively by Morrow (1986) and Hiller (1991). In general, 

compared to nonhygroscopic particles of the same initial size, the deposition of hygroscopic aerosols in 

different regions of the lung may be higher or lower, depending on the initial size. For particles with 

initial sizes larger than 0.5 μm (aerodynamic diameter), the influence of hygroscopicity would be to 

increase total deposition with a shift in regional deposition from the distal to larger proximal airways; for 

smaller particles deposition would tend to decrease. A thorough review of respiratory deposition and 

clearance of particulate matter is available elsewhere (U.S. EPA, 1996, 2004). The intent herein was to 

briefly mention some issues specific to SOx. 

2.7.3. Distribution and Elimination of SOX

When SO2 contacts the fluids lining the airway, it dissolves into the aqueous fluid and forms 

hydrogen (H+

) ions and bisulfite (HSO3

−

) and sulfite (SO3

2−

) anions (ATS, “Health effects of outdoor air 

pollution. Committee of the Environmental and Occupational Health Assembly of the American Thoracic 

Society,” 1996). The majority of anions are expected to be present as HSO3

−

 at a concentration 

proportional to the gas phase concentration of SO2 (Ben-Jebria et al., 1990). Because of the chemical 

reactivity of these anions, various reactions are possible, leading to the oxidation of SO3

2−

 to SO4

2−

 (see 

Section 12.2.1, U.S. EPA, 1982). Clearance of SO3

2−

 from the respiratory tract may involve several 

intermediate chemical reactions and transformations. Gunnison and Benton (1971) identified S-sulfonate 

in blood as a reaction product of inhaled SO2. Following inhalation of SO2, the clearance half-time of 4.1 

days for S-sulfonate in rabbits has been reported (Gunnison and Palmes, 1973). 

Some SO2 is also removed by desorption of from the respiratory tract. Desorption is expected when 

the partial pressure of SO2 in airway lining fluids exceeds that of the air. Speizer and Frank (1966) found 

that on expiration, 12% of the SO2 absorbed during inspiration was desorbed into the expired air. During 

the first 15 min after the 25- to 30-min SO2 exposure, another 3% was desorbed. In total, 15% of the 

amount of SO2 originally inspired and absorbed was desorbed from the nasal mucosa. Frank et al. (1969) 

reported that up to 18% of the SO2 was desorbed within ~10 min after exposure. 

 2-67 
Chapter 3. Integrated Health Effects 

This integrated discussion is structured to provide a coherent framework for the assessment of 

health risks associated with human exposure to ambient SO2 in the U.S.. The main goals of this chapter 

are: (1) to integrate newly available epidemiologic, human clinical, and animal toxicological evidence 

with consideration of key findings and conclusions from the 1982 AQCD for Sulfur Oxides and First 

Addendum (U.S. EPA, 1982), 1986 Second Addendum (U.S. EPA, 1986b), and 1994 Supplement to the 

Second Addendum, (U.S. EPA, 1994c); and (2) to draw conclusions about the causal role of SO2 in 

relation to a variety of health effects. These causal determinations utilize the framework outlined in 

Chapter 1. 

This chapter is organized to present morbidity and mortality associated with short-term exposures 

to SO2, followed by morbidity and mortality associated with long-term exposures. Human clinical studies 

examining the effect of peak exposures (less than 1-h, generally 5-10 min) of SO2 on respiratory 

symptoms and lung function are discussed first. Later sections describe the findings of epidemiologic 

studies that examine the association between short-term (generally 24-h avg) and long-term (generally 

months to years) ambient SO2 exposure and health outcomes, such as respiratory symptoms in children 

and asthmatics, emergency department (ED) visits and hospital admissions for respiratory and 

cardiovascular diseases, and premature mortality. The human clinical and epidemiologic evidence are 

presented with relevant animal toxicological data, when available. 

The evaluation of epidemiologic evidence involves consideration of sources of uncertainty, as 

discussed in Chapter 1, including exposure error, potential confounders or effect modifiers, statistical 

modeling issues, publication bias, and multiple testing. Efforts have been made to assess the impact of 

these uncertainties in the evaluation of the epidemiologic literature. For example, in studies examining 

multiple single-day lag models, the pattern of association across the various lags was evaluated. 

Additional focus was placed on results from distributed and moving avg lags as they are able to examine 

multiday effects. Both single- and multiple-pollutant models were considered and examined for 

robustness of results. Additional analyses of multiple model specifications for adjustment of temporal or 

meteorological trends were regarded as sensitivity analyses. Further, the evaluation of the epidemiologic 

evidence also considered study population and sample size, with particular emphasis placed on multicity 

studies that by their very nature can reduce uncertainty related to publication bias. Other factors 

considered were study location (North America versus other regions), meaningfulness and validity of the 

health endpoint measurements, and appropriateness of the statistical analyses methods used. These 

considerations led to emphasis of certain studies in the chapter text, tables, and figures. 

Animal toxicological studies may provide further evidence for the potential mechanism of an 

observed effect; however, most of these studies have been conducted at concentrations vastly exceeding 

current ambient conditions. In discussing the mechanisms of SOX toxicity, studies conducted under 

atmospherically relevant conditions are emphasized whenever possible; studies at higher levels are also 

considered, due to species-to-species differences and potential differences in sensitivity between study 

subjects and especially susceptible human populations. 

This chapter focuses on important recent scientific studies, with emphasis on those conducted at or 

near current ambient concentrations. Given their respective strengths and limitations, evidence from 

human clinical, epidemiologic, and animal toxicological studies was considered in order to evaluate the 

causality of SOX–health effects associations. The annexes supplement the information included here by 

presenting more details of the literature. 

 3-1 
3.1. Respiratory Morbidity Associated with Short-Term 

Exposure 

3.1.1. Summary of Findings from the Previous Review 

The majority of the SO2 human clinical studies discussed in the 1982 AQCD for SOX evaluated 

respiratory effects of SO2 exposure in healthy adults, with some limited data from clinical studies of 

adults with asthma. SO2-related respiratory effects such as increased airway resistance and decreased 

forced expiratory volume in 1 s (FEV1) were observed in healthy individuals at concentrations > 1.0-5.0 

ppm, and in asthmatics at concentrations < 1.0 ppm. The 1986 Second Addendum (U.S. EPA, 1986b) and 

1994 Supplement to the Second Addendum (U.S. EPA, 1994c) reviewed several additional controlled 

studies involving both healthy and asthmatic individuals. In general, these studies found no pulmonary 

effects of SO2 exposure in healthy subjects exposed to concentrations ≤ 1.0 ppm (Bedi et al., 1984; 

Folinsbee et al., 1985; Kulle et al., 1984; Stacy et al., 1983). However, in exposures of asthmatic adults, 

respiratory effects were observed following short-term exposures (5-10 min) to levels < 1.0 ppm (Balmes 

et al., 1987; Horstman et al., 1986; Linn et al., 1987). 

Only a few epidemiologic studies reviewed in the 1982 AQCD were useful in determining the 

concentration-response relationship of respiratory health effects from short-term exposure to SO2. The 

most notable study was by Lawther et al. (1970), which examined the association between air pollution 

and worsening health status in bronchitic patients residing in London, England. It was concluded in the 

1982 AQCD that worsening of health status among chronic bronchitic patients was associated with daily 

black smoke (BS) levels of 250-500 μg/m3

 in the presence of SO2 levels in the range of 191-229 ppb. In 

the 1986 Second Addendum, additional studies investigated morbidity associated with short-term 

exposure to SO2. The most relevant study was by Dockery et al. (1982), which examined pulmonary 

function in school children in Steubenville, OH, as part of the Harvard Six Cities Study. This study found 

that small but statistically significant reversible decrements in forced vital capacity (FVC) and forced 

expiratory volume in 0.75 s (FEV0.75) were associated with increases in 24-h avg concentrations of total 

suspended particles (TSP) at levels ranging up to 220-420 μg/m3

 and SO2 at levels ranging up to 107-

176 ppb. However, it was impossible to separate the relative contributions of TSP and SO2, and no 

threshold level for the observed effects could be discerned from the wide range of exposure levels. 

Epidemiologic evidence for an association between SO2 and respiratory morbidity, as indicated by 

increased use of ED facilities or increased hospital admissions for respiratory diseases, was also reported 

in the 1982 AQCD. Overall, these results suggested increased upper respiratory tract morbidity during 

episodic marked elevations of PM or SO2 (400-500 ppb), especially among older adults. The 1982 AQCD 

further concluded that the studies reviewed provided essentially no evidence for an association between 

asthma attacks and acute exposures at typical ambient PM or SO2 levels in the U.S. (the mean annual avg 

SO2 concentrations from 1972 to 1977 was approximately 6 ppb, with 90th percentile values ranging from 

15 to 20 ppb). 

The 1982 AQCD reported numerous effects on the respiratory system in animals exposed to SO2. 

Effects were generally observed at levels exceeding those found in the ambient environment, and 

included morphological changes, altered pulmonary function, lipid peroxidation, and changes in host lung 

defenses. The immediate effect of acute SO2 exposure in animals was increased pulmonary resistance to 

airflow, a measure of bronchoconstriction. Bronchoconstriction was reported to be the most sensitive 

indicator of lung function effects. 

Collectively, the human clinical, epidemiologic, and animal toxicological, studies provided 

biological plausibility and coherent evidence of an adverse effect of ambient SO2 on respiratory health. 

Since the 1982 AQCD, 1986 Second Addendum, and 1994 Supplement to the Second Addendum, 

additional studies have been conducted to determine the relationship between short-term exposures to 

 3-2 
ambient SO2 and adverse respiratory health effects, including respiratory symptoms, lung function, airway 

inflammation, airway hyperresponsiveness (AHR), lung host defenses, and ED visits and hospitalizations 

for respiratory causes. The epidemiologic, human clinical, and animal toxicological evidence on the 

effects of SO2 on these various endpoints are discussed below. The findings of the previous review are 

integrated below with the current literature. 

3.1.2. Potential Mode of Action for Respiratory Health Effects 

The 1982 AQCD (U.S. EPA, 1982) gave background information on the biochemistry of SO2, 

chemical reactions of bisulfite (HSO3

–

), metabolism of SO2, and the activating or inhibiting effects of 

bisulfite on various enzymes. SO2 readily dissolves in water, rapidly becoming hydrated to form sulfurous 

acid, which at physiological pH substantially dissociates to form bisulfite and sulfite (SO3

2–) ions. In vitro 

studies have shown that SO2 and/or bisulfite readily react with nucleic acids, proteins, lipids, and other 

classes of biomolecules. Bisulfite participates in three important types of reactions with biomolecules: 

sulfonation (sulfitolysis), autooxidation with generation of free radicals, and addition to cytosine. 

Products of sulfonation reactions have been shown to be long-lived in vivo and may be highly reactive. 

Products of autooxidation may be responsible for the initiation of lipid peroxidation, which, among other 

effects, could damage plasma membranes. In addition, bisulfite can react with nucleic acids to convert 

cytosine to uracil, thus resulting in mutational events. A principal mechanism of detoxification of SO2

(and sulfite/bisulfite) occurs through the enzymatic activity of sulfite oxidase, resulting in the production 

of sulfate. Sulfite oxidase is a molybdenum-containing enzyme, and the 1982 AQCD noted that depleting 

its activity in an animal model through a low-molybdenum diet supplemented with the competitive 

inhibitor tungsten resulted in a significant lowering of the LD50 for intraperitoneally injected bisulfite. It 

was also noted that while in vitro exposure to SO2 or sulfite/bisulfite had been shown to either activate or 

inhibit a variety of enzymes, no such effects had yet been demonstrated for in vivo exposure. 

As discussed in the 1982 AQCD, the immediate effect of acute SO2 exposure in animals is 

bronchoconstriction. Reactions of SO2 with respiratory tract fluids can result in the production of 

bisulfite, sulfite, and a lowering of the pH, which may be involved in the bronchoconstrictive response. It 

is now widely appreciated that bronchoconstriction following SO2 exposure is mediated by 

chemosensitive receptors in the tracheobronchial tree. Rapidly activating receptors (RARs) and sensory 

C-fiber receptors found at all levels of the respiratory tract are sensitive to irritant gases such as SO2 

(Coleridge and Coleridge, 1994; Widdicombe, 2006). Activation of these vagal afferents stimulates 

central nervous system reflexes resulting in bronchoconstriction, mucus secretion, mucosal vasodilation, 

cough, apnea followed by rapid shallow breathing, and effects on the cardiovascular system such as 

bradycardia and hypotension or hypertension (Coleridge and Coleridge, 1994; Widdicombe and Lee, 

2001; Widdicombe 2003). 

Early experiments demonstrated that SO2-induced reflexes were mediated by cholinergic 

parasympathetic pathways involving the vagus nerve and inhibited by atropine (Grunstein et al., 1977; 

Nadel et al., 1965a; 1965b). Bronchoconstriction was found to involve smooth muscle contraction since 

β-adrenergic agonists such as isoproterenol reversed the effects (Nadel et al., 1965a; 1965b). Histamine 

was also thought to be involved in SO2-induced bronchoconstriction (U.S. EPA, 1982). 

More recent experiments in animal models conducted since 1982 have demonstrated that both 

cholinergic and noncholinergic mechanisms may be involved in SO2-induced effects. In two studies 

utilizing bilateral vagotomy, vagal afferents were found to mediate the immediate ventilatory responses to 

SO2 (Wang et al., 1996), but not the prolonged bronchoconstrictor response (Barthelemy et al., 1988). 

Other studies showed that atropine failed to block SO2-induced bronchoconstriction, and that a local axon 

reflex resulting in C-fiber secretion of neuropeptides (i.e., neurogenic inflammation) was responsible for 

the effect (Atzori et al., 1992; Hajj et al., 1996). Neurogenic inflammation has been shown to play a key 

role in animal models of airway inflammatory disease (Groneberg et al., 2004). 

 3-3 
In humans, the mechanisms responsible for SO2-induced bronchoconstriction are not fully 

understood. In non-asthmatics, near complete attenuation of bronchoconstriction has been demonstrated 

using the anticholinergic agents atropine and ipratropium bromide (Snashall and Baldwin, 1982; Tan et 

al., 1982; Yildirim et al., 2005). However, in asthmatics, these same anticholinergic agents (Field et al., 

1996; Myers et al., 1986), as well as short- and long-acting β2-adrenergic agonists (Gong et al., 1996; 

Linn et al., 1988), theophylline (Koenig et al., 1992), cromolyn sodium (Myers et al., 1986), nedocromil 

sodium (Bigby and Boushey, 1993) and leukotriene receptor antagonists (Gong et al., 2001; Lazarus et al., 

1997) only partially blocked SO2-induced bronchoconstriction (see Annex Table D-1, (U.S. EPA, 1994c). 

That none of these therapies have been shown to completely attenuate the effects of SO2 implies the 

involvement of both parasympathetic pathways and inflammatory mediators in asthmatics. Strong 

evidence of this was borne out in a study by Myers et al. (1986), in which asthmatic adults were exposed 

to SO2 following pretreatment with cromolyn sodium (a mast cell stabilizer), atropine (a muscarinic 

receptor antagonist), and the two medications together. While both treatments individually provided some 

protection against the bronchoconstrictive effects of SO2, there was a much stronger and statistically 

significant effect following concurrent administration of the two medications. 

It has been proposed that inflammation contributes to the enhanced sensitivity to SO2 seen in 

asthmatics by altering autonomic responses (Tunnicliffe et al., 2001), enhancing mediator release (Tan et 

al., 1982) and/or sensitizing C-fibers and RARs (Lee and Widdicombe, 2001). Whether local axon 

reflexes also play a role in SO2-induced bronchoconstriction in asthmatics is not known (Groneberg et al., 

2004; Lee and Widdicombe, 2001; Widdicombe, 2003). However, differences in respiratory tract 

innervation between rodents and humans suggest that C-fiber mediated neurogenic inflammation may be 

unimportant in humans (Groneberg et al., 2004; Widdicombe and Lee, 2001; 2003). 

3.1.3. Respiratory Effects Associated with Peak (5-10 min) Exposure

SO2-induced respiratory effects among exercising asthmatics are well-documented, and have been 

consistently observed following peak exposures (defined here as 5-10 min exposures to relatively high 

concentrations, e.g., 0.2-1.0 ppm) (Balmes et al., 1987; Bethel et al., 1985; Horstman et al., 1986; 1988; 

Linn et al., 1984; 1987; 1988; 1990; Schachter et al., 1984; Sheppard et al., 1981). SO2-induced 

decrements in lung function have been observed in asthmatics at concentrations as low as 0.1 ppm when 

SO2 is administered via mouthpiece (Koenig et al., 1990; Sheppard et al., 1981). However, these 

exposures cannot be directly compared to exposures occurring among freely breathing subjects as a larger 

fraction of administered SO2 reaches the tracheobronchial airways during oral breathing (see Section 

2.7.1, Kirkpatrick et al., 1982; Linn et al., 1983a). Since the publication of the 1994 Supplement, several 

additional human clinical studies have been published that provide supportive evidence of SO2-induced 

decrements in lung function and increases in respiratory symptoms among exercising asthmatics (see 

Annex Table D-2). Descriptions of older studies were presented in the 1994 Supplement, and are not 

described in great detail in this document. However, based in part on recent guidance from the American 

Thoracic Society (ATS) regarding what constitutes an adverse health effect of air pollution (ATS, 2000), 

key older studies described in the 1994 Supplement were reviewed and analyzed along with studies 

published since 1994. In their official statement, the ATS concluded that an air pollution-induced shift in a 

population distribution of a given health-related endpoint (e.g., lung function in asthmatic children) 

should be considered adverse, even if this shift does not result in the immediate occurrence of illness in 

any one individual in the population. The ATS also recommended that transient loss in lung function with 

accompanying respiratory symptoms attributable to air pollution should be considered adverse. However, 

it is important to note that symptom perception is highly variable among asthmatics even during severe 

episodes of asthmatic bronchoconstriction. An asymptomatic decrease in lung function may pose a 

significant health risk to asthmatic individuals as it is less likely that these individuals will seek treatment 

(Eckert et al., 2004; Fritz et al., 2007). Therefore, whereas the conclusions in the 1994 Supplement were 

 3-4 
based on SO2 exposure concentrations which resulted in large decrements in lung function along with 

moderate to severe respiratory symptoms, the current review of data from human clinical studies focused 

on moderate to large SO2-induced decrements in lung function along with respiratory symptoms ranging 

from mild (perceptible wheeze or chest tightness) to severe (breathing distress requiring the use of a 

bronchodilator). 

3.1.3.1. Respiratory Symptoms 

The 1994 Supplement to the Second Addendum described in detail several studies that evaluated 

respiratory symptoms following controlled human exposures to SO2. Briefly, following 5-min exposures 

to 0, 0.2, 0.4, and 0.6 ppm SO2 during moderate to heavy levels of exercise (V ̇ E = 48 L/min), Linn et al. 

(1983b) reported that the severity of respiratory symptoms (i.e., cough, chest tightness, throat irritation) 

among asthmatics increased with increasing SO2 concentration. Relative to clean air exposures, exposures 

to SO2 resulted in statistically significant increases in respiratory symptoms at concentrations of 0.4 and 

0.6 ppm. In a subsequent study, Linn et al. (1987) observed a significant effect of SO2 on respiratory 

symptoms in asthmatics who were engaged in slightly lower levels of exercise (V ̇ E = 40 L/min) for a 

duration of 10 min. Clear increases in respiratory symptoms were observed at concentrations of 0.6 ppm, 

with 43% of asthmatic subjects experiencing SO2-induced symptoms. Some evidence of SO2-induced 

increases in respiratory symptoms was also demonstrated at concentrations as low as 0.4 ppm, with 15% 

of subjects experiencing symptoms (Smith, 1994). It was also observed that these symptoms abated < 1 h 

after exposure. Balmes et al. (1987) reported that 7 out of 8 asthmatic adults developed respiratory 

symptoms, including wheezing and chest tightness, following 3-min exposures to 0.5 ppm SO2 via 

mouthpiece during eucapnic hyperpnea (V ̇ E = 60 L/min). 

Additional human clinical studies published since the 1994 Supplement to the Second Addendum 

have provided support for previous conclusions regarding the effect of peak exposures to SO2 on 

respiratory symptoms. In a human clinical study of SO2-sensitive asthmatics, Gong et al. (1995) reported 

that respiratory symptoms (i.e., shortness of breath, wheeze, and chest tightness) increased with 

increasing SO2 concentration (0, 0.5, and 1.0 ppm SO2) following exposures of 10 min with varying 

levels of exercise. It was also observed that exposure to 0.5 ppm SO2 during light exercise evoked a more 

severe symptomatic response than heavy exercise in clean air. Trenga et al. (1999) observed a significant 

correlation between decreases in FEV1 and increases in respiratory symptoms following 10 min exposures 

via mouthpiece to 0.5 ppm SO2. 

3.1.3.2. Lung Function 

In controlled exposures of healthy human subjects to SO2, respiratory effects including increased 

respiration rates, decrements in peak flow, bronchoconstriction, and increased airway resistance have 

been observed at concentrations > 1 ppm (Abe, 1967; Amdur et al., 1953; Andersen et al., 1974; Frank et 

al., 1962; Lawther, 1955; 1975; Sim and Pattle, 1957; Snell and Luchsinger, 1969). SO2-induced 

decrements in lung function can be potentiated by increasing ventilation rate, either through eucapnic 

hyperpnea or by performing exercise during exposure. This effect is likely due to an increased uptake of 

SO2 resulting from both the increase in minute ventilation as well as a shift from nasal breathing to 

oronasal breathing. 

It has been clearly established that subjects with asthma are more sensitive to the respiratory effects 

of SO2 exposure than healthy individuals without asthma. Asthmatic individuals exposed to SO2

concentrations as low as 0.2-0.3 ppm for 5-10 min during exercise have been shown to experience 

moderate or greater bronchoconstriction, measured as an increase in specific airway resistance (sRaw) of 

≥ 100% or decrease in FEV1 of ≥ 15% after correction for exercise-induced responses in clean air (Bethel 

et al., 1985; Linn et al., 1983b; 1984; 1987; 1988; 1990). It has been consistently demonstrated that these 

 3-5 
decrements in lung function are more pronounced following exposures to higher concentrations of SO2

(0.4-0.6 ppm), with a greater fraction of asthmatics affected (Linn et al., 1983b; 1987; 1988; 1990; 

Magnussen et al., 1990; Roger et al., 1985). Gong et al. (1995) demonstrated a concentration-response 

relationship between SO2 and lung function by exposing 14 unmedicated, SO2-sensitive asthmatics to 0, 

0.5, and 1 ppm SO2 under 3 different levels of exercise. It was shown that increasing SO2 concentration 

had a greater effect on sRaw and FEV1 than increasing exercise level. In some cases, bronchoconstrictive 

responses to SO2 can occur in as little as 2 min after the start of exposure (Balmes et al., 1987; Horstman 

et al., 1988). SO2-induced decrements in lung function appear to be transient, and the magnitude of effect 

has not been observed to increase with repeat exposures. There is evidence of a diminished response to 

SO2 when repeat exposures (10 min) occur within 5 h of the initial exposure. However, when exposure to 

SO2 occurs during a 30-min period with continuous exercise, the response to SO2 develops rapidly and is 

maintained throughout the 30-min exposure (Kehrl et al., 1987; Linn et al., 1984; 1987). Although the 

majority of human clinical studies have been conducted at 20-25°C and 70-85% relative humidity, there is 

some evidence that the respiratory effects of SO2 are exacerbated when exposure occurs in cold or dry 

ambient conditions (Bethel et al., 1984; Linn et al., 1985b). 

Since some of the studies involving asthmatic subjects have used change in sRaw as the endpoint 

of interest while others measured changes in FEV1 or both, a comparison of FEV1 and sRaw based on 

data from Linn et al. (1987, 1990) was provided in the 1994 Supplement to the Second Addendum. Based 

on simple linear interpolation of the data from these two studies, a 100% increase in sRaw corresponded 

to a 12 to 15% decrease in FEV1 and a 200% increase in sRaw corresponded to a 25 to 30% decrease in 

FEV1. 

One of the aims of the Linn et al. (1987) study was to determine how the intensity of response 

varied with asthma severity or status. In this study, 24 normal, 21 atopic (but not asthmatic), 16 mild 

asthmatic, and 24 moderate/severe asthmatic subjects were exposed to SO2 concentrations between 0 and 

0.6 ppm. While the moderate/severe asthmatics experienced greater decrements in lung function than 

mild asthmatics following exposure to clean air during exercise, their increases in response to increasing 

SO2 concentrations were similar to those of the mild asthmatic group. Thus, it was concluded by the 

authors that respiratory response to SO2 was not strongly dependent on the clinical severity of asthma. 

However, the apparent lack of correlation between SO2 response and asthma severity should be 

interpreted with caution, since the SO2 response may have been attenuated by medication usage. 

Classification of asthma severity in this study was based on medication use to control asthma. Individuals 

who required regular medication to manage asthma were classified as “moderate/severe” asthmatics, 

while asthmatic subjects who did not use medication between episodes were classified as “mild” 

asthmatics. Three of the moderate/severe asthmatics were unable to withhold medication usage prior to 

most exposures. Trenga et al. (1999) observed that 25 out of 47 adult asthmatics experienced a drop in 

FEV1 versus baseline of between 8 and 44% (mean = 17.2%) following a 10 min mouthpiece exposure to 

0.5 ppm SO2 during moderate exercise. However, severity of asthma, as defined by medication use, was 

not shown to be a predictor of sensitivity to SO2. In a study of medication-dependent moderate 

asthmatics, Linn et al. (1990) found that normal treatment (typically regular use of a long-acting 

bronchodilator) did not prevent the airway responses to SO2 and exercise. However, SO2-induced 

bronchoconstriction was significantly reduced when normal medication was supplemented with 

administration of a short-acting beta agonist immediately preceding exposure. 

Quick-relief and long-term-control asthma medications have been shown to provide varying 

degrees of protection against the bronchoconstrictive effect of SO2 in mild and moderate asthmatics (see 

Annex Table D-1; (U.S. EPA, 1994c; Gong et al., 1996; 2001; Lazarus et al., 1997; Linn et al., 1988; 

1990; Myers et al., 1986). While no therapy has been shown to completely eliminate the respiratory 

effects of SO2 in asthmatics, some short- and long-acting asthma medications are capable of significantly 

reducing SO2-induced bronchoconstriction (Gong et al., 1996; 2001; Koenig et al., 1987; Linn et al., 

1990). However, asthma is often poorly controlled even among severe asthmatics due to inadequate drug 

therapy or poor compliance among those who are on regular medication (Rabe et al., 2004). Mild 

asthmatics, who constitute the majority of asthmatic individuals, are much less likely to use asthma 

 3-6 
medication than asthmatics with more severe disease (O’Byrne, 2007; Rabe et al., 2004). It is therefore 

reasonable to conclude that all asthmatics, mild, moderate and severe, are at high risk of experiencing 

adverse respiratory effects of SO2 exposure. 

One of the key studies discussed in the 1994 Supplement to the Second Addendum was by 

Horstman et al. (1986). In this study, 27 asthmatic subjects were exposed to concentrations of SO2

between 0 and 2 ppm SO2 for 10 min on different days under exercising conditions (V ̇ E = 42 L/min). The 

authors reported that for 22% of the subjects, the concentration of SO2 needed to produce a doubling of 

sRaw compared to clean air exposure [PC(SO2)] was < 0.5 ppm, with 2 subjects (7.4%) experiencing 

moderate decrements in lung function following exposure to concentrations of SO2 at or below 0.3 ppm 

(see Figure 3-1). For approximately 15% of the subjects, the PC(SO2) was > 2 ppm, with approximately 

35% of asthmatic subjects experiencing a doubling in sRaw versus clean air at ≤ 0.6-ppm SO2. 

SO2-induced decrements in lung function (increased sRaw and decreased FEV1) have frequently 

been associated with increases in respiratory symptoms among asthmatics (Balmes et al., 1987; Gong et 

al., 1995; Linn et al., 1983b; 1987; 1988; 1990). Linn et al. (1987) exposed 40 mild and moderate 

asthmatics during 10 min periods of exercise to 0, 0.2, 0.4, and 0.6 ppm SO2. The effect of SO2 on lung 

function and respiratory symptoms was assessed immediately following exposure, and the individualspecific results have been made available to the U.S. EPA by the study authors (Smith, 1994). Following 

exposure to 0.6 ppm SO2 and after adjusting for effects of exercise in clean air, 21 of the 40 subjects 

demonstrated moderate or greater decrements in lung function, defined as a ≥15% decrease in FEV1, a 

≥100% increase in sRaw, or both. Of these 21 responders, 14 (67%) also experienced mild to severe 

respiratory symptoms (6 mild, 6 moderate, and 2 severe). In the same study, 14 asthmatics experienced 

moderate or greater decrements in lung function at 0.4 ppm SO2, 5 of whom (36%) also experienced mild 

to moderate respiratory symptoms (2 mild, 3 moderate). Five asthmatics experienced moderate or greater 

decrements in lung function at the lowest SO2 concentration tested (0.2 ppm), with 1 of the 5 (20%) also 

experiencing mild respiratory symptoms. 

Source: Horstman et al. (1986). 

Figure 3-1. Distribution of individual airway sensitivity to SO2. Each data point represents the 

value of PC(SO2) for an individual subject. PC(SO2) is defined as the provocative 

concentration of SO2 causing a doubling of sRaw compared to clean air exposure. 

 3-7 
It has been proposed that, as in asthmatics, individuals with COPD may also be more susceptible to 

SO2-induced respiratory health effects. However, this group has not been extensively studied in human 

clinical studies. Among a group of older adults with physician-diagnosed COPD, Linn et al. (1985a) 

reported no significant effect on lung function following 15 min exposures to SO2 at concentrations of 0.4 

and 0.8 ppm. While it was concluded that older adults with COPD appear to be less sensitive to SO2 when 

compared with younger adult asthmatics, the authors suggested that the lack of response may have been 

due in part to the very low levels of exercise used in the study (V ̇ E = 18 L/min), which would result in a 

lower dose of SO2 reaching the lower airway. In contrast to studies with asthmatics, most of the subjects 

in this study regularly used bronchodilators and were permitted their use up to 4 h prior to the study. 

3.1.3.3. Airway Inflammation 

A very limited number of human clinical studies have investigated the role of airway inflammation 

in the asthmatic response following peak exposure to SO2. Gong et al. (2001) observed an SO2-induced 

increase in sputum eosinophil counts in exercising asthmatics 2 h after a 10 min exposure to 0.75 ppm 

SO2. The results of this study provide some evidence that SO2 may elicit an inflammatory response in the 

airways of asthmatics which extends beyond the short time period typically associated with SO2 effects. 

3.1.3.4. Mixtures and Interactive Effects 

The interaction of SO2 with other common air pollutants or the sequential exposure of SO2 after 

prior exposure to another pollutant can potentially modify SO2-induced respiratory effects. However, only 

a few human clinical studies have looked at the interactive effects of coexisting ambient air pollutants. In 

a human clinical study designed to simulate an ambient “acid summer haze,” Linn et al. (1997) exposed 

healthy and asthmatic children (9–12 years of age) for 4 h with intermittent exercise to a mixture of SO2

(0.1 ppm), H2SO4 (100 μg/m3

), and O3 (0.1 ppm). Compared with exposure to filtered air, exposure to the 

air pollution mixture did not result in statistically significant changes in lung function or respiratory 

symptoms. 

In a human clinical study of asthmatic adolescents (12- to 16-years-old), Koenig et al. (1983) 

evaluated changes in FEV1 following a 10-min mouthpiece exposure during moderate exercise to 

1 mg/m3

 NaCl alone and in combination with 0.5 and 1.0 ppm SO2. Statistically significant decreases of 

15 and 23% were reported in FEV1 following exposure to 1 mg/m3

 NaCl in combination with 0.5 and 

1.0 ppm SO2, respectively. No significant changes in FEV1 were observed between pre- and 

post-exposure to 1 mg/m3

 NaCl without SO2. The effect observed in this study may be the result of the 

presence of hygroscopic particles that can carry SO2 deeper into the lung. 

Koenig et al. (1990) also examined the effect of 15-min exposures to 0.1 ppm SO2 via mouthpiece 

in adolescent asthmatics engaged in moderate levels of exercise. Immediately preceding this exposure, 

subjects were exposed for 45 min to 0.12 ppm O3 during intermittent moderate exercise. Subjects also 

underwent two additional exposure sequences with the same exercise regimen: 15-min exposure to 

0.1 ppm SO2 following a 45-min exposure to clean air, and 15-min exposure to 0.12 ppm O3 following a 

45-min exposure to 0.12 ppm O3. The authors found that the change in FEV1 relative to baseline was 

significantly different following the O3-SO2 exposure (8% decrease) when compared to the change 

following the air-SO2 or O3-O3 exposures (decreases of 3% and 2%, respectively). In a more recent study 

using a mouthpiece exposure system, Trenga et al. (2001) reported that among adult asthmatics, exposure 

to O3 (0.12 ppm for 45 min) resulted in a slight increase in lung function responses to SO2 at a 

concentration of 0.25 ppm (6.5% decrease in FEV1 with pre-exposure to O3, compared with a 3.4% 

decrease in FEV1 with pre-exposure to filtered air). Hazucha and Bates (1975) demonstrated a synergistic 

effect of concurrent exposure to SO2 (0.37 ppm) and O3 (0.37 ppm) on lung function in healthy 

asthmatics; however, no such effect was observed in a similar study conducted by Bedi et al. (1979). 

 3-8 
Jörres and Magnussen (1990) and Rubinstein et al. (1990) investigated the effects of a prior NO2

exposure on SO2-induced bronchoconstriction in asthmatic adults. While Jörres and Magnussen suggested 

that prior exposure to NO2 increased the responsiveness to SO2, Rubinstein et al. did not find that NO2

exacerbated the effects of SO2. Linn et al. (1980) reported no difference in lung function or respiratory 

symptoms among a group of exercising asthmatics exposed to both clean air and a combination of NO2

(0.5 ppm) and SO2 (0.3 ppm). 

3.1.3.5. Summary of Evidence on the Effect of Peak Exposure on Respiratory Health 

Collectively, evidence from earlier studies considered in the previous review, along with a limited 

number of new human clinical studies, consistently indicates that with elevated ventilation rates a large 

percentage of asthmatic individuals (up to 60%) experience moderate or greater decrements in lung 

function, frequently accompanied by respiratory symptoms, following peak exposures to SO2 at 

concentrations of 0.4-0.6 ppm (Balmes et al., 1987; Gong et al., 1995; Horstman et al., 1986; Linn et al., 

1983b; 1987; 1988; 1990). SO2-induced decrements in lung function have also been observed at lower 

SO2 concentrations (0.2-0.3 ppm) in a smaller fraction (~5-30%) of asthmatic subjects (Bethel et al., 

1985; Linn et al., 1987; 1988; 1990; Sheppard et al., 1981). At these concentrations, SO2-induced 

decrements in lung are less likely to be accompanied by respiratory symptoms (Linn et al., 1983b; 1987; 

1988; 1990; Roger et al., 1985). These findings are consistent with the current understanding of the 

potential modes of action for respiratory health as described in Section 3.1.2. Among asthmatics, both the 

magnitude of SO2-induced decrements in lung function and the percent of individuals affected have 

consistently been shown to increase with increasing exposure to SO2 concentrations between 0.2 and 1.0 

ppm. This is summarized in Table 3-1 along with supporting evidence of SO2-induced increases in 

respiratory symptoms at various exposure concentrations. The table includes data from all studies where 

individual data are presented or have been made available by the authors (Smith, 1994). This information 

represents the response to SO2 among groups of relatively healthy asthmatics and cannot necessarily be 

extrapolated to the most sensitive asthmatics in the population who are likely more susceptible to the 

respiratory effects of exposure to SO2. 

Although the vast majority of human clinical studies involving controlled exposure to SO2 have 

been conducted in adult asthmatics, there is a relatively strong body of evidence to suggest that 

adolescents may experience many of the same respiratory effects at similar SO2 exposure concentrations 

(Koenig et al., 1981, 1983; 1987; 1988; 1990; 1992). It should be noted, however, that in all of these 

studies involving adolescents, SO2 was administered via inhalation through a mouthpiece rather than an 

exposure chamber. This exposure technique bypasses nasal absorption of SO2, likely resulting in a relative 

increase of pulmonary SO2 uptake (see Section 2.7.1) (Linn et al., 1983a). 

 3-9 
Table 3-1. Percentage of asthmatic adults in controlled human exposures experiencing SO2

induced decrements in lung function. 

Cumulative Percentage of 

Responders (Number of Subjects)1

sRaw 

≥ 100% Ç ≥ 200% Ç ≥ 300% Ç

SO2 FEV1

Conc 

(ppm) 

Exposure 

Duration 

No. 

Subj 

Ventilation 

(L/min) 

Lung 

Func ≥ 15% È ≥ 20% È ≥ 30% È Study 

Respiratory 

Symptoms: 

Supporting 

Studies 

10 min 40 ~40 sRaw 5% (2) 0 0 Linn et al. (1987)2 0.2 

10 min 40 ~40 FEV1 13% (5) 5% (2) 3% (1) Linn et al. (1987) 

5 min 19 ~50-60 sRaw 32% (6) 16% (3) 0 

5 min 9 ~80-90 sRaw 22% (2) 0 0 

Bethel et al. (1985) 

Bethel et al. (1985) 

0.25 

10 min 28 ~40 sRaw 4% (1) 0 0 Roger et al. (1985) 

10 min 20 ~50 sRaw 10% (2) 5% (1) 5% (1) Linn et al. (1988)3

10 min 21 ~50 sRaw 33% (7) 10% (2) 0 Linn et al. (1990)3

10 min 20 ~50 FEV1 15% (3) 0 0 Linn et al. (1988) 

0.3 

10 min 21 ~50 FEV1 24% (5) 14% (3) 10% (2) Linn et al. (1990) 

Limited evidence of 

SO2-induced 

increases in 

respiratory symptoms 

in some asthmatics: 

Linn et al. (1983b; 

1984; 1987; 1988; 

1990), Schachter et al. 

(1984) 

0.4 10 min 40 ~40 sRaw 23% (9) 8% (3) 3% (1) Linn et al. (1987) 

10 min 40 ~40 FEV1 30% (12) 23% (9) 13% (5) Linn et al. (1987) 

5 min 10 ~50-60 sRaw 60% (6) 40% (4) 20% (2) Bethel et al. (1983) 

10 min 28 ~40 sRaw 18% (5) 4% (1) 4% (1) Roger et al. (1985) 

0.5 

10 min 45 ~30 sRaw 36% (16) 16% (7) 13% (6) Magnussen et al. 

(1990)5

Stronger evidence 

with some statistically 

significant increases in 

respiratory symptoms: 

Balmes et al. (1987)5, 

Gong et al. (1995), 

Linn et al. (1983b; 

1987), Roger et al. 

(1985) 

10 min 40 ~40 sRaw 35% (14) 28% (11) 18% (7) Linn et al. (1987) 

10 min 20 ~50 sRaw 60% (12) 35% (7) 10% (2) Linn et al. (1988) 

10 min 21 ~50 sRaw 62% (13) 29% (6) 14% (3) Linn et al. (1990) 

10 min 40 ~40 FEV1 53% (21) 48% (19) 20% (8) Linn et al. (1987) 

10 min 20 ~50 FEV1 55% (11) 55% (11) 5% (1) Linn et al. (1988) 

0.6 

10 min 21 ~50 FEV1 43% (9) 33% (7) 14% (3) Linn et al. (1990) 

10 min 28 ~40 sRaw 50% (14) 25% (7) 14% (4) Roger et al. (1985)4 1.0 

10 min 10 ~40 sRaw 60% (6) 20% (2) 0 Kehrl et al. (1987) 

Clear and consistent 

increases in SO2-

induced respiratory 

symptoms: Linn et al. 

(1983b; 1984; 1987; 

1988; 1990), Gong et 

al. (1995), Horstman 

et al. (1988) 

1Data presented from all references from which individual data were available. Percentage of individuals who experienced greater than or equal to a 100, 200, or 300% increase in 

specific airway resistance (sRaw), or a 15, 20, or 30% decrease in FEV1. Lung function decrements are adjusted for effects of exercise in clean air (calculated as the difference between 

the percent change relative to baseline with exercise/SO2 and the percent change relative to baseline with exercise/clean air). Quality control of data was performed separately by two 

EPA staff scientists. 

2Responses of mild and moderate asthmatics reported in Linn et al. (1987) have been combined. Data reported only for the first 10 min period of exercise in the first round of exposures. 

3Analysis includes data from only mild (Linn et al., 1988) and moderate (Linn et al., 1990) asthmatics who were not receiving supplemental medication. 

4One subject was not exposed to 1.0 ppm due to excessive wheezing and chest tightness experienced at 0.5 ppm. For this subject, the values used for 0.5 ppm were also used for 1.0 

ppm under the assumption that the response at 1.0 ppm would be equal to or greater than the response at 0.5 ppm. 

5Indicates studies in which exposures were conducted using a mouthpiece rather than a chamber. 

 3-10 
3.1.4. Respiratory Effects Associated with Short-Term (≥ 1 h) 

Exposure 

3.1.4.1. Respiratory Symptoms 

Epidemiologic studies have examined the association between ambient SO2 concentrations and 

respiratory symptoms in both adults and children. In air pollution field studies, respiratory symptoms are 

usually assessed using questionnaire forms (or “daily diaries”) completed by study subjects. Questions 

address the daily experience of coughing, wheezing, shortness of breath (or difficulty breathing), 

production of phlegm, and others. 

Children 

Epidemiologic studies on respiratory symptoms published since the last review are summarized in 

Annex Table F-1; key studies are discussed in detail below. 

 Source: Mortimer et al. (2002). 

Figure 3-2. Odds ratios (95% CI) for incidence of morning asthma symptoms of 846 asthmatic 

children from the National Cooperative Inner-City Asthma Study. Effects associated 

with a 20 ppb increase in 3-h avg SO2 with a lag of 1-2 day moving average are 

presented. SO2 effect estimates from single- and multipollutant models are shown. 

The strongest epidemiologic evidence for an association between respiratory symptoms and 

exposure to ambient SO2 comes from two large U.S. multicity studies (Mortimer et al., 2002; Schildcrout 

et al., 2006). Mortimer et al. examined 846 asthmatic children from eight U.S. urban areas in the National 

Cooperative Inner-City Asthma Study (NCICAS) for summertime air pollution-related respiratory 

 3-11 
symptoms. Median 3-h avg SO2 (8 to 11 a.m.) levels ranged from 17 ppb in Detroit, MI to 37 ppb in East 

Harlem, NY. Morning symptoms were found to be most strongly associated with an avg of a 1- to 2-day 

lag of SO2 concentrations. In multipollutant models with O3 and NO2 (measured in seven cities), the SO2

association remained robust (see Figure 3-2). When PM10 was also included in the multipollutant models, 

the SO2 effect estimate decreased only slightly; however, it became nonsignificant, possibly due to 

reduced statistical power (only three of eight cities were included in the analysis adjusting for PM10) or 

collinearity resulting from adjustment of multiple pollutants (in addition to PM10, O3 and NO2 were also 

adjusted for in this model). 

In the Childhood Asthma Management Program (CAMP) study, the association between ambient 

air pollution and asthma exacerbations in children (n = 990) from eight North American cities was 

investigated (Schildcrout et al., 2006). SO2 measurements were available in seven of the eight cities. The 

median 24-h avg SO2 concentrations ranged from 2.2 ppb (interquartile range [IQR]: 1.7, 3.1) in San 

Diego, CA to 7.4 ppb (IQR: 5.3, 10.7) in St. Louis, MO. Results for the associations between asthma 

symptoms and all pollutants are shown in Figure 3-3. Analyses indicate that although SO2 was positively 

related to increased risk of asthma symptoms at all lags, only the 3-day moving avg was statistically 

significant. No associations were observed between SO2 and rescue inhaler use. Stronger associations 

were observed for CO and NO2. The effect estimates appear to be slightly larger in joint-pollutant models 

with CO or NO2, particularly at a 2-day lag, but did not change much when PM10 was jointly considered. 

Source: Schildcrout et al. (2006). 

Figure 3-3. Odds ratios (95% CI) for daily asthma symptoms of 990 asthmatic children from the 

Childhood Asthma Management Program Study. Effects associated with a 10 ppb 

increase in within-subject concentrations of 24-h avg SO2 are presented. Data 

collected from November 1993 to September 1995 were used. Results from single- and 

joint-pollutant models are shown. 

 3-12 
A longitudinal study of 1,844 schoolchildren during the summer from the Harvard Six Cities Study 

suggested that the association between SO2 and respiratory symptoms could be confounded by PM10

(Schwartz et al., 1994). The median 24-h avg SO2 concentration during this period was 4.1 ppb (10th–

90th percentile: 0.8, 17.9; max 81.9). SO2 concentrations were found to be associated with cough 

incidence and lower respiratory tract symptoms. Of the pollutants examined, PM10 had the strongest 

associations with respiratory symptoms. In two-pollutant models, the effect of PM10 was found to be 

robust to adjustment for other copollutants, while the effect of SO2 was substantially reduced after 

adjustment for PM10. Because the PM10 concentrations were correlated strongly to SO2-derived sulfate 

particles (r = 0.80), the diminution of the SO2 effect estimate may indicate that for PM10 dominated by 

fine sulfate particles, PM10 has a slightly stronger association than SO2. This study further investigated the 

concentration-response function and observed a nonlinear relationship between SO2 concentrations and 

respiratory symptoms. Though an increasing trend was observed at concentrations as low as 10 ppb, no 

statistically significant increase in the incidence of lower respiratory tract symptoms was seen until 

concentration exceeded a 24-h avg SO2 of 22 ppb. 

In the Pollution Effects on Asthmatic Children in Europe (PEACE) study, a multicenter study of 14 

cities across Europe, the effects of acute exposure to various pollutants including SO2 on the respiratory 

health of children with chronic respiratory symptoms (n = 2,010) was examined during the winter of 

1993–1994 (Roemer et al., 1998). Mean 24-h avg SO2 concentrations ranged from 1 ppb in the urban area 

of Umeå, Sweden, to 43 ppb in the urban area of Prague, Czech Republic. No associations were observed 

between SO2 and daily prevalence of respiratory symptoms or bronchodilator use at any of the single- and 

multiday lags considered. In addition, no associations were observed for any of the other pollutants 

examined. It should be noted that during the study period, there were only two major air pollution 

episodes, at the beginning and end of the study period. In the epidemiologic model, the control for time 

trend was accomplished through the use of linear and quadratic terms. Given the timing of the air 

pollution episodes, the quadratic trend term would have removed most of the air pollution effect. 

Other studies that participated in the PEACE study and analyzed results for longer periods of time 

have observed statistically significant associations between SO2 and respiratory symptoms in children. 

Van der Zee et al. (1999) looked at the association between respiratory symptoms and SO2 in 7- to 11-

year-old children (n = 633) with and without chronic respiratory symptoms in the Netherlands. Significant 

associations with lower respiratory tract symptoms and increased bronchodilator use were observed for 

SO2, as well as PM10, BS, and sulfate, in symptomatic children living in urban areas (n = 142). In a twopollutant model with PM10, the results were robust for bronchodilator use, but slightly reduced for lower 

respiratory tract symptoms. A subgroup analysis of this cohort was conducted by Boezen et al. (1999). 

They examined 7- to 11-year-old children (n = 459) in the Netherlands and tested them for AHR and 

atopy. It was hypothesized that children with AHR, as measured using a methacholine (MCh) challenge, 

and atopy, indicated by raised serum total IgE (> 60 kU/L, the median value), may be susceptible to the 

effects of air pollution. One of the strengths of this study was the use of AHR and serum IgE 

concentration to indicate susceptibility; these measurements would be less prone to error than selfreported chronic respiratory symptoms. A total of 121 children were found to have AHR and relatively 

high serum total IgE; 67 had AHR and relatively low serum total IgE, 104 had no AHR but had a 

relatively high serum total IgE concentration, and 167 were found to have neither AHR nor relatively high 

serum total IgE. For the subset of children with relatively low serum total IgE with or without AHR, no 

associations were observed between SO2 and any respiratory symptoms. However, for children with 

relatively high serum total IgE either with or without AHR, the prevalence of lower respiratory tract 

symptoms increased with increasing SO2 concentrations. For children with AHR and relatively high 

serum total IgE, the OR for the prevalence of lower respiratory tract symptoms was 1.70 (95% CI: 1.26, 

2.29) with a 5-day moving avg for every 10 ppb increase in SO2. For children without AHR but with 

relatively high serum total IgE, the OR was 1.82 (95% CI: 1.33, 2.50) with a 5-day moving avg. 

Additional studies have examined the relationship between respiratory symptoms and ambient SO2

concentrations and generally found positive associations, including two U.S. studies (Delfino et al., 

2003a; Neas et al., 1995) and several European studies (Hoek and Brunekreef, 1995; Peters et al., 1996a; 

 3-13 
Roemer et al., 1993; Segala et al., 1998; Timonen and Pekkanen, 1997). However, some did not find a 

consistent association between respiratory symptoms and SO2 concentrations (Hoek and Brunekreef, 

1993; Romieu et al., 1996). None of these studies examined possible confounding of the SO2 effect by 

copollutants. 

*Note that van der Zee et al. (1999) and Ward et al. (2002a) presented results for prevalence of cough. 

Figure 3-4. Odds ratios (95% CI) for incidence of cough among children, grouped by season. For 

single-day lag models, current day and/or previous day SO2 effects are shown, except 

for Ségala et al. (1998b), which only presented results for a 3-day lag. Multiday lag 

models represent the effect of the mean concentration from the range of days noted. 

Risk estimates are standardized per 10 ppb increase in 24-h avg SO2 level. 

Figure 3-4 and Figure 3-5 present the odds ratios for SO2-related cough, and lower respiratory tract 

or asthma symptoms, respectively, among children from epidemiologic studies published since the last 

NAAQS review. All studies that reported quantitative results with relevant data are included in the figure. 

The results for cough were somewhat variable with wide confidence intervals, as shown in Figure 3-4. 

 3-14 
The studies conducted in the summer generally indicate increased risk of cough from exposure to SO2. A 

more consistent effect of SO2 is observed on lower respiratory tract or asthma symptoms (Figure 3-5). 

Although there is some variability in the individual effect estimates, the majority of the odds ratios appear 

to be greater than one. As was the case with cough, stronger associations with lower respiratory tract or 

asthma symptoms were observed in the summer, as opposed to the winter. There was some variability 

among the different lags of exposure; however, effects were generally observed with current day or 

previous day exposure and, in some cases, with a distributed lag of 2 to 3 days. 

*Note that van der Zee et al. (1999) and Schildcrout et al. (2006) presented results for prevalence of lower respiratory tract or asthma symptoms. 

Figure 3-5. Odds ratios (95% CI) for the incidence of lower respiratory tract or asthma symptoms 

among children, grouped by season. Risk estimates are standardized per 10 ppb 

increase in 24-h avg SO2 level. For single-day lag models, current day and/or previous 

day SO2 effects are shown. Multiday lag models represent the effect of the moving 

average from the range of days noted. 

3-15 
Overall, recent epidemiologic studies provided evidence for an association between ambient SO2

exposures and increased respiratory symptoms in children, particularly those with asthma or chronic 

respiratory symptoms. Recent U.S. multicity studies observed significant associations between SO2 and 

respiratory symptoms at a median range of 17 to 37 ppb (75th percentile: ~25 to 50) across cities for 3-h 

avg SO2 (NCICAS, Mortimer et al., 2002) and 2.2 to 7.4 ppb (90th percentile: 4.4 to 14.2) for 24-h avg 

SO2 (CAMP, Schildcrout et al., 2006). However, an earlier study that examined the concentrationresponse function found that a statistically significant increase in the incidence of lower respiratory tract 

symptoms was not observed until concentrations exceeded a 24-h avg SO2 of 22 ppb, though an 

increasing trend was observed at concentrations as low as 10 ppb (Harvard Six Cities Study, Schwartz et 

al., 1994). In the limited number of studies that examined potential confounding by copollutants through 

multipollutant models, the SO2 effect was generally found to be robust after adjusting for PM and other 

copollutants. More details of the literature published since the last review are found in Annex Table F-1. 

Adults 

Compared to the number of studies conducted with children, fewer epidemiologic studies were 

performed that examined the effect of ambient SO2 exposure on respiratory symptoms in adults. Most of 

these studies focused on potentially susceptible populations, i.e., those with asthma or COPD. One of the 

larger studies was conducted by van der Zee et al. (2000) in 50- to 70-year-old adults, with (n = 266) and 

without (n = 223) chronic respiratory symptoms in the Netherlands. In adults both with and without 

chronic respiratory symptoms, no consistent associations were observed between SO2 levels and respiratory symptoms or medication use. A subgroup analysis of this cohort examining SO2-related respiratory 

symptoms in individuals with AHR and atopy was conducted by Boezen et al. (2005). The subgroup of 

individuals with elevated serum total IgE, both with (n = 48) and without (n = 112) AHR, were found to 

be more susceptible to air pollutants when contrasted with those who did not have elevated serum total 

IgE (n = 167). Significant associations were observed between previous-day 24-h avg SO2 concentrations 

and the prevalence of upper respiratory tract symptoms in those with elevated serum total IgE. Stratified 

analyses by gender indicated that, among those with AHR and elevated IgE, only males (n = 25) were at a 

higher risk for respiratory symptoms. The OR for these males was 3.54 (95% CI: 1.79, 7.07) increase in 

24-h avg SO2 for a 5-day moving avg, compared with 1.05 (95% CI: 0.59, 1.91) for the females. 

Studies by Desqueyroux et al. (2002a; 2002b) examined the association between air pollution and 

respiratory symptoms in other potentially susceptible populations, i.e., those with severe asthma (n = 60, 

mean age 55 years) and COPD (n = 39, mean age 67 years), in Paris, France. The mean 24-h avg SO2

concentration was 3 ppb (range: 1, 10) in the summer and 7 ppb (range: 1, 31) in the winter. No 

associations were observed between SO2 concentrations and the incidence of asthma attacks or episodes 

of symptom exacerbation in severe asthmatics or individuals with COPD. Among the pollutants 

considered, O3 was found to have the strongest effect in these studies. 

Several other European studies did observe an association between ambient SO2 concentrations and 

respiratory symptoms in adults with asthma or chronic bronchitis (Higgins et al., 1995; Neukirch et al., 

1998; Peters et al., 1996a). Only one of these studies examined possible confounding of the association by 

copollutants. Higgins et al. (1995) examined the effect of summertime air pollutant exposure on respiratory symptoms in 62 adults with either asthma, COPD, or both. The max 24-h avg SO2 level was 45 ppb. 

An association was observed between SO2 and symptoms of wheeze, and it remained robust after 

adjustment for O3 and NO2. The effects of PM were not examined in this study. 

Results from the epidemiologic studies examining the association between SO2 and respiratory 

symptoms in adults were generally mixed, with some showing positive associations and others finding no 

relationship at current ambient levels. There was limited epidemiologic evidence which suggested that 

atopic adults may be at increased risk for SO2-induced respiratory symptoms. The overall epidemiologic 

evidence that 24-h avg SO2 exposures at or near ambient concentrations has an effect on adults is inconclusive. However, as discussed in Section 3.1.3.1, human clinical studies have observed an effect of peak 

exposures to SO2 on respiratory symptoms, particularly among SO2-sensitive asthmatics, with 10 min 

 3-16 
exposures to SO2 concentrations as low as 0.2-0.6 ppm under exercise conditions. These effects in clinical 

studies are at levels that have sometimes been measured in ambient air for similarly short-time durations. 

3.1.4.2. Lung Function 

The 1982 AQCD reported bronchoconstriction, indicated by increased pulmonary resistance, as the 

most sensitive indicator of lung function effects of acute SO2 exposure, based on the observations of 

increased pulmonary resistance in guinea pigs that were acutely exposed to 0.16 ppm SO2. Since then, 

only a few animal toxicological studies have measured lung function at or near ambient levels of SO2. 

These recent studies, and those using higher concentrations of SO2, are summarized in Annex Table E-1. 

Increased pulmonary resistance and decreased dynamic compliance were observed in conscious guinea 

pigs exposed to 1 ppm SO2 for 1 h (Amdur et al., 1983). Effects were seen immediately after exposure 

and were not present 1 h post-exposure. No changes in tidal volume, minute volume or breathing 

frequency were found. These same investigators also exposed guinea pigs to 1 ppm SO2 for 3 h/day for 6 

days (Conner et al., 1985). No changes were observed in pulmonary function or respiratory parameters, 

i.e., diffusing capacity for CO, functional reserve capacity, vital capacity, total lung capacity, respiratory 

frequency, tidal volume, pulmonary resistance or pulmonary compliance. In another study, Barthelemy et 

al. (1988) demonstrated a 16% increase in airway resistance following a 45-min exposure of anesthetized 

rabbits to 0.5 ppm SO2 via an endotracheal tube. This latter exposure is more relevant to oronasal than 

nasal breathing. 

Children 

Most epidemiologic studies discussed in the previous section on respiratory symptoms also 

examined lung function. In these studies self-administered PEF meters were primarily used to assess lung 

function. PEF follows a circadian rhythm, with the highest values found during the afternoon and lowest 

values during the night and early morning (Borsboom et al., 1999). Therefore, these studies generally 

analyzed PEF data stratified by time of day. The epidemiologic studies on lung function are summarized 

in Annex Table F-1. 

Mortimer et al. (2002) examined 846 asthmatic children from eight U.S. urban areas in the 

NCICAS for changes in PEF related to air pollution. The mean 3-h avg SO2 was 22 ppb across the eight 

cities during the study period of June through August, 1993. No associations were observed between SO2

concentrations and morning or evening PEF. Of all the pollutants examined, including PM10, O3, and NO2, 

only O3 was associated with changes in morning PEF. 

In another U.S. study (Neas et al. 1995), 83 children from Uniontown, PA reported twice-daily PEF 

measurements during the summer of 1990. The mean daytime 12-h avg SO2 concentration was 14.5 ppb 

(max 44.9). No associations were observed between daytime 12-h avg SO2 concentrations and mean 

deviation in evening PEF, even after concentrations were weighted by the proportion of hours spent 

outdoors during the prior 12-h. Statistically significant associations were observed for O3, total sulfate 

particles, and particle-strong acidity. 

A study by van der Zee et al. (1999) observed associations between ambient SO2 concentrations 

and daily PEF measurements in 7- to 11-year-old children (n = 142) with chronic respiratory symptoms 

living in urban areas of the Netherlands. The OR for a > 10% decrement in evening PEF per 10 ppb 

increase in 24-h avg SO2 was 1.20 (95% CI: 0.97, 1.47) with same-day exposure. A greater effect was 

observed at a 2-day lag, OR = 1.40 (95% CI: 1.18, 1.67), and this effect remained robust in a twopollutant model with PM10, OR = 1.34 (95% CI: 1.08, 1.64). 

Multipollutant analyses also were conducted in a study by Chen et al. (1999), which examined the 

effects of short-term exposure to air pollution on the pulmonary function of 895 children, ages 8 to 13 

years, in three communities in Taiwan. The daytime 1-h max SO2 the day before spirometry ranged from 

0 to 72.4 ppb. In a single-pollutant model, 1-h max SO2 concentration at a 2-day lag was significantly 

 3-17 
associated with FVC, -50.80 mL (95% CI: -97.06, -4.54), or a 2.6% decline, per 40 ppb 1-h max SO2. 

However, in multipollutant models, authors noted that only O3 remained significantly associated with 

FVC and FEV1. Effect estimates for SO2 in multipollutant models were not provided. 

While additional studies have observed associations between ambient SO2 concentrations and 

changes in lung function in children (e.g., Hoek and Brunekreef, 1993; Peters et al., 1996a; Roemer et al., 

1993; Segala et al., 1998; Timonen and Pekkanen, 1997), several other studies did not find a significant 

association between SO2 and lung function parameters (e.g., Delfino et al., 2003a; Peacock et al., 2003; 

Romieu et al., 1996). In addition, within studies that did observe an association, the correlations between 

SO2 and other pollutants, particularly PM indices, were high [for example, r = 0.8-0.9 with TSP] (Peters 

et al., 1996a) making it difficult to separate the contributions of individual pollutants. 

In conclusion, while some epidemiologic studies observed a positive association between shortterm SO2 exposure and lung function in children, several others, including a large U.S. multicity study, 

did not observe such an association. The limited evaluation of potential confounding by copollutants also 

indicated mixed results. Overall, the evidence is insufficient to conclude that short-term exposure to 

ambient SO2 has an independent effect on lung function in children. 

Adults 

Only a limited number of epidemiologic studies have been conducted examining the association 

between ambient SO2 concentrations and lung function in adults, as in the case of respiratory symptoms. 

In a cross-sectional survey, Xu et al. (1991) investigated the effects of indoor and outdoor air pollutants 

on the respiratory health of 1,140 adults (aged 40 to 69 years) living in residential, industrial, and 

suburban areas of Beijing, China. The annual mean concentrations of SO2 in residential, industrial, and 

suburban areas from 1981 to 1985 were 49 ppb, 22 ppb, and 7 ppb, respectively. Log-transformed SO2

and TSP were significantly associated with reductions in FEV1 and FVC. The authors cautioned that since 

SO2 and TSP concentrations were strongly correlated, the effect of SO2 could not be separated. 

Van der Zee et al. (2000) observed an association between SO2 and morning PEF in 50- to 70-yearold adults (n = 138) with chronic respiratory symptoms living in urban areas of the Netherlands. No 

associations were observed with evening PEF. The OR for a > 20% decrement in PEF was 1.21 (95% CI: 

0.76, 1.92) per 10 ppb increase in 24-h avg SO2 with same-day exposure and 1.56 (95% CI: 1.02, 2.39) at 

a 1-day lag. No associations were observed for a > 10% decrement in PEF. The authors hypothesized that 

while SO2 level did not have much effect on PEF in most subjects, a small subgroup of individuals 

experienced fairly large PEF decrements when SO2 levels were high. No multipollutant analyses were 

conducted. 

Higgins et al. (1995) examined the association between pulmonary function and air pollution in 75 

adults with either asthma, COPD, or both. Exposure to SO2 was associated with increased variation in 

PEF, but not with mean or minimum PEF. The SO2 effects on PEF variation were robust to adjustment for 

O3 and NO2. Effects of PM were not considered. Neukirch et al. (1998) also observed associations 

between lung function and SO2 concentrations in a study of asthmatic adults in Paris, France; however, 

significant associations were found for all pollutants examined, including BS, PM13, and NO2. Other 

epidemiologic studies observed only weak relationships between ambient SO2 concentrations and lung 

function in adults (Peters et al., 1996a; Taggart et al., 1996). 

Evidence from human clinical studies clearly indicates that asthmatic individuals experience 

moderate or greater decrements in lung function, as well as increased respiratory symptoms, following 

peak exposure (5-10 min) to SO2 (Balmes et al., 1987; Gong et al., 1995; Horstman et al., 1986; Linn et 

al., 1983b; 1987). These effects were seen at peak concentrations as low as 0.2-0.6 ppm. In a human 

clinical study by Tunnicliffe et al. (2003) that evaluated the effect of 1-h exposures to 0.2 ppm SO2 in 

resting healthy and asthmatic subjects, no significant changes were observed in lung function as measured 

by FEV1, FVC, and maximal midexpiratory flow (MMEF). However, these results are not unexpected 

given that subjects were exposed while at rest. 

 3-18 
In summary, the epidemiologic studies examining adults do not provide strong evidence for an 

association between short-term exposure to ambient SO2 and lung function. While some studies did 

observe associations between SO2 exposure and decrements in lung function parameters, the strong 

correlation between SO2 and various copollutants in most studies, and the lack of evidence evaluating 

potential confounding by copollutants, limit interpretation of independent effects of SO2 on lung function. 

3.1.4.3. Airway Inflammation 

The animal toxicological studies on airway inflammation are summarized in Annex Table E-2. In 

one study, guinea pigs were exposed to 1 ppm SO2 for 3 h/day for 1-5 days and bronchoalveolar lavage 

was performed (Conner et al., 1989). No change in numbers of total cells or neutrophils in lavage fluid 

was observed over this time period. However, in two models of allergic sensitization, SO2 exposure 

increased airway inflammation. In one study Park et al. (2001a), guinea pigs were exposed to 0.1 ppm 

SO2 for 5 h/day for 5 days and sensitized with 0.1% ovalbumin aerosols for 45 min on days 3-5. One 

week later, animals were subjected to bronchial challenge with 1.0% ovalbumin and bronchoalveolar 

lavage and histopathologic examination were performed 24 h later. Results demonstrated increased 

numbers of eosinophils in lavage fluid, and an infiltration of inflammatory cells, bronchiolar epithelial 

cell damage and plugging of the airway lumen with mucus and cells in the bronchial tissues of animals 

treated with both SO2 and ovalbumin, but not in animals treated with ovalbumin or SO2 alone. In a second 

study, rats which were sensitized and challenged with ovalbumin and exposed to 2 ppm SO2 for 1 h/day 

for 7 days had an increased number of inflammatory cells in bronchoalveolar lavage fluid and an 

enhanced histopathological response compared with those treated with ovalbumin or SO2 alone (Li et al., 

2007). Similarly, ICAM-1, a protein involved in regulating inflammation, and MUC5AC, a mucin 

protein, were upregulated in lungs and trachea to a greater extent in rats treated with ovalbumin and SO2

than those treated with ovalbumin or SO2 alone. Further experiments are required to determine whether 

exposure to near ambient SO2 also enhance inflammatory responses in non-allergic and allergic rats. 

In a human clinical study, Tunnicliffe et al. (2003) measured levels of exhaled NO (eNO) in 

asthmatic and healthy adult subjects, before and after 1-h exposure to 0.2 ppm SO2 under resting 

conditions. While eNO concentrations were higher in the asthmatic than in healthy subjects, no significant 

difference was observed between pre- and postexposure in either group. 

One epidemiologic study by Adamkiewicz et al. (2004) examined eNO as a biological marker for 

inflammation in 29 older adults (median age 70.7 years) in Steubenville, OH. The mean 24-h avg SO2

concentration was 12.5 ppb (IQR 11.5). The authors reported that, while significant and robust 

associations were observed between increased daily levels of fine PM (PM2.5) and increased eNO, no 

associations were observed with any of the other pollutants examined, including SO2, NO2, and O3. 

Overall, the very limited human clinical and epidemiologic evidence is insufficient to conclude that 

exposure to SO2 at current ambient concentrations is associated with inflammation in the airway. 

However, toxicological studies indicated that repeated exposures to SO2, at concentrations as low as 0.1 

ppm in guinea pigs, may exacerbate inflammatory responses in allergic animals. 

3.1.4.4. Airway Hyperresponsiveness and Allergic Sensitization 

The toxicological studies describing SO2-induced effects on airway responsiveness and allergic 

sensitization in guinea pigs, rabbits, dogs, and sheep are summarized in Annex Table E-3. In one study, 

Amdur et al. (1988) exposed guinea pigs for 1 h to 1 ppm SO2 and measured airway responsiveness to 

acetylcholine 2 h later. No AHR was observed. In a second study, Douglas et al., (1994) found no AHR 

following a histamine challenge 24 h after exposure of rabbits to 5 ppm SO2 for 2 h. In a third study, 

exposure of sheep for 4 h to 5 ppm SO2 failed to result in AHR following carbachol (Abraham et al., 

1981). In a fourth study, a 5-min exposure to 30 ppm but not to 10 ppm SO2 resulted in AHR in dogs 

 3-19 
challenged with methacholine (Lewis and Kirchner, 1984). Collectively, these results show that a single 

exposure to SO2 at a concentration of 10 ppm or less failed to induce AHR following challenge in 4 

different animal models. 

However, two other studies demonstrated increased airway responsiveness in guinea pigs exposed 

repeatedly to SO2 and allergen. Riedel et al. (1988) studied the effect of SO2 exposure on local bronchial 

sensitization to inhaled antigen. Guinea pigs were exposed by inhalation to 0.1, 4.3 and 16.6 ppm SO2 for 

8 h/d for 5 days. During the last 3 days, SO2 exposure was followed by exposure to nebulized ovalbumin 

for 45 min. Following bronchial provocation with inhaled ovalbumin (0.1%) one week later, airway 

obstruction was measured by whole body plethysmography. In addition, specific antibodies against 

ovalbumin were measured in serum and bronchaolveolar fluids. Results show significantly higher 

bronchial obstruction in animals exposed to SO2 (at all concentration levels) with ovalbumin compared 

with animals exposed only to ovalbumin. In addition, significant increases in anti-ovalbumin IgG 

antibodies were detected in bronchoalveolar lavage fluid of animals exposed to 0.1, 4.3 and 16.6 ppm SO2 

and in serum from animals exposed to 4.3 and 16.6 ppm SO2 compared with controls exposed only to 

ovalbumin. These results demonstrate that repeated exposure to SO2 can enhance allergic sensitization in 

the guinea pig at a concentration as low as 0.1 ppm. In a second study, guinea pigs were exposed to 0.1 

ppm SO2 for 5 h/day for 5 days and sensitized with 0.1% ovalbumin aerosols for 45 min on days 3 to 5 

(Park et al., 2001a). One week later, animals were subjected to bronchial challenge with 1.0% ovalbumin 

and lung function was evaluated 24 h later by whole body plethysmography. Results demonstrated a 

significant increase in enhanced pause (Penh), a measure of airway obstruction, in animals exposed to SO2

with ovalbumin but not in animals treated with ovalbumin or SO2 alone. These experiments also indicate 

that near ambient levels of SO2 may play a role in exacerbating allergic responses in the guinea pig. 

In a human clinical study evaluating SO2-induced AHR to an inhaled allergen (house dust mite), 

Devalia et al. (1994) found that neither SO2 (0.2 ppm) nor NO2 (0.4 ppm) enhanced airway response to 

the allergen in asthmatic individuals. However, following concurrent exposure (6 h) to SO2 and NO2

while at rest, subjects did exhibit an increased response to the inhaled allergen. In a subsequent study, 

Rusznak et al. (1996) confirmed these findings and observed that the combination of SO2 and NO2

enhanced airway response to house dust mite antigen up to 48 hours post-exposure. 

A limited number of epidemiologic studies also examined the association between SO2 and AHR. 

These studies are summarized in Annex Table F-1. Søyseth et al. (1995a) investigated the effect of shortterm exposure to SO2 and fluoride on the number of capillary blood eosinophils, and the prevalence of 

AHR in schoolchildren, ages 7 to 13 yr, (n = 620) from two regions in Norway, a valley containing an 

SO2-emitting aluminum smelter (Ardal) and a similar but nonindustrialized valley (Laerdal). The median 

24-h avg SO2 concentration was 8 ppb (10th–90th percentile: 1, 33) in the exposed area and 1 ppb (10th–

90th percentile: 0, 4) in the nonindustrialized valley. The mean number of eosinophils was significantly 

greater in children living near the aluminum smelter compared to the nonindustrialized area. However, 

within children in the exposed area, a negative concentration-response relationship was observed between 

mean eosinophils and previous-day 24-h avg SO2. The observed association between SO2 and eosinophils 

was limited to atopic children. In children living in the exposed area, a statistically significant positive 

association was observed between prevalence of AHR and previous-day 24-h avg SO2 concentrations. 

Similar associations were observed for fluoride. The authors hypothesized that recent exposure to SO2

may have induced changes in the airway leading to AHR, in addition to recruitment of eosinophils to the 

airways in atopic subjects. Exposure to PM was not assessed in this study. 

A study by Taggart et al. (1996) examined the effect of summertime air pollution levels in 

northwestern England on AHR in nonsmoking, asthmatic subjects (n = 38) aged 18 to 70 years who were 

determined to be MCh reactors. Subjects were tested multiple times, for a total of 109 evaluable challenge 

tests, with a range of two to four tests per subject. The max 24-h avg SO2 concentration during the study 

period was 40 ppb. This study reported that 24-h avg SO2 levels were marginally associated with a 

decreased dose of MCh required for a 20% drop in the postsaline FEV1 (PD20FEV1). 

In summary, the animal toxicological evidence suggests that repeated exposures to SO2 at 

concentrations as low as 0.1 ppm in guinea pigs can exacerbate AHR following allergic sensitization. Two 

 3-20 
recent human clinical studies have demonstrated an increase in airway response to an inhaled allergen in 

asthmatic subjects following exposures to a combination of 0.2-ppm SO2 and 0.4-ppm NO2. These 

findings are consistent with the very limited epidemiologic evidence that suggests that exposure to SO2

may lead to AHR in atopic children and asthmatic adults. 

3.1.4.5. Respiratory Illness-Related Absences 

An additional concern has been the potential for SO2 exposure to enhance susceptibility to, or the 

severity of illness resulting from respiratory infections, especially in children. School absenteeism is used 

as an indicator of morbidity in children caused by acute conditions. Respiratory conditions are the most 

frequent cause, particularly influenza and the common childhood infectious diseases. Studies discussed in 

this section are summarized in Annex Table F-1. Park et al. (2002) examined the association between air 

pollution and school absenteeism in 1,264 first- to sixth-grade students attending school in Seoul, Korea. 

The study period extended from March 1996 to December 1999, with a mean 24-h avg SO2 concentration 

of 9.19 ppb (SD 4.61). Note that analyses were performed using Poisson Generalized Additive Model 

(GAM) with default convergence criteria. Same-day SO2 concentrations were positively associated with 

illness-related absences (16% excess risk [95% CI: 13, 22] per 10 ppb increase in 24-h avg SO2), but 

inversely associated with non-illness–related absences (9% decrease [95% CI: 2, 15]). PM10 and O3

concentrations also were positively associated with illness-related absences. In two-pollutant models 

containing SO2 and either PM10 or O3, the SO2 estimates were robust. 

A study by Pönka (1990) observed results that were consistent with those from the Park et al. 

(2002) study. Pönka found that absenteeism due to febrile illnesses among children in day care centers 

and schools, and in adults was significantly higher on days of higher SO2 concentrations (> 8.1 ppb 

weekly mean of 1-h avg), compared to days of lower SO2 concentrations in Helsinki, Finland. In addition, 

on days of higher SO2 concentrations, the mean weekly number of cases of upper respiratory tract 

infections and tonsillitis reported from health centers increased. Temperature, but not NO2, was also found 

to be associated with febrile illnesses and respiratory tract infections. From these epidemiologic studies, it 

is unknown whether SO2 increases susceptibility to infection or whether its presence exacerbates 

morbidity following infection. 

Pino et al. (2004) examined the association between air pollution and respiratory illnesses in a 

cohort of 504 infants recruited at 4 months of age from primary health care units in southeastern Santiago, 

Chile. The infants were followed through the first year of life. The mean 24-h avg SO2 concentration was 

11.6 ppb (5th–95th percentile: 3.0, 29.0). The most frequent diagnosis during follow-up was wheezing 

bronchitis. No associations were observed between current-day or previous-day SO2 and wheezing 

bronchitis, but with a 7-day lag, a 21% (95% CI: 8, 39) excess risk in wheezing bronchitis was observed 

per 10 ppb increase in 24-h avg SO2. However, it should be noted that stronger associations were 

observed with PM2.5, which was well-correlated with SO2 (r = 0.73). These epidemiologic studies are 

summarized in Annex Table F-1. 

To summarize, very few studies have examined the association between ambient SO2 

concentrations and absences from school or work as a result of respiratory illnesses. The limited evidence 

indicates a possible association between exposure to SO2 concentrations and increased respiratory 

illnesses, particularly among young children; however, this association was also seen with PM, which was 

correlated with SO2. 

3.1.4.6. Emergency Department Visits and Hospitalizations for Respiratory Diseases 

Total respiratory causes for ED visits and hospital admissions typically include asthma, bronchitis 

and emphysema (collectively referred to as COPD), upper and lower respiratory tract infections, 

pneumonia, and other minor categories. Temporal associations between ED visits or hospital admissions 

 3-21 
for respiratory diseases and the ambient concentrations of SO2 have been the subject of more than fifty 

peer-reviewed research publications since 1994. In addition to considerable statistical and analytical 

refinements, recent studies have examined responses of morbidity in different age groups, the effect of 

seasons on ED and hospital usage, and multipollutant models to characterize the effects of copollutant 

mixtures. The epidemiologic studies of ED visits and hospital admissions for respiratory causes are 

summarized in Annex Table F-2. 

All Respiratory Diseases 

There are relatively few studies of ED visits for all respiratory causes in contrast to the quantity of 

studies that examine hospital admissions for all respiratory causes. Collectively, studies of ED visits and 

hospitalizations provide evidence to support an association between ambient SO2 levels and ED visits and 

hospitalizations for all respiratory causes. When analyses were restricted by age, the results among 

children (0-14 years) and older adults (65+ years) were mainly positive, though not all statistically 

significant. The studies that examined the association of these outcomes and SO2 levels among adults (15-

64 years) reported a mix of positive and negative results. When all age groups were combined, the results 

of ED and hospitalization studies were mainly positive; however, the excess risk estimates were generally 

smaller compared to the children and older adults groups (see additional discussion in Section 4.2.3). It is 

possible that the effects observed in the combined age groups were driven by increases in the very young 

or older adult subpopulations. SO2-related relative risks from the hospitalization and ED studies, 

separated by analyses among all ages and age-specific analyses, are shown in Figure 3-6. All studies that 

reported quantitative data are included in the figure (studies using GAM with default convergence criteria 

are not included). Figure 3-6, as well as Figure 3-7 are presented to assess the general consistency of the 

findings. Overall, the effect estimates in this figure range from a -5% to 20% excess risk in ED visits or 

hospital admissions for respiratory causes per 10 ppb increase in 24-h avg SO2, with the large majority of 

studies suggesting an increase in risk. 

Wilson et al. (2005) examined ED visits for all respiratory causes in Portland, ME from 1996–2000 

and in Manchester, NH from 1998–2000. The mean 1-h max SO2 concentration in Portland was 11.1 ppb 

(SD 9.1), and was higher during the winter months (mean 17.1 ppb [SD 12.0]) and lower in the summer 

(mean 9.1 ppb [SD 8.0]). In Manchester, the mean 1-h max SO2 concentration was 16.5 ppb (SD 14.7 

ppb), and also was higher in the winter months (mean 25.7 ppb [SD 15.8]) than in the summer months 

(mean 10.6 ppb [SD 15.1]). Though the authors reported the 1-h max SO2 concentrations, they used the 

24-h avg SO2 concentrations in their analyses. When all ages where included in analyses, Wilson et al. 

(2005) found positive associations between ED visits and SO2, with a 7% (95% CI: 3.0, 12) and 1% (95% 

CI: -3.0, 5.0) excess risk per 10 ppb increase in 24-h avg SO2 at a 0-d lag in Portland, ME and 

Manchester, NH, respectively. 

Peel et al. (2005) investigated ED visits for all respiratory causes in Atlanta, GA from 1993–2000. 

This study included 484,830 ED visits. The mean 1-h max SO2 concentration was 16.5 ppb (SD 17.1). 

The researchers found a weak positive relationship between ED visits and SO2, though the increased risk 

was not statistically significant (1.6% [95% CI: −0.6, 3.8] excess risk per 40 ppb increase in 1-h max 

SO2). Tolbert et al. (2007) recently reanalyzed these data with four additional years of data and found 

similar results (0.8% [95% CI: -0.7, 2.3]). An analysis by Dab et al. (1996) examined the association 

between SO2 and hospital admissions for all respiratory causes in Paris, France, using both the 24-h avg 

and 1-h max. It should be noted that these researchers observed similar effect estimates for both exposure 

metrics; however, only the estimate using 24-h avg was statistically significant (1.1% [95% CI: 0.1, 2.0] 

excess risk per 10 ppb increase in 24-h avg SO2 versus 1.9% [95% CI: −1.3, 5.0]) per 40 ppb increase in 

1-h max SO2). 

 3-22 
Figure 3-6. Relative risks (95% CI) of SO2-associated emergency department visits and 

hospitalizations for all respiratory causes among all ages and separated by age group. 

Risk estimates are standardized per 10 ppb increase in 24-h avg SO2 concentrations 

or 40 ppb increase in 1-h max SO2 (*). 

 3-23 
When analyses were stratified to include only children (0-14 years), evidence of a modest 

association between SO2 and ED visits or hospitalizations for all respiratory causes in children was 

reported in several Australian (Barnett et al., 2005; Petroeschevsky et al., 2001) and European (Anderson 

et al., 2001 [using GAM default convergence criteria]; Atkinson et al., 1999a; 1999b) studies. Excess 

risks ranging from 3% to 22% per 10 ppb increase in 24-h avg SO2 were reported by these studies. In a 

multicity study spanning Australia and New Zealand, Barnett et al. (2005) compared hospital admission 

data collected from 1998–2001 with ambient SO2 concentrations, where the mean 24-h avg SO2

concentration ranged from 0.9 to 4.8 ppb. The authors found a 22% (95% CI: 5, 42) excess risk per 10 

ppb increment in 24-h avg SO2 among children (1-4 years) in these cities. Petroeschevsky et al. (2001) 

found similar results for the 0-4 age group in their Brisbane, Australia study (22.4% increase [95% CI: 

8.7, 37.7]). However, some additional U.S. (Wilson et al., 2005), European (Fusco et al., 2001 [using 

GAM default convergence criteria]; Ponce de Leon et al., 1996), and Latin American (Braga et al., 1999; 

2001) studies did not find statistically significant associations between ambient SO2 concentrations and 

hospitalizations for all respiratory causes among children. 

Wilson et al. (2005) found a positive association between ED visits and SO2, with a 16% (95% CI: 

7.0, 22.0) excess risk per 10 ppb increase in 24-h avg SO2 at a 0-d lag in Portland, and a 7% (95% CI: -

5.0, 21.0) in Manchester, NH when only older adults (65+ years) were considered. In another two-city 

study, Schwartz (1995) compared 13,740 hospital admission among older adults in New Haven, CT and 

Tacoma, WA from 1988–1990 with ambient SO2 concentrations. The mean 24-h avg SO2 concentration 

was 29.8 ppb (90th percentile: 159) in New Haven and 16.8 ppb (90th percentile: 74) in Tacoma. Schwartz 

found positive associations between hospitalizations and SO2, with a 2% (95% CI: 1.0, 3.0) excess risk at 

a 2-d lag in New Haven and 3% (95% CI: 1.0, 6.0) excess risk at a 0-d lag in Tacoma per 10 ppb increase 

in 24-h avg SO2. In two-pollutant models, the SO2 effect estimate from New Haven, but not Tacoma, was 

found to be robust to adjustment for PM10. Here, the term robust is used to indicate that there was little 

change in the magnitude of the central estimate, though statistical significance may have been lost. In 

Vancouver, BC, both Fung et al. (2006) and Yang et al. (2003b) also found positive associations between 

hospitalizations among older adults and SO2. In a multipollutant model including coefficient of haze 

(CoH), NO2, O3, and CO, the SO2 effect estimate diminished slightly (Yang et al., 2003b). 

Additional evidence of a positive association between ED visits or hospitalizations for all 

respiratory causes among older adults and SO2 comes from several European (Spix et al., 1998; Sunyer et 

al., 2003a; Vigotti et al., 1996) and Australian (Petroeschevsky et al., 2001) studies. Excess risks ranging 

from 1% to 12% per 10 ppb increase in 24-h avg SO2 were reported by these studies. Petroeschevsky et 

al. (2001) examined 33,710 hospital admissions in Brisbane, Australia from 1987–1994. The mean 

24-h avg SO2 concentration was 4.1 ppb, and was highest in the winter months (4.8 ppb) and lowest in the 

spring (3.7 ppb). Petroeschevsky et al. found a 12% (95% CI: 2.0, 23.0) excess risk per 10 ppb increase in 

24-h avg SO2 at 0-d lag. Additional European studies did not find statistically significant associations 

between ambient SO2 concentrations and ED visits or hospitalizations for all respiratory causes among 

older adults (Anderson et al., 2001 [using GAM with default convergence criteria]; Atkinson et al., 1999a; 

1999b; Ponce de Leon et al., 1996; Schouten et al., 1996). 

In summary, studies generally observed small, positive associations between ambient SO2 concentrations and ED visits and hospitalizations, particularly among children and older adults (65+ years). The 

positive evidence from these studies is supported by the results of panel, human clinical, and limited 

toxicological studies that also found a positive relationship between SO2 levels and adverse respiratory 

outcomes. 

Asthma 

Studies of ED visits and hospitalizations provide evidence to support an association between 

ambient SO2 levels and ED visits and hospitalizations for asthma. The results from the hospitalization and 

ED studies, separated by analyses among all ages and age-specific analyses, are shown in Figure 3-7. 

Overall, central effect estimates in the figure range from a -10% to 40% excess risk in ED visits and 

 3-24 
hospitalizations for asthma per 10 ppb increase in 24-h avg SO2. Most of the effect estimates are positive 

(suggesting an association with SO2 and ED visits and hospitalizations for asthma), though few are 

statistically significant at the 95% confidence level. 

When all ages were included in the analyses, Wilson et al. (2005) found a positive association 

between ED visits and SO2, with an 11% (95% CI: 2, 20) excess risk per 10 ppb increase in 24-h avg SO2

at a 0-d lag in Portland, ME and a positive, though not statistically significant association in Manchester, 

NH (6% increase [95% CI: -4, 17]). Ito et al. (2007b) found a 36% (95% CI: 22.2, 51.2) excess risk in 

asthma ED visits per 10 ppb increase in 24-h avg SO2 during warm months in New York City. This effect 

was robust to the inclusion of PM2.5 in the model, though this association was diminished once NO2 was 

included in the model. Another study conducted in New York City (NY DOH, 2006) found a 10% (95% 

CI: 5, 15) excess risk in asthma hospital admissions per 10 ppb increase in 24-h avg SO2 for Bronx 

residents, but a null association for the residents of Manhattan (-1% [95% CI: -11, 11]). A study 

conducted in Atlanta (Peel et al., 2005) found a null relationship between asthma ED visits and 1-h max 

SO2 (0.2% increase [95% CI: -3.2, 3.4]). A study by Jaffe et al. (2003) examined the association between 

SO2 and ED visits for asthma in three cities in Ohio – Cincinnati, Cleveland, and Columbus – in 

asthmatics aged 5 to 34 years. The mean 24-h avg SO2 concentrations were 14 ppb (range: 1–50) in 

Cincinnati, 15 ppb (range: 1-64) in Cleveland, and 4 ppb (range: 0–22) in Columbus. A positive 

association was observed in the multicity analysis, with a 6.1% (95% CI: 0.5, 11.5) excess risk in asthma 

visits observed per 10 ppb increase in 24-h avg SO2. In the city-stratified analyses, significant 

associations were observed only for Cincinnati (17.0% [95% CI: 4.6, 30.8]). 

When analyses were stratified to include children (0-14 years) only, Wilson et al. (2005) found 

positive, but not statistically significant associations between ED visits and SO2 in Portland, ME (5% 

[95% CI: -12, 25]) and Manchester, NH (20% [95% CI: -3, 49]). Similarly, Lin et al. (2003b) observed a 

positive association between hospitalizations for asthma and SO2 among girls (30% [95% CI: 6, 60]), and 

a negative association for boys (-10% [95% CI: -23.4, 5.8] Toronto, ON; mean 24-h avg SO2 of 5.36 ppb 

[SD 5.90]). Stronger evidence comes from a study of childhood asthma hospitalizations conducted in 

Bronx County, New York (Lin et al., 2004e). In this study, the authors conducted a case-control study of 

children aged 0-14 years and examined the association of daily ambient SO2 concentrations (categorized 

into quartiles of both avg and max levels) and cases admitted to the hospital for asthma or controls who 

were admitted for reasons other than asthma. The mean 24-h avg SO2 was below 17 ppb for both cases 

and controls across all lag days examined. The authors found that cases were exposed to higher 24-h avg 

SO2 than controls. When the highest exposure quartile was compared with the lowest, the ORs were 

strongest when a 3-day lag was employed (OR 2.16 [95% CI: 1.77, 2.65] for 24-h avg SO2; OR 1.86 

[95% CI: 1.52, 2.27] for 1-h max SO2). The results were positive and statistically significant for all lag 

days examined. These results suggest a consistent positive association between SO2 exposure and 

hospitalizations for childhood asthma. 

Additional evidence of a positive association between ED visits or hospitalizations for asthma 

among children and SO2 comes from several European (Anderson et al., 1998; Atkinson et al., 1999a; 

1999b; Hajat et al., 1999; Sunyer et al., 1997; 2003a [using GAM with default convergence criteria]; 

Thompson et al., 2001) and Asian (Park et al., 2002 [using GAM with default convergence criteria] ) 

studies. Excess risks ranging from 2% to 10% per 10 ppb increase in 24-h avg SO2 were reported by these 

studies. Several of these studies observed that the SO2 effect estimate was robust to adjustment for BS and 

NO2 (Anderson et al., 1998; Sunyer et al., 1997), but one study observed that the SO2 effect diminished 

considerably with adjustment for PM10 and benzene (Thompson et al., 2001). Atkinson et al. (1999a) 

compared 165,032 respiratory hospital admissions in London from 1992–1994 with ambient SO2 levels 

(mean 24-h avg of 7.2 ppb [SD 4.7]). They found a 10% (95% CI: 4.0, 16.0) excess risk per 10 ppb 

increase in 24-h avg SO2 at 1-d lag for children aged 0-14 years. Additional European (Fusco et al., 2001 

[using GAM with default convergence criteria]), Australian (Barnett et al., 2005; Petroeschevsky et al., 

2001), Asian (Ko et al., 2007b; Lee et al., 2006a) and Latin American (Gouveia and Fletcher, 2000) 

studies did not find statistically significant associations between ambient SO2 concentrations and 

hospitalizations for all respiratory causes among children. 

 3-25 
Figure 3-7. Relative risks (95% CI) of SO2-associated emergency department visits and 

hospitalizations for asthma among all ages and age-specific groups. Risk estimates 

are standardized per 10 ppb increase in 24-h avg SO2 concentrations or 40 ppb 

increase in 1-h max SO2 (*). 

 3-26 
In summary, small, positive associations were observed between ambient SO2 concentrations and 

ED visits and asthma hospitalizations. Evidence from these studies is further supported by the results of 

panel and human clinical studies that have also found SO2-related respiratory effects in asthmatics. 

Chronic Obstructive Pulmonary Disease 

There are relatively few studies that have examined the association of ED visits and 

hospitalizations for COPD and ambient SO2 levels, and very little evidence that an association exists. A 

recent study (Ko et al., 2007a) found a significant association between hospital admissions for COPD (not 

including asthma) in Hong Kong (1.8% [95% CI: 0.3, 3.8]) excess risk per 10 ppb increase in 24-h avg 

SO2 concentration). Three additional studies reported positive and statistically significant results for 

COPD and SO2; all three studies included asthma in their diagnostic definition of COPD (Anderson et al., 

2001; Moolgavkar, 2003a; Sunyer et al., 2003b). Anderson et al. (1999) reported a 12% (95% CI: 5.0, 

20.0) excess risk per 10 ppb increase in 24-h avg SO2 among children, while Moolgavkar (2003b) and 

Sunyer et al. (2003b) found 5% and 2% excess risks per 10 ppb increase in 24-h avg SO2 among older 

adult populations, respectively. Other studies examining COPD did not find statistically significant results 

(Atkinson et al., 1999a; Burnett et al., 1999 [using GAM with default convergence criteria]; Michaud et 

al., 2004). 

Overall, this limited and inconsistent evidence does not support a relationship between ED visits 

and hospitalizations for COPD and ambient SO2 levels. 

Respiratory Diseases Other than Asthma or COPD 

Studies of ED visits or hospital admissions for other respiratory diseases looked at several other 

specific outcomes. There are limited studies with mixed results for upper respiratory tract infections 

(Burnett et al., 1999 [using GAM with default convergence criteria]; Hajat et al., 2002 [using GAM with 

default convergence criteria]; Lin et al., 2005; Peel et al., 2005), pneumonia (Barnett et al., 2005; 

Moolgavkar et al., 1997 [using GAM with default convergence criteria]; Peel et al., 2005), bronchitis 

(Barnett et al., 2005; Michaud et al., 2004), and allergic rhinitis (Hajat et al., 2002 [using GAM with 

default convergence criteria]; Villeneuve et al., 2006b). The limited evidence is suggestive of an 

association between SO2 levels and ED visits or hospitalizations for lower respiratory tract diseases 

(Atkinson et al., 1999a; Farhat et al., 2005 [using GAM with default convergence criteria]; Hajat et al., 

2002 [using GAM with default convergence criteria]; Lin et al., 1999; Martins et al., 2002 [using GAM 

with default convergence criteria]). All of the studies that characterized this relationship found a positive 

and statistically significant excess risk associated with increases in SO2. Excess risks ranging from 3% to 

33% per 10 ppb increase in 24-h avg SO2 were reported by these studies. 

In summary, only a few studies provide results for respiratory health outcomes other than asthma 

and COPD, and these results are mixed. This makes it difficult to draw conclusions about the effects of 

SO2 on these diseases. Limited evidence is indicative of an association between ambient SO2 levels and 

ED visits for lower respiratory tract diseases. 

Summary of Evidence on Emergency Department Visits and Hospitalizations for Respiratory 

Diseases 

Small, positive associations exist between ambient SO2 concentrations and ED visits and 

hospitalizations for all respiratory causes, particularly among children and older adults (65+ years). 

Similar associations are found for asthma. The SO2-related changes in ED visits or hospital admissions 

for respiratory causes ranged from -5% to 20% excess risk, with the large majority of studies suggesting 

an increase in risk. Mean 24-h avg SO2 levels ranged from 1 to 30 ppb in these studies, with maximum 

values ranging from 12 to 75 ppb. No association was observed between SO2 levels and ED visits and 

 3-27 
hospitalizations for COPD. Given the limited number of studies with mixed results, it is difficult to draw 

conclusions about the effect of SO2 on other respiratory diseases, though studies of lower respiratory tract 

diseases are somewhat indicative of an association. 

The potential influence of copollutants has not been systematically considered in the epidemiologic 

literature. A limited subset of the studies examined potential confounding by copollutants using 

multipollutant regression models. Figure 3-8 presents SO2 excess risk estimates with and without 

adjustment for various copollutants. PM and NO2 are the main foci, since these pollutants have been 

found to be highly-correlated with SO2 in epidemiologic studies and have known respiratory health 

effects. Multipollutant regression analyses indicated that although copollutant adjustment had varying 

degrees of influence on the SO2 effect estimates, the effect of SO2 on respiratory health outcomes 

appeared to be generally robust and independent of the effects of gaseous copollutants, including NO2 

(Anderson et al., 1998; Lin et al., 2004c; Sunyer et al., 1997), and O3 (Anderson et al., 1998; Hajat et al., 

1999; Tsai et al., 2006; Yang et al., 2003b; 2005). The evidence for PM10 was less consistent, with three 

studies finding that positive SO2 effect estimates became negative, though not statistically significant, 

with the inclusion of PM10 in the model (Galan et al., 2003; Schwartz, 1995 [in New Haven, CT]; Tsai et 

al., 2006 [in Tacoma, WA]). Several other studies found the effects estimates for SO2 to be generally 

robust to the inclusion of PM10 in the model (Burnett et al., 1997b; Hagen et al., 2000; Hajat et al., 1999 

[using GAM with default convergence criteria]; Schwartz, 1995 [in New Haven, CT]). The studies that 

examined PM2.5 and PM10-2.5 in copollutant models found that the SO2 estimates were generally robust to 

the adjustment for PM of these size fractions (Burnett et al., 1997b; Ito et al., 2007; Lin et al., 2003b; NY 

DOH, 2006). 

The results of several studies (Anderson et al., 1998; Hajat et al., 1999; Schouten et al., 1996; Spix 

et al., 1998; Wong et al., 1999a) have demonstrated a greater increase in ED visits and hospitalizations for 

respiratory illnesses during the summer months, despite the fact that the avg concentrations for SO2 in 

some of the areas studied were greatest in winter. In contrast, some studies found the associations between 

ED visits and hospital admissions and respiratory disease with similar increases in SO2 to be greater in 

winter than summer (Vigotti et al., 1996; Walters et al., 1994). Other studies were unable to discern a 

seasonal difference in ED visits and hospitalizations for respiratory causes (Castellsague et al., 1995; 

Tenias et al., 1998; Wong et al., 2002c [using GAM with default convergence criteria]). These effects 

were not consistent across age groups. Warmer months were more likely to show evidence of an 

association with adverse respiratory outcomes in children, while older adults appeared more likely to be 

affected during the cooler months. These seasonal associations remain somewhat uncertain and require 

additional investigation. 

In conclusion, a large number of epidemiologic studies provide evidence of an association between 

ambient SO2 concentrations and ED visits and hospitalizations for all respiratory causes, in particular 

among children and older adults (65+ years), and for asthma. The findings are generally robust when 

additional copollutants are included in the model. These associations are supported by panel studies that 

observed SO2-related increases in asthma and other respiratory symptoms in children, and human clinical 

and animal toxicological studies that found a positive relationship between SO2 exposure and various 

respiratory outcomes. 

 3-28 
Figure 3-8. Relative risks (95% CI) of SO2-associated emergency department visits (*) and 

hospitalizations for all respiratory causes and asthma, with and without copollutant 

adjustment. Risk estimates are standardized per 10 ppb increase in 24-h avg SO2

concentrations or 40 ppb increase in 1-h max SO2. 

 3-29 
3.1.4.7. SO2-PM Interactions and Other Mixture Effects 

As discussed earlier, SO2 is a component of complex air pollution mixtures that vary 

geographically and temporally (e.g., by hour, week, and season). The 1982 AQCD addressed the question 

of possible effects of PM on the response to SO2. It was noted that sorption of SO2 onto liquid or solid 

particles, which may act as carriers, tended to increase its potency in animal toxicological experiments. 

However, the mechanism for the effect was not known. Since then, additional animal studies have 

demonstrated respiratory responses following inhalation of SO2 which was adsorbed onto metal oxide or 

carbon particles. These studies are summarized in Annex Table E-4 and both confirm and extend earlier 

findings. In all of the recent studies, the resulting particles were submicron in size; they would be 

expected to deposit in the lower respiratory tract. Acute and subacute exposures to SO2 and PM resulted 

in additive or more-than-additive effects on pulmonary resistance (Amdur et al., 1983; Chen et al., 1991), 

diffusing capacity for CO (Amdur et al., 1988), AHR following an acetylcholine challenge (Amdur et al., 

1988; Chen et al., 1992), and decreased host defense responses (Clarke et al., 2000; Jakab et al., 1996). 

Many of these studies reported transformation of SO2 to sulfite, sulfate, sulfur trioxide and H2SO4

depending on conditions of temperature and relative humidity (Amdur et al., 1983; 1988; Chen et al., 

1991; Clarke et al., 2000; Jakab et al., 1996). Respiratory responses observed in these experiments were 

in some cases attributed to the formation of particular sulfur-containing species. For example, repeated 

exposure to 20 μg/m3

 carbon black-associated sulfate resulted in impaired host defenses (Clarke et al., 

2000). However, the relevance of these animal toxicological studies has been called into question because 

concentrations of both PM (1 mg/m3

 and higher) and SO2 (1 ppm and higher) utilized in these studies are 

much higher than ambient levels. Furthermore, the SO2-adsorbed PM utilized in some of these studies is 

not representative of ambient PM. For example, some of the laboratory-generated aerosols contained 

sulfite but atmospheric chemistry studies do not indicate significant amounts of sulfite ion in atmospheric 

PM. In summary, animal toxicological studies conducted since the last review suggest that SO2 effects 

may be potentiated by coexposure to PM but the relevance of these results to ambient exposures is not 

clear. 

The 1982 AQCD also described an informative study of complex air pollutants which was 

conducted in dogs (Stara et al., 1980). In dogs that were exposed to SO2 + H2SO4, with or without 

irradiated or non-irradiated auto exhaust concentrations relevant to urban exposures, functional lung 

changes were observed at 61 months of exposure and at 2 years after exposures ended. Morphological and 

biochemical changes were observed at 2.5-3 years after exposure. Additional animal studies have been 

conducted since the 1982 AQCD which involved binary mixtures, laboratory-generated complex mixtures 

(e.g., simulation of regional air pollution), or actual ambient air mixtures. Annex Tables E-5 through E-7 

summarize results from short-term studies on possible toxicity relationships between SO2 and O3, SO2 and 

sulfates, as well as the effects of complex air pollution mixtures in healthy animals and disease models. 

Possible interactions between SO2 and cold air were also examined (Annex Table E-8). Generally, most 

studies with ambient or laboratory-generated complex mixtures did not include a SO2-only exposure 

group, making it difficult to determine the contribution of SOX. No definitive conclusions can be made 

from these studies. 

3.1.4.8. Summary of Evidence on the Effect of Short-Term (≥ 1 h) Exposure on 

Respiratory Health 

Numerous epidemiologic studies have observed associations between short-term (≥ 1-h, generally 

24-h avg) exposure to SO2 and respiratory health effects, ranging from respiratory symptoms to ED visits 

and hospital admissions for respiratory causes. The associations between ambient SO2 concentrations and 

several respiratory outcomes were generally consistent, with the large majority of studies showing 

positive associations, and multicity studies, as well as several single-city studies, indicating statistically 

significant findings. The effects on lung inflammation and AHR related to short-term exposure to SO2 at 

 3-30 
levels as low as 0.1 ppm found in animal toxicological studies provide a degree of coherence and 

biological plausibility for the observed epidemiologic associations. In addition, the causal respiratory 

effects of peak exposures (5-10 min) of SO2 at levels as low as 0.2 ppm found in the human clinical 

studies of asthmatics (see Section 3.1.3.) provide further evidence of biological plausibility for the effects 

associated with short-term exposure to SO2. 

Two recent multicity studies (Mortimer et al., 2002; Schildcrout et al., 2006) and several other 

studies (Delfino et al., 2003a; Neas et al., 1995; van der Zee et al., 1999) have found an association 

between short-term ambient SO2 concentrations and respiratory symptoms in children. In the limited 

number of studies that assessed potential confounding by copollutants using multipollutant models, the 

SO2 effect on respiratory symptoms was generally found to be robust to adjustment for copollutants. 

These findings indicate an association between short-term exposure to ambient SO2 exposure and 

respiratory symptoms in children, particularly those with asthma. Several recent studies (Desqueyroux et 

al., 2002a; 2002b; van der Zee et al., 2000) found no association between ambient SO2 levels and 

respiratory symptoms in adults, though there was limited epidemiologic evidence which suggested that 

atopic adults as well as children may be at increased risk for SO2-induced respiratory symptoms (Boezen 

et al., 1999; 2005). 

Epidemiologic studies do not provide strong evidence for an association between short-term 

ambient SO2 exposure and lung function in either children (Delfino et al., 2003a; Mortimer et al., 2002; 

Roemer et al., 1998) or adults (e.g., Peters et al., 1996a; Taggart et al., 1996). Several other studies 

reported positive results; however, the generally mixed findings, as well as the relative lack of evidence 

available to evaluate potential confounding by copollutants, limits the causal interpretation of ambient 

SO2 on lung function. 

Only one epidemiologic study (Adamkiewicz et al., 2004) evaluated inflammation, as indexed by 

eNO, and found no association with SO2 exposure. Animal toxicological studies found that repeated 

exposure to SO2 leads to increased airway inflammation in two models involving animals which were 

sensitized to an antigen (Li et al., 2007; Park et al., 2001a). Studies of other ambient pollutants indicate 

that influx of macrophages and other inflammatory cells, with the related release of cytokines, is a 

common response to injury. 

Effects of short-term exposure to SO2 on AHR have been observed. In two animal toxicological 

studies, repeated exposure to 0.1 ppm SO2 led to AHR in guinea pigs sensitized to an antigen (Park et al., 

2001a; Riedel et al., 1988). Human clinical studies by Devalia et al. (1994) and Rusznak et al. (1996) 

demonstrated enhanced airway responses to an inhaled allergen in asthmatic subjects following exposure 

to a combination of SO2 (0.2 ppm) and NO2 (0.4 ppm). This effect was not observed following exposure 

to either SO2 or NO2 alone. These findings of increased airway resistance are in concordance with the 

limited epidemiologic study results that showed SO2-induced increases in AHR among atopic children 

and asthmatic adults (Soyseth et al., 1995a; Taggart et al., 1996). 

Epidemiologic studies provide evidence for an association between ambient SO2 levels and ED 

visits and hospitalizations for all respiratory diseases in two susceptible populations: children (Dab et al., 

1996; Petroeschevsky et al., 2001; Walters et al., 1994) and older adults (65+ years) (Fung et al., 2006; 

Schwartz, 1995; Spix et al., 1998; Wong et al., 1999a). Evidence for an association between ambient SO2

levels and these outcomes in non-elderly adults was weaker. A modest association between ambient SO2

and ED visits and hospitalizations for asthma was also observed. SO2 effect estimates were generally 

robust to the inclusion of copollutants, including PM, O3, CO and NO2, indicating that the observed effects 

of SO2 on respiratory endpoints is independent of the effects of other ambient air pollutants. 

In summary, recent epidemiologic studies, supported by a limited number of animal toxicological 

studies conducted at near ambient concentrations, indicate an association between short-term (≥ 1-h, 

generally 24-h avg) exposure to SO2 and several measures of respiratory health, including respiratory 

symptoms, inflammation, airway hyperreponsiveness, and ED visits and hospitalizations for respiratory 

causes. The epidemiologic evidence further observed that the SO2-related respiratory effects were more 

pronounced in asthmatic children and older adults (65+ years). 

 3-31 
3.1.5. Evidence of the Effects of SO2 on Respiratory Morbidity from 

Intervention Studies 

Many epidemiologic studies have examined the association of short-term SO2 concentrations and 

various respiratory morbidity outcomes. These studies collectively suggest that increased ambient SO2

concentrations are associated with increased risk of respiratory outcomes, ranging from respiratory 

symptoms to ED visits and hospitalizations. Further contributing to the evidence base are intervention 

studies that reported decreases in respiratory morbidity following improvements in air quality, particularly 

reductions in SO2 concentrations. 

In Hong Kong, a sudden change in regulation in July 1990 required all power plants and road 

vehicles to use fuel oil with a sulfur content of ≤ 0.5% by weight. These regulations were enforced 

quickly, and provided opportunities to observe changes in morbidity before and after the intervention. 

Peters et al. (1996b) followed 3,521 children (mean age 9.5 years) residing in two districts with good and 

poor air quality before the intervention from 1989 to 1991. The intervention resulted in large reductions in 

SO2 (up to 80% in polluted district), along with a modest reduction in sulfate (38% in polluted district). 

Only a small change in TSP levels was observed after the intervention (15% decline in polluted district). 

In 1989 and 1990, an excess risk of respiratory symptoms was observed in the polluted district. After the 

intervention, there was a greater decline in reported symptoms of cough, sore throat, phlegm, and 

wheezing in the polluted compared with the unpolluted district. For example, the OR for cough, 

comparing the polluted to the unpolluted district, was 1.22 (95% CI: 1.05, 1.42) in 1989 and 1990, and 

decreased to 0.92 (95% CI: 0.73, 1.15) in 1991. 

A study by Keles et al. (1999) evaluated the prevalence of chronic rhinitis among high school 

students before and after installation of a natural gas network for domestic heating and industrial works, 

in a polluted area of Istanbul, Turkey. Concentrations of CO, NO2, and hydrocarbons were relatively low 

compared to SO2 and TSP in this area. After the intervention, the annual mean TSP concentration declined 

by 23% from 89.7 μg/m3

 to 68.8 μg/m3. An even greater decline (46%) was observed for SO2, from an 

annual mean of 70.8 ppb to 38.2 ppb. The prevalence of rhinitis decreased significantly from 62.5% to 

51% of the student population (p < 0.05) following the installation of the natural gas network. Symptoms 

of rhinitis were associated with air pollution levels, but not with any of the other factors considered, 

including sex, household crowding, heating source, and smoking status. Although the effects from TSP 

could not be separated from SO2 effects, this study demonstrated that reductions in both pollutants (with 

greater declines in SO2) resulted in significant reductions in the prevalence of chronic rhinitis in a highly 

polluted area. 

Another study in Germany observed that reductions in air pollutant levels were associated with 

improvement in reported respiratory symptoms. Heinrich et al. (2002) examined the influence of reduced 

air pollution levels on respiratory symptoms in children aged 5 to 14 years (n = 7,632). Questionnaires 

were collected from the children during 1992–1993, 1995–1996, and 1998–1999 in three study areas. 

During the study period, SO2 concentrations decreased by more than 90% and TSP concentrations 

decreased by approximately 60%. Concentrations of nucleation-mode particles (10-30 nm) increased 

during this time period. For most respiratory outcomes, the prevalence continued to decline in each of the 

three surveys. The temporal changes followed similar trends in all three study areas. Stronger effects 

between SO2 and prevalence of respiratory symptoms were observed among children without indoor 

exposures. For those without indoor exposures, ORs of 1.21 (95% CI: 1.11, 1.32) were observed for 

prevalence of bronchitis and 1.11 (95% CI: 1.02, 1.22) for frequent colds per 5-ppb increase in the annual 

mean of SO2. Frye et al. (2003) reported changes in lung function parameters associated with declines in 

SO2 concentrations in 2,493 children during this period as well. The researchers observed a 0.6% (95% 

CI: 0.1, 1.2) increase in FVC and a 0.4% (95% CI: -0.1, 0.9) increase in FEV1 per 5-ppb decrease in the 

annual mean of SO2. They concluded that the decreasing prevalence of respiratory symptoms and the 

increase in lung function following decreases in air pollution levels might indicate the reversibility of 

adverse health effects in children. 

 3-32 
In summary, these studies observed that improvements in air quality, in particular large decreases in 

SO2 concentrations, were associated with improvements in respiratory symptoms and lung function. 

However, the decreased respiratory morbidity following large reductions in ambient SO2 concentrations 

does not preclude the possibility that other constituents of the pollution mixture that share the same 

source as SO2 are also responsible for adverse effects. In the German and Turkey studies, both SO2 and 

TSP concentrations decreased dramatically. Although PM10 levels before and after the intervention were 

stable in Hong Kong, large reductions in ambient nickel and vanadium were observed concomitantly with 

reductions of sulfur after the intervention (Hedley et al., 2006). Animal toxicological studies conducted at 

higher concentrations (≥ 1 mg/m3

 PM and ≤ 1 ppm SO2) suggest that SO2 effects may be potentiated by 

coexposure to PM, but the relevance of these results to ambient exposures is not clear. The improvements 

in respiratory health may be jointly attributable to declines in both SO2 and PM. Considered collectively 

with the larger body of evidence from epidemiologic, human clinical, and animal toxicological studies, 

these intervention studies are supportive of SO2-related effects on respiratory morbidity. 

3.1.6. Summary of Evidence of the Effect of Short-Term SO2 Exposure 

on Respiratory Health 

Evaluation of the health evidence led to the conclusion that there is a causal relationship between 

respiratory morbidity and short-term exposure to SO2. This conclusion is supported by the consistency, 

coherence, and plausibility of findings observed in human clinical studies with 5-10 min exposures, 

epidemiologic studies using largely 24-h avg exposures, and animal toxicological studies using exposures 

of minutes to hours. 

The strongest evidence for this causal relationship comes from human clinical studies reporting 

respiratory symptoms and decreased lung function following peak exposures of 5-10 min duration to SO2. 

These effects have been observed consistently across studies involving exercising mild to moderate 

asthmatics. Statistically significant decrements in lung function accompanied by respiratory symptoms 

including wheeze and chest tightness have been clearly demonstrated following exposure to 0.4-0.6 ppm 

SO2. Although studies have not reported statistically significant respiratory effects following exposure to 

0.2-0.3 ppm SO2, some asthmatic subjects (5-30%) have been shown to experience moderate to large 

decrements in lung function at these exposure concentrations. 

A larger body of evidence supporting this determination of causality comes from numerous 

epidemiologic studies reporting associations with respiratory symptoms, ED visits, and hospital 

admissions with short-term SO2 exposures, generally of 24-h avg. Important new multicity studies and 

several other studies have found an association between 24-h avg ambient SO2 concentrations and 

respiratory symptoms in children, particularly those with asthma. Furthermore, limited epidemiologic 

evidence indicates that atopic children and adults may be at increased risk for SO2-induced respiratory 

symptoms. Generally consistent and robust associations also were observed between ambient SO2

concentrations and ED visits and hospitalizations for all respiratory causes, particularly among children 

and older adults (65+ years), and for asthma. Results of experiments in laboratory animals support these 

observations; studies in animals sensitized with antigen demonstrate that repeated exposure to near 

ambient SO2 levels (as low as 0.1 ppm in guinea pigs) can exacerbate allergic responses including airway 

inflammation and AHR. 

Mean 24-h avg SO2 levels ranged from 1 to 30 ppb in the epidemiologic studies, with maximum 

values ranging from 12 to 75 ppb. In the human clinical studies, respiratory effects were observed in 

exercising asthmatics following 5-10 min exposure to SO2 at levels as low as 0.2 ppm. 5-min SO2 data 

acquired from a limited number of ambient monitoring sites across the U.S. during the years 1997 to 2006 

indicated that 0.2% of the hourly maximum 5-min avg were at or above a concentration of 0.2 ppm. It is 

difficult to unequivocally relate the 24-h avg SO2 concentrations typically assessed in epidemiologic 

studies with the peak exposures in the human clinical studies. The apparent gap between the SO2

 3-33 
concentrations at which respiratory health effects are observed in the epidemiologic studies and the 

human clinical studies may be partially attributable to the differences in the study type (e.g., sample size, 

study subject selection, exposure conditions). Collectively, the findings from both human clinical and 

epidemiologic studies provide a strong basis for concluding a causal relationship between respiratory 

morbidity and short-term exposure to SO2. 

3.2. Systemic Morbidity Associated with Short-Term SO2

Exposure 

3.2.1. Summary of Findings from the Previous Review 

The studies reviewed in the 1982 AQCD primarily investigated respiratory health outcomes. There 

were no key animal toxicological or human clinical studies available at the last review to address effects 

of SO2 exposure on the cardiovascular system. The only report was a study in dogs exposed to air 

pollutant mixtures (SO2 + H2SO4 with or without nonirradiated or irradiated auto exhaust) (Stara et al., 

1980). No changes were observed in cardiovascular function at the end of 3 years of exposure and 3 years 

after exposure. No epidemiologic studies linking exposure to SO2 with cardiovascular physiological 

endpoints or ED visits or hospital admissions for cardiovascular causes were examined in the last review. 

Furthermore, no studies of SO2 effects on other organ systems were addressed in the 1982 AQCD. 

3.2.2. Cardiovascular Effects Associated with Short-Term Exposure 

The biological basis for SO2-related cardiovascular health effects may lie in the stimulation of 

chemosensitive receptors found in the respiratory tract which respond to irritants like SO2. Vagallymediated responses may affect the cardiovascular system by inducing bradycardia and either hypotension 

or hypertension, as discussed in Section 3.1.2. Alternatively oxidation reactions mediated by the SO2

metabolites sulfite and bisulfite which have been absorbed into the systemic circulation may potentially 

alter cardiovascular function. In general, vagally-mediated responses have been observed at lower 

concentrations of SO2 than oxidative injury. 

Since 1982, several animal toxicological studies have addressed the effects of SO2 on 

cardiovascular endpoints. These are summarized below and in Annex Table E-9. In addition, there is one 

noteworthy study examining the hematological effects of short-term SO2 exposure (Annex Table E-10). 

Acute exposure of rats to 0.87 ppm SO2 for 24 h resulted in increased hematocrit, sulfhemoglobin and 

osmotic fragility as well as decreased whole blood and packed cell viscosities (Baskurt, 1988). These 

results indicate a systemic effect of inhaled SO2 and are consistent with an oxidative injury to red blood 

cells. Only one study since 1982 measured systemic levels of sulfite or bisulfite following SO2 inhalation 

(Gunnison et al., 1987; Annex Table E-11). Further studies are required to confirm that inhalation 

exposures of SO2 at or near ambient levels increase blood sulfite and bisulfite levels sufficiently for 

oxidative injury to blood cells or other tissues. 

Recent epidemiologic studies have examined the association between air pollution and 

cardiovascular effects, including increased heart rate (HR), reduced heart rate variability (HRV), 

incidence of ventricular arrhythmias, changes in blood pressure, incidence of myocardial infarctions (MI), 

and ED visits and hospitalizations due to cardiovascular causes. The results of these cardiovascular 

studies are summarized in Annex Tables F-3 and F-4. 

 3-34 
3.2.2.1. Heart Rate and Heart Rate Variability 

Heart rate variability (HRV) is generally determined by analyses of time (e.g., standard deviation of 

normal R-R intervals [SDNN]) and frequency domains (e.g., low frequency [LF] / high frequency [HF] 

ratio by power spectral analysis, reflecting autonomic balance) measured during 24 h of 

electrocardiography (ECG). Brook et al. (2004) stated that HRV, resting heart rate, and blood pressure are 

modulated by a balance between the two determinants of autonomic tone (the sympathetic and 

parasympathetic nervous systems). An imbalance of cardiac autonomic control may predispose 

susceptible people to greater risk of ventricular arrhythmias and mortality from cardiovascular causes 

(Brook et al., 2004; Liao et al., 2004). 

A limited number of human clinical studies examined the effect of SO2 on HRV. During a 

controlled exposure of 12 healthy subjects and 12 subjects with asthma to 0.2 ppm SO2 for 1 h under 

resting conditions, Tunnicliffe et al. (2001) reported that HF power, LF power, and total power were 

higher with SO2 exposures compared to air exposure in the healthy subjects, but that these indices were 

reduced during SO2 exposure in the subjects with asthma. The LF/HF ratios were unchanged in both 

groups. The authors postulated two autonomic pathways for SO2-mediated bronchoconstriction. In 

healthy subjects, the dominant pathway was proposed to be the rapidly adapting receptor/C-fiber route, 

which results in activation of a central nervous system reflex with an increase in vagal tone. In the 

asthmatic subjects, proximal airway narrowing was proposed as the dominant response, possibly through 

neurogenic inflammation. This likely causes a compensatory central nervous system-mediated reduction 

in vagal tone, resulting in bronchodilation of the distal airway. While there were no detectable changes in 

symptoms or lung function in either of the groups, this study provides some evidence that exposure to 

SO2 may elicit systemic responses at these low levels (0.2 ppm). 

In a similar study, Routledge et al. (2006) exposed patients with stable angina as well as healthy 

subjects to 50 μg/m3

 carbon particles and to 0.2 ppm SO2, each alone and in combination, for 1 h under 

resting conditions. HRV, C-reactive protein, and coagulation markers were measured. The authors 

reported that for the healthy subjects, SO2 exposure was associated with a decrease in HRV markers of 

cardiac vagal control 4 h after exposure. However, it should be noted that there was no apparent 

difference in the absolute value of the root mean square of successive RR interval differences (r-MSSD) 

at 4 h postexposure between the control, SO2, carbon, and carbon/SO2 groups. The significant difference 

reported in the change in r-MSSD from baseline to 4 h postexposure with SO2 appears to be due to a 

higher baseline value of r-MSSD preceding the SO2 exposure compared to the baseline value of r-MSSD 

preceding the air exposure. There were no changes in HRV among the patients with stable angina. The 

authors noted that this lack of response in the heart patients may be due to a drug treatment effect rather 

than decreased susceptibility; a large portion of the angina patients were taking beta blockers, which are 

known to increase indices of cardiac vagal control. 

In an epidemiologic study, Liao et al. (2004) investigated short-term associations between ambient 

pollutants and cardiac autonomic control from the fourth cohort examination (1996 through 1998) of the 

population-based Atherosclerosis Risk in Communities (ARIC) study using a cross-sectional study 

design. Men and women aged 45 to 64 years (n = 6,784) from three U.S. study centers in North Carolina, 

Minnesota, and Mississippi were examined. Resting, supine, and 5-min beat-to-beat R-R interval data 

were collected. The mean 24-h avg SO2 level measured 1 day prior to the HRV measurement was 4 ppb 

(SD 4). In addition to SO2, the potential effects of PM10, O3, CO, and NO2 were evaluated. Previous-day 

SO2 concentrations were positively associated with HR and inversely associated with SDNN and LF 

power. Consistently more pronounced associations were suggested between SO2 and HRV among persons 

with a history of coronary heart disease. Significant associations with HRV indices also were observed for 

PM10 and the other gaseous pollutants. When the regression coefficients for each individual pollutant 

model were compared, the effects of PM10 on HRV were considerably larger than the effects for the 

gaseous pollutants, including SO2. No multipollutant analyses were conducted. 

Gold et al. (2000; 2003) examined the effect of short-term changes in air pollution on HRV in a 

panel study of 21 older adults (aged 53 to 87 years) in Boston, MA. The study participants were observed 

 3-35 
up to 12 times from June to September 1997. The mean 24-h avg SO2 concentration was 3.2 ppb (range: 

0, 12.6). The 24-h avg SO2 concentration was associated with decreased HR in the first 5-min rest period, 

but not in the overall 25-min study protocol. The effect estimate for SO2 slightly diminished but remained 

marginally significant in a two-pollutant model with PM2.5. The inverse association between SO2 and HR 

observed in this study are in contrast to the SO2-related increases in HR observed by Liao et al. (2004) 

and Peters et al. (1999). No associations were observed between HRV and SO2. The strongest associations 

with HRV were observed for PM2.5 and O3. 

Another study of air pollutants and HRV was conducted in Boston, MA on 497 men from the 

Normative Aging Study (Park et al., 2005b). The best 4-consecutive-min interval from a 7-min sample 

was used for the HRV calculations. For the exposure variable, 4, 24, and 48 h moving averages matched 

on the time of the ECG measurement for each subject were considered. The mean 24-h avg SO2

concentration was 4.9 ppb (range: 0.95, 24.7). Associations with measures of HRV were reported for 

PM2.5 and O3, but not with SO2 for any of the averaging periods. In another study conducted in Boston, 

MA, Schwartz et al. (2005) found significant effects of increases in PM2.5 on measures of HRV, while 

no associations with SO2 were observed. Other studies examined the relationship of SO2 with HRV (Chan 

et al., 2005; de Paula Santos et al., 2005; Holguin et al., 2003; Luttmann-Gibson et al., 2006). Most of 

these studies, with the exception of de Paula Santos et al., did not observe associations with SO2. 

In the limited number of epidemiologic studies that examined a possible effect of SO2 on HRV, 

there were some positive findings; however, results reported from the human clinical studies were 

inconsistent. The overall evidence is insufficient to conclude that SO2 has an effect on cardiac autonomic 

control. 

3.2.2.2. Repolarization Changes 

In addition to the role played by the autonomic nervous system in arrhythmogenic conditions, 

myocardial vulnerability and repolarization abnormalities are believed to be key factors contributing to 

the mechanism of such diseases. 

Two in vitro studies (Nie and Meng, 2005, 2006) conducted with a 1:3 molar:molar mixture of the 

SO2 derivatives bisulfite and sulfite demonstrated effects of a 10-μm bisulfite:sulfite mixture on sodium 

and L-type calcium currents (which included changes in inactivation and/or activation, recovery from 

inactivation, and inactivation/activation time constants) in ventricular myocytes. These in vitro 

observations suggested a potential role for L-type calcium current in cardiac injury following SO2

exposure. Additional toxicological studies are necessary to evaluate repolarization changes at ambient 

levels of SO2. 

In an epidemiologic study, Henneberger et al. (2005) examined the association of repolarization 

parameters (QT duration, T-wave complexity, variability of T-wave complexity, and T-wave amplitude) 

with air pollutants in patients with preexisting coronary heart disease (n = 56, all males) in East Germany. 

The patients were examined repeatedly once every 2 weeks for 6 months, for a total of 12 ECG 

recordings. The mean 24-h avg SO2 concentration was 2 ppb (range: 1, 4). Ambient SO2 concentrations 

during the 24-h preceding the ECG were associated with the QT interval duration, but not with any other 

repolarization parameters. Stronger associations were observed between PM indices and QT interval 

duration, T-wave amplitude, and T-wave complexity. 

Evidence from the limited number of in vitro toxicological studies indicates that L-type calcium 

current may have a role in cardiac injury following SO2 exposure at higher than ambient concentrations. 

In the single epidemiologic study of SO2 and repolarization changes, an association between SO2 and one 

of several repolarization parameters examined (QT duration) was observed; however, stronger 

associations were reported for PM. 

 3-36 
3.2.2.3. Cardiac Arrhythmias 

One toxicological study examined the effects of PM, ultrafine carbon, and SO2 on spontaneous 

arrhythmia frequency in 18-month-old rats (Nadziejko et al., 2004). The rats were exposed to 1 ppm SO2

for 4 h. No significant change in the frequency of spontaneous arrhythmias was found with SO2 and 

ultrafine carbon exposure. However, rats exposed to concentrated ambient PM had a significantly greater 

increase in the frequency of delayed beats than rats exposed to air. 

In a panel study of 100 patients with implanted cardioverter defibrillators (ICDs) in Eastern 

Massachusetts, Peters et al. (2000a) tested the hypothesis that patients with ICDs would experience lifethreatening arrhythmias after an air pollution episode. The mean 24-h avg SO2 concentration measured at 

two sites in Boston during the study period was 7 ppb (5th–95th percentile: 1, 19). ICDs monitor ECG 

abnormalities, and treat ventricular fibrillation or ventricular tachycardias by administering shock therapy 

to restore the normal cardiac rhythm. The ICD device also stores information on each tachyarrhythmia 

and shock. There was no association between SO2 and defibrillator discharges in the 33 subjects who had 

any defibrillator discharges during the follow-up period or in the 6 subjects who had at least 10 

discharges. There was some evidence that NO2 was associated with increased defibrillatory interventions 

in the subjects with any defibrillator discharges. Among the patients with at least 10 events, NO2, CO, and 

PM2.5 were found to be associated with defibrillator discharges. 

In a follow-up study designed to confirm the findings of Peters et al. (2000a), Dockery et al. (2005) 

used a larger sample of ICD patients in Boston (n = 203) with a longer follow-up period. The median 

concentration of 48-h avg SO2 averaged across multiple sites in Boston was 4.9 ppb (IQR 4.1). No 

significant associations were found between ventricular arrhythmic episode days and any of the air 

pollutants. However, when the analysis was stratified by recent arrhythmias (i.e., within 3 days), there 

was evidence of an excess risk of ventricular arrhythmia with SO2, PM2.5, black carbon, NO2, and CO. 

Since PM2.5, black carbon, NO2, and CO were correlated with each other and with SO2, the authors noted 

that differentiating the independent effects of the pollutants would be difficult. A case-crossover analysis 

of the same data by Rich et al. (2005) also observed associations with 48-h avg SO2, but the SO2 effect 

was not found to be robust to adjustment by PM2.5. In a similar study conducted in St. Louis, MO, an 

excess risk was associated with SO2 concentrations in the 24 h prior to an arrhythmia, but not with PM2.5

and O3 (Rich et al., 2006a). In this study, none of the other measured pollutants (PM, elemental carbon, 

O3, CO, NO2) were correlated with SO2. The authors suggested that the different effects observed in St. 

Louis and Boston may be due to differences in the source or mix of air pollutants in these cities. Finally, 

findings from a time series study of tachyarrythmic events among 518 patients over a 10 year period in 

Atlanta do not indicate an association with SO2, nor with the other pollutants studied including PM2.5 and 

its components (Metzger et al., 2007). 

Additional studies have examined the relationship of SO2 with arrhythmias in Vancouver, and 

observed associations at very low ambient SO2 concentrations (mean 24-h avg SO2 of ~2.5 ppb with a 

max of 8.1 ppb). Vedal et al. (2004) stated that of all pollutants examined, the only one with somewhat 

consistent positive associations with arrhythmia events was SO2. In season-stratified analyses, SO2 was 

positively associated with arrhythmias in the winter, while in the summer the association was negative. 

On the other hand, in the Rich et al. (2004) study, positive associations were observed in the summer but 

not in the winter. The authors stated that it was difficult to interpret these findings. 

Collectively, the epidemiologic evidence for an association between short-term exposure to SO2

and arrhythmias is inconsistent. The limited toxicological evidence does not provide biological plausiblity 

for an effect. 

3.2.2.4. Blood Pressure 

Two animal toxicological studies examined the effect of SO2 on blood pressure Hälinen et al. 

(2000a) examined blood pressure changes in guinea pigs which were hyperventilated to simulate exercise, 

 3-37 
and exposed to 1, 2.5, and 5 ppm SO2 in cold, dry air. After 10-min exposures to each SO2 concentration, 

separated by 15-min exposures to clean, warm, humid air, a transient increase in blood pressure was 

observed during 5 ppm SO2 exposure in cold, dry air. In a second study (Hälinen et al., 2000b), 

hyperventilated guinea pigs were exposed to cold, dry air alone or to 1 ppm SO2 in cold, dry air for 60 

min. The study reported similar increases in blood pressure and HR with exposure to cold, dry air or to 

SO2 in cold, dry air. The increase in HR was gradual, while increases in blood pressure generally occurred 

during the first 10 to 20 min of exposure. Similar effects were observed with exposure to cold, dry air or 

to SO2 in cold, dry air, suggesting that effects were associated with cold, dry air rather than with SO2. 

Ibald-Mulli et al. (2001) examined the association between blood pressure and SO2 using survey 

data from the MONICA (Monitoring Trends and Determinants in Cardiovascular Disease) Project. Blood 

pressure measurements were taken from 2,607 men and women. The mean 24-h avg SO2 concentration 

was 23 ppb (range: 5, 91). An increase in systolic blood pressure was associated with 24-h avg SO2 and 

TSP. However, in a two-pollutant model with TSP, the effect of SO2 on blood pressure was substantially 

reduced and became nonsignificant while the effect of TSP was robust. 

In a study by de Paula Santos et al. (2005), changes in blood pressure in association with SO2 were 

investigated in vehicular traffic controllers (n = 48) aged 31 to 55 years living in São Paulo, Brazil, where 

vehicles are the primary source of air pollution. The mean 24-h avg SO2 level, measured at six different 

stations around the city, was 7 ppb (SD 3). Blood pressure was measured every 10 min when subjects 

were awake (6 a.m. to 11 p.m.) and every 20 min during sleep (11 p.m. to 6 a.m.). Results indicated that 

SO2, as well as CO, were associated with increases in systolic and diastolic blood pressure. However, 

when a two-pollutant model was used to test the robustness of the associations, only the CO effect 

remained statistically significant. 

Very few studies have examined the effects of short-term SO2 exposure on blood pressure. 

Collectively, the limited toxicological and epidemiologic evidence that exposure to SO2 has effects on 

blood pressure is inconclusive. 

3.2.2.5. Blood Markers of Cardiovascular Risk 

Folsom et al. (2001) demonstrated that elevated levels of fibrinogen, white blood cell count, factor 

VIII coagulant activity (factor VIII-C), and von Willebrand factor were associated with risk of 

cardiovascular disease. Schwartz (2001) investigated the association between various blood markers of 

cardiovascular risk and air pollution among subjects in the Third National Health and Nutrition 

Examination Survey (NHANES III) in the U.S. conducted between 1989 and 1994 across 44 counties. 

The NHANES III is a random sample of the U.S. population with oversampling for minorities (30% of 

NHANES sample) and the elderly (20% of the sample). The mean SO2 concentration was 17.2 ppb (IQR 

17) across the 25 counties where data were available. This study looked at fibrinogen levels, platelet 

counts, and white blood cell counts. After controlling for age, ethnicity, gender, body mass index, and 

smoking status and number of cigarettes per day, SO2 was found to be positively associated with white 

blood cell counts. PM10 was associated with all blood markers. In two-pollutant models, PM10 remained a 

significant predictor of white blood cell counts after controlling for SO2, but not vice versa. 

A recent cross-sectional study (Liao et al., 2005) investigated the effects of air pollution on plasma 

hemostatic and inflammatory markers in the ARIC study (n = 10,208). The authors hypothesized that 

short-term exposure to air pollutants was associated with increased levels of inflammatory markers and 

lower levels of albumin, as serum albumin is inversely associated with inflammation. The mean 24-h avg 

SO2 concentration was 5 ppb (SD 4). Significant curvilinear relationships were observed between SO2 and 

factor VIII-C, white blood cell counts, and serum albumin. The authors noted that since no biological 

explanation could be offered for the “U”-shaped curve between SO2 and factor VIII-C and the “inverse 

U”-shape between SO2 and albumin, generalization of the association should be exercised with caution. 

No associations were observed between SO2 and fibrinogen or von Willebrand factor. 

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In another large cross-sectional study of 7,205 office workers in London, Pekkanen et al. (2000) 

examined the association between plasma fibrinogen and ambient air pollutants. The mean 24-h avg SO2

was 9 ppb (10th–90th percentile: 5, 19). Associations with fibrinogen were observed for all pollutants 

examined, either in all-year or summer-only analyses, except for SO2 and O3. 

Taken together, results from the limited number of studies is insufficient to determine the effect of 

SO2 on various blood markers of cardiovascular risk. 

3.2.2.6. Acute Myocardial Infarction 

The association between air pollution and the incidence of MI was examined in a small number of 

studies. As part of the Determinants of Myocardial Infarction Onset Study, Peters et al. (2001) examined 

772 patients with MI living in greater Boston, MA. A case-crossover design was used to assess changes in 

the risk of acute MI after exposure to potential triggers. The mean 24-h avg SO2 was 7 ppb (range: 1, 20) 

during the study period. Similarly, the mean 1-h avg SO2 was 7 ppb (range: 0, 23). In an analysis that 

considered both the 2-h avg (between 60 and 180 min before the onset of symptoms) and 24-h avg 

(between 24 and 48 h before the onset) concentrations jointly, the study found no significant association 

between risk of MI and SO2. Of all the pollutants considered, only PM2.5 and PM10 were found to be 

associated with an excess risk of MI. In a study of 5,793 confirmed cases of acute MI in King County 

Washington, Sullivan et al. (2005) also used a case-crossover design to investigate the association of 

ambient levels of several air pollutants 1, 2, 4 and 24 h before the MI onset. No association with SO2 (or 

with PM2.5) was observed. The mean SO2 level was 9 ppb (range: 0-39 ppb) at the time of the study. 

In the MONICA Project, the effect of air pollution on acute MI was studied in Toulouse, France, 

using a case-crossover study design (Ruidavets et al., 2005b). The mean 24-h avg SO2 level was 3 ppb 

(5th–95th percentile: 1, 5). A total of 399 cases of acute MI were recorded during the study period. O3, but 

not SO2, was found to be associated with the incidence of acute MI. Exposure to PM was not considered 

in this study. 

Only a limited number of studies examined the association between ambient SO2 concentrations 

and incidence of acute MI. These studies provide no evidence that exposure to SO2 increases the risk of 

MI. 

3.2.2.7. Emergency Department Visits and Hospitalizations for Cardiovascular 

Diseases 

The current review includes more than 30 peer-reviewed studies that address the effect of SO2

exposure on ED visits or hospitalizations for cardiovascular diseases. These studies are discussed briefly 

in this section and further summarized in Annex Table F-4. 

All Cardiovascular Diseases 

The disease grouping of all cardiovascular diseases typically includes all diseases of the circulatory 

system (e.g., heart diseases and cerebrovascular diseases, ICD9 Codes 390-459). A summary of the 

associations reported for ambient SO2 concentrations with all cardiovascular diseases are presented in 

Figure 3-9. 

In a study of 11 cities in Spain, an excess risk of 3.6% (95% CI: 0.6, 6.7) per 10 ppb increase in 

24-h avg SO2 at a 0-1 day lag was observed for all cardiovascular disease admissions (Ballester et al., 

2006). The mean 24-h avg SO2 level in the cities studied was 6.6 ppb. In addition, time-series data linking 

SO2 with hospital admissions for cardiovascular diseases in three metropolitan areas in the U.S. (i.e., 

Cook, Maricopa, Los Angeles Counties) was conducted (Moolgavkar 2000; reanalysis 2003). Among 

older adults (65+ years) in Los Angeles County, a 13.7% (95% CI: 11.3, 16.1) excess risk in admissions 

 3-39 
was observed per 10 ppb increase in 24-h avg SO2 at lag 0 day, in the reanalysis using a Generalized 

Linear Model (GLM) and natural splines to adjust for temporal trends rather than GAM. The median 

24-h avg SO2 level for Los Angeles County was 2 ppb during the study period. Results for Maricopa and 

Cook counties were not presented in the reanalysis. 

Figure 3-9. Relative risks (95% CI) of SO2-associated emergency department visits (*) and 

hospitalizations for all cardiovascular causes, arranged by age group. Risk estimates 

are standardized per 10 ppb increase in 24-h avg SO2 concentrations or 40 ppb 

increase in 1-h max SO2. 

In a large single city study Metzger et al. (2004) examined approximately 4.4 million hospital visits 

to 31 hospitals from 1993 to 2000 in Atlanta, GA and reported a 1.4% (95% CI: -1.5, 4.4) excess risk in 

ED visits for cardiovascular causes per 40-ppb increase in 1-h max SO2. Peel et al. (2006) conducted 

 3-40 
analyses using the same dataset to compare results obtained with a case-crossover design to the Metzer et 

al. (2007) results, which were obtained using a time series approach. Peel et al. (2006) and Metzger et al. 

(2004) report similar findings. The median 1-h max SO2 level in Atlanta during the study period was 11 

ppb (10th–90th percentile: 2–39). Results from several single-city studies in Europe, Australia, and 

Taiwan indicated positive associations with SO2 (Atkinson et al., 1999a; Ballester et al., 2001; Jalaludin et 

al., 2006; Petroeschevsky et al., 2001; Poloniecki et al., 1997), though others observed negative 

associations (Chang et al., 2005; Llorca et al., 2005) (see Figure 3-9). 

Specific Cardiovascular Diseases 

Several studies examined the effect of ambient SO2 on hospital admissions for cardiac disease 

(ICD9 Codes 390-429), ischemic heart disease (IHD, ICD9 Codes 410-414), dysrhythmia (ICD9 Code 

427), congestive heart failure (CHF, ICD9 Code 428), MI (ICD9 Code 410) or cerebrovascular diseases 

(ICD9 Codes 430-438). In a study of the six cities of Metropolitan Toronto, Burnett et al. (1997b) 

reported an association between cardiac hospitalizations and ambient SO2 (23.8% [95% CI: 5.5, 45.2]) 

excess risk per 40 ppb increase in 1-h max SO2) that was robust to the inclusion of other gaseous 

pollutants in the model. PM was not identified as an independent risk factor for hospital admissions in 

this study. A European multicity study reported statistically significant positive associations with cardiac 

disease admissions (Ballester et al., 2006). However, adjustment for PM10 and CO in two-pollutant 

models diminished the association reported by Ballester et al. by approximately 50%. Findings for cardiac 

disease admissions reported in several additional single city studies conducted in the U.S., Canada, 

Australia and Europe were inconsistent (Fung et al., 2005b; Jalaludin et al., 2006; Llorca et al., 2005; 

Michaud et al., 2004). 

Analyses restricted to diagnoses of IHD (Jalaludin et al., 2006; Lee et al., 2003b; Lin et al., 2003a; 

Metzger et al., 2004; Peel et al., 2007), CHF (Burnett et al., 1997c; Koken et al., 2003; Metzger et al., 

2004; Morris et al., 1995; Peel et al., 2007; Wellenius et al., 2005a) dysrhythmia (Koken et al., 2003; 

Metzger et al., 2004; Peel et al., 2007), MI (Koken et al., 2003; Lin et al., 2003a), and angina pectoris 

(Hosseinpoor et al., 2005) were also conducted. Metzger et al. (2004) observed weak nonsignificant or 

negative associations of 1-h max SO2 with IHD, CHF, and dysrhythmia. Using the same dataset, Peel et 

al. (2007) investigated effect modification of cardiovascular disease outcomes across comorbid disease 

status categories, including hypertension, diabetes, COPD, dysrhythmia, and CHF. Authors observed only 

weak nonsignificant or negative associations for IHD, CHF, and dysrhythmia with ambient 1-h max SO2

level in any comorbid disease category. Both increases in admissions or ED visits (Jalaludin et al., 2006; 

Koken et al., 2003; Wellenius et al., 2005a) and weak or negative associations (Burnett et al., 1997; 

Hosseinpoor et al., 2005; Lee et al., 2003b; Lin et al., 2003a) were reported in other studies. 

Studies of the effect of SO2 on cerebrovascular admissions were also considered. Positive 

associations were reported for ischemic stroke (Villeneuve et al., 2006a; Wellenius et al., 2005a, 2005b). 

However, Wellenius et al. (2005b) reported stronger associations for NO2 and CO than SO2, and the 

association reported by Villenueve et al. (2006a) was diminished in two-pollutant models. No meaningful 

positive associations of ambient SO2 with cerebrovascular diseases were observed in several other studies 

(Henrotin et al., 2007a; Jalaludin et al., 2006; Metzger et al., 2004; Peel et al., 2007; Tsai et al., 2003a). 

Summary of Evidence on Emergency Department Visits and Hospitalizations from 

Cardiovascular Diseases 

Several studies have observed positive associations between ambient SO2 concentrations and ED 

visits or hospital admissions for cardiovascular diseases (e.g., all cardiovascular diseases, cardiac 

diseases, cerebrovascular diseases) particularly among individuals 65+ years of age, but results are not 

consistent across studies. The strongest evidence comes from a large multicity study conducted in Spain 

Ballester et al. (2006) that observed statistically significant positive associations between ambient SO2

 3-41 
and cardiovascular disease admissions; however, the SO2 effect was found to diminish by half with PM10

and CO adjustment. Only a limited number of studies assessed potential confounding by copollutants 

despite the moderate to strong correlation between SO2 and various copollutants in most studies. While 

some studies indicated that the association of SO2 with cardiovascular hospitalizations were generally 

robust to adjustment for BS and PM10 (Ballester et al., 2001; Fung et al., 2005b), several other studies, 

including that by Balleseter et al. (2006), observed that the effect of SO2 on cardiovascular ED visits and 

hospitalizations may be confounded by copollutant exposures. Jalaludin et al. (2006) reported a 3% 

excess risk in cardiovascular disease hospital admissions per 0.75 ppb incremental change in 24-h avg 

SO2 in single-pollutant models, which was reduced to null when CO was included. Chang et al. (2005) 

noted that the observed negative association of SO2 with all cardiovascular disease hospitalizations was 

strengthened after adjusting for NO2, PM10, and CO in two-pollutant models. The authors attributed this 

finding to possible collinearity problems between SO2 and copollutants. None of the epidemiologic 

studies examined effects of possible interactions among copollutants. 

3.2.2.8. Summary of Evidence on the Effects of Short-Term SO2 Exposure on 

Cardiovascular Health 

Biologically plausible modes of action (e.g., vagally-mediated irritant responses and oxidative 

injury) that could explain short-term SO2 effects on the cardiovascular system were summarized in a 

previous section of this chapter (Section 3.1.2). However, consideration of these modes of action in light 

of findings from additional animal toxicological, human clinical, and epidemiologic studies has led to the 

conclusion that the evidence as a whole is inadequate to infer a causal relationship. 

Specifically, evidence from human clinical and epidemiologic studies of HRV in healthy persons as 

well as persons with asthma or cardiovascular disease was inconsistent and did not support an effect of 

SO2 on the autonomic nervous system, despite some positive findings. In the single epidemiologic study 

of SO2 and repolarization changes, an association with QT interval duration was observed. While in vitro 

studies suggested a potential role for L-type calcium current in cardiac injury, the relevance of these 

studies to ambient exposures is unknown. Epidemiologic evidence from studies of the effect of SO2 on 

ICD-recorded arrhythmias was inconsistent. Furthermore, studies of blood pressure and blood markers of 

cardiovascular risk failed to provide consistent evidence to suggest a role for SO2 in cardiovascular 

disease development. Finally, although some studies of hospital admissions and ED visits for 

cardiovascular diseases reported positive and statistically significant associations with SO2, findings were 

inconsistent across this body of literature as a whole. Many researchers were unable to distinguish the 

effect of SO2 from correlated copollutants while others reported a reduction in the SO2 effect in twopollutant models. The inconsistency of the evidence, lack of coherence across and within disciplines, as 

well as limitations inherent to the observational studies (e.g., inadequate control of copollutant exposures) 

contributed to this causal determination. 

3.2.3. Other Effects Associated with Short-Term SO2 Exposure 

The short-term effects of SO2 on other organ systems were not examined in the previous review. A 

review of animal toxicological studies published since the 1982 AQCD indicates a limited number of 

research inquiries addressing the systemic effects of short-term SO2 exposure in various other organs. The 

most recent studies on these are summarized in Annex Tables E-12 through E-16. 

Of note are three ex vivo acute exposure studies using SO2 derivatives (sulfite and bisulfite) on 

hippocampal or dorsal root ganglion neurons isolated from Wistar rats (Du and Meng, 2004a, 2004b, 

2006). Perturbations were observed in potassium-, sodium-, and calcium-gated channels at concentrations 

of 0.01-100 μM. These authors speculated that such effects might correlate with the neurotoxicity that has 

 3-42 
been associated with SO2 inhalation. However effects on the nervous system have generally been studied 

using chronic exposures ≥ 5 ppm SO2. Effects observed at these levels are of questionable significance in 

evaluating the health effects at ambient levels. These studies are summarized in Annex Table E-12. 

3.3. Mortality Associated with Short-Term SO2 Exposure 

3.3.1. Summary of Findings from the Previous Review 

The studies available to review in the 1982 AQCD were mostly from historical data including 

London, England, and New York City air pollution episodes. Effects of SOX (mainly SO2) were 

investigated along with PM indices because they shared a common source, coal burning, and separating 

their associations with mortality was a challenge that many of the earlier episodic studies could not 

resolve. The SO2 levels observed in these air pollution episodes were several orders of magnitude higher 

than the current avg levels observed in U.S. cities (e.g., in the 1962 New York City episode, SO2 in 

Manhattan peaked at 400 to 500 ppb). Some of these London and New York City studies suggested that 

PM, not SO2, was associated with observed mortality, but the 1982 AQCD could not resolve the relative 

roles of these two pollutants and suggested that the clearest mortality associations were seen when both 

pollutants were at high levels (24-h avg values of both BS and SO2 exceeding 1000 μg/m3

 [~400 ppb for 

SO2]) and less so at lower ranges although the review of the studies and reanalyses found no clear 

evidence of a threshold for SO2. 

The 1986 Second Addendum to the 1982 AQCD reviewed more reanalyses of the London data and 

analyses of New York City, Pittsburgh, and Athens data. While these reanalyses and some new analyses 

confirmed earlier findings (and suggested stronger evidence of BS effects than of the SO2 effects), given 

the remaining uncertainties with exposure error and statistical modeling, there was not sufficient 

information to quantitatively determine concentration-response relationships at lower concentrations of 

either PM or SO2. 

A series of short-term mortality effects studies in the late 1980s and early 1990s (Dockery et al., 

1992; Fairley, 1990; Pope, 1989; Pope et al., 1992; Schwartz and Dockery, 1992a, 1992b) showed 

associations between mortality and PM indices at relatively low levels. Since then, a large number of 

epidemiologic studies have investigated the adverse health effects of air pollution with hypotheses mainly 

focused on PM, and SO2 was often analyzed as one of the potential confounders in these studies. 

3.3.2. Mortality and Short-Term SO2 Exposure in Multicity Studies and 

Meta-Analyses 

In reviewing the range of SO2 mortality effect estimates, multicity studies provide especially useful 

information because they analyze data from multiple cities using a consistent method, avoiding potential 

publication bias. There have been several multicity studies from the U.S., Canada, and Europe, some of 

which will be discussed in the sections below. Meta-analysis studies also provide useful information on 

describing heterogeneity of effect estimates across studies; however, in contrast to multicity studies, the 

observed heterogeneity may reflect the variation in analytical approaches across studies. In addition, the 

effect estimate from a meta-analysis may be subject to publication bias, unless the analysis specifically 

examines such bias and adjusts for it. These studies, as well as many other single-city studies, are 

summarized in Annex Table F-5. 

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3.3.2.1. Multicity Studies 

National Morbidity, Mortality, and Air Pollution Study 

The time-series analysis of the largest 90 U.S. cities (Samet et al., 2000a; reanalysis Dominici et 

al., 2003) in the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) is by far the largest 

multicity study conducted to date to investigate the mortality effects of air pollution, but its primary focus 

was PM10. It should also be noted that, according to the table of mean pollution levels in the original 

report (Samet et al., 2000a), SO2 data were missing in 28 of 90 cities. Annual 24-h avg mean SO2 levels 

ranged from 0.4 ppb (Riverside, CA) to 14.2 ppb (Pittsburgh, PA), with a mean of 5.9 ppb during the 

study period of 1987 to 1994. The analysis in the original report used GAM models with default 

convergence criteria. Dominici et al. (2003) reanalyzed the data using GAM with stringent convergence 

criteria as well as using GLM. It should be noted that this model’s adjustment for weather effects employs 

more terms than other time-series studies in the literature, suggesting that the model adjusts for potential 

confounders more aggressively than the models in other studies. 

Source: Dominici et al. (2003). 

Figure 3-10. All cause mortality excess risk estimates for SO2 from the National Morbidity, 

Mortality, and Air Pollution Study. Posterior means and 95% posterior intervals of 

national average estimates of SO2 effects on all-cause mortality from non-external 

causes per 10 ppb increase in 24-h avg SO2 at 0, 1, and 2-day lags within sets of the 62 

cities with pollutant data available. 

Figure 3-10 shows the all-cause mortality excess risk estimates for SO2 from Dominici et al. 

(2003). The mortality excess risk estimate with a 1-day lag was 0.60% (95% CI: 0.26, 0.95) per 10 ppb 

increase in 24-h avg SO2. PM10 and O3 (in summer) appeared to be more strongly associated with 

 3-44 
mortality compared to the other gaseous pollutants. The model with PM10 and NO2 resulted in an 

appreciably reduced SO2 excess risk estimate, 0.38% (95% CI: -0.62, 1.38) per 10 ppb increase in 

24-h avg SO2. These results suggest that the observed SO2-mortality association could be confounded by 

PM10 and NO2. The authors stated that the results did not indicate associations of SO2, NO2, and CO with 

all-cause mortality. 

Canadian Multicity Studies 

There have been three Canadian multicity studies conducted by the same group of investigators 

examining the association between mortality and short-term exposure to air pollutants (Burnett et al., 

1998a; 2000; 2004). This section focuses on Burnett et al. (2004) as this study is the most extensive 

Canadian multicity study, both in terms of the length and coverage of cities. The discussion in this study 

focused on NO2, because NO2 was the best predictor of short-term mortality fluctuations among the 

pollutants. This was also the case in the Burnett et al. (1998a) study of the gaseous pollutants in 11 

Canadian cities. The mean 24-h avg SO2 levels across the 12 cities was 5.8 ppb, with city means ranging 

from 1 ppb in Winnipeg to 10 ppb in Halifax. The population-weighted avg was 5 ppb. The mean SO2

levels in this study were similar to those in the NMMAPS (mean 24-h avg SO2 levels across the 62 

NMMAPS cities was 5.9 ppb). 

All-cause (nonaccidental), cardiovascular, and respiratory mortality were analyzed in Burnett et al. 

(2004). For SO2, PM2.5, PM10-2.5, PM10 (arithmetic addition of PM2.5 and PM10-2.5), CoH, and CO, the 

strongest mortality association was found at a 1-day lag, whereas for NO2, it was the 3-day moving avg 

(i.e., avg of 0, 1, and 2-day lags), and for O3, it was the 2-day moving avg. The daily 24-h avg values 

showed stronger associations than the daily 1-h max values for all the gaseous pollutants and CoH except 

for O3. The SO2 all-cause mortality excess risk estimate was 0.74% (95% CI: 0.29, 1.19) per 10 ppb 

increase in the 24-h avg SO2 with a 1-day lag. After adjusting for NO2, the SO2 effect estimate was 

reduced to 0.42% (95% CI: 0.01, 0.84), while the NO2 effect estimate was only slightly affected. In this 

analysis, no regression analysis using both SO2 and PM was conducted. The Burnett et al. (2000) analysis 

observed that the simultaneous inclusion of SO2 and PM2.5 in the model reduced the SO2 effect estimate 

by half, whereas the PM2.5 estimate was only slightly reduced. Overall, these results suggest that SO2 was 

not an important predictor of daily mortality in the Canadian cities and that its mortality associations 

could be confounded with NO2 or PM. 

Air Pollution and Health: A European Approach 

Several Air Pollution and Health: a European Approach (APHEA) analyses have reported SO2

mortality excess risk estimates. Katsouyanni et al. (2006) examined the association of PM10, BS, and SO2

with all-cause mortality in 12 European cities using the standard APHEA (GLM) approach. The same data 

set was reanalyzed to adjust for the seasonal cycles (Samoli et al., 2001; 2003). The reanalysis by Samoli 

et al. (2003) produced results that were similar to those in the original analysis by Katsouyanni et al. 

(2006). Since the original analysis presented more results, including multipollutant model results, 

discussion will focus on this analysis. 

The study by Katsouyanni et al. (1997) includes seven western European cities (Athens, Barcelona, 

Cologne, London, Lyon, Milan, and Paris) and five central eastern European cities (Bratislava, Kracow, 

Lodz, Poznan, and Wroclaw). The data covered at least 5 consecutive years for each city within the years 

1980 through 1992. The SO2 levels in these cities were generally higher than in the U.S. or Canada, with 

the median 24-h avg SO2 ranging from 5 ppb in Bratislava to 28 ppb in Kracow. Analysis was restricted to 

days when PM and SO2 concentrations did not exceed 200 μg/m3

 (76 ppb for SO2) to evaluate the effects 

of moderate to low exposures. The data were analyzed by each center separately following a standardized 

method, but the lag for the “best” model was allowed to vary in these cities from 0 to 3 days. The cityspecific effect estimates were then examined in the second stage for source of heterogeneity using city-

 3-45 
specific variables such as mean pollution and weather variables, accuracy of the air pollution 

measurements, health of the population, smoking prevalence, and geographical differences. 

The city-specific estimates were found to be heterogeneous and, among the explanatory variables, 

only the separation between western and central eastern European cities resulted in more homogeneous 

groups. The all-cause mortality excess risk estimates were 1.14% (95% CI: 0.88, 1.39), 1.99% (95% CI: 

1.15, 2.83), and 0.46% (95% CI: -0.23, 1.15) for all the 12 cities combined, western cities, and central 

eastern cities, respectively, per 10 ppb increase in the 24-h avg SO2 at variable single-day lags. Zmirou et 

al. (1998) analyzed cardiovascular and respiratory mortality in 10 of the 12 APHEA cities and observed 

that the cause-specific mortality excess risk estimates were higher than those for all-cause mortality. As in 

the analyses of all-cause mortality, SO2 effect estimates for these cause specific deaths were higher in 

western European cities than in central eastern European cities. 

Seasonal analyses indicated that the summer estimate was slightly higher than the winter estimate 

in the western cities, but the difference was not statistically significant. The results for the two-pollutant 

model with SO2 and BS were presented for the western cities, with a similar extent (~30%) of reductions 

in the estimates of both pollutants (1.31% [95% CI: 0.40, 2.23] for SO2). Furthermore, for western cities, 

they estimated effects for SO2 for days with high or low BS levels and the corresponding BS effects for 

days with high or low SO2 levels and found that the effects were similar in the stratified data. From these 

results, Katsuoyanni et al. (2006) suggested that the effects of the two pollutants were independent. 

Overall, the APHEA studies provide some suggestive evidence that the effect of short-term 

exposure to SO2 on mortality is independent of PM. This is somewhat in contrast to the U.S. and 

Canadian studies. The SO2 levels were much higher in the European cities, but the type of PM 

constituents also might be different. 

The Netherlands Study 

In the Netherlands studies by Hoek et al. (2000; 2001; renalysis by Hoek, 2003) the association 

between air pollutants and mortality was examined in a large population (14.8 million for the entire 

country) over the period of 1986 through 1994. The Netherlands were not part of the APHEA analysis. 

The median 24-h avg SO2 level in the Netherlands was 4 ppb (6 ppb for the four major cities). All the 

pollutants examined, including PM10, BS, O3, NO2, SO2, CO, sulfate, and nitrate, were associated with allcause mortality, and for single-day models, a 1-day lag showed the strongest associations for all the 

pollutants. The following effect estimates are all from the GLM models with natural splines for 

smoothing functions. The SO2 excess risk estimate in a single-pollutant model was 1.31% (95% CI: 0.69, 

1.93) per 10 ppb increase in 24-h avg SO2 at a 1-day lag and 1.78% (95% CI: 0.86, 2.70) at an avg of 0- 

to 6-day lag. Seasonal analyses showed slightly greater effect estimates during the summer compared to 

the winter. SO2 was most highly correlated with BS (r = 0.70). The simultaneous inclusion of SO2 and BS 

reduced the effect estimates for both pollutants (SO2 effect estimate was 1.07% [95% CI: -0.27, 2.42] per 

10 ppb increase with an avg of 0- to 6-day lag of 24-h avg SO2). PM10 was less correlated with SO2 (r = 

0.65), and the simultaneous inclusion of these pollutants resulted in an increase in the SO2 effect estimate. 

These results from the analysis of the Netherlands data suggested some indication of confounding 

between SO2 and BS. 

Cause specific analysis showed larger excess risk estimates for COPD (3.61% [95% CI: -0.29, 

7.67] per 10 ppb increase in the avg of 0- through 6-day lags of 24-h avg SO2) and pneumonia (6.56% 

[95% CI: 1.16, 12.24]) deaths compared to that from all causes, but because essentially all of the 

pollutants showed larger effect estimates for these sub-categories, it is difficult to interpret these estimates 

as effects of SO2 alone. Similarly, the effect estimates for heart failure (7.1% [95% CI: 2.6, 11.7]) and 

thrombosis-related deaths (9.6% [95% CI: 3.1, 16.6]) were larger than that for total cardiovascular (2.7% 

[95% CI: 1.3, 4.1]) causes. Since the same pattern was seen for other pollutants as well, it is difficult to 

interpret these cause-specific effect estimates due to SO2 alone or any one of the pollutants analyzed. 

 3-46 
Other European Multicity Studies 

Other European multicity studies were conducted in 8 Italian cities (Biggeri et al., 2005), 9 French 

cities (Le Tertre et al., 2002), and 13 Spanish cities (Ballester et al., 2002). The studies by Le Tertre et al. 

and Ballester et al. were conducted using GAM methods with the default convergence setting. 

Biggeri et al. (2005) analyzed eight Italian cities (Turin, Milan, Verona, Ravenna, Bologna, 

Florence, Rome, and Palermo) for mortality and hospital admissions (mortality data were not available for 

Ravenna and Verona). The study period varied from city to city between 1990 and 1999. Only singlepollutant models were examined in this study. The SO2 excess risk estimates were 4.14% (95% CI: 1.05, 

7.33), 4.94% (95% CI: 0.41, 9.67), and 7.37% (95% CI: -3.58, 19.57) per 10 ppb increase with an avg of 

0- and 1-day lag of 24-h avg SO2 for all-cause, cardiovascular, and respiratory deaths, respectively. Since 

all the pollutants showed positive associations with these mortality categories and the correlations among 

the pollutants were not presented, it is not clear how much of the observed associations are shared or 

confounded. The mortality excess risk estimates were not heterogeneous across cities for all the gaseous 

pollutants. It should be noted that in Turin, Milan, and Rome, the mean SO2 values declined by 50% from 

the first half to the second half of the study period, while the levels of other pollutants declined by smaller 

fractions. This also complicates the interpretation of SO2 effect estimates in this study, which are much 

higher than those from the APHEA studies. 

The Le Tertre et al. (2002) study of nine French cities examined BS, SO2, NO2, and O3 by generally 

following the APHEA protocol, but using GAM with default convergence criteria and using the avg of 

lags 0 and 1 day for combined estimates. SO2 data were not available in one of the nine cities (Toulouse). 

All four pollutants were positively associated with mortality outcomes. The study did not report 

descriptions of correlation among the pollutants or conduct multipollutant models, and therefore, it is 

difficult to assess the potential extent of confounding among these pollutants. The SO2 effect estimates 

were homogeneous across cities, with the exception of Bordeaux, which was the only city that used strong 

acidity as a proxy for SO2. 

The Spanish Multicentre Study on Air Pollution and Mortality (EMECAM) examined the 

association of PM indices (i.e., PM10, TSP, BS) and SO2 with mortality in 13 cities (Ballester et al., 2002). 

These studies followed the APHEA protocol, but using GAM with default convergence criteria. The daily 

mean 24-h avg SO2 concentrations ranged from 3 to 17 ppb. In the seven cities where 1-h max SO2 data 

were also available, mean concentrations ranged from 21 to 43 ppb. The combined effect estimates for 

all-cause and respiratory mortality were statistically significant for both 24-h avg SO2 and 1-h max SO2. 

Controlling for PM indices substantially diminished the effect estimates for 24-h avg SO2, but not for 

1-h max SO2. The authors reported that these results could indicate an independent impact of peak values 

of SO2 more than an effect due to a longer exposure. 

3.3.2.2. Meta-Analyses of Air Pollution-Related Mortality Studies 

Meta-Analysis of All Criteria Pollutants 

Stieb et al. (2002) reviewed time-series mortality studies published between 1985 and 2000, and 

conducted a meta-analysis to estimate combined effects for PM10, CO, NO2, O3, and SO2. Since many of 

the studies reviewed in that analysis used GAM with default convergence parameters, Stieb et al. (2003) 

updated the estimates by separating the GAM versus non-GAM studies. In addition, separate combined 

estimates were presented for single- and multipollutant models. There were more GAM estimates than 

non-GAM estimates for all the pollutants except for SO2. For SO2, there were 29 non-GAM estimates 

from single-pollutant models and 10 estimates from multipollutant models. The lags and multiday 

averaging used in these estimates varied. The combined estimate for all-cause mortality was 0.95% (95% 

CI: 0.64, 1.27) per 10 ppb increase in 24-h avg SO2 from the single-pollutant models and 0.85% (95% CI: 

0.32, 1.39) from the multipollutant models. Because these estimates are not from an identical set of 

 3-47 
studies, the difference (or lack of a difference, as in this case) between the two estimates may not 

necessarily be due to the effect of adding a copollutant in the model. Note that the data extraction 

procedure of this meta-analysis for the multipollutant models was to include from each study the 

multipollutant model that resulted in the greatest reduction in effect estimates compared with that 

observed in single-pollutant models. It should also be noted that all the multicity studies whose combined 

estimates have been discussed in the previous section were published after this meta-analysis. 

Health Effects Institute Review of Air Pollution Studies in Asia 

The Health Effects Institute (HEI) conducted a comprehensive review of air pollution health effects 

studies in Asia (HEI, 2004) that summarized the results from mortality and hospital admission studies 

published in the peer-reviewed scientific literature from 1980 through 2003. Of the 138 papers the report 

identified, most were studies conducted in East Asia (mainland China, Taipei, Hong Kong, South Korea, 

and Japan). The levels of SO2 in these Asian cities were generally higher than in U.S. or Canadian cities, 

with more than half of these studies reporting mean 24-h avg SO2 levels of > 10 ppb. Based on a 

comparison of the reported mean SO2 levels from the same cities in different time periods, it is clear that 

the SO2 levels declined significantly in the 1990s. The meta-analysis used the most recent estimate for 

each city to reflect recent pollution levels. Based on the criteria of having at least one year of data, model 

adjustment for major time-varying confounders, and reporting effect estimates per unit increase in air 

pollution, the meta-analysis included 28 time-series studies (11 from South Korea, 6 from mainland 

China, 6 from Hong Kong, and 1 each from Taipei, India, Singapore, Thailand, and Japan). The lags 

selected to compute combined estimates were inevitably variable; a systematic approach was used to 

favor the a priori lag stated in the study, followed by the most significant lag, and then the largest effect 

estimate. 

Among the health outcomes examined in the meta-analysis, all-cause mortality was addressed in 

the largest number of studies (13 studies) and SO2 was the most frequently studied pollutant (11 studies). 

The report generally focused on the results of single-pollutant models, as there were too few studies with 

results of comparable multipollutant models to allow meaningful analysis. The SO2 mortality effect 

estimates showed evidence of heterogeneity. The combined estimate for all-cause mortality was 1.49% 

(95% CI: 0.86, 2.13) per 10 ppb increase in 24-h avg SO2. One of the limitations noted in the report was 

that some degree of publication bias was present in these studies. 

3.3.3. Evidence of the Effect of SO2 on Mortality from an Intervention 

Study 

Many time-series studies provide estimates of excess risk of mortality, but a question remains as to 

the likelihood of a reduction in deaths when SO2 levels are actually reduced. A sudden change in 

regulation in Hong Kong in July 1990 required the conversion to fuel oil with low sulfur content. The 

reduction in respiratory symptoms among children living in the polluted district in Hong Kong after the 

intervention was previously discussed in Section 3.1.5. Hedley et al. (2002) examined changes in 

mortality rates following the intervention. The SO2 levels after the intervention declined about 50% (from 

about 17 ppb to 8 ppb), but the levels for PM10, NO2, and sulfate did not change and O3 levels slightly 

increased. The seasonal mortality analysis results showed that the apparent reduction in seasonal death 

rate occurred only during the first winter, and this was followed by a rebound (i.e., higher than expected 

death rate) in the following winter, then returned to the expected pattern three to five years after the 

intervention. Using Poisson regression of the monthly deaths, the avg annual trend in death rate 

significantly declined after the intervention for all causes (2.1%), respiratory causes (3.9%), and 

cardiovascular causes (2.0%), but not from other causes. These results seem to suggest that a reduction in 

SO2 leads to an immediate reduction in deaths and a continuing decline in the annual trend in death rates. 

 3-48 
Hedley et al. (2002) estimated that the expected average gain in life expectancy per year due to the lower 

SO2 levels was 20 days for females and 41 days for males. 

Interpreting these results is somewhat complicated by an upward trend in mortality across the 

intervention point, which the authors noted was due to increased population size and aging. The results 

suggest that such an upward trend is less steep after the introduction of low sulfur fuel. While the Poisson 

regression model of monthly deaths does adjust for trend and seasonal cycles, the regression model does 

not specifically address the influence of influenza epidemics, which can vary from year to year. This issue 

also applies to the analysis of warm to cool season change in death rates. The most prominent feature of 

the time-series plot (or the fitted annual cycle of monthly deaths) presented in this study is the lack of a 

winter peak for respiratory and all-cause mortality during the year immediately following the 

intervention. Much could be made of this lack of a winter peak, but no discussion of the potential impact 

of (or a lack of) influenza epidemics is provided. These issues complicate the interpretation of the 

estimated decline in upward trend of mortality rate or the apparent lack of winter peak. 

The decline in mortality following the intervention does not preclude the possibility that other 

constituents of the pollution mixture that share the same source as SO2 are responsible for the adverse 

effects. Even though PM10 levels before and after the intervention were stable in Hong Kong, it is possible 

that constituents that do not explain a major fraction of PM may have declined. As also noted previously 

in Section 3.1.5, Hedley et al. (2006) noted large reductions in ambient nickel and vanadium 

concomitantly with reductions of sulfur after the intervention. SO2 also may be serving as a modifier of 

the effect of respirable particles. Thus, while the Hong Kong data are supportive of SO2-mortality effects, 

the possibility remains that mortality effects may be caused by constituents of SO2-associated sources. 

3.3.4. Summary of Evidence on the Effects of Short-Term SO2

Exposure on Mortality 

The epidemiologic evidence on the effect of short-term exposure to SO2 on all-cause 

(nonaccidental) and cardiopulmonary mortality is suggestive of a causal relationship at ambient 

concentrations. The epidemiologic studies are generally consistent in reporting positive associations 

between SO2 and mortality; however, there was a lack of robustness of the observed associations to 

adjustment for copollutants. 

Figure 3-11 presents all-cause SO2 mortality excess risk estimates from the multicity studies and 

meta-analyses. The mortality effect estimates from single-pollutant models range from 0.6% (NMMAPS) 

to 4.1% (Italian 8-cities study) per 10 ppb increase in 24-h avg SO2 concentrations, but given the large 

confidence band in the Italian study, a more stable range may be 0.6 to 2%. It is noteworthy that the SO2

effect estimates for the NMMAPS and Canadian 12-city studies are quite comparable (0.6 and 0.7%, 

respectively), considering the differences in the modeling approach. The heterogeneity of estimates in the 

multicity studies and meta-analyses may be due to several factors, including the differences in model 

specifications, averaging/lag time, SO2 levels, and effect-modifying factors. Only the APHEA study 

examined possible sources of heterogeneity for SO2-related mortality. They examined several potential 

effect modifiers such as the mean levels of pollution and weather variables, accuracy of the air pollution 

measurements, health of the population, smoking prevalence, and geographical differences. The only 

variable that could explain the heterogeneity of city-specific effect estimates was the geographic 

separation (western versus central eastern European cities) for both SO2 and BS, but heterogeneity in the 

SO2 effect estimates remained within the western cities. 

 3-49 
Figure 3-11. Relative risks (95% CI) of SO2-associated all-cause (nonaccidental) mortality, with and 

without copollutant adjustment, from multicity and meta-analysis studies. Effect 

estimates are standardized per 10 ppb increase in 24-h avg SO2 concentrations. For 

multipollutant models, results from the models that resulted in the greatest reduction 

in the SO2 effects are shown. (NMMAPS = National Morbidity, Mortality, and Air 

Pollution Study; APHEA = Air Pollution and Health: a European Approach.) 

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*Note: Le Tertre et al. (2002) and Ballester et al. (2002) performed analyses using Poisson GAM with default convergence criteria. 

Figure 3-12. Relative risks (95% CI) of SO2-associated mortality for all (nonaccidental), respiratory, 

and cardiovascular causes from multicity studies. Effect estimates are standardized 

per 10 ppb increase in 24-h avg SO2 concentrations. (APHEA = Air Pollution and 

Health: a European Approach.) 

Several multicity studies provided effect estimates for broad cause-specific categories, typically 

respiratory and cardiovascular mortality. A summary of these effect estimates, along with the all-cause 

mortality estimates for comparison, are presented in Figure 3-12. These results from multicity studies 

suggest that the mortality effect estimates for cardiovascular and respiratory causes were generally larger 

than that for all-cause mortality, though in some cases the effects were not statistically significant, 

possibly because of reduced statistical power by which to examine cause-specific associations. In these 

 3-51 
studies, the effect estimates for respiratory mortality were also found to be larger than the cardiovascular 

mortality effect estimates, suggesting a stronger association of SO2 with respiratory mortality compared to 

cardiovascular mortality. This finding is consistent with the observed greater effects of SO2 on respiratory 

morbidity compared to cardiovascular morbidity. 

As shown previously in Figure 3-11, the mortality effect estimates from the multipollutant models 

in the multicity studies suggest some extent of confounding between SO2 and PM and/or NO2, as 

indicated by the instability of the effect estimates in multipollutant models. It should be noted, however, 

that interpretation of the single- versus multipollutant model results are complicated by potential 

interaction among copollutants and differing degrees of measurement error for correlated pollutants. 

Very few studies specifically examined possible interactions among the copollutants. Katsouyanni 

et al. (2006) examined the effect estimates for SO2 and BS in seven western European cities for subsets 

stratified by high and low levels of the other pollutant and found that the estimates were similar for days 

with low or high levels of the other pollutant. From these results, Katsouyanni et al. suggested that the 

effects of SO2 and BS were independent. 

In summary, recent epidemiologic studies have reported associations between mortality and SO2, 

often at mean 24-h avg levels of < 10 ppb. The range of the excess risk estimates for SO2 on all-cause 

mortality is 0.4 to 2% per 10 ppb increase in 24-h avg SO2 in several multicity studies and meta-analyses. 

The effect estimates for more specific categories may be larger. The larger European study suggests that 

the observed heterogeneity in SO2 effect estimates is at least in part regional. The intervention study from 

Hong Kong supports the idea that a reduction in SO2 levels results in a reduction in deaths, but this does 

not preclude the possibility that the causal agent is not SO2 but rather something else that is associated 

with SO2 sources. Results from the multicity studies suggest that SO2-mortality excess risk estimates may 

be confounded to some extent by copollutants, making a definitive distribution of effects among the 

pollutants difficult. However, the interpretation of multipollutant model results also requires caution 

because of possible interaction among the copollutants and influence of varying measurement error. Very 

limited information was available to determine possible interaction effects between SO2 and PM or other 

copollutants. Overall, the evidence that SO2 is causally related to mortality at current ambient levels is 

suggestive, but limited by potential confounding and lack of understanding regarding the interaction of 

SO2 with copollutants in the epidemiologic data. 

3.4. Morbidity Associated with Long-Term SO2 Exposure 

3.4.1. Summary of Findings from the Previous Review 

The 1982 AQCD addressed some effects of long-term SO2 exposure. It was reported that 

bronchoconstriction resulted from chronic exposure to 5.1 ppm SO2 in dogs but not in monkeys. This 

increased pulmonary resistance was thought to occur as a result of morphological changes in the airway 

or hypersecretion of mucus leading to airway narrowing. However, there were no remarkable pulmonary 

pathological findings in monkeys and dogs in these studies. This could have been due to the conventional 

light microscopic examination applied, which could not detect alterations in surface membranes or subtle 

changes in cilia. 

It was also noted that repeated exposures of rats ≥ 50 ppm SO2 produced chronic bronchitis similar 

to that seen in humans although there was no evidence to suggest that bronchitis developed in humans at 

ambient levels of SO2. Furthermore, nasal mucosal alterations were observed in mice exposed to 10 ppm 

SO2 for 72 h by inhalation. Lack of data on morphological effects of SO2 at near ambient concentrations 

was noted. In addition, some alterations in lung host defenses were discussed with chronic exposure to 

SO2 at doses exceeding ambient concentrations. 

 3-52 
In the 1982 AQCD, only a few epidemiologic studies provided sufficient quantitative evidence 

relating respiratory symptoms or pulmonary functions changes to long-term exposure to SO2. Briefly, a 

study by Lunn et al. (1967) in Sheffield, England, provided the strongest evidence of an association 

between pulmonary function decrements and increased frequency of lower respiratory tract symptoms in 

5- to 6-year-old children chronically exposed to ambient BS (annual level of 230 to 301 μg/m3

) and SO2

levels (69 to 105 ppb). A follow-up study by Lunn et al. (1970) found no effect with much lower levels of 

BS (range: 48, 169 μg/m3

) and SO2 (range: 36, 97 ppb); it was suggested that this might be due to 

insufficient power to detect small health effect changes. 

The 1986 Second Addendum presented three additional studies that examined the effects of longterm exposure on respiratory health. A study by Ware et al. (1986) reported that respiratory symptoms 

were associated with annual avg TSP in the range of ~30 to 150 μg/m3

 in children (n = 8,380) from six 

U.S. studies. Only cough was found to be significantly associated with SO2. Although the increase in 

symptoms did not appear concomitantly with any decrements in lung function, this may indicate different 

mechanisms of effect. Other studies by Chapman et al. (1995) and Dodge et al. (1985) also observed 

increased prevalence of cough among children and young adults living in areas of higher SO2

concentrations; however, it was noted that the observed effects might have been due to intermittent high 

SO2 peak concentrations. 

In addition to respiratory effects from long-term exposure to SO2, the potential carcinogenicity of 

SO2 or other SOX was also examined in the previous review. The 1982 AQCD concluded that little or no 

clear epidemiologic evidence substantiated the hypothesized links between SO2 or other SOX and cancer, 

though there was some animal toxicological evidence that led to the conclusion that SO2 may be 

considered a suspect carcinogen/cocarcinogen. There was very limited consideration of the effects of 

long-term exposure to SO2 on other organ systems. 

Since the 1982 AQCD and the 1986 Second Addendum, a number of animal toxicological and 

epidemiologic studies have investigated the effect of long-term exposure to SO2 on respiratory morbidity, 

including asthma, bronchitis and respiratory symptoms, lung function, morphological effects, and lung 

host defense. Additional studies have examined the effect of long-term SO2 exposure on genotoxic and 

carcinogenic effects, cardiovascular effects, and prenatal and neonatal outcomes, which are also briefly 

discussed in this section. 

3.4.2. Respiratory Effects Associated with Long-Term Exposure to SO2

3.4.2.1. Asthma, Bronchitis, and Respiratory Symptoms 

Several epidemiologic studies have examined the association between long-term exposure to SO2

and other air pollutants on asthma, bronchitis, and a variety of respiratory symptoms. These studies are 

summarized in Annex Table F-6. In the Six Cities Study of Air Pollution and Health, cross-sectional 

associations between air pollutants and respiratory symptoms were examined in 5,422 white children aged 

10 to 12 years old from Watertown, MA, St. Louis, MO, Portage, WI, Kingston-Harriman, TN, 

Steubenville, OH, and Topeka, KS (Dockery et al., 1989). Annual means of 24-h avg SO2 concentrations 

ranged from 3.5 ppb in Topeka to 27.8 ppb in Steubenville. Except for O3, the correlations among pairs of 

pollution measures varied between 0.53 and 0.98. No associations were observed between SO2 and a 

variety of respiratory symptoms, including bronchitis, chronic cough, chest illness, persistent wheeze, and 

asthma. Stronger associations were observed for PM indices. 

Dockery et al. (1996) examined the respiratory health effects of acid aerosols in 13,369 white 

children aged 8 to 12 years old from 24 communities in the U.S. and Canada between 1988 and 1991. The 

city-specific annual mean SO2 concentration was 4.8 ppb, with a range of 0.2 to 12.9 ppb. With the 

exception of the gaseous acids, nitrous and nitric acid, none of the particulate or gaseous pollutants, 

including SO2, were associated with increased asthma or any asthmatic symptoms. Stronger associations 

 3-53 
with particulate pollutants were observed for bronchitis and bronchitic symptoms. For SO2, the only 

significant association found was with chronic phlegm, with an OR of 1.19 (95% CI: 1.00, 1.40) per 

5 ppb increase in SO2. 

Herbarth et al. (2001) performed a meta-analysis of three cross-sectional surveys conducted in East 

Germany investigating the relationship between lifetime exposure (from birth to completion of 

questionnaire survey) to SO2 and TSP in children and the prevalence of chronic bronchitis. Using a 

logistic model that included variables on parental predisposition (mother or father with bronchitis) and 

environmental tobacco smoke exposure, the authors reported that the OR for bronchitis due to a lifetime 

exposure to SO2 was 3.51 (95% CI: 2.56, 4.82) (the concentration change for which the OR was based 

was not presented). No associations were found between TSP and the prevalence of bronchitis in children. 

As part of the international SAVIAH (Small-Area Variation in Air Pollution and Health) study, 

Pikhart et al. (2001) examined the respiratory health effects from long-term exposure to SO2 in children (n 

= 6,959) from two central European cities with high pollution levels (Prague, Czech Republic, and 

Poznan, Poland). A novel technique was used to estimate the outdoor concentrations of SO2 at a smallarea level. Outdoor SO2 was measured by passive samplers at 130 sites in the two cities during 2-week 

periods. Concentrations of SO2 at each location in the study areas were estimated from these data by 

modeling using a geographic information system (GIS). The estimated mean exposure to outdoor SO2 was 

32 ppb (range: 25, 37) in Prague and 31 ppb (range: 17, 53) in Poznan. The prevalence of wheezing or 

whistling in the past 12 months was associated with SO2 (OR of 1.08 [95% CI: 1.03, 1.13] per 5 ppb 

increase in SO2). Moreover, the lifetime prevalence of wheezing or whistling (OR 1.03 [95% CI: 1.00, 

1.07]) and lifetime prevalence of physician-diagnosed asthma (OR 1.09 [95% CI: 1.00, 1.19]) also were 

associated with SO2 levels. In the SAVIAH study, the only other pollutant considered in relation to health 

outcomes was NO2. An earlier publication by Pikhart et al. (2000) presented preliminary results of the 

Prague data and indicated that the observed associations between NO2 and respiratory symptoms were 

generally similar to that of SO2. 

The International Study of Asthma and Allergies in Children (ISAAC) included thousands of 

children in several European countries and Taiwan (Hirsch et al., 1999; Hwang et al., 2005; PenardMorand et al., 2005; Ramadour et al., 2000; Studnicka et al., 1997). Pénard-Morand et al. examined the 

effect of long-term exposures to air pollution and prevalence of exercise-induced bronchial reactivity 

(EIB), flexural dermatitis, asthma, allergic rhinitis, and atopic dermatitis in 9,615 children aged 9 to 11 

years in six French communities. Using 3-year averaged concentrations of SO2, the investigators reported 

that the prevalence of EIB reactivity, lifetime asthma, and allergic rhinitis were significantly associated 

with increases in SO2 exposure. The estimated 3-year averaged concentration of SO2 was 2 ppb in the 

low-exposure schools and 4 ppb in the high-exposure schools. In a single-pollutant model, the ORs were 

2.37 (95% CI: 1.44, 3.77) for EIB and 1.58 (95% CI: 1.00, 2.46) for lifetime asthma per 5 ppb increase in 

SO2. In this study, SO2 was correlated with PM10 (r = 0.76) but not with O3 (r = -0.02). Using a twopollutant model that included PM10, the associations of SO2 with EIB and lifetime asthma were fairly 

robust (< 5% change). 

In a German study of 5,421 children, the annual mean SO2 concentration was associated with 

morning cough reported in the last 12 months, but not bronchitis (Hirsch et al., 1999). This study further 

observed that the association of SO2 and other air pollutants with respiratory symptoms were stronger in 

nonatopic than in atopic children. The authors noted that these findings were in line with the hypothesis 

that these air pollutants induce nonspecific irritative rather than allergic inflammatory changes in the 

airway mucosa, as irritative effects would affect the clinical course in nonatopic children more strongly 

than in atopics whose symptoms are also determined by allergen exposure. 

In contrast to the studies noted above, other studies using the ISAAC protocol did not observe an 

association between long-term exposure to SO2 and respiratory symptoms. In France, Ramadour et al. 

(2000) performed an epidemiologic survey of 2,445 children aged 13 to 14 years living in communities 

with contrasting levels of air pollution to determine the relationship between long-term exposure to 

gaseous air pollutants and prevalence rate of rhinitis, asthma, and asthma symptoms. The avg SO2

concentrations during the 2-month survey period ranged from 7 ppb to 22 ppb across the seven 

 3-54 
communities. This study found no relationship between the mean levels of SO2, NO2, or O3 and the 

above-mentioned symptoms. Another study of 843 children from eight nonurban communities in Austria 

did not observe consistent associations between SO2 and prevalence of asthma and symptoms (Studnicka 

et al., 1997). Compared to the lowest SO2 concentration category, the ORs in the higher SO2

concentration categories (third and fourth quartiles) did not exceed one for any of the symptoms 

examined (wheeze, cough, bronchitis, and asthma). 

A cohort study was conducted by Goss et al. (2004) to examine the effect of air pollutants on a 

potentially susceptible population, patients with cystic fibrosis. Study participants included 11,484 

patients (mean age 18.4 years) enrolled in the Cystic Fibrosis Foundation National Patient Registry in 

1999–2000. Exposure was assessed by linking air pollution values from ambient monitors with the 

patient’s home ZIP code. During the study period, the mean SO2 concentration was 4.9 ppb (SD 2.6, IQR: 

2.7, 5.9). This study found no association between SO2 and the odds of having two or more pulmonary 

exacerbations. One of the limitations addressed by the authors was the lack of information regarding 

tobacco use or environmental tobacco smoke, an important risk factor for pulmonary exacerbations. 

Several studies examined the effects of long-term exposure to SO2 on asthma, bronchitis, and 

respiratory symptoms. The studies reported positive associations in children; the notable exception was 

the Harvard Six Cities Study. However, there were inconsistencies in the results observed: some found 

effects on bronchitic but not asthmatic symptoms; others found the converse. A major limitation was that 

some subjects were asked to recall prevalence of symptoms in the last 12 months or in a lifetime; such 

long recall periods may have caused significant recall bias. Another concern is the high correlation of 

long-term avg SO2 and copollutant concentrations, particularly PM, and the very limited evaluation of 

potential confounding in these studies. Overall, the epidemiologic studies do not provide sufficient 

evidence to conclude that long-term exposure to SO2 has an effect on asthma, bronchitis, or respiratory 

symptoms. 

3.4.2.2. Lung Function 

Only a few new animal toxicological studies involving longer-term inhalation exposures to SO2

were conducted since the last review. These studies are summarized here and in Annex Table E-1. Rabbits 

that were neonatally immunized to Alternaria tenuis and exposed to 5 ppm SO2 for 13 weeks beginning in 

the neonatal period (Douglas et al., 1994) did not demonstrate alterations in lung resistance, dynamic 

compliance, trans-pulmonary pressure, tidal volume, respiration rate or minute volume. Similarly, no 

changes in physiological function were noted in dogs exposed to 15 ppm SO2 for 2 h/day and 4-5 

days/week for 5 months (Scanlon et al., 1987), although changes were noted at 50 ppm. However, Smith 

et al. (1989) found decreased residual volume and quasistatic compliance in rats at 4 months of exposure 

to 1 ppm SO2 for 5 h/day and 5 days/week. 

Only a limited number of epidemiologic studies examined the association between long-term 

exposure to SO2 and changes in lung function. The Harvard Six Cities Study by Dockery et al. (1989) 

reported that no associations were observed between lung function and long-term exposure to air 

pollution, including SO2, in a cohort of more than 5,000 children. An analysis of NHANES II data by 

Schwartz (1989), which included information on children and youths from 44 cities but was limited by a 

cross-sectional study design, also did not observe an association with SO2, though inverse associations of 

FVC and FEV1 with annual concentrations of TSP, NO2 and O3 were found. Additional studies conducted 

in Europe observed mixed results. 

In a longitudinal cohort study of 1,150 children in nine communities in Austria, Frischer et al. 

(1999) examined the effect of long-term exposure to air pollutants on lung function. Lung function was 

measured in the spring and fall over a 3-year period from 1994 through 1996. Annual mean SO2

concentrations ranged from 2 to 6 ppb across the nine communities. The authors reported no consistent 

associations between SO2, PM10, or NO2 and lung function. For SO2, a negative parameter estimate was 

observed during the summer, but a positive estimate was found during the winter period. Horak et al. 

 3-55 
(2002a, 2002b) extended the study of Frischer et al. (1999) with an additional year of data. The mean SO2

concentration was 6 ppb in the winter and 3 ppb in the summer. This study found a positive association 

between wintertime SO2 concentrations and changes in FVC, which became null with PM10 in a twopollutant model. 

Jedrychowski et al. (1999) conducted a prospective cohort study of 1,001 preadolescent children 

from two areas of Krakow, Poland, that differed in ambient air pollutants. In the city center, which had 

higher air pollution, the mean annual level of SO2 was 16.7 ppb (SD 12.5). In comparison, the mean 

annual SO2 level in the control area was 12.1 ppb (SD 8.4). A similar difference in TSP levels was 

observed between the city center and control area. The adjusted ORs comparing the city center to the 

control area for the occurrence of slower lung function growth over a two-year period were 2.10 (95% CI: 

1.27, 3.46) for FVC and 2.10 (95% CI: 1.27, 3.48) for FEV1 in boys. The adjusted ORs for girls were 1.54 

(95% CI: 0.89, 2.64) for FVC and 1.51 (95% CI: 0.90, 2.53) for FEV1. However, as both TSP and SO2

levels were higher in the city center, the observed effects on lung function growth cannot be specifically 

attributable to SO2. 

One notable study examined the potential effect of long-term exposure to air pollution on lung 

function in adults. The study by Ackermann-Liebrich et al. (1997) included 9,651 adults aged 18 to 60 

years old residing in eight different areas in Switzerland (Study on Air Pollution and Lung Diseases in 

Adults [SAPALDIA]). They observed a 0.1% decrease in FEV1 per 5 ppb increase in SO2 for adults. 

Significant associations also were observed for PM10 and NO2. The limited number of study areas and 

high intercorrelation between the pollutants made it difficult to assess the effect of an individual pollutant. 

The authors concluded that air pollution from fossil fuel combustion, which was the main source of air 

pollution for SO2, NO2, and PM10 in Switzerland, was associated with decrements in lung function 

parameters in this study. 

Collectively, the results from the limited number of animal toxicological and epidemiologic studies 

on the effect of long-term exposure to ambient SO2 on lung function is inconclusive. 

3.4.2.3. Morphological Effects 

Several animal toxicological studies of morphological effects resulting from subacute to chronic 

SO2 exposures have been published since the 1982 AQCD. These studies are summarized in Annex Table 

E-17. No alveolar lesions (including electron microscopic evaluation) or changes in numbers of tracheal 

secretory cells were observed in guinea pigs exposed to 1 ppm SO2 for 3 h/day for 6 days (Conner et al., 

1985). No pulmonary or nasal lesions were observed in rats exposed to 5 ppm SO2 for 2 h/day and 

5 days/week for 4 weeks (Wolff et al., 1989). A weakness of the latter study is that histopathological 

methods were not reported. However, a third study reported histopathological changes in the respiratory 

system involving lesions in the bronchioles. Smith et al. (1989) exposed rats for 4 to 8 months to 1 ppm 

SO2 for 5 h/day and 5 days/week and observed increased incidence of bronchiolar epithelial hyperplasia 

and a small increase (12%) in numbers of nonciliated epithelial cells in terminal respiratory bronchioles at 

4 but not 8 months of exposure. A limitation of the study was the examination of a single concentration, 

which does not allow for concentration-response assessment or identification of a no-effect-level. 

In summary, evidence from these animal toxicological studies is insufficient to conclude that longterm exposure to ambient SO2 causes prolonged effects on lung morphology. 

3.4.2.4. Lung Host Defense 

The 1982 AQCD reported some detrimental effects of SO2 on lung host defenses that generally 

occurred at concentrations exceeding ambient exposure concentrations. In rats exposed to 0.1 ppm SO2

for ~2 to 3 weeks, clearance of labeled particles from the lung was accelerated at 10 and 23 days 

following exposure. In rats exposed to 1 ppm for ~2 to 3 weeks, clearance was accelerated at 10 days and 

 3-56 
slowed down at 25 days. Tracheal mucus flow was decreased with a 1-year exposure of dogs to 1 ppm 

SO2, but was unaffected by a 30-min exposure of donkeys to 25 ppm SO2. Studies in mice suggested no 

effect on susceptibility to bacterial infection with exposure to SO2 concentrations of ≤ 5 ppm for 3 

months. Antiviral defenses were impaired in mice exposed to 7-10 ppm SO2 for 7 days. No alterations in 

pulmonary immune system were reported with chronic exposure of mice to 2 ppm SO2. 

Several studies on lung host defense have been conducted since the last review and are summarized 

in Annex Table E18. Only one study published after the last review evaluated mucociliary clearance in 

rats after exposure to SO2. In this subchronic study, no effect on clearance of radiolabeled particles from 

the lung was observed in rats exposed to 5 ppm SO2 for 2 h/day for 4 weeks (Wolff et al., 1989). These 

findings are in contrast to the altered clearance reported in the 1982 AQCD. Three other recent studies 

were conducted evaluating the effects of 10 ppm SO2 on immune responses. Impairment of host defense 

responses was seen following continuous exposure to SO2 for 1-3 weeks (Azoulay-Dupuis et al., 1982), 

but not in response to a single 4 h exposure (Clarke et al., 2000; Jakab et al., 1996). 

In summary, animal toxicological studies do not provide sufficient evidence to assess the effects of 

long-term exposure to ambient SO2 on lung host defense. 

3.4.2.5. Summary of Evidence on the Effects of Long-Term Exposure on Respiratory 

Health 

The overall epidemiologic evidence on the respiratory effects of long-term exposure to SO2 is 

inadequate to infer a causal relationship. Studies that examined the effects of long-term exposure to SO2

on asthma, bronchitis, and respiratory symptoms observed positive associations in children. However, the 

variety of outcomes examined and the inconsistencies in the observed results make it difficult to assess 

the impact of long-term exposure of SO2 on respiratory symptoms. In the limited number of studies 

examining the SO2 associations with lung function, results were generally mixed. A major consideration 

in evaluating SO2-related health effects in these epidemiologic studies of long-term exposure is the high 

correlation among the pollutant levels observed, particularly between long-term avg SO2 and PM 

concentrations. The lack of evidence available to evaluate potential confounding by copollutants limits 

the ability to make a causal determination based on these studies. 

A limited number of animal toxicological have examined the effect of long-term exposure to SO2

on lung function. Results from these studies do not provide strong biological plausibility for effects of 

long-term ambient exposure to SO2 on respiratory morbidity. These studies observed no effects on 

physiological lung function at SO2 concentrations ≤ 5 ppm in rabbits and dogs; however, one study found 

decreased residual volume and quasistatic compliance at 1 ppm SO2 in rats. In addition, no morphological 

changes were found in guinea pigs exposed subacutely to 1 ppm SO2, or in rats exposed subchronically to 

5 ppm SO2. While mild, bronchiolar epithelial hyperplasia was observed in rats exposed to 1 ppm for 4 

months, this change was not apparent at 8 months. Furthermore, animal toxicological studies provide no 

evidence for decrements in lung host defense at or near ambient levels of SO2. 

Overall, the available evidence from the generally limited number of epidemiologic and animal 

toxicological studies is inadequate to infer that respiratory effects occur from long-term exposure to SO2

at ambient concentrations. 

3.4.3. Carcinogenic Effects Associated with Long-Term Exposure 

The 1982 AQCD concluded that little or no clear epidemiologic evidence substantiated the 

hypothesized links between SO2 or other SOX and cancer. From the toxicological studies, it was noted that 

while there were some indications of carcinogenicity for both SO2 and SO2 + benzo[a]pyrene (B[a]P), 

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complex exposure regimens, problematic dose determinations, and/or inadequately reported experimental 

details led to the conclusion that SO2 could only be considered a suspect carcinogen/cocarcinogen. 

Since the last review, numerous studies have examined the genotoxic effects of SO2. These are 

summarized in Annex Table E-19. SO2 and its metabolite sulfite were found not to be mutagenic or to 

induce DNA damage in vitro (Pool-Zobel et al., 1990; Pool et al., 1988b). However, inhalation studies 

demonstrated increased mouse bone marrow micronucleated polychromatic erythrocytes and DNA 

damage in cells isolated from various organs when mice were exposed for 4-6 h/day for 7 days to 5-30 

ppm SO2 (Meng et al., 2002; 2005b; Ruan et al., 2003). These in vivo studies suggest that inhaled SO2

may have systemic effects at high concentrations, but they are of questionable significance in evaluating 

the effects of SO2 at ambient levels. 

The carcinogenic potential of SO2 was examined in animal toxicological studies which are 

summarized in Annex Table E-11. Gunnison et al. (1988) conducted a two-part study in which rats were 

exposed either for 21 weeks (6 h/day, 5 days/week) by inhalation to 0, 10, or 30 ppm SO2, or for 21 weeks 

to two tungsten-supplemented, molybdenum-deficient diets. This latter regimen induced a condition of 

sulfite oxidase deficiency, resulting in elevated systemic levels of sulfite:bisulfite relative to control 

values (e.g., in plasma, from 0 to 44 μM; and in tracheal tissue, from 33 to 69 or 550 nmol/g wet weight). 

Beginning with week 4, some groups from each regimen received weekly tracheal installations of 1-mg 

B[a]P for 15 weeks. Overall results indicated that squamous cell carcinoma was not induced, or in the 

B[a]P groups coinduced or promoted, by SO2 inhalation or elevated systemic sulfite:bisulfite. Researchers 

found a very high incidence of animals with tumors in the groups exposed to only B[a]P (128 of144 

animals). As a result, carcinogenicity or cocarcinogenicity of SO2 or sulfite:bisulfite could only have been 

detected as a shortening of tumor induction time or an increase in rate of tumor appearance, and neither 

was observed. As noted by the authors, these findings do not support the conclusion that SO2 exposure 

enhances the carcinogenicity of B[a]P . It was proposed that SO2 exposure, by elevating systemic 

sulfite:bisulfite, would generate glutathione-S-sulfonates, which in turn could inhibit glutathione Stransferase (GST) and reduce intracellular GSH and, thus, interfere with a major detoxication pathway for 

B[a]P. See Annex Table E-20 for discussion (Menzel et al. 1986). 

Two similar studies were published that investigated the ability of 10 to 11 months of exposure (16 

h/day) to 4 ppm SO2, 6 ppm NO2, or their combination to affect the carcinogenicity of either urban 

suspended PM (SPM) (Ito et al., 1997) or diesel exhaust particle (DEP) extract-coated carbon particles 

(Ohyama et al., 1999). The former study found that, while exposure to SPM extract-coated carbon 

particles significantly increased pulmonary endocrine cell (PEC) hyperplasia, coexposure to SO2, NO2, or 

their combination was without additional affect. Also, irrespective of gas coexposure, SPM extract-coated 

carbon particles demonstrated a few PEC papillomas versus control frequencies of zero. 

Using Syrian golden hamsters, Heinrich et al. (1989) investigated whether coexposure to 10 ppm 

SO2 and 5 ppm NO2 for 6 to 8 months (5 days/week, 19 hours/day) could enhance tumorigenesis induced 

by a single subcutaneous injection of diethylnitrosamine (DEN) during week 2. The combined gas 

exposure did not affect body weight gain and only minimally shortened survival times. Compared to the 

DEN groups, serial sacrifices of gas-exposed animals demonstrated progressively increasing numbers of 

tracheal mucosal cells and aberrant tracheal cell cilia. In the lung, effects related to gas mixtures were 

largely limited to a progressive type of alveolar lesion that involved the lining of bronchiolar epithelium 

and the appearance of pigment-containing AM and to a mild, diffuse thickening of the alveolar septa. 

Exposure to the combined gases by itself did not induce tumors of the upper respiratory tract, nor did it 

enhance the induction of such tumors by DEN. 

In addition to the animal toxicological studies that examined the genotoxic and carcinogenic 

potential of SO2, a limited number of recent epidemiologic studies have investigated the relationship 

between long-term exposure to SO2 and lung cancer incidence and mortality. These studies are 

summarized in Annex Table F-7. Nyberg et al. (2000) conducted a case-control study of men aged 40 to 

75 years with (n = 1,042) and without (n = 2,364) lung cancer in Stockholm County, Sweden. They 

mapped residence addresses to a GIS database to assign individual exposures to SO2 from defined 

emission sources (mainly local oil-fueled residential heating). Available SO2 measurement data were used 

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to calibrate the model. In this study, SO2 was considered an indicator of air pollution from residential 

heating. Exposure to NO2, considered to be a marker of traffic pollution, also was evaluated in this study. 

The 90th percentile 30-year avg SO2 level was 30 ppb. After adjusting for potential confounders (e.g., 

smoking, occupational exposures), long-term avg heating-related SO2 exposure was not associated with 

an increase in risk of lung cancer incidence. A weak association for the 30-year avg traffic-related NO2

exposure was observed. 

Very similar results were reported in a Norwegian study by Nafstad et al. (2003). The study 

population is a cohort of 16,209 men who enrolled in a study of cardiovascular diseases in 1972. The 

Norwegian cancer registry identified 422 incident cases of lung cancer. SO2 exposure data were modeled 

based on residence using data for observed concentrations and emission from point sources (e.g., industry 

and heating of buildings and private homes) and traffic. Once again, no association was observed between 

long-term exposure to SO2 and lung cancer incidence. 

Three additional European cohort studies examined the associations between long-term exposure to 

air pollution and lung cancer mortality (Beelen et al., 2008; Filleul et al., 2005; Nafstad et al., 2004) in 

cohorts ranging in size from 14,284 to 120,852 subjects, who were followed for 9 to > 20 years. 

Consistent with the results for lung cancer incidence, none of these studies observed an association 

between long-term SO2 exposure and lung cancer mortality. These studies are discussed in further detail 

in Section 3.5.2.2. 

Similar to the European cohort studies, studies conducted in the U.S. generally did not observe an 

association between long-term exposure to SO2 and lung cancer mortality. In the reanalysis of the Harvard 

Six Cities Study, Krewski et al. (2000) estimated a RR of 1.03 (95% CI: 0.91, 1.16) per 5 ppb increase in 

avg SO2 over the study period, while Pope et al. observed a positive but not statistically significant (RR 

~1.04 per 5 ppb increase in avg SO2 from 1982 to 1998) association in the extended analysis of the 

American Cancer Society (ACS) cohort. The California Seventh-day Adventists study by Abbey et al. 

(1999) did observe a statistically significant association between lung cancer mortality and SO2 (and most 

of the pollutants examined including PM10, sulfate, O3, and NO2), but the number of lung cancer deaths in 

this cohort was very small (12 for female, 18 for male) and, therefore, it is difficult to interpret these 

estimates. More detailed discussions of these studies are provided in Section 3.5.2.1. 

In conclusion, the toxicological studies indicate that SO2 at high concentrations may cause DNA 

damage but fails to induce carcinogenesis, cocarcinogenesis, or tumor promotion. Furthermore, results 

from the limited number of epidemiologic studies examining the association between long-term exposure 

to ambient SO2 and excess risk of lung cancer incidence and mortality are inconclusive. 

3.4.4. Cardiovascular Effects Associated with Long-Term Exposure 

The effects of SO2 on the cardiovascular system were not addressed in the 1982 AQCD. Since then, 

animal toxicological studies have reported oxidation and glutathione (GSH) depletion (Langley-Evans et 

al., 1996; Meng et al., 2003b; Wu and Meng, 2003) in the hearts of rodents which were exposed by 

inhalation to SO2. However, as concentrations of SO2 used in these studies were 5 ppm and above, the 

oxidative injury observed is probably not relevant to cardiovascular effects seen at ambient levels of SO2. 

These and other animal toxicology studies measuring cardiovascular endpoints are summarized in Annex 

Table E-9. 

 A recent epidemiologic study examined the association between long-term exposure to air 

pollution, including SO2, and one or more fatal or nonfatal cardiovascular events. In the Women’s Health 

Initiative cohort study, Miller et al. (2007) studied 65,893 postmenopausal women between the ages of 50 

and 79 years without previous cardiovascular disease in 36 U.S. metropolitan areas from 1994 to 1998. 

Subjects’ exposures to air pollution were estimated using residents’ five-digit ZIP code, assigning the 

annual mean levels of air pollutants measured at the nearest monitor. A total of 1,816 women had one or 

more fatal or nonfatal cardiovascular events, including 261 deaths from cardiovascular causes. Hazard 

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ratios for the first cardiovascular event were estimated. The results for models that only included subjects 

with non-missing exposure data for all pollutants (n = 28,402 subjects, resulting in 879 cardiovascular 

events) are described here. In the single-pollutant models, PM2.5 showed the strongest associations with 

cardiovascular events among the pollutants (Hazard Ratios = 1.24 [95% CI: 1.04, 1.48] per 10 μg/m3

increase in annual avg), followed by SO2 (1.07 [95% CI: 0.95, 1.20] per 5 ppb increase in the annual avg). 

In the multipollutant model where all the pollutants (i.e., PM2.5, PM10-2.5, CO, SO2, NO2, O3) were 

included in the model, the PM2.5 association with overall cardiovascular events was even stronger (1.53 

[95% CI: 1.21, 1.94]). The association with SO2 also became stronger (1.13 [95% CI: 0.98, 1.30]). 

Correlations among these pollutants were not described and, therefore, the extent of confounding among 

these pollutants in these associations could not be examined, but among all the air pollutants considered, 

PM2.5 was clearly the best predictor of cardiovascular events. 

The available toxicological and epidemiologic evidence to assess the effect of long-term exposure 

to SO2 on cardiovascular health is too limited to make any conclusions at this time. 

3.4.5. Prenatal and Neonatal Outcomes Associated with Long-Term 

Exposure 

Several animal toxicological studies examined developmental effects of SO2 and are summarized in 

Annex Table E-13. No changes in birth weight or neurobehavioral development were noted in mouse 

pups prenatally exposed to 5-30 ppm SO2 (Petruzzi et al., 1996), while some behavioral modifications 

were seen in adults exposed prenatally to these same levels (Fiore et al., 1998). However, effects observed 

at such high concentrations of SO2 are of questionable relevance. 

In recent years, the effects of prenatal and neonatal exposure to air pollution have been examined in 

epidemiologic studies by several investigators (see Annex Table F-8). The most common endpoints 

studied are low birth weight, preterm delivery, and measures of intrauterine growth. Preterm birth and low 

birth weight may result in serious long-term health outcomes for the infant. Preterm birth is the leading 

cause of infant mortality and is a major determinant of a variety of adverse neurodevelopmental outcomes 

and chronic adverse respiratory effects (Berkowitz and Papiernik, 1993). Low birth weight has also been 

linked with increased risk of infant mortality and morbidity. Other studies have examined associations 

between maternal exposure to ambient air pollution and sudden infant death syndrome (SIDS) and 

neonatal hospitalizations. 

These studies analyzed air pollution data and birth certificates from a given area. In evaluating the 

results of these studies, it is important to consider the limitations of these data. For example, the reliability 

and validity of birth certificate data have been reviewed (Buescher et al., 1993; Piper et al., 1993) and 

have been found to vary in degrees of reliability by specific variables. The variables considered the most 

reliable include birth weight, maternal age, race, and insurance status. Whereas gestational age, parity and 

delivery type (vaginal vs. cesarean) were reasonably reliable, obstetrical complications and maternal 

lifestyle factors such as smoking and alcohol consumption were not reliable. Another concern in these 

studies regards adequate control for potential confounders. While most of these studies adequately 

controlled for maternal education, parity, age, and sex of child, many did not adjust for SES, occupational 

exposures, indoor pollution levels, maternal smoking, alcohol use, prenatal care, or concurrent 

temperature exposures as fetal growth is associated with all of these factors. This makes overall 

comparisons across studies a difficult task. 

While most studies analyzed avg SO2 exposure for the whole pregnancy, many also considered 

exposure during specific trimesters, or other time periods (e.g., first and last months of gestation). 

Different exposure periods have been examined because the biological mechanisms and timing of critical 

exposures that link air pollution to adverse birth outcomes are yet to be determined. For example, fetal 

growth is much more variable during the third trimester; therefore, exposure during the third trimester 

would have the greatest likelihood of an association. However, insufficient placentation during the first 

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trimester may be associated with early environmental insult, whereby subsequent fetal growth is hindered. 

Similarly, it is possible that preterm delivery is associated with insufficient placentation resulting from 

early exposure. Furthermore, preterm delivery may be the result of acute exposures just prior to delivery. 

Figure 3-13. Relative risks (95% CI) for low birth weight, grouped by trimester of SO2 exposure. 

Risk estimates are standardized per 5 ppb increase in SO2 concentrations. 

Epidemiologic studies examining the effects of air pollutants on low birth weight are summarized 

in Figure 3-13. Maisonet et al. (2001) examined the association between air pollution and low birth 

weight in six northeastern cities: Boston, MA; Hartford, CT; Philadelphia, PA; Pittsburgh, PA; 

Springfield, MA; and Washington, DC. The study population consisted of 89,557 singleton, full-term, live 

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births (37-44 weeks of gestation) born between January 1994 and December 1996. Low birth weight was 

classified as < 2,500 g (5.5 lbs.). This study observed an association between low birth weight and SO2

concentrations during each trimester among Caucasians; however, the association was not consistent in 

other races and ethnicities. 

An excess risk for low birth weight associated with ambient SO2 concentrations was reported by 

Dugandzic et al. (2006) in a large cohort study of 74,284 women with full-term, singleton births from 

1988–2000 in Nova Scotia, Canada. The mean 24-h avg SO2 concentration over the study period was 10 

ppb (IQR 7). These investigators found that exposure only during the first trimester was associated with 

increased risk of low birth weight. The RR was 1.14 (95% CI: 1.04, 1.26) per 5 ppb increase in SO2 level. 

Liu and Krewski (2003) found similar results in a study of pregnancy outcomes and air pollution in 

Vancouver, Canada. The mean 24-h avg SO2 concentration was 4.9 ppb (IQR 7.7) from 1985 to 1998. 

Maternal exposure during the first month was associated with an increased risk of low birth weight (OR 

1.11 [95% CI: 1.01, 1.22]). Additional studies from the U.S., Europe, Latin America and Asia have 

reported positive associations between low birth weight and maternal exposure to SO2 during the first 

(Bell et al., 2007; Bobak, 2000; Ha et al., 2001; Mohorovic, 2004; Yang et al., 2003a), second (Bobak, 

2000; Gouveia et al., 2003; Lee et al., 2003a) and third (Bobak, 2000; Lin et al., 2004b; Wang et al., 

1997) trimesters. 

Preterm delivery, intrauterine growth retardation (IUGR), and birth defects are additional adverse 

birth outcomes that have been associated with ambient SO2 levels. In a time-series analysis using data 

from four Pennsylvania counties, Sagiv et al. (2005) reported that the mean 6-week SO2 exposure prior to 

birth was associated with increased risk of preterm birth, with a RR of 1.05 (95% CI: 1.00, 1.10) per 

5 ppb increase in SO2. A 5 ppb increase in SO2 concentrations three days before birth was associated with 

a RR of 1.02 (95% CI: 0.99, 1.05). The authors discussed two plausible mechanisms for the effects of air 

pollution on preterm birth: (1) changes in blood viscosity due to inflammation as a result of air pollution 

(Peters et al., 1997); and (2) maternal infection during pregnancy as a consequence of impaired immunity 

from air pollution exposure. Liu and Krewski (2003) reported that SO2 exposure during the last month of 

pregnancy was associated with preterm birth, with an OR of 1.09 (95% CI: 1.01, 1.19) for a 5 ppb 

increase in SO2, in Vancouver, Canada. Similar results were found for studies conducted in the Czech 

Republic (Bobak, 2000), Korea (Leem et al., 2006), and Beijing (Xu et al., 1995). 

Liu and Krewski (2003) further reported that SO2 exposure during the last month of pregnancy was 

associated with IUGR (OR 1.07 [95% CI: 1.01, 1.13]). However, in a later study in the Canadian cities of 

Calgary, Edmonton and Montreal, Liu et al. (2007) did not observe associations between maternal 

exposure to SO2 and increased risk of IUGR. 

Two Brazilian studies examined exposure to SO2 and neonatal deaths. Pereira et al. (1998) found a 

positive association between SO2 and intrauterine mortality in São Paulo during a 2-year period, though 

the effect was sensitive to model specifications and did not support a concentration-response relationship. 

The most robust association with intrauterine mortality was observed for an index of three gaseous 

pollutants (NO2, SO2, CO). Lin et al. (2004b) found that a 5 ppb increase in SO2 was associated with an 

increase of 8.8% (95% CI: 5.8, 11.8). A similar relationship was found for PM10. The creation of an index 

containing both PM10 and SO2 allowed the observation of their cumulative effects on daily death counts. 

The result of this analysis was similar in magnitude to the effect of SO2 alone. An ecologic cohort study 

of infant mortality in the U.S. found no association with annual averages of SO2 concentration (Lipfert et 

al., 2000a). 

Gilboa et al. (2005) conducted a population-based case-control study to investigate the association 

between maternal exposure and air pollutant exposure during weeks 3-8 of pregnancy, the risk of selected 

cardiac birth defects and oral clefts in live births, and fetal deaths between 1997 and 2000 in seven Texas 

counties. When the highest quartile of exposure was compared to the lowest, the authors observed a 

positive association between SO2 and isolated ventricular septal defects (OR 2.16 [95% CI: 1.51, 3.09]). 

Although this is the only study to have examined the effect of maternal exposure to SO2 on birth defects, 

it supports the notion that the developing embryo and growing fetus are susceptible to maternal air 

pollution exposure. 

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Several studies examined adverse health outcomes in relation to SO2 concentrations during the 

neonatal period. Dales et al. (2006) evaluated hospitalizations for respiratory disorders in neonates < 4 

weeks of age from hospitals in 11 large Canadian cities during a 15-year study period (populationweighted avg 24-h avg SO2 of 4.3 ppb). The researchers observed a 5.5% (95% CI: 2.8, 8.3) excess risk in 

respiratory hospitalizations associated with a 10 ppb increase in 24-h avg SO2 concentrations with a 2-d 

lag. This effect was slightly attenuated after adjusting for PM10 and gaseous copollutants. To investigate 

the influence of ambient SO2 concentrations on SIDS, Dales et al. (2004) conducted a time-series analysis 

comparing daily rates of SIDS and daily SO2 concentrations from 12 large, Canadian cities during a 16-

year period. The mean 24-h avg SO2 level across the 12 cities was 5.51 ppb (IQR 4.92). There was an 

18.0% (95% CI: 4.4, 33.4) excess risk in SIDS incidence for a 10 ppb increase in 24-h avg SO2 levels. 

The authors concluded that the effect of SO2 was independent of sociodemographic factors, temporal 

trends, and weather. 

In summary, epidemiologic studies on birth outcomes have observed positive associations between 

SO2 exposure and low birth weight; however, toxicological studies provide very little biological 

plausibility for reproductive outcomes related to SO2 exposure. The inconsistent results across trimesters 

of pregnancy and the lack of evidence regarding confounding by copollutants further limit the 

interpretation of these studies. The limited number of studies addressing preterm delivery, IUGR, birth 

defects, neonatal hospitalizations, and infant mortality make it difficult to draw conclusions regarding the 

effect of SO2 on these outcomes. 

3.4.6. Other Organ System Effects Associated with Long-Term 

Exposure 

The 1982 AQCD presented only one chronic exposure study which was relevant to nervous system 

effects. Dogs were exposed for 68 months to a mixture of SO2 and H2SO4 (Stara et al., 1980). No effects 

on visual-evoked brain potentials during or immediately after exposure to the SOX mixture were 

observed. Since then, numerous studies have examined brain lipid content, lipid peroxidation and 

glutathione content and antioxidant enzymes following inhalation exposure of rodents to SO2 at 

concentrations of 10 ppm or higher. Concentrations of 5 ppm or higher SO2 were used in studies 

examining neurobehavior and neurodevelopment in mice. These studies are summarized in Annex Table 

E-12. 

In the past 25 years, numerous animal toxicological studies have evaluated the effects of long-term 

SO2 exposure on other organ systems such as reproductive, hematological, gastrointestinal, renal, 

lymphatic, and endocrine systems. Most of these studies used concentrations of SO2 of 5 ppm or higher. 

Many of these studies examined alteration profiles of lipid peroxidation and antioxidant levels (LangleyEvans et al., 1996; Meng et al., 2003c; 2004) and are summarized in Annex Table E-10, E-13 through E16, and E-21. 

3.5. Mortality Associated with Long-Term SO2 Exposure 

3.5.1. Summary of Findings from the Previous Review 

At the time of the 1982 AQCD, the available studies on the effects of long-term exposure to SO2 on 

mortality were all ecological cross-sectional studies. This study design could not take into consideration 

such confounders as cigarette smoking, occupational exposures, and social status. In addition, there were 

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questions regarding how representative the aerometric data used were for community exposure. 

Therefore, it was concluded that the epidemiologic studies did not provide valid quantitative data relating 

respiratory disease or other types of mortality to long-term (annual avg) exposures to SO2 or PM. 

The 1986 Secondary Addendum reviewed more studies of this type, with information on more 

detailed components of PM (inhalable and fine particles, and particulate sulfate). While some studies 

suggested importance of the size of PM, the fundamental problem of the study design made it difficult to 

interpret the effect estimates. The 1986 Secondary Addendum also reviewed a Japanese study in which 

the death rates from asthma and chronic bronchitis in a highly polluted section of Yokkaichi, an industrial 

city with large SO2 emissions from the largest oil-fired power plant in Japan, were compared with those in 

a less polluted area of the same city (Imai et al., 1986). SOX levels (measured using the lead peroxide 

method) averaged across several monitoring sites in the polluted harbor area ranged from around 1.0 to 

2.0 mg/day (annual avg) during 1964 through 1972 and then steadily declined to less than 0.5 mg/day in 

1982. This is in contrast to levels consistently < 0.3 mg/day in the low pollution areas throughout 1967 

through 1982. Annual avg levels for other pollutants (i.e., NO2, TSP, oxidants) monitored in the high 

pollution area were consistently low from 1974 through 1982. The results indicated elevated rates of 

chronic bronchitis mortality in the highly polluted area compared to the less polluted area, but the 1986 

Secondary Addendum could not conclude that this was due to SO2 alone, because sulfate or other 

particulate SOX such as H2SO4 could have been responsible. 

Several, more recent studies have examined long-term exposure effects of air pollution, including 

SO2, on mortality. These studies are summarized in Annex Table F-9. As with short-term exposure 

studies, the focus of most of these studies was mainly on PM though some focused on traffic-related air 

pollution. They all used Cox proportional hazards regression models with adjustment for potential 

confounders. The designs of these studies were better than earlier cross-sectional studies as the outcome 

and most of the potential confounders (e.g., smoking history, occupational exposure) were measured on 

an individual basis. However, the geographic scale and method for exposure estimates varied across these 

studies. 

3.5.2. Associations of Mortality and Long-Term Exposure in Key 

Studies 

3.5.2.1. U.S. Cohort Studies 

Harvard Six Cities Studies 

Dockery et al. (1993) conducted a prospective cohort study to study the effects of air pollution with 

the main focus on PM components in six U.S. cities. These cities were chosen based on levels of air 

pollution, with Portage, WI and Topeka, KS representing the least polluted cities and Steubenville, OH 

representing the most polluted city. Mean SO2 levels ranged from 1.6 ppb in Topeka to 24.0 ppb in 

Steubenville from 1977 to 1985. Cox proportional hazards regression was conducted with data from a 14- 

to 16-year follow-up study of 8,111 adults in the six cities. Dockery et al. (1993) reported that lung cancer 

and cardiopulmonary mortality were more strongly associated with the concentrations of inhalable and 

fine PM, and sulfate particles, than with the levels of TSP, SO2, NO2, or acidity of the aerosol. 

Krewski et al. (2000) conducted a sensitivity analysis of the Harvard Six Cities Study and 

examined associations between gaseous pollutants (i.e., O3, NO2, SO2, and CO) and mortality. SO2

showed positive associations with total mortality (RR = 1.05 [95% CI: 1.02, 1.09] per 5 ppb increase in 

avg SO2 over the study period) and cardiopulmonary deaths (1.05 [95% CI: 1.00, 1.10]), but in this 

dataset SO2 was highly correlated with PM2.5 (r = 0.85), sulfate (r = 0.85), and NO2 (r = 0.84). 

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American Cancer Society Cohort Studies 

Pope et al. (1993) investigated associations between long-term exposure to PM and the mortality 

outcomes in the ACS cohort. Ambient air pollution data from 151 U.S. metropolitan areas in 1981 were 

linked with individual risk factors in 552,138 adults who resided in these areas when enrolled in the 

prospective study in 1982. Death outcomes were ascertained through 1989. PM2.5 and sulfate were 

associated with total, cardiopulmonary, and lung cancer mortality, but not with mortality for all other 

causes. Gaseous pollutants were not analyzed in this study. 

Krewski and co-investigators (Jerrett et al., 2003a; Krewski et al., 2000) conducted an extensive 

sensitivity analysis of the Pope et al. (1993) ACS data, augmented with additional gaseous pollutants data. 

The mean SO2 concentrations were 7.18 ppb in the warm season (April to September) and 11.24 ppb in 

the cool season (October to March). Among the gaseous pollutants examined, only SO2 showed positive 

associations with mortality. The RR for total mortality was 1.06 (95% CI: 1.05, 1.07) per 5 ppb increase 

in the annual avg SO2. Analysis using SO2 measured in different seasons produced a somewhat higher 

estimate for the warm season than that for the cool season (7% compared to 5% excess risk per 5 ppb 

increase). Although the subjects in the ACS cohort came from all regions of the U.S., the majority of the 

151 cities were located in the eastern U.S., where both SO2 and sulfate tend to be higher. PM2.5 levels are 

also higher in the east. To address the influence of these spatial patterns, which may confound 

associations between mortality and these pollutants, Krewski et al. (2000) conducted extensive two-stage 

regression modeling. In these models, the association between SO2 and mortality was diminished but 

persisted after adjusting for sulfate, PM2.5, and other variables. For example, in the spatial filtering model 

(which resulted in the largest reduction of the SO2 effect estimate when sulfate was included), the SO2

total mortality RR estimate was 1.07 (95% CI: 1.03, 1.11) in the single-pollutant model and 1.04 (95% 

CI: 1.02, 1.06) with sulfate in the two-pollutant model. The effect estimates for PM2.5 and sulfate also 

were diminished when SO2 was included in the models. The result further showed that SO2 effect 

estimates were generally insensitive to adjustment for spatial correlation. Thus, these results suggest that 

the association between SO2 and mortality may be confounded with PM, but the association cannot be 

accounted for by PM2.5 or sulfate alone. Krewski et al. (2000) noted that their reanalysis of the ACS and 

Harvard Six Cities studies suggested that mortality might be attributed to more than one component of the 

complex mixture of ambient air pollutants in urban areas in the U.S.. 

The original Pope et al. (1993) study and the Krewski et al. (2000) reanalysis both used the air 

pollution exposure estimates that are based on the average over the Metropolitan Statistical Area (MSA), 

which consists of multiple counties. To investigate the effects of geographic scale over which the air 

pollution exposures are averaged, (Willis et al., 2003), reanalyzed the ACS cohort data using the exposure 

estimates averaged over the county scale, and compared the results with those based on the MSA-scale 

avg exposure. Less than half of the cohort used in the MSA-based study was used in the county-scale 

based analysis, because of the limited availability of sulfate monitors and the reduced sample size due to 

the loss of subjects when using the five-digit ZIP codes. The mean (9.3 ppb versus 10.7 ppb) and range 

(0.0 to 29.3 ppb versus 0.0 to 27.2 ppb) of the MSA- and county-level SO2 data sets were similar. In the 

analysis comparing the two-pollutant model with sulfate and SO2, they found that the inclusion of SO2

reduced sulfate effect estimates substantially (> 25%) in the MSA-scale model but not substantially 

(< 25%) in the county-scale model. In the MSA-level analysis (with 113 MSAs), the SO2 RR estimate 

was 1.04 (95% CI: 1.02, 1.06) per 5 ppb increase, with sulfate in the model. In the county-level analysis 

(91 counties) with sulfate in the model, the corresponding estimate was smaller (1.02 [95% CI: 1.00, 

1.05]). It should also be noted that the correlation between covariates were different between the MSAlevel data and county-level data. The correlation between SO2 and sulfate was 0.48 in the MSA-level data, 

but it was 0.56 in the county-level data. The correlation between poverty rate and SO2 was -0.16 in the 

MSA-level data, but it was 0.15 in the county-level data. Thus, the extent of confounding between SO2

and PM components as well as among other covariates in the model can be affected by the geographic 

scale of aggregation of exposure estimates. It is not clear, however, if the smaller geographic scale 

increases or decreases exposure characterization error for SO2, because a certain extent of smoothing 

 3-65 
(averaging) over distance may reduce very local concentration peaks that are not relevant to the city-wide 

population. 

Pope et al. (2002) extended analysis of the ACS cohort with double the follow-up time (to 1998) 

and triple the number of deaths compared to the original Pope et al. (1993) study. In addition to PM2.5, all 

the gaseous pollutant data were retrieved for the extended period and analyzed for their associations with 

death outcomes. As in the 1995 analysis, the air pollution exposure estimates were based on the MSAlevel averages. PM2.5 was associated with total, cardiopulmonary, and lung cancer mortality but not with 

deaths for all other causes. SO2 was associated with all the mortality outcomes, including all other causes 

of deaths. The SO2 RR estimate for total mortality was 1.03 (95% CI: 1.02, 1.05) per 5 ppb increase (1982 

to 1998 avg). The association of SO2 with mortality for all other causes (sulfate also showed this pattern) 

makes it difficult to interpret the effect estimates. This lack of specificity for SO2 (in contrast to PM) is 

not consistent with causal inference. 

The EPRI-Washington University Veterans’ Cohort Mortality Studies 

Lipfert et al. (2000b) conducted an analysis of a national cohort of ~70,000 male U.S. military 

veterans who were diagnosed as hypertensive in the mid 1970s and were followed up for about 21 years 

(up to 1996). This cohort was 35% black and 57% were current smokers (81% of the cohort had been 

smokers at one time). PM2.5, PM10, PM10-2.5, TSP, sulfate, CO, O3, NO2, SO2, and lead were examined in 

this analysis. No mean or median level of SO2 was reported. The county of residence at the time of entry 

to the study was used to estimate exposures. Four exposure periods (from 1960 to 1996) were defined, 

and deaths during each of the three most recent exposure periods were considered. The results for SO2

were presented only qualitatively as part of their preliminary screening regression results. Lipfert et al. 

(2000b) noted that lead and SO2 were not found to be associated with mortality, thus were not considered 

further. They also noted that the pollution effect estimates were sensitive to the regression model 

specification, exposure periods, and the inclusion of ecological and individual variables. The authors 

reported that indications of concurrent mortality risks were found for NO2 and peak O3. 

Lipfert et al. (2006b) examined associations between traffic density and mortality in the same 

cohort, whose follow-up period was extended to 2001. As in their 2000 study, four exposure periods were 

considered but included more recent years. The 95th percentiles of daily avg in each of the exposure 

periods were considered for SO2. For the 1997–2001 data period, the estimated mortality RR for SO2 was 

0.99 (95% CI: 0.97, 1.01) per 5 ppb increase in a single-pollutant model. They reported that traffic density 

was a better predictor of mortality than ambient air pollution variables with the possible exception of O3. 

The log-transformed traffic density variable was only weakly correlated with SO2 (r = 0.32) and PM2.5 (r 

= 0.50) in this data set. 

Lipfert et a. (2006a) further extended analysis of the veterans’ cohort data to include the EPA’s 

Speciation Trends Network (STN) data, which collected chemical components of PM2.5. They analyzed 

the STN data for year 2002, again using county-level averages. PM2.5 and gaseous pollutants data for 

1999 through 2001 were also analyzed. As in the previous Lipfert et al. (2006b) study, traffic density was 

the most important predictor of mortality, but associations were also seen for elemental carbon, vanadium, 

nickel, and nitrate. O3, NO2, and PM10 also showed positive but weaker associations. Once again, no 

associations were observed between long-term exposure to SO2 and mortality. 

Seventh-day Adventist Study 

Abbey et al. (1999) investigated associations between long-term ambient concentrations of PM10, 

sulfate, SO2, O3, and NO2 (1973 through 1992) and mortality (1977 through 1992) in a cohort of 6,338 

nonsmoking California Seventh-day Adventists. Monthly indices of ambient air pollutant concentrations 

at 348 monitoring stations throughout California were interpolated to ZIP codes according to home or 

work location of study participants, cumulated, and then averaged over time. They reported associations 

between PM10 and total mortality for males and nonmalignant respiratory mortality for both sexes. SO2

 3-66 
was not associated with total mortality (RR 1.07 [95% CI: 0.92, 1.24] for males and 1.00 [95% CI: 0.88, 

1.14] for females per 5 ppb increase in multiyear avg SO2), cardiopulmonary deaths, or respiratory 

mortality for either gender. 

3.5.2.2. European Cohort Studies 

A study by Beelen et al. (2008) examined associations between traffic-related air pollution and 

mortality. They analyzed data from the Netherlands Cohort Study on Diet and Cancer with 120,852 

subjects who were followed from 1987 to 1996. BS, NO2, SO2, PM2.5, and four types of traffic-exposure 

estimates were analyzed. While the local traffic component was estimated for BS, NO2, and PM2.5, no 

such attempt was made for SO2, because there was “virtually no traffic contributions to this pollutant.” 

Thus, only “background” SO2 levels were reflected in the exposure estimates. Traffic intensity on the 

nearest road was associated with all-cause mortality and a larger RR was observed for respiratory 

mortality. Results were similar for BS, NO2 and PM2.5, but no associations were found for SO2 (RR = 0.98 

[95% CI: 0.93, 1.03] per 5 ppb increase in multiyear avgSO2). 

Nafstad et al. (2004) investigated the association between mortality and long-term exposure to air 

pollution exposure in a cohort of Norwegian men followed from 1972–1973 through 1998. Data from 

16,209 males (aged 0 to 49 years) living in Oslo, Norway, in 1972–1973 were linked with data from the 

Norwegian Death Register and with estimates of the avg annual air pollution levels at the participants’ 

home addresses. PM was not considered in this study because measurement methods changed during the 

study period. Exposure estimates for NOX and SO2 were constructed using models based on subject 

addresses, emission data for industry, heating, and traffic, and measured concentrations. While NOX was 

associated with total, respiratory, lung cancer, and ischemic heart disease deaths, SO2 did not show any 

associations with mortality. The authors noted that the SO2 levels were reduced by a factor of 7 during the 

study period (from 5.6 ppb in 1974 to 0.8 ppb in 1995), whereas NOX did not show any clear downward 

trend. 

Filleul et al. (2005) investigated long-term effects of air pollution on mortality in 14,284 adults 

who resided in 24 areas from seven French cities when enrolled in the Air Pollution and Chronic 

Respiratory Diseases (PAARC) survey in 1974. Daily measurements of SO2, TSP, BS, NO2, and NO were 

made in the 24 areas for 3 years (1974 through 1976). Models were run before and after exclusion of six 

area monitors influenced by local traffic as determined by a NO:NO2 ratio of > 3. Before exclusion of the 

six areas, none of the air pollutants was associated with mortality outcomes. After exclusion of these 

areas, analyses showed associations between total mortality and TSP, BS, NO2, and NO but not SO2 (RR 

= 1.01 [95% CI: 0.97, 1.06] per 5 ppb multiyear average) or acidimetric measurements. It should be noted 

that SO2 levels in these French cities declined markedly between the 1974 through 1976 period and the 

1990 through 1997 period by a factor of 2 to 3, depending on the city. The changes in air pollution levels 

over the study period complicate interpretation of reported effect estimates. 

3.5.2.3. Cross-Sectional Analysis Using Small Geographic Scale 

Elliott et al. (2007) examined associations of BS and SO2 with mortality in Great Britain using a 

cross-sectional analysis. However, unlike the earlier ecological cross-sectional mortality analyses in the 

U.S. in which mortality rates and air pollution levels were compared using large geographic boundaries 

(i.e., MSAs or counties), in the Elliot et al. analysis, the mortality rates and air pollution were compared 

using a much smaller geographic unit, the electoral ward, with a mean area of 7.4 km2

 and a mean 

population of 5,301 per electoral ward. Death rates were computed for four successive 4-year periods 

from 1982 to 1994 and associated with 4-year exposure periods from 1966 to 1994. The number of deaths 

from all causes in the 10,520 wards was 420,776. Of note, SO2 levels declined from 41.4 ppb in the 1966 

to 1970 period to 12.2 ppb in 1990 to 1994. This type of analysis does not allow adjustments for 

 3-67 
individual risk factors, but the study did adjust for SES data available for each ward from the 1991 

census. Social deprivation and air pollution were more highly correlated in the earlier exposure windows. 

They observed associations for both BS and SO2 and mortality outcomes. The estimated effects were 

stronger for respiratory illness than other causes of mortality for the most recent exposure period and most 

recent mortality period (when pollution levels were lower). The adjustment for social deprivation reduced 

the effect estimates for both pollutants. The adjusted mortality RRs for SO2 for the pooled mortality 

periods using the most recent exposure windows were 1.021 (95% CI: 1.018, 1.024) for all causes, 1.015 

(95% CI: 1.011, 1.019) for cardiovascular, and 1.064 (95% CI: 1.056, 1.072) for respiratory causes per 

5 ppb increase in SO2. The effect estimates for the most recent mortality period using the most recent 

exposure windows were larger. Simultaneous inclusion of BS and SO2 reduced effect estimates for BS but 

not SO2. Elliott et al. (2007) noted that the results were consistent with those reported in the Krewski et al. 

(2000) reanalysis of the ACS study. This analysis was ecological, but the exposure estimates in the 

smaller area compared to that in the U.S. cohort studies may have resulted in less exposure 

misclassification error, and the large underlying population appears to be reflected in the narrow 

confidence bands of effect estimates. The results from this study suggest an association between longterm exposures (especially in recent years) to SO2 and mortality. 

3.5.3. Summary of Evidence on the Effect of Long-Term Exposure on 

Mortality 

The available epidemiologic evidence on the effect of long-term exposure to SO2 on mortality is 

inadequate to infer a causal relationship at this time. The ecological cross-sectional studies examined in 

the 1982 AQCD and 1986 Secondary Addendum found suggestive relationships between long-term 

exposure to SO2 and mortality. However, there were concerns as to whether the observed association was 

due to SO2 alone, because sulfate or other particulate SOX such as H2SO4 could have been responsible. In 

the more recent longitudinal cohort studies, once again, positive associations have been observed between 

long-term exposure to SO2 and mortality; however, several issues affect the interpretation of these results. 

Figure 3-14 presents all-cause mortality RR estimates associated with long-term exposure to SO2

from the U.S. and European cohort studies. The overall range of RRs spans 0.97 to 1.07 per 5 ppb 

increase in the annual (or longer period) avgSO2. The analyses of the Harvard Six Cities and the ACS 

cohort data, which likely provide effect estimates that are most useful for evaluating possible health 

effects in the U.S., observed RRs of 1.02 to 1.07. Note that each of the U.S. cohort data has its own 

advantages and limitations. The Harvard Six Cities data have a small number of exposure estimates, but 

the study cities were carefully chosen to represent a range of air pollutant exposures. The ACS cohort had 

far more subjects, but the population was more highly educated than the representative U.S. population. 

Since educational status appeared to be an important effect modifier of air pollution effects in both 

studies, the overall effect estimate for the ACS cohort may underestimate that for the more general 

population. However, it should also be noted that several other U.S. and European studies did not observe 

an association between long-term exposure to SO2 and mortality. 

The geographic scale of analysis appears to influence SO2 effect estimates and exposure error. In a 

reanalysis of the ACS data, the county-level analysis showed a smaller SO2 effect estimate than MSAlevel analysis. For sulfate, the opposite pattern was found. Thus, the impact of the geographic scale of 

analysis may also depend on the spatial distribution of air pollutants. The cross-sectional analysis in Great 

Britain using small-scale electoral wards observed an effect estimate similar to the lower end of the range 

of effect estimates for all-cause mortality from U.S. cohort studies, though it is not clear if the effect 

estimates from this cross-sectional study are directly comparable to those from cohort studies. 

 3-68 
Figure 3-14. Relative risks (95% CI) of SO2-associated all-cause (nonaccidental) mortality, with and 

without adjustment for sulfate, from longitudinal cohort studies. Effect estimates are 

standardized per 5 ppb increase in SO2 concentrations. The exposure estimates for 

Krewski et al. (2000) and Pope et al. (2002) are based on MSA (Metropolitan Statistical 

Area)-level averaging; Lipfert et al. (2006b) used county-level averaging. 

 3-69 
Another important issue that these studies could not resolve was the possible confounding and/or 

interaction among PM indices and SO2. The possibility that the observed effects may not be due to SO2, 

but other constituents that come from the same source as SO2, or that PM may be more toxic in the 

presence of SO2 or other components associated with SO2, cannot be ruled out. For example, the ACS 

cohort came from all regions of the U.S., but a major fraction of the ACS cities were located in the eastern 

U.S., where both SO2 and sulfate levels tend to be higher. Therefore, even with sophisticated spatial 

modeling, separating possible confounding of SO2 effects by PM is challenging. Future and on-going 

studies that take into consideration within- versus between-city variation of these pollutants may help 

elucidate this issue. 

Overall, the results from two major U.S. epidemiologic studies observe an association between 

long-term exposure to SO2 or sulfur-containing particulate air pollution and mortality. However, several 

other U.S. and European cohort studies did not observe an association. The lack of consistency across 

studies, inability to distinguish potential confounding by copollutants, and uncertainties regarding the 

geographic scale of analysis limit the interpretation of a causal relationship. 

 3-70 
Chapter 4. Public Health Impact 

This chapter addresses several issues relating to the broader public health impact from exposure to 

ambient SO2. The first section discusses the shape of the concentration-response relationship for SO2, 

with consideration of interindividual variability in responses and evaluation of the limited evidence 

available to assess threshold values for health effects. The next section identifies characteristics of 

subpopulations which may experience increased risks from SO2 exposures, through either enhanced 

susceptibility (e.g., as a result of pre-existing disease, genetic factors, age) and/or differential vulnerability 

associated with increased exposure (e.g., close proximity to sources, activities). 

4.1. Assessment of Concentration-Response Function and 

Potential Thresholds 

An important consideration in characterizing the public health impacts associated with SO2

exposure is whether the concentration-response relationship is linear across the full concentration range, 

or if there are concentration ranges where there are departures from linearity (i.e., nonlinearity). Of 

particular interest is the shape of the concentration-response curve at and below the level of the current 

SO2 NAAQS level of a 24-h avg level of 0.14 ppm or the annual avg of 0.03 ppm, and whether a 

threshold of effect may exist among these lower concentrations. The assumption of a threshold indicates 

an ambient SO2 concentration below which adverse health outcomes are not elicited. Lack of a threshold 

implies that exposure to even the lowest measured ambient SO2 concentrations has the potential to cause 

toxicity. 

In the context of risk assessment for human health, it is important to consider the distinction 

between individual thresholds and population thresholds. There is wide variability in the human 

population, and a threshold for a population is defined by the threshold for the most sensitive individual 

in that population (Gaylor et al., 1988). Some human clinical studies provide individual-level response 

data in relation to different levels of SO2 exposure; this allows evaluation of both the percentage of 

individuals showing responses across the range of exposures as well as the concentration at which an 

individual begins to indicate a response. Very few epidemiologic studies, and no human clinical studies, 

evaluate whether there is a population-level threshold, which is the concentration of SO2 that must be 

exceeded to elicit a health response in the study population. 

Human clinical and epidemiologic studies that examined the shape of the concentration-response 

function are presented below. The discussion focuses on respiratory morbidity effects associated with 

short-term exposure to SO2, for which the strongest causal evidence exists. 

4.1.1. Evidence from Human Clinical Studies 

In human clinical studies of exercising asthmatics, moderate SO2-induced decrements in lung 

function have been observed at the lowest levels tested (i.e., 0.2 to 0.3 ppm, 5 to 10 min exposures) in 

some individuals (approximately 5-30% of subjects). Statistically significant respiratory effects have been 

consistently observed at concentrations of 0.4-0.6 ppm, with 20-60% of asthmatics experiencing moderate 

to large decrements in lung function following 5-10 min exposures (see Table 3-1). Smaller, yet 

statistically significant decrements in lung function have also been demonstrated at SO2 concentrations < 

0.2 ppm when preceded by exposure to O3 (see Section 3.1.3). Human clinical studies are valuable in 

characterizing the concentration-response relationship in relatively healthy asthmatics, but cannot be used 

 4-1 
to evaluate potential population threshold directly, as controlled human exposure studies have not been 

conducted at SO2 concentrations below 0.1 ppm and do not include the most sensitive asthmatics. 

 Source: Linn et al. (1987; 1988; 1990); Smith (1994) 

Figure 4-1. Percent of mild and moderate asthmatics ( VúE = 40-50 L/min) experiencing an SO2-

induced increase in (a) sRaw of 100% or a decrease in (b) FEV1 of 15%, adjusted 

for effects of moderate to heavy exercise in clean air. The data represents lung 

function measurements from 40, 41, 40, and 81 subjects at concentrations of 0.2, 0.3, 

0.4, and 0.6 ppm, respectively. A two parameter logistic model was fit to the data using 

Bayesian estimation with noninformative priors (Lunn et al., 2000).1

 

1

 The form of the logistic model was p ! 1 1 " " SO2

/ ( exp( *log( ))) # $ , where p is the fraction of asthmatics who 

experienced a moderate or greater decrement in lung function as defined above. Median estimates are presented along with upper and lower 95% 

confidence intervals. 

4-2 
With increasing exposure concentration between 0.2 and 1.0 ppm, there is a clear increase both in 

magnitude of respiratory effect and percent of asthmatics affected (Table 3-1). A subset of the data 

presented in this table was taken from a series of studies conducted by Linn et al. (1987; 1988; 1990) and 

is presented graphically in Figures 4-1 through 4-3. In these studies, mild and moderate asthmatics were 

exposed for 10 min to SO2 concentrations between 0 and 0.6 ppm during moderate to heavy exercise. 

These particular studies were selected for inclusion in this meta-analysis owing to similarities between 

exposure protocols, with all subjects being exposed to multiple concentrations of SO2. In the 1987 study, 

subjects were exposed to SO2 concentrations of 0, 0.2, 0.4, and 0.6 ppm, while in the 1988 and 1990 

studies, subjects were exposed to concentrations of 0, 0.3, and 0.6 ppm. The percent of asthmatics 

experiencing moderate or greater SO2-induced decrements in lung function (increase in sRaw ≥ 100% or 

decrease in FEV1 ≥ 15%) is shown in Figure 4-1. At 0.2 ppm, between 5 and 13% of subjects are affected, 

and this fraction increases with increasing concentration, with approximately 50% of subjects 

experiencing respiratory effects at a concentration of 0.6 ppm. 

Figures 4-2 and 4-3 present the concentration-response relationship between SO2 and decrements in 

lung function among asthmatics in the Linn et al. (1987; 1988; 1990) studies. Concentration response 

relationships are presented for all asthmatics, as well as for SO2-sensitive asthmatics, i.e., those 

asthmatics experiencing significant decrements in lung function at the highest exposure concentration 

used (0.6 ppm). This analysis demonstrates a clear increase in the magnitude of respiratory effects with 

increasing exposure concentration, with more marked effects observed at lower concentrations among the 

SO2-sensitive asthmatics. The results of a study by Gong et al. (1995) support this conclusion: the authors 

observed a linear relationship between SO2 concentration (0, 0.5, and 1.0 ppm) and both lung function 

(decrease in FEV1, and increase in sRaw) and respiratory symptoms. 

Figure 4-2. SO2-induced increase in sRaw among mild and moderate asthmatics following 10 min 

exposures with moderate to heavy exercise (VE = 40-50 L/min). Responses presented 

for all asthmatics as well as SO2-sensitive asthmatics, defined here as asthmatics 

experiencing a ≥100% SO2 induced increase in sRaw at 0.6 ppm. The analysis includes 

data from 40 subjects exposed to concentrations of 0.0, 0.2, 0.4, and 0.6 ppm, 14 of 

whom were SO2-sensitive (Linn et al., 1987), as well as 41 subjects exposed to 0.0, 0.3, 

and 0.6, 25 of whom were SO2-sensitive (Linn et al., 1988; 1990). Error bars = 1 SE. 

 4-3 
Figure 4-3. SO2-induced decrease in FEV1 among mild and moderate asthmatics following 10 min 

exposures with moderate to heavy exercise (VE = 40-50 L/min). Responses presented 

for all asthmatics as well as SO2 sensitive asthmatics, defined here as asthmatics 

experiencing a 15% SO2 induced decrease in FEV1 at 0.6 ppm. The analysis includes 

data from 40 subjects exposed to concentrations of 0.0, 0.2, 0.4, and 0.6 ppm, 21 of 

whom were SO2 sensitive (Linn et al., 1987), as well as 41 subjects exposed to 0.0, 0.3, 

and 0.6, 20 of whom were SO2 sensitive (Linn et al., 1988; 1990). Error bars = 1 SE. 

4.1.2. Evidence from Epidemiologic Studies 

Although there are numerous epidemiologic studies that examined the association between SO2 and 

various health effects, only a few of these studies attempted to evaluate the concentration-response 

function. Most studies assumed a linear or log-linear relationship between ambient SO2 concentrations 

and the health outcome in their evaluations. 

Epidemiologic studies have examined the concentration-response relationship for SO2 using 

various statistical methods, including the comparison of effect estimates in increasing quartiles or 

quintiles, plotting the risk observed against increasing SO2 concentrations, and using nonparametric 

smoothed curves to assess the nonlinearity of the SO2-effect relationship. Most of the epidemiologic 

studies that examined the concentration-response function between SO2 exposure and respiratory 

morbidity observed that the relationship could not be distinguished from linear across the entire 

concentration range. 

The association between asthma hospitalizations and ambient 24-h avg SO2 concentrations was 

examined in a case-control study of children in Bronx County, NY (Lin et al., 2004d). The 24-h avg 

concentration ranged from 2.9 to 66.4 ppb. The authors categorized 24-h avg SO2 concentrations and 

estimated ORs for each category using the lowest exposure group as the reference (2.9 to 9.2 ppb). They 

observed an increasing linear trend across the range of concentrations, with more marked effects observed 

at 24-h avg SO2 concentrations greater than 40 ppb (Figure 4-4). A similar concentration-response 

relationship for daily 1-h max SO2 levels was also observed. During the years 2003-2005, the 24-h avg 

SO2 concentrations for the 90th and 95th percentiles were 10 and 13 ppb, respectively. 

4-4 
Source: Lin et al. (2004d) 

Figure 4-4. Adjusted odds ratios of asthma hospitalizations by groupings of 24-h avg SO2

concentrations in Bronx County, New York. All groups were compared with the lowest 

exposure group (2.9-9.2 ppb). ORs for 24-h avg SO2 concentrations on the same day, 

as well as from a 2-day, 3-day, and 4-day moving average lag are presented. 

The Harvard Six Cities Study by Schwartz et al. (1994) investigated the concentration-response 

function and observed a nonlinear relationship between SO2 concentrations and respiratory symptoms. A 

figure plotting the relative odds of incidence of lower respiratory tract symptoms against SO2

concentrations lagged 1 day indicated that no statistically significant increase in the incidence of lower 

respiratory tract symptoms was seen until SO2 concentrations exceeded a 24-h avg of 22 ppb though an 

increasing trend was observed at concentrations as low as 10 ppb (see Figure 4-5). In a study of 

respiratory hospitalizations, Ponce de Leon et al. (1996) found that a weak relationship with SO2 was only 

observable at 24-h avg SO2 concentrations above 23 ppb. In both the Schwartz et al. (1994) and Ponce de 

Leon et al. (1996) studies, a statistically significant increased risk was observable only at 24-h avg SO2

concentrations that were above the 90th percentile. The nonlinearity observed in these concentrationresponse functions is dependent on only a few influential observations; thus, should be interpreted with 

caution. 

4-5 
Source: Schwartz et al. (1994). 

Figure 4-5. Relative odds ratio of incidence of lower respiratory tract symptoms smoothed 

against 24-h avg SO2 concentrations on the previous day, controlling for temperature, 

city, and day of week. 

A study by Jaffe et al. (2003) examined the association between SO2 and ED visits for asthma in 

three cities in Ohio, and found significant associations only in Cincinnati using Poisson regression 

analysis. To examine the concentration-response function, they also conducted quintile analyses. In 

Cincinnati, an increasing linear trend in risk was observed across the range of concentrations. Wong et al. 

(2002a [using GAM with default convergence criteria]) constructed a plot of risk against 24-h avg SO2

concentrations to examine the concentration-response relationship in Hong Kong and London. In general, 

a linear relationship between risk of respiratory hospitalizations and SO2 was observed across the range of 

SO2 concentrations in Hong Kong, but not in London. Several other studies that examined the 

concentration-response relationship found that the association between respiratory hospitalizations and 

SO2 did not deviate from linearity (Atkinson et al., 1999b; Burnett et al., 1997b; Hajat et al., 1999; Hajat 

et al., 2002). 

Discerning a possible population-level threshold for air pollution-related effects in epidemiologic 

studies is quite challenging. Using PM2.5 as an example, Brauer et al. (2002a) examined the relationship 

between ambient concentrations and mortality risk in a simulated population with specified common 

individual threshold levels. They found that no population threshold was detectable when a low threshold 

level was specified for individuals. Other factors that may make it difficult to detect a threshold if one 

exists include wide interindividual variability in sensitivity to SO2 exposure, and the inadequacy of 

currently deployed ambient monitors for accurate and precise measurements at lower 24-h avg SO2 levels. 

Ambient concentrations of SO2 have been declining since the 1980s and are now at or very near the limit 

of detection (~3 ppb) of the ambient monitors in the regulatory network. The mean 24-h avg SO2

concentration across the metropolitan statistical areas (MSAs) from 2003 through 2005 was 4 ppb (5th–

95th percentile: 1-13). Thus, there is greater uncertainty at the lower concentration range compared to the 

higher concentrations, which likely limits the ability to detect any potential threshold that may exist. 

 4-6 
The overall evidence from epidemiologic studies that evaluate the concentration-response 

relationship is not sufficient to conclude that the relationship deviated from linearity; however, it should 

be noted that these studies generally lack power to distinguish between linear and non-linear response 

forms. 

4.1.3. Summary of Evidence on Concentration-Response Functions 

and Thresholds 

In the previous two sections, evidence from human clinical and epidemiologic studies on the 

concentration-response function that may inform identification of any potential population threshold was 

presented. Results from human clinical studies indicate wide interindividual variability in response to SO2

exposures, with peak (5 to 10 min) exposures at levels as low as 0.2-0.3 ppm eliciting respiratory 

responses in some asthmatic individuals. A clear increase in the magnitude of respiratory effects was 

observed with increasing exposure concentrations between 0.2 and 1.0 ppm during 5-10 min SO2

exposures. 

Several epidemiologic studies that examined the concentration-response function between shortterm (24-h avg or 1-h max) exposure to SO2 and respiratory morbidity observed that the relationship 

could not be distinguished from linear across the entire concentration range. Given the various limitations 

in observing a possible threshold in population studies, the lack of evidence does not necessarily indicate 

that there is indeed no threshold in SO2 health effects. Some epidemiologic studies did report that though 

there was generally an increasing trend at the lower SO2 concentrations, a marked increase in SO2-related 

respiratory health effects was observed at higher concentrations. However, as these observations were 

based on a few potentially influential data points (24-h avg SO2 concentrations above the 90th percentile), 

the results should be interpreted with caution. The overall limited evidence from epidemiologic studies 

examining the concentration-response function of SO2 health effects is inconclusive regarding the 

presence of an effect threshold at current ambient levels. 

4.2. Susceptible and Vulnerable Populations 

Not all individuals exposed to pollutants respond similarly. Some subpopulations are at increased 

risk to the detrimental effects of pollutant exposure; additionally, considerable interindividual variability 

exists within sensitive subpopulations. The NAAQS are intended to provide an adequate margin of safety 

for both general populations and sensitive subpopulations, or those subgroups potentially at increased risk 

for ambient air pollution health effects. 

In general, a sensitive population might exhibit an adverse health effect to a pollutant at 

concentrations lower than those needed to elicit the same response in the general population, or exhibit a 

more severe adverse effect than the general population when exposed to the same pollutant 

concentrations. The term susceptibility generally encompasses innate or acquired factors that make 

individuals more likely to experience effects with exposure to pollutants. Genetic or developmental 

factors can lead to innate susceptibility, while acquired susceptibility may result from age, from disease, 

or personal risk factors such as smoking, diet, or exercise; personal risk factors such as smoking, diet, or 

exercise habits are also associated with the development of heart and lung diseases. In addition, new 

attention has been paid to the concept of some population groups having increased vulnerability to 

pollution-related effects due to factors including SES (e.g., reduced access to health care) or particularly 

elevated exposure levels. Factors potentially contributing to susceptibility or vulnerability to air pollution 

are included in Table 4-1. 

 4-7 
It is important to recognize that there is some overlap between the general concepts of 

susceptibility and vulnerability. For example, life stage is an important factor potentially determining both 

susceptibility and vulnerability. Children may be particularly vulnerable because of differences in 

exposure arising from their behavior and susceptible due to absorption or metabolism rates. Aging may 

also increase susceptibility to adverse effects from a pollutant because as individuals age, their bodies’ 

ability to defend against and respond to injury may diminish. 

The previous review of the SO2 NAAQS identified certain groups within the population that may 

be more susceptible to the effects of SO2 exposure, including asthmatics, individuals not diagnosed as 

asthmatic but with atopic disorders (e.g., allergies), and individuals with COPD or cardiovascular disease. 

Recent information on potentially susceptible or vulnerable subpopulations is assessed below. 

Table 4-1. Factors Potentially Contributing to Susceptibility or Vulnerability to Air Pollution 

Susceptibility Factors Vulnerability Factors 

Respiratory diseases (e.g., asthma) 

Cardiovascular diseases 

Genetic factors 

Age, Gender 

Race/ethnicity 

Pro-inflammatory conditions, e.g., diabetes 

Obesity 

Adverse birth outcomes (e.g., low birth weight) 

Increased activity patterns 

Decreased air conditioning use 

Increased level of exertion 

Work environment (e.g., outdoor workers) 

Lower SES 

Lower education level 

Residential location (e.g., proximity to roadways) 

Geographic location (West vs. East) 

4.2.1. Pre-existing Disease 

A recent report of the NRC (2004) emphasized the need to evaluate the effect of air pollution on 

susceptible groups, including those with respiratory illnesses and cardiovascular diseases. Generally, 

asthma, COPD, conduction disorders, CHF, diabetes, and MI are conditions believed to put persons at 

greater risk of adverse events associated with air pollution. Asthmatics are known to be one of the most 

SO2-responsive subgroups in the population; the evidence related to respiratory illness, including asthma 

and other factors, is discussed in further detail below. 

4.2.1.1. Pre-existing Respiratory Diseases 

The 1982 AQCD concluded that asthmatics are more susceptible to respiratory effects from SO2

exposures than the general public. This conclusion was primarily drawn from the strong human clinical 

evidence. Recent epidemiologic studies have strengthened this conclusion, reporting associations between 

a range of health outcomes with both short-term and long-term SO2 exposures in subjects with respiratory 

disease. 

In human clinical studies, asthmatics have been shown to be more responsive to respiratory effects 

of SO2 exposures than healthy non-asthmatics. While SO2-attributable decrements in lung function 

generally have not been demonstrated at concentrations ≤ 1.0 ppm in non-asthmatics (Lawther et al., 

1975; Linn et al., 1987; Schachter et al., 1984), statistically significant increases in respiratory symptoms 

and decreases in lung function have consistently been observed in exercising asthmatics following peak (5 

to 10 min) SO2 exposures to concentrations of 0.4-0.6 ppm (Gong et al., 1995; Horstman et al., 1986; 

 4-8 
Linn et al., 1983b). Moderate or greater SO2-induced decrements in lung function have also consistently 

been observed at lower SO2 concentrations (0.2-0.3 ppm) in some asthmatics (Bethel et al., 1985; Linn et 

al., 1987; 1988; 1990; Sheppard et al., 1981). It is important to note that the most severe asthmatics in the 

population are excluded from participating in controlled human exposures to SO2. Therefore, it is not 

unreasonable to presume that the most sensitive asthmatics would respond at lower levels than those used 

in human clinical studies. There is no evidence from human clinical studies that individuals with COPD 

have increased susceptibility to SO2-induced respiratory effects. 

A number of epidemiologic studies reported increased respiratory morbidity associated with SO2

exposures in asthmatics. Notably, two U.S. multicity studies observed associations between ambient SO2

concentrations and respiratory symptoms in asthmatic children (Mortimer et al., 2002; Schildcrout et al., 

2006). Additional studies also have indicated generally positive associations for asthma among children 

and included a U.S. study (Delfino et al., 2003a) and several European studies (Higgins et al., 1995; 

Neukirch et al., 1998; Peters et al., 1996a; Roemer et al., 1993; Segala et al., 1998; Taggart et al., 1996; 

Timonen and Pekkanen, 1997; van der Zee et al., 1999). Studies of adults found no consistent association 

between respiratory symptoms among asthmatics and SO2 concentrations (Desqueyroux et al., 2002a; 

2002b; Romieu et al., 1996; van der Zee et al., 2000). 

A positive association between ambient SO2 concentrations and ED visits and hospitalizations 

provides further evidence that asthmatics are susceptible to the effects of SO2. The associations between 

ambient concentrations of 24-h avg SO2 and ED visits and hospitalizations for asthma in the U.S. are 

generally positive (Jaffe et al., 2003; Lin et al., 2004d; Michaud et al., 2004; Wilson et al., 2005), though 

a large time-series study conducted in Atlanta, GA did not find an association between ambient 1-h max 

SO2 levels and asthma ED visits (Peel et al., 2005). Studies conducted outside the U.S. (Atkinson et al., 

1999a; 1999b; Hajat et al., 1999; Sunyer et al., 1997; Thompson et al., 2001) also generally found 

positive results. 

In summary, substantial evidence from epidemiologic studies suggests that individuals with 

preexisting respiratory diseases, particularly asthma, are more susceptible to respiratory health effects, 

though not mortality, from SO2 exposures than the general public. The observations from human clinical 

studies indicating increased sensitivity to SO2 exposures in asthmatic subjects compared to healthy 

subjects provide coherence and biological plausibility for these observations in epidemiologic studies. 

4.2.1.2. Pre-existing Cardiovascular Diseases 

The evidence available to evaluate the susceptibility of populations with cardiovascular disease for 

SO2-related health effects is very limited. One human clinical study observed no evidence to suggest that 

patients with stable angina were more susceptible to SO2-related health effects compared with healthy 

subjects (Routledge et al., 2006). The authors noted that this lack of response in the heart patients may be 

due to a drug treatment effect rather than decreased susceptibility. Liao et al. (2004) investigated shortterm associations between ambient pollutants and cardiac autonomic control and observed that 

consistently more pronounced associations were found between SO2 and HRV among persons with a 

history of coronary heart disease. In another epidemiologic study, Henneberger et al. (1996) examined the 

association of repolarization parameters with air pollutants in East German men with preexisting coronary 

heart disease. Ambient SO2 concentrations during the 24-h preceding the ECG were associated with the 

QT interval duration, but not with any other repolarization parameters. 

Evidence is inconsistent in studies analyzing the associations between ambient levels of air 

pollutants and ED visits or hospitalizations for cardiovascular diseases. A recent epidemiologic study 

investigated the association of SO2 with cardiac-related hospital admissions among persons with 

preexisting cardiopulmonary conditions and observed no associations with ambient 1-h max SO2 level for 

any cardiac disease investigated (i.e., ischemic heart disease [IHD], CHF, and dysrhythmia) across strata 

of comorbid disease status, including hypertension, diabetes, and COPD (Peel et al., 2007). 

 4-9 
Goldberg et al. (2003) compared the risk estimates for death with the underlying cause of CHF and 

those deaths classified as having CHF one year before death and did not find associations between air 

pollution and those with CHF as an underlying cause of death. The authors found associations between 

some of the air pollutants examined (coefficient of haze [CoH], SO2, and NO2) and the deaths that were 

classified as having CHF one year before death, but the association with the specific cause of death was 

not unique to SO2. This pattern of association, including but not specific to SO2, with specific causes of 

death also was observed in an additional cohort of patients with CHF (Kwon et al., 2001). 

In conclusion, the very limited evidence examining the susceptibility of individuals with 

preexisting cardiovascular disease to adverse health effects from ambient SO2 exposures is inconclusive. 

4.2.2. Genetic Factors for Oxidant and Inflammatory Damage from Air 

Pollutants 

A consensus now exists among scientists that genetic factors related to health outcomes and 

ambient pollutant exposures merit serious consideration (Gilliland et al., 1999; Kauffmann, 2004). 

Several criteria must be satisfied in selecting and establishing useful links between polymorphisms in 

candidate genes and adverse respiratory effects. First, the product of the candidate gene must be 

significantly involved in the pathogenesis of the effect of interest, which is often a complex trait with 

many determinants. Second, polymorphisms in the gene must produce a functional change in either the 

protein product or in the level of expression of the protein. Third, in epidemiologic studies, the issue of 

confounding by other genes or environmental exposures must be carefully considered. 

Several glutathione S-transferase (GST) families have common, functionally important 

polymorphic alleles (e.g., homozygosity for the null allele at the GSTM1 and GSTT1 loci, homozygosity 

for the A105G allele at the GSTP1 locus) that significantly reduce expression of enzyme function in the 

lung. Exposure to radicals and oxidants from air pollution induces decreases in GSH that increase GST 

transcription. Individuals with genotypes that result in enzymes with reduced or absent glutathione 

peroxidase activity are likely to have reduced oxidant defenses and increased susceptibility to inhaled 

oxidants and radicals. 

Gilliland et al. (2002) examined effects of GSTM1, GSTT1, and GSTP1 genotypes and acute 

respiratory illness, specifically respiratory illness-related absences from school. The goal was to examine 

potential susceptibilities on this basis, but not specifically to air pollutants. They concluded that fourth 

grade schoolchildren who inherited a GSTP1 Val-105 variant allele had a decreased risk of respiratory 

illness-related school absences, indicating that GSTP1 genotype influences the risk and/or severity of 

acute respiratory infections in school-aged children. 

Lee et al. (2004) studied ninth grade schoolchildren with asthma in Taiwan for a geneenvironmental interaction between GSTP1-105 genotypes and outdoor pollution. They examined general 

district air pollution levels of low (mean SO2 level of 3.6 ppb from 1994 to 2001), moderate (mean SO2 of 

6.2 ppb), and high (mean SO2 of 8.6 ppb) and found that compared with individuals with any Val-105 

allele in the low air pollution district, Ile-105 homozygotes in the high air pollution district had a 

significantly increased risk of asthma. 

Gauderman et al. (2002) describe a study method that uses principal components analysis 

computed on single nucleotide polymorphism (SNP) markers to test for an association between a disease 

and a candidate gene. For example, they evaluated the association between respiratory symptoms in 

children and four SNPs in the GSTP1 locus, using data from the Southern California Children’s Health 

Study (CHS). The authors observed stronger evidence of an association using the principal components 

approach (p = 0.044) than using either a genotype-based (p = 0.13) or haplotype-based (p = 0.052) 

approach. This method may be applied to relationships in this and other databases to evaluate aspects of 

air pollutants such as SO2. 

 4-10 
Figure 4-6. Relative risks (95% CI) of age-specific associations between short-term exposure to 

SO2 and respiratory ED visits* and hospitalizations. Risk estimates are standardized 

per 10 ppb increase in 24-h avg SO2 concentrations or 40 ppb increase in 1-h max SO2. 

Winterton et al. (2001) attempted to identify a genetic biomarker for susceptibility to SO2. They 

screened 62 asthmatic subjects for SO2 responsiveness using an inhalation challenge and collected genetic 

material via buccal swabs to test for associations between SO2 sensitivity and specific gene 

 4-11 
polymorphisms. Subjects inhaled 0.5 ppm SO2 by mouthpiece for 10 min while wearing noseclips during 

moderate exercise on a treadmill. Subjects were defined as SO2-sensitive if FEV1 was decreased 12%. 

Genetic polymorphisms as biomarkers of susceptibility were evaluated in five regions coding for the β2-

adrenergic receptor, the α subunit of the interleukin-4 (IL-4) receptor, the Clara cell secretory protein 

(CC16), tumor necrosis factor-α (TNF-α), and lymphotoxin-α (also known as TNF-β). The authors found 

a significant association between response to SO2 and the homozygous wild-type allele of TNF-α position 

-308. All of the SO2-sensitive subjects had the homozygous wild-type allele for TNF-α position -308, 

while 61% of the nonresponders had this genotype. Homozygosity for the TNF-1 allele was associated 

with a 5-fold increased risk of physician-diagnosed asthma relative to other genotypes. None of the other 

polymorphisms showed significant trends. 

In summary, the differential effects of air pollution in general among genetically diverse 

subpopulations have been examined for a number of GST genes and other genotypes. The limited number 

of studies may provide some insight into susceptible groups and a potential genetic role in such. Only one 

of these studies specifically examined SO2 as the exposure of interest, and it found a significant 

association with the homozygous wild-type allele for TNF-α. Khoury et al. (2000a) states that while 

genomics is still in its infancy, opportunities exist for developing, testing, and applying its tools to public 

health research of outcomes with possible environmental causes. At this time, there are insufficient data 

on which to base a conclusion regarding the effect of SO2 exposure on genetically distinct subpopulations. 

4.2.3. Age-Related Susceptibility 

The American Academy of Pediatrics (2004) notes that children and infants are among the most 

susceptible to many air pollutants, including SO2. Eighty percent of alveoli are formed postnatally and 

changes in the lung continue through adolescence; furthermore, the developing lung is highly susceptible 

to damage from exposure to environmental toxicants (Dietert et al., 2000). Children also have increased 

vulnerability as they spend more time outdoors, are highly active, and have high minute ventilation, 

which collectively increase the dose they receive (Plunkett et al., 1992; Wiley et al. 1991a, 1991b). 

A number of epidemiologic studies have observed increased respiratory symptoms in children 

associated with increasing SO2 exposures (Mortimer et al., 2002; Schildcrout et al., 2006; Schwartz et al. 

1994), though there is no evidence from a limited number of studies suggesting this same effect in adults 

(Desqueyroux et al. 2002a, 2002b; van der Zee et al., 2000). Similarly, adverse respiratory effects have 

been observed in adolescents following SO2 exposure in a laboratory setting (Koenig et al., 1981; 1983; 

1987; 1988; 1990). However, there is no evidence from human clinical studies to suggest that the 

respiratory effects in adolescents are more severe than those observed in adults. 

Older adults are frequently classified as being particularly susceptible to air pollution. The basis of 

the increased sensitivity in the older adults is not known, but one possibility is that it may be related to 

changes in the respiratory tract lining fluid antioxidant defense network (Kelly and Mudway, 2003) or a 

general reduction in immune competence. 

A number of studies, investigating the association between ambient SO2 levels and ED visits or 

hospital admissions for all respiratory causes or asthma, stratified their analyses by age group. Figure 4-6 

summarizes the evidence of age-specific associations between SO2 and acute respiratory ED visits and 

hospitalizations. Several studies demonstrated that the excess risk of ED visits or hospitalizations for all 

respiratory causes or asthma was higher for children (e.g. Atkinson et al., 1999a; 1999b; Petroeschevsky 

et al., 2001) and older adults (e.g. Petroeschevsky et al., 2001; Wilson et al., 2005; Wong et al. 1999) 

when compared to the risk for adults or all ages together. This is more clearly depicted in the summary 

density curves in Figure 4-7 and Figure 4-8, created using the effect estimates presented in Figure 4-6. As 

shown in these two figures, the effect estimates for children and older adults are slightly larger than that 

for adults or all ages for both all respiratory diseases and asthma ED visits and hospitalizations. 

 4-12 
Figure 4-7. Summary density curves of the relative risks of age-specific associations between 

short-term exposure to SO2 and ED visits and hospitalizations for all respiratory 

causes. Risk estimates are standardized per ppb increase in 24-h avg SO2

concentrations or 40 ppb increase in 1-h max SO2. Density curves drawn from effect 

estimates in Figure 4-6. 

Figure 4-8. Summary density curves of the relative risks of age-specific associations between 

short-term exposure to SO2 and ED visits and hospitalizations for asthma. Risk 

estimates are standardized per ppb increase in 24-h avg SO2 concentrations or 40 ppb 

increase in 1-h max SO2. Density curves drawn from effect estimates in Figure 4-6. 

Cakmak et al. (2007b) reported that among seven Chilean urban centers, the percent increase in 

nonaccidental mortality associated with a 10 ppb increase in 24-h avg SO2 was 3.4% (95% CI: 0.7, 6.1) 

for those < 65 years of age and 5.6% (95% CI: 2.2, 9.1) for those > 85 years of age. The authors 

concluded that older adults are particularly susceptible to dying from air pollution, and suggested that 

concentrations deemed acceptable for the general population may not adequately protect those aged > 85 

years. 

 4-13 
There is limited epidemiologic evidence to suggest that children and older adults (65+ years) are 

more susceptible to the adverse respiratory effects associated with ambient SO2 concentrations when 

compared to the general population. 

4.2.4. Other Potentially Susceptible Populations 

Although data specific to SO2 exposures is lacking for the susceptibility factors listed below, 

several other potentially susceptible groups deserve specific mention in this document. These include 

individuals in a chronic pro-inflammatory state (e.g., diabetics), obese individuals, and children born 

prematurely or with low birth weight. 

Chronic inflammation appears to enhance susceptibility for air pollution-related cardiovascular 

events in older individuals, persons with diabetes, coronary artery disease, obesity, and past MIs (Bateson 

and Schwartz, 2004; Goldberg et al., 2001; Peel et al., 2007; Zanobetti and Schwartz, 2002). Dubowsky et 

al. (2006) reported that individuals with conditions associated with both chronic inflammation and 

increased cardiac risk were more vulnerable to the short-term pro-inflammatory effects of air pollution. 

This included individuals with diabetes; obesity; and concurrent diabetes, obesity and hypertension. 

Zanobetti and Schwartz (2001) reported more than twice the risk for hospital admissions for heart disease 

in persons with diabetes than in persons without diabetes associated with exposure to ambient air 

pollution, indicating that persons with diabetes are an important at-risk group. Data from the Third 

National Health and Nutrition Examination Survey indicated that 5.1% of the U.S. population older than 

20 years of age has diagnosed diabetes and an additional 2.7% has undiagnosed diabetes (Harris et al., 

1998). Moreover, another study found that subjects with impaired glucose tolerance without type II 

diabetes also had reduced HRV (Schwartz, 2001). This may indicate that the at-risk population may be 

even larger. 

Mortimer et al. (2000) reported that among asthmatic children, birth characteristics continue to be 

associated with increased susceptibility to air pollution later in life, demonstrating that air pollutioninduced asthma symptoms were more severe in children born prematurely or of low birth weight. 

Specifically, the authors revealed asthmatic children born more than three weeks prematurely or weighing 

less than 2,500 grams (5.5 pounds) had a six-fold decrease in breathing capacity associated with air 

pollution compared to full-weight, full-term children. The low birth weight and premature children also 

reported a five-fold greater incidence of symptoms like wheezing, coughing and tightness in the chest. 

4.2.5. Factors that Potentially Increase Vulnerability to SO2

A limited amount of information exists on exposures to SO2 among vulnerable populations. As 

noted above, vulnerability is characterized by extrinsic factors that may increase a population’s risk from 

air pollution, such as being more highly exposed than the general population, or reduced SES. Because 

indoor and personal SO2 concentrations are generally much lower than outdoor or ambient measurements, 

individuals that spend most of their time indoors, such as older adults, are not anticipated to be vulnerable 

to high SO2 exposures, though in some cases they may be more susceptible to the effects of these 

exposures than the general population due to preexisting health factors. Another factor that potentially 

alters vulnerability to SO2 is air conditioning use due to the reduced penetration of SO2 into buildings 

when windows are closed. 

Other individuals with increased vulnerability include those who spend a lot of time outdoors at 

increased exertion levels, for example outdoor workers and individuals who exercise or play outdoor 

sports. Exercise may cause an increase in uptake of SO2 resulting from an increase in ventilation rate and 

accompanying shift from nasal to oronasal breathing. Children, who generally spend more time playing 

outdoors, may qualify as both a susceptible population (due to their developing physiology) and as a 

 4-14 
vulnerable population since ambient SO2 concentrations are several-fold higher than indoor 

concentrations. 

SES is a known determinant of health and there is evidence that SES modifies the effects of air 

pollution (Makri and Stilianakis, 2007; O’Neill et al., 2003). Both higher exposures to air pollution and 

greater susceptibility to its effects may contribute to a complex pattern of risk among those with lower 

SES. Conceptual frameworks have been proposed to explain the relationship between SES, susceptibility 

and exposure to air pollution. Common to these frameworks is the consideration of the broader social 

context in which people live and its effect on health in general (Gee and Payne-Sturges, 2004; O’Neill et 

al., 2003), as well as on maternal and child health (Morello-Frosch and Shenassa, 2006), and asthma 

(Wright et al., 2007) specifically. Multilevel modeling approaches that allow parameterization of 

community level stressors such as increased life stress, as well as individual risk factors, are considered 

by these authors. In addition, statistical methods that allow for temporal and spatial variability in exposure 

and susceptibility, have been discussed in the recent literature (Jerrett and Finkelstein, 2005; Kunzli, 

2005). 

Several studies have examined modification by SES indicators on the association between 

mortality and PM (Finkelstein et al., 2003; Jerrett et al., 2004; Martins et al., 2004; O’Neill et al., 2003; 

Romieu et al., 2004) or other indices such as traffic density, distance to roadway or a general air pollution 

index (Finkelstein et al., 2005; Ponce et al., 2005; Woodruff et al., 2003). However, modification of SO2

associations has been examined in a few studies. For example, in a study conducted in 10 large Canadian 

cities, living in communities in which individuals have lower household education and income levels 

increased the individual’s vulnerability to air pollution (Cakmak et al., 2006). These effects were 

statistically significant for several gaseous criteria pollutants, but not for SO2. In addition, Finkelstein et 

al. (2003) evaluated neighborhood levels of income and air pollution in southern Ontario, Canada. They 

found that both income and SO2 levels were associated with mortality differences. Specifically, among 

people with below-median income, the relative risk for those with above-median exposure to SO2 was 

1.18 (95% CI: 1.11, 1.26); the corresponding relative risk among subjects with above-median income was 

1.03 (95% CI: 0.83, 1.28). Overall, there is very limited evidence available from which conclusions on the 

human health effects from the interaction between SES and SO2 can be drawn. 

Other factors that may potentially increase vulnerability to SO2 are residential or geographic 

location. However, residential location is not as strong of a predictor of exposure vulnerability for SO2 as 

for traffic-related pollutants, because meteorological conditions have a greater impact on pollutant plume 

direction from primary point sources such as coal-fired power plants. 

4.2.6. Summary of Potentially Susceptible and Vulnerable Populations 

In summary, subgroups considered to be potentially susceptible and/or vulnerable include children 

and older adults; people with other respiratory disease; genetic factors; SES; and populations 

experiencing heightened exposure levels (e.g., those living near roadways or other “hot spots” or engaged 

in outdoor work or exercise). Also of concern are individuals who generally may not be inherently 

susceptible to SO2-related health effects but may experience transient increases in airways sensitivity to 

SOX induced by other respiratory irritants such as recent viral respiratory infection (Stempel and Boucher, 

1981). These groups comprise a large fraction of the U.S. population. Given the heterogeneity of 

individual responses to air pollution, the severity of health effects experienced by a susceptible subgroup 

may be much greater than that experienced by the population at large (Zanobetti et al., 2000). 

 4-15 
Chapter 5. Summary and Conclusions 

Previous chapters present the most policy-relevant information related to the review of the NAAQS 

for SOX. This chapter integrates key findings from atmospheric sciences, ambient air data analyses, 

exposure assessment, dosimetry, and health evidence. The EPA framework for causal determinations 

described in Chapter 1 has been applied to the body of evidence in order to judge the scientific data about 

exposure to SOX and health effects in a two-step process. The first step is to determine the weight of 

evidence in support of causation at relevant pollutant exposures and characterize the strength of any 

resulting causal classification. The EPA framework applied here employs a five-level hierarchy for causal 

determination: 

 Causal relationship 

 Likely to be a causal relationship 

 Suggestive of a causal relationship 

 Inadequate to infer a causal relationship 

 Suggestive of no causal relationship 

The second step evaluates the entirety of policy-relevant quantitative evidence regarding the 

concentration-response relationships including levels and exposure durations at which effects are 

observed, and subpopulations that experience effects that differ from the general population. This 

integration of evidence results in identification of a study or set of studies that best estimates the 

concentration-response relationships for the U.S. population, given the current state of knowledge. 

Together the two steps in the framework lead to: 1) causal determinations for a range of health outcomes, 

and 2) characterization of the magnitude of these responses, including susceptible or vulnerable 

subpopulations, over a range of relevant exposures. 

This chapter summarizes and integrates the newly available scientific evidence that best informs 

consideration of the policy-relevant questions that frame this review, presented in Chapter 1. Section 5.1 

presents trends in emissions of SO2 and provides a brief summary of the ambient air quality at short- and 

long-term exposures ranging from 5 min to one year. Section 5.2 discusses the evidence for the

occurrence and plausibility of health effects following short- and long-term exposure to ambient SO2, and 

the levels at which these health effects occur. Section 5.3 integrates the evidence and discusses important 

uncertainties identified in the interpretation of the scientific evidence. Section 5.4 presents the evidence 

for potentially susceptible and vulnerable populations to health effects from SO2 exposure. Finally, 

conclusions based on the available scientific evidence for human health effects associated with SO2

exposure are presented in Section 5.5. 

5.1. Emissions and Ambient Concentrations of SO2

Anthropogenic SO2 is emitted mainly by fossil fuel combustion (chiefly coal and oil) and metal 

smelting. The largest source of emissions is from elevated point sources such as the stacks of power 

plants and industrial facilities. Since 1990, in response to controls applied under the Acid Rain Program 

(U.S. EPA, 2006a), SO2 emissions from these sources have declined substantially. Emissions demonstrate 

a strong gradient increasing from west to east, owing to the high concentration of SO2-emitting electric 

generating utilities in the Ohio River Valley, the Southeast, Texas, Illinois, and Missouri. Policy Relevant 

Background (PRB) levels of SO2 are estimated to be in the range of a few hundredths of a ppb (< 1% of 

typical ambient levels) across most of the U.S., though much higher values are found in areas affected by 

volcanic or geothermal activity. 

 5-1 
The levels of the current primary NAAQS for SOX are 0.14 ppm for 24-h avg SO2 concentrations 

and 0.03 ppm for annual avg SO2 concentrations, not to be exceeded more than once per year. 

Exceedances of the current primary NAAQS for SOX have become rare in recent years, as both the mean 

24-h and annual avg SO2 concentrations in the U.S. for the years 2003 to 2005 were ~4 ppb. For the 24-h 

avg, 99th percentile and maximum values were ~25 ppb and ~145 ppb, respectively. The 99th percentile 

and maximum values of the annual avg were ~13 ppb and ~15 ppb, respectively. 

Mean 1-h max concentrations in these years were ~13 ppb, with a 99th percentile value of ~95 ppb 

and maximum value of ~700 ppb. The large differences between 99th percentile and maximum values for 

the shorter term averages suggest that the maxima are strongly limited spatially and temporally and are 

not a major determinant of the mean values. 

5-min avg SO2 data are collected without a specific regulatory mandate. Hourly maximum 5-min 

avg data were voluntarily supplied from 98 monitors located in certain regions of the U.S. during the 

period 1997 to 2006, with only 16 monitors providing all twelve 5-min averages in each hour. The median 

value of the hourly maximum 5-min avg in this limited data set ranged from 1 to 8 ppb, while the 99th 

percentile value ranged from 21 to 184 ppb, depending on location. Because of the nonuniform 

spatiotemporal distribution of the existing 5-min data set across the U.S., these values are likely not 

representative of nationwide 5-min distributions. 

5.2. Health Effects of SO2 

Evaluation of the health evidence, with consideration of issues related to atmospheric sciences, 

exposure assessment, and dosimetry, led to the conclusion that there is a causal relationship between 

respiratory morbidity and short-term exposure to SO2. This conclusion is supported by the consistency, 

coherence, and plausibility of findings observed in the human clinical, epidemiologic, and animal 

toxicological studies. In human clinical studies, respiratory effects were observed following 5-10 min 

exposures to SO2 at concentrations ≥ 0.2 ppm in asthmatics engaged in moderate to heavy levels of 

exercise. In the epidemiologic studies, respiratory effects were observed in areas where the maximum 

ambient 24-h avg SO2 concentration was below the current 24-h avg NAAQS level of 0.14 ppm. Mean 

24-h avg SO2 levels ranged from 1 to 30 ppb in these epidemiologic studies conducted in the U.S. and 

abroad, with maximum 24-h avg SO2 values ranging from 12 to 75 ppb. Animal toxicological studies 

indicate that repeated exposures to SO2, at concentrations as low as 0.1 ppm in guinea pigs, may 

exacerbate airway inflammation and hyperresponsiveness in allergic animals. 

The respiratory health effects of SO2 are consistent with the mode of action of SO2 as it is currently 

understood. The immediate effect of SO2 on the respiratory system is bronchoconstriction. This response 

is mediated by chemosensitive receptors in the tracheobronchial tree. These receptors trigger reflexes at 

the central nervous system level resulting in bronchoconstriction, mucus secretion, mucosal vasodilation, 

cough, and apnea followed by rapid shallow breathing. In some cases, local nervous system reflexes also 

may be involved. Asthmatics are more sensitive to the effects of SO2 likely resulting from preexisting 

inflammation associated with this disease. This inflammation may lead to enhanced release of mediators, 

alterations in the autonomic nervous system and/or sensitization of the chemosensitive receptors. These 

biological processes are likely to underlie decreased lung function and increased hyperresponsiveness 

observed in response to SO2 exposure. 

The definitive evidence for the causal relationship comes from human clinical studies reporting 

respiratory symptoms and decreased lung function in exercising asthmatics following 5-10 min exposures 

to SO2 at concentrations which have sometimes been measured in ambient air for similarly short-time 

durations. With increased ventilatory rates during exercise, the pattern of SO2 absorption shifts from the 

upper airways to the tracheobronchial airways in conjunction with a shift from nasal to oronasal 

breathing. Mode of breathing is an important determinant of the severity of SO2-induced 

bronchoconstriction, with the greatest responses occurring during oral breathing followed by oronasal 

 5-2 
breathing and the smallest responses observed during nasal breathing. In the human clinical studies, 

5-30% of relatively healthy exercising asthmatics experienced moderate or greater decrements in lung 

function (≥ 100% increase in sRaw or ≥ 15% decrease in FEV1) with peak exposures to SO2

concentrations of 0.2-0.3 ppm (see Table 5-1). At concentrations ≥ 0.4 ppm, a greater percentage (20-

60%) of asthmatics experience SO2-induced decrements in lung function, which are frequently 

accompanied by respiratory symptoms. A clear concentration-response relationship has been 

demonstrated following exposures to SO2 at concentrations between 0.2 and 1.0 ppm, both in terms of 

increasing severity of effect and percentage of asthmatics adversely affected. Animal toxicological studies 

have also reported bronchoconstriction with short-term exposures of 1 ppm SO2 (see Table 5-2). The 

limited 5-min SO2 data acquired from ambient monitors located in certain regions of the U.S. during the 

years 1997 to 2006 indicated that 0.2% of the hourly maximum 5-min avg were greater than 0.2 ppm. 

Table 5-1. Key health effects of short-term exposure to SO2 observed in human clinical studies. 

Concentration Exposure Effects Study 

5-10 min Moderate to large reductions in FEV1 and increases in sRaw observed among some 

asthmatic adults (5-30%) during moderate to heavy exercise. Bronchial 

responsiveness to SO2 may be enhanced when preceded by exposure to O3. Limited 

evidence of SO2-induced increases in respiratory symptoms. 

Bethel et al. (1985); Horstman et al. 

(1986);Koenig et al. (1990); Linn et al. 

(1983b; 1987; 1988; 1990); Schachter 

et al. (1984); Sheppard et al. (1981); 

Trenga et al. (2001) 

0.2-0.3 ppm 

1-6 h Enhanced airway responses to an inhaled allergen following exposure to SO2 with 

NO2 in resting asthmatics. No evidence of respiratory symptoms or decrements in 

lung function in resting asthmatics or healthy adults. The evidence that SO2 exposure 

may lead to changes in heart rate variability is weak and inconsistent. 

Devalia et al. (1994); Routledge et al. 

(2006); Rusznak et al. (1996); 

Tunnicliffe et al. (2001; 2003) 

1-10 min Moderate or greater decrements in lung function clearly demonstrated in asthmatics 

during exercise, with significant interindividual variability in response (~20-35% of 

asthmatics experiencing moderate or greater decrements in lung function). 

Respiratory effects observed following 5-10 min of exposure are generally not 

enhanced by repeat exposures. Respiratory symptoms (e.g., wheezing, chest tightness) are observed at concentrations as low as 0.4 ppm and have been shown to 

increase with increasing exposure concentrations. 

Balmes et al. (1987); Gong et al. 

(1995); Horstman et al. (1986); Koenig 

et al. (1983); Linn et al. (1983b; 1987); 

Magnussen et al. (1990); Schachter 

et al. (1984); Sheppard et al. (1981); 

Trenga et al. (1999) 

0.4-0.5 ppm 

~1-h Decrements in lung function among asthmatics following 10 min of exercise at the end 

of a 60-75 min exposure are statistically significant, but less severe than effects 

observed following a 10 min period of exercise at the start of the exposure. 

Linn et al. (1987); Roger et al. (1985) 

1-10 min Clear and consistent SO2-induced increases in respiratory symptoms observed 

among exercising asthmatics. Moderate to large decrements in lung function 

demonstrated in 35-60% of asthmatics. Respiratory effects attributed to SO2 among 

asthmatics during exercise may be diminished after cessation of exercise, even with 

continued SO2 exposure. No respiratory effects reported in healthy, non-asthmatics. 

Balmes et al. (1987); Gong et al. 

(1995); Hackney et al. (1984); Horstman et al. (1986; 1988); Koenig et al. 

(1983); Linn et al. (1987; 1988; 1990); 

Roger et al.(1985); Schachter et al. 

(1984) 

0.6-1.0 ppm 

1-6 h Decrements in lung function among asthmatics following 5-10 min of exercise at the 

end of a 1-6 h exposure are statistically significant, but less severe than effects 

observed following a 5-10 min period of exercise at the start of the exposure. 

Linn et al. (1984; 1987); Hackney et al. 

(1984); Roger et al. (1985) 

 

 5-3 
Table 5-2. Key respiratory health effects of exposure to SO2 in animal toxicological studies. 

Study Exposure Species Effects 

LUNG FUNCTION 

Amdur et al. 

(1983) 

1 ppm (2.62 mg/m3); 1 h; 

head only 

Hartley guinea pigs, 

male, age not reported, 

200-300 g, n = 18-23/ 

group 

An 11% increase in pulmonary resistance and 12% decrease in dynamic compliance 

were observed. Neither effect persisted into the 1 h period following exposure. No 

effects were observed for breathing frequency, tidal volume, or min volume. 

Barthelemy 

et al. (1988) 

0.5 or 5 ppm 

(1.3 or 13.1 mg/m3); 45 min; 

intratracheal 

Rabbit, sex not reported, 

adult, mean 2.0 kg, 

n = 5-9/group; rabbits 

were mechanically 

ventilated 

Lung resistance increased by 16% and 50% in response to 0.5 and 5 ppm SO2, 

respectively. 

Conner et al. 

(1985) 

1 ppm (2.62 mg/m3); 3-h/day 

for 6 days; nose only; animals 

evaluated for up to 48-h 

following exposure 

Hartley guinea pig, male, 

age not reported, 

250-320 g, n = ≤18/ 

group/time point 

No effect was observed on residual volume, functional reserve capacity, vital capacity, 

total lung capacity, respiratory frequency, tidal volume, pulmonary resistance, 

pulmonary compliance, diffusing capacity for carbon monoxide or alveolar volume at 1 

or 48 h after last exposure. 

INFLAMMATION AND MORPHOLOGY 

Li et al. 

(2007) 

2 ppm (5.24 mg/m3); 1 h/day 

for 7 days; with and without 

exposure to ovalbumin 

Wistar rats, male, age not 

reported 

Increased number of inflammatory cells in BAL fluid, increased levels of MUC5AC and 

ICAM-1 and an enhanced histopathological response compared with those treated with 

ovalbumin or SO2 alone 

Park et al. 

(2001a) 

0.1 ppm (0.26 mg/m3); 5 h/day 

for 5 days; whole body; with 

and without exposure to 

ovalbumin 

Dunkin-Hartley guinea 

pig, male, age not reported, 250-350 g, 

n = 7-12/group 

After bronchial challenge, the ovalbumin/SO2-exposed group had significantly 

increased eosinophil counts in BAL fluids compared with all other groups, including the 

SO2–only group. The bronchial and lung tissue of the ovalbumin/SO2–exposed group 

showed infiltration of inflammatory cells, bronchiolar epithelial damage, and mucus and 

cell plug in the lumen. 

Conner et al. 

(1989) 

1 ppm (2.62 mg/m3); 3-h/day 

for 1-5 days; nose only; bronchoalveolar lavage performed 

each day 

Hartley guinea pig, male, 

age not reported, 

250-320 g, n = 4 

No change in numbers of total cells and neutrophils, protein levels or enzyme activity in 

lavage fluid following SO2 exposure. 

Conner et al. 

(1985) 

1 ppm (2.62 mg/m3); 3 h/day/6 

day; evaluated up to 72 h 

postexposure 

Hartley guinea pigs, 

male, age not reported, 

250-320 g, n = 18/ 

group/time point 

No alveolar lesions. 

Smith et al. 

(1989) 

1 ppm (2.62 mg/m3); 5-h/day, 

5 day/wk up to 4 and 8 mos 

Sprague-Dawley rats, 

male, young adult, initial 

weight not reported, n = 

12-15 data point 

Increased bronchial epithelial hyperplasia and number of nonciliated epithelial cells 

observed at 4 mos. 

AIRWAY HYPERRESPONSIVENESS AND ALLERGIC SENSITIZATION 

Park et al. 

(2001a) 

0.1 ppm (0.26 mg/m3); 5-

h/day for 5 days; whole body; 

with and without exposure to 

ovalbumin 

Dunkin-Hartley guinea 

pig, male, age not reported, 250-350 g, 

n = 7-12/group 

After bronchial challenge, the ovalbumin/SO2-exposed group had significantly 

increased enhanced pause (indicator of airway obstruction) compared with all other 

groups, including the SO2 group. Study authors concluded that low level SO2 may 

enhance the development of ovalbumin-induced asthmatic reactions in guinea pigs. 

Riedel et al. 

(1988) 

0.1, 4.3, or 16.6 ppm (0, 0.26, 

11.3, or 43.5 mg/m3); 8-h/day 

for 5 days; whole body; 

animals were sensitized to 

ovalbumin on the last 3 days 

of exposure 

Perlbright-White guinea 

pig, female, age not 

reported, 300-350 g, 

n = 5 or 6/group (14 

controls) 

Bronchial provocation with ovalbumin was conducted every other day for 2 wks, 

starting at 1 wk after the last exposure. Numbers of animals displaying symptoms of 

bronchial obstruction after ovalbumin provocation was increased in all SO2 groups 

compared to air-exposed groups. Anti-ovalbumin antibodies (IgG total and IgG1) were 

increased in BAL fluid and serum of SO2-exposed compared to air-exposed controls, 

with statistical significance attained for IgG total in BAL fluid at ≥4.3 ppm SO2 and in 

serum at all SO2 concentrations. Results indicate that in this model, repeated exposure 

to even low concentrations of SO2 can potentiate allergic sensitization of the airway. 

 

A larger body of evidence supporting this determination of causality comes from numerous 

epidemiologic studies published since the previous NAAQS review reporting associations with 

 5-4 
respiratory symptoms, ED visits, and hospital admissions with short-term SO2 exposures, generally of 24-

h avg. These studies were conducted in areas where the maximum ambient 24-h avg SO2 concentration 

was consistently below the current 24-h avg NAAQS level of 0.14 ppm. Mean 24-h avg SO2 levels 

ranged from 1 to 30 ppb, with maximum values ranging from 12 to 75 ppb, in the epidemiologic studies,. 

Important new multicity studies and several other studies have found an association between 24-h avg 

ambient SO2 concentrations and respiratory symptoms in children, particularly those with asthma. 

Furthermore, limited epidemiologic evidence indicates that atopic children and adults may be at increased 

risk for SO2-induced respiratory symptoms. Generally consistent associations also were observed between 

ambient SO2 concentrations and ED visits and hospitalizations for all respiratory causes, particularly 

among children and older adults (≥ 65 years), and for asthma. The SO2-related changes in ED visits or 

hospital admissions for respiratory causes ranged from -5% to 20% excess risk. 

The consistency and coherence of the epidemiologic evidence for respiratory effects associated 

with short-term exposure to SO2 are illustrated in Figure 5-1 and Figure 5-2, which present effect 

estimates for respiratory symptoms, ED visits, and hospitalizations in children. Associations between 

short-term ambient SO2 concentrations and respiratory symptoms, ED visits, and hospitalizations are 

largely positive, with several of the more precise effect estimates (suggestive of greater study power) 

indicating statistical significance. The epidemiologic findings of asthma symptoms with 24-h avg SO2

exposures are generally coherent with increases in symptoms reported in asthmatics in human clinical 

studies with 5-10 min exposures; it is possible that these epidemiologic associations are determined in 

large part by peak exposures within a 24-h period. The effects of SO2 on respiratory symptoms, lung 

function, and airway inflammation observed in the human clinical studies using peak exposures further 

provides a basis for a progression of respiratory morbidity resulting in increased ED visits and hospital 

admissions. Collectively, these findings provide biological plausibility for the observed associations 

between ambient SO2 levels and ED visits and hospitalizations for all respiratory diseases and asthma, 

notably in children and older adults (≥ 65 years). 

Overall, the epidemiologic evidence for respiratory morbidity is consistent, with associations 

reported in studies conducted in numerous locations using a variety of methodological approaches. The 

potential influence of copollutants has not been systematically considered in the epidemiologic literature. 

A limited subset of the studies examined potential confounding by copollutants using multipollutant 

regression models. These analyses indicated that although copollutant adjustment had varying degrees of 

influence on the SO2 effect estimates, the effect of SO2 on respiratory health outcomes appeared to be 

generally robust and independent of the effects of gaseous copollutants, including NO2 and O3. The 

evidence for PM10 was less consistent, though it was noted that in the limited number of studies that 

examined PM2.5 and PM10-2.5, the SO2 estimates were generally robust to the inclusion of these copollutants 

in the regression model. These findings suggest that the observed effects of SO2 on respiratory endpoints 

occur independent of the effects of other ambient air pollutants. 

Intervention studies provide additional evidence that supports a causal relationship between SO2

exposure and respiratory health effects. Hill (1965) emphasized that intervention studies can provide 

strong support for causal inferences. Two notable studies conducted in several cities in Germany and in 

Hong Kong reported that decreases in SO2 concentrations were associated with improvements in 

respiratory symptoms. In eastern Germany, a decrease in the prevalence of respiratory symptoms was 

correlated with a steep decline in ambient SO2 concentrations of more than 90% from 1992-1993 to 1998-

1999 (Heinrich et al., 2002). During this study period, decreases in other ambient air pollutants, including 

~60% lower TSP concentrations, also occurred in these cities. In Hong Kong (Peters et al., 1996b), 

respiratory health improved with similarly large reductions in SO2 of up to 80% in the polluted district but 

with much smaller reductions in TSP (less than 20%) compared with those in the cities in eastern 

Germany. The possibility remains that these health improvements may be partially attributable to 

declining concentrations of air pollutants other than SO2, most notably PM or constituents of PM. Animal 

toxicological studies conducted at higher concentrations (≥ 1 mg/m3

 PM and ≥ 1 ppm SO2) suggest that 

SO2 effects may be potentiated by coexposure to PM but the relevance of these results to ambient 

 5-5 
exposures is not clear. Hence, the improvements in respiratory health may be jointly attributable to 

declines in both SO2 and PM. 

Figure 5-1. Odds ratios (95% CI) for the association between short-term exposures to ambient 

SO2 and respiratory symptoms in children. Odds ratios are standardized per 10-ppb 

increase in 24-h avg SO2 level. Studies are generally presented in the order of 

increasing width of the 95% CI. 

 5-6 
Figure 5-2. Relative risks (95% CI) for the association between short-term exposures to ambient 

SO2 and emergency department (ED) visits (*) and hospitalizations for all respiratory 

diseases and asthma in children. Relative risks are standardized per 10-ppb increase 

in 24-h avg SO2 level. The studies are generally presented in the order of increasing 

width of the 95% CI. 

The ISA also evaluates the evidence of other health outcomes and exposure durations. For shortterm exposure to SO2 and mortality, the evidence was suggestive of a causal relationship. Recent 

epidemiologic studies have consistently reported positive associations between mortality and SO2, with 

slightly larger effect estimates observed for respiratory mortality compared to cardiovascular mortality. 

5-7 
However, the SO2 effect estimates were generally reduced after adjusting for copollutants in the 

regression models, indicating some extent of confounding among these pollutants. The evidence between 

short-term SO2 exposure and cardiovascular effects, and morbidity and mortality with long-term SO2

exposures is inadequate to infer a causal relationship. The key conclusions on the health effects of SO2

exposure are briefly summarized in Section 5.5. 

5.3. Integration of the Evidence 

This section highlights some key considerations for the evaluation of the evidence in this ISA. As 

discussed above, clinical studies provide the definitive evidence that short-term SO2 exposure is 

associated with respiratory morbidity. Numerous epidemiologic studies report associations for a broader 

range of respiratory health outcomes, at lower concentrations than the clinical studies and at levels below 

the current 24-h avg standard. There is, however, uncertainty about the magnitude of the epidemiologic 

effects estimates. Several sources of uncertainty and the implications for risk assessment are discussed 

below. 

Although the numerous epidemiologic studies provide supportive evidence in making a causal 

determination for the effect of SO2 on respiratory health, uncertainty remains in quantifying the 

concentration-response relationship. Exposure measurement error is a key source of this uncertainty as 

there are questions about the extent to which concentrations measured by the regulatory ambient 

monitoring network typically used in epidemiologic studies can accurately represent an individual’s 

exposure to SO2 of ambient origin. SO2 monitors currently deployed in the regulatory monitoring 

networks are adequate to determine compliance with current standards, since both the 24-h and annual 

avg standards are substantially above the operating limit of detection of these monitors. However, these 

monitors are inadequate for accurate and precise measurements at or near the current ambient mean 24-h 

avg SO2 level of ~4 ppb. Other factors contributing to exposure measurement error include the siting of 

ambient monitors, spatial variation in ambient SO2, variation in time-activity patterns, infiltration 

characteristics of microenvironments, and instrument error. 

Only a limited number of studies have focused on the relationship between personal exposure and 

ambient concentrations of SO2, in part because ambient levels have declined markedly over the past few 

decades. Current indoor and outdoor concentrations are often beneath detection limits for passive personal 

monitors used in SO2 exposure studies. In such situations, associations between ambient concentrations 

and personal exposures are inadequately characterized. However, in two studies with personal 

measurements above detection limits (Brauer et al., 1989; Sarnat et al., 2006a), a reasonably strong 

association (statistically significant regression slopes, with R2

 = 0.15-0.43) was observed between 

personal SO2 exposure and ambient concentrations. 

No studies have characterized the relationship between community avg exposure and ambient 

concentrations, which is more directly relevant to community time-series, short-term panel, and long-term 

cohort epidemiologic studies. Variations across a community in the fractional contribution of ambient SO2

to exposure generally will not influence the magnitude of the observed health effect estimate in the 

epidemiologic studies, though the standard error of the estimate would tend to be increased by 

intracommunity variations. However, for community time-series and short-term panel studies, exposure 

and analytical measurement errors would tend to bias the effect estimate towards the null, leading to 

uncertainty in accurately quantifying the magnitude of the effect. In long-term exposure studies, the 

variable ambient measurement and exposure error could also result in bias, but the extent and direction of 

this bias is unclear. 

Another factor that contributes to uncertainty in estimating the SO2-related effect from 

epidemiologic studies is that SO2 is one component of a complex air pollution mixture including various 

other components known to affect respiratory health. Several of these copollutants have been found to be 

 5-8 
correlated with SO2 in epidemiologic studies (e.g., PM, NO2, CO). Of particular interest is the correlation 

between SO2 and sulfate, the principal atmospheric oxidation product of SO2. Short-term, mostly timeseries epidemiologic studies generally use intracity ambient concentration data which show very little or 

no correlation between emitted SO2 and transformed sulfate. In contrast, long-term epidemiologic studies 

using intercity data can show correlations between SO2 and sulfate on the order of 0.8 or higher. In these 

studies, the fine-scale spatiotemporal variations in the intracity data are significantly reduced since sulfate 

has sufficient time for production from SO2, is dispersed over a wide spatial area, and can be mixed down 

to ground level. Layered over these spatial and fine-scale temporal differences are seasonal and regional 

dissimilarities driven by cities’ various SO2 emissions profiles and differing available time and sunlight 

conditions for oxidation. Therefore, the relationship between SO2 and sulfate indicates temporal and 

spatial discongruities that can influence exposures and related health effects. 

As a consequence of these uncertainties, the epidemiologic observations of SO2 health effects can 

be interpreted in different ways which are not mutually exclusive. First, the reported SO2 effect estimates 

in epidemiologic studies may reflect independent SO2 effects on respiratory health. This is supported by 

evidence from human clinical studies which indicate that peak exposures (5-10 min) to SO2 at levels as 

low as 0.2-0.3 ppm are capable of eliciting respiratory responses in asthmatics. In the epidemiologic 

studies, SO2-related effects on respiratory morbidity were observed in areas where the mean 24-h avg SO2

levels ranged from 1 to 30 ppb, with maximum values ranging from 12 to 75 ppb. There are several 

factors that may explain the difference between the concentration-response relationships in the human 

clinical and epidemiologic studies. First, human clinical studies examine effects in groups of relatively 

healthy individuals who do not represent the full range of susceptibilities present in the general 

population. In addition, clinical studies include very small numbers of subjects as compared to the 

epidemiologic studies that consider large populations exposed to ambient concentrations. These two 

factors limit the power of human clinical studies to characterize exposure-response relationships at 

ambient-relevant concentrations for the general population and sensitive subpopulations. Further, 

comparisons are being made between 5-10 min exposures in the human clinical studies and 24-h avg 

exposures values reported in the epidemiologic studies. It is difficult to know if high short-term SO2

concentrations are driving the observed associations among the general population given the limited 

number of ambient monitoring sites reporting short-term concentration data across the country. Among 

the limited number of epidemiologic studies evaluating the concentration-response function, a few studies 

found that a marked increase in SO2-related respiratory health effects was only observed at higher 

concentrations (above 90th percentile values). However, several others reported a linear relationship 

across the entire range of concentrations. 

Another interpretation is that ambient SO2 may be serving as an indicator of complex ambient air 

pollution mixtures sharing the same source as SO2 (i.e., combustion of sulfur-containing fuels or metal 

smelting). Other components of mixed emissions from these sources include trace elements such as 

vanadium, nickel, selenium, and arsenic. Distinguishing effects of individual pollutants in multipollutant 

regression models is made difficult by the possibility that a given air pollutant may be acting as a 

surrogate for a less-well-measured or unmeasured pollutant, or that several pollutants may all be acting as 

surrogates for the same mixtures of pollutants. Therefore, reported SO2-related effects may represent 

those of the overall mixture or other chemical components within the mixture. Although these issues 

complicate the interpretation of effect estimates from multipollutant regression models, the limited 

available evidence indicates that the effect of SO2 on respiratory health outcomes appeared to be generally 

robust and independent of the effects of gaseous copollutants, including NO2 and O3, as well as particulate 

copollutants, particularly PM2.5. 

 5-9 
5.4. Susceptible and Vulnerable Populations 

Evidence from epidemiologic and human clinical studies, supported by animal studies, has 

indicated that certain subgroups within the population are more susceptible and/or vulnerable to the 

effects of SO2 exposure. There is substantial evidence from epidemiologic and human clinical studies 

indicating that asthmatics are more susceptible to respiratory health effects from SO2 exposures than the 

general public. Limited epidemiologic evidence further indicates that children and older adults (≥ 65 

years) are more susceptible to the adverse respiratory effects associated with ambient SO2 concentrations 

when compared to the general population. A number of potentially susceptible groups, including obese 

individuals, individuals having a chronic pro-inflammatory state like diabetics, and children born 

prematurely or with low birth weight (< 2,500 grams), may experience increased adverse effects 

associated with exposure to air pollution, but these relationships have not been examined specifically in 

relation to SO2. The differential effects of air pollution among genetically diverse subpopulations have 

been examined for a number of glutathione-S-transferase (GST) genes and other genotypes. While limited 

in number, these studies provide some insight into a potential genetic role in the determination of 

susceptibility to air pollution. 

Human clinical studies have clearly shown that exercising asthmatics are at greatest risk of 

experiencing adverse respiratory effects related to SO2 exposure. Oronasal breathing during exercise 

increases vulnerability as it allows a larger fraction of inhaled SO2 to reach the lower airways. Therefore, 

individuals with increased vulnerability for SO2-related respiratory health effects include those who spend 

time outdoors at increased exertion levels, for example children, outdoor workers, and individuals who 

exercise or play sports. 

5.5. Conclusions 

The important findings of this ISA on the health effects of SO2 exposure, including the levels at 

which effects are observed, are briefly summarized in Table 5-3. Also summarized are conclusions drawn 

in the 1996 NAAQS review for comparison. 

Collectively, the human clinical, epidemiologic, and animal toxicological data are sufficient to 

conclude that there is a causal relationship between respiratory morbidity and short-term exposure to SO2. 

Observed associations between SO2 exposure and an array of respiratory outcomes, including respiratory 

symptoms, lung function, airway inflammation, AHR, and ED visits and hospitalizations from the human 

clinical, animal toxicological, and epidemiologic studies, in combination, provide clear and convincing 

evidence of consistency, specificity, temporal and biologic gradients, biological plausibility, and 

coherence. 

Human clinical studies consistently demonstrate respiratory morbidity among exercising asthmatics 

following peak exposures (5-10 min) to SO2 concentrations ≥ 0.4 ppm, with respiratory effects occurring 

at concentrations as low as 0.2 ppm in some asthmatics. In the epidemiologic studies, the SO2-related 

respiratory effects were consistently observed in areas where the maximum ambient 24-h avg SO2

concentration was below the current 24-h avg NAAQS level of 0.14 ppm (see Tables 5-4 and 5-5). 

Potentially susceptible and vulnerable subgroups include asthmatics, children, older adults, and 

individuals who spend a lot of time outdoors at increased exertion levels. 

In addition to respiratory morbidity related to short-term exposure to SO2, studies of other health 

outcomes and exposure durations were also evaluated in this ISA. The evidence is suggestive of a causal 

relationship between short-term exposure to SO2 and mortality. The evidence linking short-term SO2

exposure and cardiovascular effects, and morbidity and mortality with long-term exposures to SO2 is 

inadequate to infer a causal relationship. 

 5-10 
Table 5-3. Key findings on the health effects of SO2 exposure 

Short-Term Exposure: RESPIRATORY MORBIDITY 

Causal relationship 

RESPIRATORY SYMPTOMS

Conclusions from 1996 NAAQS Review: Among exercising asthmatics, there is a clear, statistically 

significant increase in respiratory symptoms following peak exposures (5-10 min) to 0.6-1.0 ppm SO2. 

Significant, but less severe symptoms are associated with peak SO2 exposures at concentrations of 

0.4-0.5 ppm in human clinical studies. 

 In the epidemiologic studies, an association with aggravation of bronchitis is consistently observed at 

24-h avg SO2 levels of 0.19 to 0.23 ppm and in some cases at levels below 0.19 ppm. 

Conclusions from Current Review: Severity and incidence of respiratory symptoms has been shown to 

increase with increasing concentrations between 0.2 and 0.6 ppm SO2 in exercising asthmatic adults 

following peak exposures (5-10 min). Statistically significant increases in symptoms are observed at 

SO2 concentrations 0.4 ppm. 

 Epidemiologic studies provide consistent evidence of an association between ambient SO2 exposure 

and increased respiratory symptoms in children, particularly those with asthma or chronic respiratory 

symptoms. Multicity studies have observed these associations at a median range of 17 to 37 ppb 

(75th percentile: ~25 to 50) across cities for 3-h avg SO2 and 2.2 to 7.4 ppb (90th percentile: 4.4 to 

14.2) for 24-h avg SO2. 

 In contrast, the epidemiologic evidence on the association between SO2 and respiratory symptoms in 

adults is inconsistent at current short-term avg ambient SO2 concentrations. 

LUNG FUNCTION 

Conclusions from 1996 NAAQS Review: Bronchoconstriction has been found to be the most sensitive 

indicator of lung function effects following acute exposure to SO2. Guinea pigs were found to be the 

most sensitive species, with bronchoconstriction observed using 0.16 ppm SO2. In human clinical 

studies, ! 10-20% of exercising asthmatic individuals experience large decrements in lung function 

(i.e., sRaw increases 200% or FEV1 decreases 20%) following 5-10 min exposures to SO2

concentrations of 0.2-0.5 ppm. At 0.6-1.0 ppm SO2, 20-25% of exercising asthmatics are similarly 

affected. 

 Small, reversible declines in lung function in children are observed in epidemiologic studies at levels of 

0.10 to 0.18 ppm but not at levels of 0.04 to 0.08 ppm. 

Conclusions from Current Review: SO2-induces moderate or greater decrements in lung function, i.e. 

increases in sRaw ( 100%) or decreases in FEV1 ( 15%) in 5-30% of exercising asthmatics at 

0.2-0.3 ppm and 20-60% of exercising asthmatics at 0.4-1.0 ppm with 5-10 min exposures. 

 Epidemiology results are inconsistent for the association between short-term avg SO2 and lung 

function in children and adults. 

AIRWAYS INFLAMMATION 

Conclusions from 1996 NAAQS Review: No conclusions in the previous review. 

Conclusions from Current Review: A limited number of health studies have evaluated the effect of SO2

on airway inflammation. One human clinical study observed an SO2-induced increase in sputum 

eosinophil counts in exercising asthmatics 2 h after a 10 min exposure to 0.75 ppm SO2. The results 

of this study provide some evidence that SO2 may elicit an inflammatory response in the airways of 

asthmatics which extends beyond the short time period typically associated with SO2 effects. 

 Animal toxicological studies indicate that repeated exposures to SO2, at concentrations as low as 0.1 

ppm in guinea pigs, may exacerbate inflammatory responses in allergic animals. 

5-11 
AIRWAYS HYPERRESPONSIVENESS 

Conclusions from 1996 NAAQS Review: No conclusions in the previous review. 

Conclusions from Current Review: Animal toxicological evidence indicates that repeated exposures to 

SO2, at concentrations as low as 0.1 ppm in guinea pigs, can exacerbate AHR following allergic 

sensitization. In a human clinical study, concurrent exposure (6 h) to 0.2 ppm SO2 and 0.4 ppm NO2

has been observed to enhance AHR to an inhaled antigen among resting asthmatics. These findings 

are consistent with the very limited epidemiologic evidence that observes an association between 

exposure to SO2 and AHR in atopic individuals. 

ED VISITS/HOSPITALIZATIONS 

Conclusions from 1996 NAAQS Review: No conclusions in the previous review. 

Conclusions from Current Review: Epidemiologic studies provide evidence of an association between 

ambient SO2 concentrations and ED visits and hospitalizations for all respiratory causes, particularly 

among children and older adults (age 65+ years), and for asthma. This finding is coherent and 

plausible with the increases in bronchoconstriction, decreases in lung functions, increases in 

respiratory symptoms, and potential increases in airway inflammation and hyperresponsiveness 

demonstrated in other epidemiologic, human clinical, and animal toxicological studies. The SO2 effect 

estimates ranged from a 5% decreased risk to a 20% excess risk per 10-ppb increase in 24-h avg 

SO2, with the large majority of studies observing an increase in risk. These effects were observed in 

studies with mean 24-h avg concentrations as low as 4 ppb, but two studies evaluating the 

concentration-response function observed that a marked increase in SO2-related effects was only 

observed higher concentrations (above 90th percentile values). 

Short-Term Exposure: CARDIOVASCULAR MORBIDITY 

Inadequate to infer a causal relationship 

CARDIOVASCULAR EFFECTS; ED VISITS/HOSPITALIZATIONS 

Conclusions from 1996 NAAQS Review: No conclusions in the previous review. 

Conclusions from Current Review: There was some positive evidence of an association between 24-h 

avg SO2 exposure and heart rate variability in the epidemiologic studies, but the evidence from two 

human clinical studies were weak and inconsistent. Some epidemiologic studies have observed 

positive associations between ambient SO2 concentrations and ED visits or hospital admissions for 

cardiovascular diseases, but results are not consistent across studies and the SO2 effect estimate was 

generally not robust to copollutant adjustment. 

Short-Term Exposure: MORTALITY 

Suggestive of a causal relationship 

NONACCIDENTAL AND CARDIOPULMONARY MORTALITY 

Conclusions from 1996 NAAQS Review: Epidemiologic studies based on historical air pollution episodes 

observed the clearest mortality associations when both BS and SO2 concentrations were at high levels 

(24-h avg values exceeding 1,000 μg/m3

 [~400 ppb for SO2]). Later studies observed that an increased risk of mortality was associated with exposure to BS and SO2 levels in the range of 0.19 to 

0.38 ppm. Because of the high colinearity between BS and SO2 levels, it is difficult to readily separate 

the effects of these pollutants on mortality. 

Conclusions from Current Review: Recent epidemiologic studies have consistently reported positive 

associations between mortality and SO2, often at mean 24-h avg levels < 10 ppb. The range of SO2

excess risk estimates for nonaccidental mortality is 0.4 to 2% per 10 ppb increase in 24-h avg SO2 in 

several multicity studies and meta-analyses. SO2 effect estimates for respiratory mortality were 

generally larger than the cardiovascular mortality estimates, suggesting a stronger association of SO2

with respiratory mortality compared to cardiovascular mortality. The SO2 effect estimates were 

generally reduced when copollutants were added in the model, indicating some extent of confounding 

among these pollutants. 

 5-12 
Long-Term Exposure: RESPIRATORY MORBIDITY 

Inadequate to infer a causal relationship 

RESPIRATORY SYMPTOMS AND LUNG FUNCTION 

Conclusions from 1996 NAAQS Review: The limited available epidemiologic data indicated associations 

between respiratory illnesses and symptoms and persistent exposures to SO2 in areas with long-term 

averages exceeding 0.04 ppm. 

Conclusions from Current Review: Several epidemiologic studies that examined the effects of long-term 

exposure to SO2 on asthma, bronchitis, and respiratory symptoms observed positive associations in 

children. However, the variety of outcomes examined and the inconsistencies in the observed results 

make it difficult to assess the direct impact of long-term exposure of SO2 on respiratory symptoms. 

The epidemiologic and animal toxicological evidence is also inadequate to infer that long-term 

exposure to SO2 has a detrimental effect on lung function. 

Long-Term Exposure: OTHER MORBIDITY 

Inadequate to infer a causal relationship 

CARCINOGENIC EFFECTS 

Conclusions from 1996 NAAQS Review: Epidemiologic evidence did not substantiate the hypothesized 

links between SO2 or other SOX and cancer, though there was some animal toxicological evidence 

that led to the conclusion that SO2 may be considered a suspect carcinogen/cocarcinogen. 

Conclusions from Current Review: Animal toxicological studies indicate that SO2 at high concentrations 

may cause DNA damage but fails to induce carcinogenesis, cocarcinogenesis, or tumor promotion. 

Epidemiologic studies did not provide evidence that long-term exposure to SO2 is associated with an 

increased incidence of or mortality from lung cancer. 

PRENATAL AND NEONATAL OUTCOMES 

Conclusions from 1996 NAAQS Review: No conclusions in the previous review. 

Conclusions from Current Review: Epidemiologic studies on birth outcomes have observed positive 

associations between SO2 exposure and low birth weight. However, the inconsistent results across trimesters of pregnancy and the lack of evidence to evaluate confounding by copollutants limit the 

interpretation of these studies. 

Long-Term Exposure: MORTALITY 

Inadequate to infer a causal relationship 

NONACCIDENTAL AND CARDIOPULMONARY MORTALITY 

Conclusions from 1996 NAAQS Review: The available studies on the effects of long-term exposure to 

SO2 on mortality were all ecological cross-sectional studies which did not take into consideration 

potential confounders. In addition, it was concluded that effects from PM and SO2 could not be 

distinguished in these studies. 

Conclusions from Current Review: Two major U.S. epidemiologic studies observed associations 

between long-term exposure to SO2 and mortality, but several other U.S. and European cohort studies 

did not observe an association. The relative risks ranged from 0.97 to 1.07 per 5-ppb increase in the 

long-term avgSO2. Evaluation of these studies is further limited by the inability to distinguish potential 

confounding by copollutants and uncertainties regarding the geographic scale of analysis. 

 5-13 
Table 5-4. Effects of short-term exposure to SO2 on respiratory symptoms among children. 

Study Population Mean Concentration 

(ppb) 

SO2 Range 

(ppb) 

SO2 Upper 

Percentile (ppb) 

Standardized Odds 

Ratio (95% CI)a

UNITED STATES 

Schildcrout et al. (2006) 

Multicity, North America 

Seattle, WA; Baltimore, MD; St. 

Louis, MO (Nov 1993–Aug 

1995); Denver, CO; San Diego, 

CA (Nov 1993–Jul 1995); 

Toronto, ON (Dec 1993–Jul 

1995); Boston, MA (Jan 1994–

Sep 1995) 

No SO2 data available in 

Albuquerque, NM 

Asthmatic children 

(n = 990) 

24-h avg: 2.2-7.4 

(range of city-specific 

medians) 

NR 75th: 3.1-10.7 

90th: 4.4-14.2 

(range of cityspecific estimates) 

Asthma symptoms (3-day sum 

lag): 

SO2 alone: 1.04 (1.00, 1.08) 

SO2 + PM10: 1.04 (0.99, 1.08) 

SO2 + NO2: 1.04 (1.00, 1.09) 

SO2 + CO: 1.05 (1.00, 1.09) 

Schwartz et al. (1994) 

Multicity, U.S. 

Watertown, MA (Apr-Aug 1985); 

Kingston-Harriman, TN; St. 

Louis, MO (Apr-Aug 1986); 

Steubenville, OH; Portage, WI 

(Apr-Aug 1987); Topeka, KS 

(Apr-Aug 1988) 

Children grades 2-5 

(n = 1,844) 

24-h avg: 4.1 (median) 6.8 (IQR) 75th: 8.2 

90th: 17.9 

Max: 81.9 

Cough incidence (4-day avg lag): 

SO2 alone: 1.15 (1.02, 1.31) 

PM10 adjusted: 1.08 (0.93, 1.25) 

NO2 adjusted: 1.09 (0.94, 1.30) 

O3 adjusted: 1.15 (1.01,1.31) 

Neas et al. (1995) 

Uniontown, PA 

Summer 1990 

Children grades 4-5 

(n = 83) 

12-h avg: 10.2 

Overnight: 5.9 

Daytime: 14.5 

11.1 (IQR) Max: 44.9 Evening cough (lag 12-h): 

1.19 (1.00, 1.42) 

Delfino et al. (2003a) 

Los Angeles, CA 

Nov 1999-Jan 2000 

Hispanic children 

w/asthma, age 10-

16 yrs 

(n = 22) 

1-h max: 7.0 2-26 90th: 11.0 Asthma symptom score >1: 

1.31 (1.10, 1.55), lag 0 

Asthma symptom score >2: 

1.37 (0.87, 2.18), lag 0 

Mortimer et al. (2002) 

Multicity, U.S. 

Bronx, NY; East Harlem, NY; 

Baltimore, MD; Washington, DC; 

Detroit, MI; Cleveland, OH; 

Chicago, IL; St. Louis, MO 

Jun-Aug 1993 

Asthmatic children, 

age 4-9 yrs 

(n = 846) 

3-h avg: 22 (shown in 

figure) 

0-75 (shown in 

graph) 

NR Asthma symptoms (lag 1-2): 

SO2 alone (8 cities): 1.19 (1.06, 

1.35) 

O3 adjusted (8 cities): 1.18 (1.05, 

1.33) 

O3 & NO2 adjusted (7 cities): 

1.19 (1.04, 1.37) 

O3, NO2 & PM10 adjusted 

(3 cities): 1.23 (0.94, 1.62) 

EUROPE 

Timonen and Pekkanen (1997) 

Kuopio (urban and suburban) 

Finland 

Winter 1994 

Children with 

asthma or cough 

symptoms, age 

7-12 yrs 

(n = 169) 

24-h avg: 2.3 1.7 (IQR) 75th: 2.7 

Max: 12.3 

Upper respiratory symptoms

(URS): 

2.70 (1.19, 6.15), lag 1 

3.15 (1.22, 8.27), lag 1-4 

 5-14 
Study Population Mean Concentration 

(ppb) 

SO2 Range 

(ppb) 

SO2 Upper 

Percentile (ppb) 

Standardized Odds 

Ratio (95% CI)a

Ward et al. (2002a, 2002b) 

Birmingham and Sandwell, UK 

Jan-Mar 1997 

May-Jul 1997 

Children, age at enrollment 9 yrs 

(n = 162) 

24-h avg: 

Winter: 5.4 (median) 

Summer: 4.7 (median) 

Winter: 2.0-18.0 

Summer: 2.0-10.0 

NR Cough (lag 0): 

Winter: 0.81 (0.59, 1.13) 

Summer: 1.21 (1.05, 1.42) 

Shortness of breath (lag 0): 

Winter: 1.05 (0.83, 1.36) 

Summer: 0.95 (0.71, 1.27) 

Wheeze (lag 0): 

Winter: 0.90 (0.67, 1.18) 

Summer: 1.13 (0.81, 1.54) 

Segala et al. (1998) 

Paris, France 

Nov 1992-May 1993 

Children with 

physician-diagnosed 

asthma, age 

7-15 yrs 

(n = 84) 

24-h avg: 8.3 (SD 5.2) 1.7-32.2 NR Asthma: 

1.73 (1.15, 2.60), lag 0 

1.60 (1.01, 2.53), lag 1 

Wheeze: 

1.22 (0.95, 1.58), lag 0 

1.13 (0.68, 1.88), lag 1 

Boezen et al. (1999) 

Amsterdam and Meppel 

(urban and rural), 

the Netherlands 

3 winters, 1992-1995 

Children with and 

without BHR and 

high serum concentrations of total IgE, 

age 7-11 yrs, 

(n = 632) 

24-h avg: 1.7-8.7 

Medians: 1.4-8.3 

(range of city-specific 

estimates) 

1.9-23.6 NR Among children with BHR and 

relatively high serum total IgE: 

Lower respiratory symptoms

(LRS): 

1.27 (1.09, 1.49), lag 0 

1.25 (1.06, 1.48), lag 1 

1.69 (1.26, 2.28), 5-day avg 

Van der Zee et al. (1999) 

Urban and nonurban areas, The 

Netherlands 

3 winters, 1992-1995 

Children with and 

without chronic 

respiratory 

symptoms, age 7-11 

yrs, 

(n = 633) 

24-h avg: 1.4-8.8 

(range of city-specific 

medians) 

NR Max: 6.5-58.5 

(range of cityspecific estimates) 

LRS, urban: 

SO2 alone: 

1.22 (1.01, 1.46), lag 0 

1.14 (0.95, 1.38), lag 1 

1.34 (0.98, 1.82), 5-day avg 

PM10 adjusted: 

1.18 (0.96, 1.45), lag 0 

1.03 (0.83, 1.27), lag 1 

1.08 (0.72, 1.63), 5-day avg 

LRS, nonurban: 

0.94 (0.79, 1.12), lag 0 

0.94 (0.78, 1.13), lag 1 

1.10 (0.75, 1.63), 5-day avg 

Cough, urban: 

0.93 (0.84, 1.03), lag 0 

1.08 (0.98, 1.19), lag 1 

1.08 (0.89, 1.30) 5-day avg 

Cough, nonurban: 

1.05 (0.96, 1.15), lag 0 

0.98 (0.90, 1.08), lag 1 

1.04 (0.83, 1.30), 5-day avg 

Hoek and Brunekreef (1994) 

The Netherlands 

3 winters, 1987-1990 

Children age 

7-11 yrs from 1 

industrial and 3 

nonindustrial 

communities 

(n = 1,078) 

24-h avg: 5.7 (SD 5.5) 0.2-36.0 NR Cough: 

1.05 (0.88, 1.26), lag 0 

LRS: 

1.33 (1.06, 1.69), lag 1 

URS: 

1.14 (0.98, 1.32), lag 0 

Pikhart et al. (2000) 

Czech Republic 

1993-1994 

Children age 

7-11 yrs from 1 

industrial and 3 

nonindustrial 

communities 

(n = 1,078) 

24-h avg: 28.2 NR 75th: 36.5 Wheeze: 

1.23 (0.95, 1.62), lag NR 

 

 5-15 
Table 5-5. Effects of short-term SO2 exposure on emergency department visits and hospital 

admissions for respiratory outcomes. 

Study Population Mean Concentration 

(ppb) SO2 Range (ppb) SO2 Upper 

Percentile (ppb)

Standardized 

Excess Risk 

(95% CI)a 

EMERGENCY DEPARTMENT VISITS – ALL RESPIRATORY 

UNITED STATES 

Wilson et al. (2005) 

Portland, ME 

Jan 1998-Dec 2000 

Manchester, NH 

Jan 1996-Dec 2000 

≈ 54,000 ED visits 

in Portland and ≈

30,000 ED visits in 

Manchester for all 

respiratory causes 

1-h max: 

Portland: 11.1 (SD 9.1) 

Manchester: 16.5 (SD 

14.7) 

NR NR Portland (lag 0): 

All ages: 7% (3, 12). 

0-14: -4% (-11, 4). 15-64: 9% (5, 14) 

65+: 16% (7, 26) 

Manchester (lag 0): 

All ages: 1% (-3, 5) 

0-14: 0% (8, 8). 15-64: 1% (-3, 5) 

65+: 7% (-5, 21) 

Tolbert et al. (2007) 

Atlanta, GA 

Jan 1993-Dec 2004 

> 1,000,000 ED 

visits for all 

respiratory causes 

from 41 hospitals 

1-h max: 14.9 1.0-149.0 75th: 20.0 

90th: 35.0 

0.8% (-0.7, 2.3), lag 0-2 

Peel et al. (2005) 

Atlanta, GA 

Jan 1993-Aug 2000 

≈ 480,000 ED visits 

for all respiratory 

causes from 31 

hospitals 

1-h max: 16.5 (SD 17.1) NR 90th: 39.0 1.6% (-0.6, 3.8), lag 0-2 

EUROPE 

Atkinson et al. (1999a) 

London, UK 

Jan 1992-Dec 1994 

98,685 ED visits 

from 12 hospitals 

24-h avg: 8.0 (SD 2.9) 2.8-30.9 90th: 11.7 All ages: 4% (1, 7), lag 1 

0-14: 9% (4, 14), lag 2 

15-64: 4% (0, 8), lag 2 

65+: -3% (-8, 3), lag 1 

EMERGENCY DEPARTMENT VISITS – ASTHMA 

UNITED STATES 

Ito et al. (2007) 

New York, NY 

Jan 1999-Dec 2002 

Asthma ED visits, all 

ages from 11 

hospitals 

24-h avg: 

All year: 7.8 (SD 4.6) 

Warm: 5.4 (SD 2.2) 

Cold: 10.2 (SD 5.1) 

IQR: 

All year: 5 

Warm: 3 

Cold: 7 

75th: 

All year: 10 

Warm: 7 

Cold: 13 

95th: 

All year: 17 

Warm: 10 

Cold: 19 

All year (lag 0-1): 8.9% (4.9, 13.0) 

Warm (lag 0-1): 

SO2 alone: 35.9% (22.2, 51.2) 

PM2.5 adjusted: 29.6% (14.3, 46.8) 

O3 adjusted: 26.8% (13.7, 41.5) 

NO2 adjusted: -1.6% (-16.7, 16.1) 

CO adjusted: 31.1% (16.7, 47.2) 

Cold (lag 0-1): 8.5% (4.8, 12.4) 

NY Department of 

Health (2006) 

Bronx & Manhattan 

Jan 1999-Dec 2000 

≈ 31,000 asthma 

ED visits from 8 

hospitals in the 

Bronx and ≈ 5,000 

asthma ED visits 

from 14 hospitals in 

Manhattan 

24-h avg: 11 (SD 7.2) NR NR Bronx (lag 0-4): 

SO2 alone: 10% (5, 15) 

PM2.5 adjusted: 10% (4, 16) 

NO2 adjusted: 10% (4, 15) 

Manhattan (lag 0-4): 

SO2 alone: -1% (-11, 11) 

PM2.5 adjusted: -3% (-14, 10) 

NO2 adjusted: 1% (-12, 14) 

Jaffe et al. (2003) 

Cincinnati, OH 

Cleveland, OH 

Columbus, OH 

Jul 1991-Jun 1996, 

summer months only 

(Jun-Aug) 

4,416 ED visits for 

asthma, age 5-34 

yrs 

24-h avg: 

Cincinnati: 14 (SD 10) 

Cleveland: 15 (SD 10) 

Columbus: 4 (SD 3) 

Cincinnati: 1-50 

Cleveland: 1-64 

Columbus: 0-22 

NR Cincinnati: 17% (5, 31), lag 2 

Cleveland: 3% (-4, 11), lag 2 

Columbus: 13% (-14, 49), lag 3 

All cities: 6% (1, 11), best lags 

 5-16 
Standardized 

Excess Risk 

(95% CI)a 

Study Population Mean Concentration 

(ppb) SO2 Range (ppb) SO2 Upper 

Percentile (ppb)

Wilson et al. (2005) 

Portland, ME 

Jan 1998-Dec 2000 

Manchester, NH 

Jan 1996-Dec 2000 

≈ 8,100 asthma ED 

visits in Portland 

and ≈ 4,700 asthma 

ED visits in 

Manchester 

1-h max: 

Portland: 11.1 (SD 9.1) 

Manchester: 16.5 (SD 

14.7) 

NR NR Portland (lag 0): 

All ages: 11% (2, 20). 0-14: 5% (-12, 25) 

15-64: 12% (1, 23). 65+: 12% (-15, 47) 

Manchester (lag 0): 

All ages: 6% (-4, 17). 0-14: 20% (-3, 49) 

15-64: 3% (-8, 16). 65+: 12% (-29, 75) 

Peel et al. (2005) 

Atlanta, GA 

Jan 1993-Aug 2000 

≈ 110,000 asthma 

ED visits, all ages 

from 31 hospitals 

1-h max: 16.5 (SD 17.1) NR 90th: 39.0 0.2% (-3.2, 3.4), lag 0-2 

EUROPE 

Atkinson et al. (1999a) 

London, UK 

Jan 1992-Dec 1994 

28,435 asthma ED 

visits from 12 

hospitals 

24-h avg: 8.0 (SD 2.9) 2.8-30.9 50th: 7.4 

90th: 11.6 

All ages: 7% (2, 13), lag 1 

0-14: 15% (7, 23), lag 1 

15-64: 6% (-1, 14), lag 1 

Hajat et al. (1999) 

London, UK 

Jan 1992-Dec 1994 

General practitioner 

visits for asthma 

24-h avg: 

All year: 8.0 (SD 2.9) 

Warm: 7.7 (SD 2.4) 

Cool: 8.3 (SD 3.4) 

NR 90th: 

All year: 11.6 

Warm: 10.7 

Cool: 12.4 

All ages: 6.6% (1.3, 11.9), lag 0-2 

0-14: 6.6% (-1.0, 14.7), lag 0-3 

15-64: 5.2% (-1.5, 12.3), lag 0-3 

65+: 7.2% (-4.3, 20.1), lag 0-1 

Boutin-Forzano et al. 

(2004) 

Marseille, France 

Apr 1997-Mar 1998 

549 ED visits for 

asthma 

24-h avg: 8.5 0.0-35.3 NR 3-49 yrs: 0.6% (-1.4, 2.7), lag 0 

Galan et al. (2003) 

Madrid, Spain 

Jan 1995-Dec 1998 

4,827 ED visits for 

asthma 

24-h avg: 8.9 (SD 5.8) 1.9-45.6 75th: 11.8 

90th: 16.5 

All ages: 4.9% (-4.2, 15.0), lag 0 

Tenias et al. (1998) 

Valencia, Spain 

Jan 1993-Dec 1995 

734 ED visits for 

asthma 

24-h avg: 10.0 

Cold: 11.9 

Warm: 8.2 

1-h max: 21.2 

Cold: 24.3 

Warm: 18.1 

NR 24-h avg: 

75th: 12.9 

95th: 16.0 

1-h max: 

75th: 27.1 

95th: 35.8 

> 14 yrs: 

13.9% (-7.0, 39.4), lag 0 

Castellsague et al. 

(1995) 

Barcelona, Spain 

Jan 1986-Dec 1989 

ED visits for asthma 

from 4 hospitals 

24-h avg: 

Summer: 15.3 

Winter: 19.5 

NR Summer: 

75th: 20.3 

95th: 30.8 

Winter: 

75th: 25.2 

95th: 35.3 

15-64 yrs: 

Summer: 5.5% (-2.1, 13.8), lag 2 

Winter: 2.1% (-4.2, 9.0), lag 1 

HOSPITAL ADMISSIONS – ALL RESPIRATORY 

UNITED STATES 

Schwartz (1995) 

New Haven, CT 

Tacoma, WA 

Jan 1988-Dec 1990 

≈ 8,800 admissions 

in New Have and ≈

4,000 admissions in 

Tacoma for all 

respiratory causes, 

ages 65+ yrs 

24-h avg: 

New Haven: 29.8 

Tacoma: 16.8 

IQR: 

New Haven: 24.8 

Tacoma: 11.5 

New Haven: 

75th: 38.2 

90th: 60.7 

Tacoma: 

75th: 21.4 

90th: 28.2 

New Haven: 

SO2 alone: 2% (1, 3), lag 2 

PM10 adjusted: 2% (1, 3), lag 2 

Tacoma: 

SO2 alone: 3% (1, 6), lag 0 

PM10 adjusted: -1% (-4, 3), lag 0 

Schwartz et al. (1996) 

Cleveland, OH 

1988-1990 

Hospital 

admissions, ages 

65+ yrs 

24-h avg: 35.0 IQR: 25 75th: 45.0 

90th: 61.0 

0.8% (-0.3, 1.5), lag 0-1 

 5-17 
Standardized 

Excess Risk 

(95% CI)a 

Study Population Mean Concentration 

(ppb) SO2 Range (ppb) SO2 Upper 

Percentile (ppb)

CANADA 

Fung et al. (2006) 

Vancouver, BC 

Jun 1995-Mar 1999 

≈ 41,000 respiratory 

admissions for ages 

65+ yrs 

24-h avg: 3.46 (SD 1.82) 0.0-12.5 NR 13% (1, 26), 7-day avg 

Cakmak et al. (2007a) 

Multicity, Canada 

Calgary, Edmonton, 

Halifax, London, 

Ottawa, Saint John, 

Toronto, Vancouver, 

Windsor, Winnipeg 

Apr 1993-Mar 2000 

> 200,000 hospital 

admissions for all 

respiratory causes 

24-h avg: 4.6 (all cities) 

2.8-10.2 (range of cityspecific means) 

0-75 Max: 14-75 

(range of cityspecific estimates) 

SO2 alone: 

2.4% (1.1, 4.0) 

O3, NO2 & CO adjusted: 

1.1% (0.2, 2.0) 

SO2 + O3 + NO2: 

14.9% (7.8, 22.3) 

(best lags selected for each city) 

Yang et al. (2003b) 

Vancouver, BC 

Jan 1986-Dec 1998 

Respiratory hospital 

admissions among 

young children 

(< 3 yrs) and elderly 

(65+ yrs) 

24-h avg: 4.84 (SD 2.84) IQR: 3.5 75th: 6.25 

Max: 24.00 

< 3 yrs (lag 2): 

SO2 alone: 3% (-6, 15) 

O3 adjusted: 3% (-8, 12) 

65+ yrs (lag 0): 

SO2 alone: 6% (0, 12) 

O3 adjusted: 6% (0, 12) 

Burnett et al. (1997b) 

Toronto, ON 

Jun 1992-Sep 1994, 

summer months only 

(May-Sep) 

All respiratory 

hospital admissions 

1-h max: 7.9 0-26 75th: 11 

95th: 18 

SO2 alone: 38.4% (13.1, 69.2), lag 0-3 

PM2.5 adj: 24.3% (-0.5, 55.2), lag 0-3 

PM10-2.5 adj: 25.5% (0.8, 56.4), lag 0-3 

PM10 adj: 23.1% (-2.9, 56.0), lag 0-3 

Luginaah et al. (2005) 

Windsor, ON 

Apr 1995-Dec 2000 

All respiratory 

admissions from 

4 hospitals 

1-h max: 27.5 (SD 16.5) 0-129 NR All ages (lag 1): Female: 8.7% (-2.7, 21.4)

Male: -9.5% (-19.7, 1.9) 

0-14 (lag 1): Female: 24.4% (2.3, 51.4) 

Male: -9.7% (-24.4, 7.8) 

15-64 (lag 1): Female: 6.5% (-14.0, 32.3)

Male: -5.9% (-29.5, 25.4) 

65+ (lag 1): Female: 6.3% (-9.9, 25.4) 

Male: -11.9% (-26.9, 6.1) 

EUROPE 

Oftedal et al. (2003) 

Drammen, Norway 

Jan 1994-Dec 2000 

All respiratory 

hospital admissions 

24-h avg: 1.1 (SD 0.8) NR NR All ages: 

71.8% (15.5, 152.7), lag NR 

Llorca et al. (2005) 

Torrelavega, Spain 

Jan 1992-Dec 1995 

Hospital admissions 

from one hospital 

24-h avg: 5.0 (SD 6.3) NR NR All ages: 

1.0% (-2.8, 4.7), lag NR 

Atkinson et al. (1999b) 

London, UK 

Jan 1992-Dec 1994 

165,032 respiratory 

hospital admissions 

24-h avg: 8.0 (SD 2.9) 2.8-30.9 90th: 11.7 All ages: 3% (0, 6), lag 1 

0-14: 8% (4, 12), lag 1 

15-64: 3% (-1, 7), lag 3 

65+: 3% (-0, 7), lag 3 

Schouten et al. (1996) 

Multicity, The 

Netherlands 

Amsterdam, Rotterdam 

Apr 1977-Sep 1989 

All respiratory 

hospital admissions 

24-h avg: 

Amsterdam: 10.5 

Rotterdam: 15.0 

1-h max: 

Amsterdam: 24.4 

Rotterdam: 37.2 

NR NR Amsterdam (lag 0-3): 

15-64 yrs: -2.3% (-5.5, 0.9) 

65+: 0.2% (-2.8, 3.3) 

Rotterdam (lag 0-2): 

15-64: -2.9% (-6.2, 0.5) 

 5-18 
Standardized 

Excess Risk 

(95% CI)a 

Study Population Mean Concentration 

(ppb) SO2 Range (ppb) SO2 Upper 

Percentile (ppb)

Spix et al. (1998) 

Multicity, Europe 

London, UK; 

Amsterdam & 

Rotterdam, the 

Netherlands; Paris, 

France; Milan, Italy 

Jan 1977-Dec 1991 

All respiratory 

hospital admissions 

24-h avg: 

London: 10.9 

Amsterdam: 7.9 

Rotterdam: 9.4 

Paris: 8.6 

Milan: 24.8 

NR NR 15-64 yrs: 0.5% (-0.4, 1.3), lag NR 

65+: 1.1% (0.3, 2.4), lag NR 

Dab et al. (1996) 

Paris, France 

Jan 1987-Sep 1992 

Respiratory hospital 

admissions from 27 

hospitals 

All year: 

24-h avg: 11.2 

1-h max: 22.5 

Warm season 

24-h avg: 7.6 

1-h max: 16.1 

Cold season 

24-h avg: 15.1 

1-h max: 29.4 

NR 99th: 

All year: 

24-h avg: 50.0 

1-h max: 87.5 

Warm: 

24-h avg: 18.5 

1-h max: 50.3 

Cold: 

24-h avg: 56.0 

1-h max: 100.9 

All ages: 

1.1% (0.1, 2.1), lag 0-2 

Ponce de Leon et al. 

(1996) 

London, UK 

1987-1988; 1991-1992 

All respiratory 

hospital admissions 

24-h avg: 12.1 (SD 4.7) NR 75th: 14.7 

90th: 17.7 

95th: 20.3 

All ages: 0.8% (-0.7, 2.4) 

0-14: 0.9% (-1.5, 3.3), lag 1 

15-64: 2.0% (-0.5, 4.7), lag 1 

65+: 2.0% (-0.3, 4.4), lag 2 

Walters et al. (1994) 

Birmingham, UK 

Jan 1988-Dec 1990 

All respiratory 

hospital admissions 

24-h avg: All year: 14.7 

Spring: 16.1 

Summer: 14.2 

Autumn: 15.4 

Winter: 12.9 

NR Max: 47.5 All ages: 

Summer: 1.5% (0.3, 2.7), lag 0 

Winter: 4.5% (2.3, 6.5), lag 0 

Hagen et al. (2000) 

Drammen, Norway 

Jan 1994-Dec 1997 

All respiratory 

hospital admissions 

24-h avg: 

Winter: 21. Spring: 18 

Summer:15. Autumn:19 

Number of monitors: 1 

Winter: 11-33 

Spring: 13-29 

Summer: 5-24 

Autumn: 16-23 

NR All ages: 

92.8% (16.8, 218.8), lag NR 

Vigotti et al. (1996) 

Milan, Italy 

Jan 1980-Dec 1989 

All respiratory 

hospital admissions 

24-h avg: 

All year: 44.9 

Winter: 94.8 

Summer: 11.6 

1.1-315.7 75th: All year: 62.0

Winter: 125.0 

Summer: 15.0 

95th: All year: 143.5

Winter: 201.0 

Summer: 23.9 

15-64 yrs: 1.3% (0.0, 2.5), lag 0 

65+: 1.0% (0.0, 2.3), lag 0 

AUSTRALIA 

Barnett et al. (2005) 

Multicity, Australia/New 

Zealand 

Auckland, Brisbane, 

Canberra, Christchurch, 

Melbourne, Perth, 

Sydney 

Jan 1998-Dec 2001 

All respiratory 

hospital admissions 

24-h avg: 

Auckland: 4.3 

Brisbane: 1.8 

Christchurch: 2.8 

Sydney: 0.9 

NA in Canberra, 

Melbourne, and Perth 

1-h max: 

Brisbane: 7.6 

Christchurch: 10.1 

Sydney: 3.7 

NA in Auckland, 

Canberra, Melbourne, 

and Perth 

24-h avg: 

Auckland: 0-24.3 

Brisbane: 0-8.2 

Christchurch: 0-11.9

Sydney: 0-3.9 

1-h max: 

Brisbane: 0-46.5 

Christchurch: 0.1-

42.1 

Sydney: 0.1-20.2 

NR 1-4 yrs: 21.8% (4.5, 41.5), lag 0-1 

5-14: 15.8% (-34.2, 104.0), lag 0-1 

 5-19 
Standardized 

Excess Risk 

(95% CI)a 

Study Population Mean Concentration 

(ppb) SO2 Range (ppb) SO2 Upper 

Percentile (ppb)

Petroeschevsky (2001) 

Brisbane, Australia 

Jan 1987-Dec 1994 

33,710 respiratory 

hospital admissions 

24-h avg: 4.1 

1-h max: 9.2 

NR NR All ages: 8.0% (3.0, 13.1), lag 0 

0-4: 22.4% (8.7, 37.7), lag 0-4 

5-14: 21.1% (-5.5, 55.1), lag 0-4 

15-64: 3.3% (-10.5, 11.8), lag 1 

65+: 12.1% (1.9, 23.4), lag 0 

LATIN AMERICA 

Gouveia and Fletcher 

(2000) 

São Paulo, Brazil 

Nov 1992-Sep 1994 

All respiratory 

hospital admissions 

24-h avg: 6.9 (SD 3.4) 1.2-22.9 75th: 8.3 

95th: 13.5 

<5 yrs: 3.7% (-1.7, 9.4), lag 1 

ASIA 

Wong et al. (1999b) 

Hong Kong 

Jan 1994-Dec 1995 

All respiratory 

admissions from 12 

hospitals 

24-h avg: 6.4 1.0-25.7 75th: 9.4 All ages: 3.5% (1.1, 5.7), lag 0 

0-4: 1.3% (-2.4, 4.9), lag 0 

5-64: 2.1% (-1.1, 5.7), lag 0 

65+: 6.2% (3.2, 9.9), lag 0 

HOSPITAL ADMISSIONS – ASTHMA 

UNITED STATES 

Lin et al. (2004d) 

New York, NY 

Jun 1991-Dec 1993 

2,629 cases; 2,236 

controls, aged 0-14 

yrs 

24-h avg: 

Cases: 16.78 

Controls: 15.57 

2.88-66.35 NR 19% (11, 29), lag NR 

Sheppard et al. (1999; 

reanalysis 2003) 

Seattle, WA 

Jan 1987-Dec 1994 

7,837 asthma 

hospital admissions 

for patients <65 yrs 

24-h avg: 8.0 NR 75th: 10.0 

90th: 13.0 

2.1% (-4.0, 6.2), lag 0 

CANADA 

Lin et al. (2003) 

Toronto, ON 

Jan 1981-Dec 1993 

7,319 asthma 

hospital admissions 

among 6-12 yr olds 

24-h avg: 5.36 (SD 5.90) 0-57.0 75th: 8.00 Girls (7-day avg): 

SO2 alone: 29.8% (5.8, 60.1) 

PM2.5 & PM10-2.5 adj: 42.3% (11.6, 80.2) 

Boys (7-day avg): 

SO2 alone: -9.8% (-23.4, 5.8) 

PM2.5 & PM10-2.5 adj: -12.6% (-27.3, 5.8) 

EUROPE 

Atkinson et al. (1999b) 

London, UK 

Jan 1992-Dec 1994 

≈ 42,000 hospital 

admissions for 

asthma 

24-h avg: 8.0 (SD 2.9) 2.8-30.9 90th: 11.7 All ages: 5% (1, 10), lag 1 

0-14: 10% (4, 16), lag 1 

15-64: 7% (0, 14), lag 3 

65+: 9% (-2, 23), lag 2y 

Schouten et al. (1996) 

Multicity, The 

Netherlands 

Amsterdam, Rotterdam 

Apr 1977-Sep 1989 

All hospital 

admissions for 

asthma 

24-h avg: 

Amsterdam: 10.5 

Rotterdam: 15.0 

1-h max: 

Amsterdam: 24.4 

Rotterdam: 37.2 

NR NR Amsterdam: 

All ages: 

-6.0% (-10.7, -1.1), lag 0-3 

Sunyer et al. (1997) 

Multicity, Europe: 

Barcelona, Spain; 

Helsinki, Finland; Paris, 

France; London, UK 

Jan 1986-Dec 1992 

All hospital 

admissions for 

asthma 

24-h avg: 

Barcelona: 15.4 

Helsinki: 6.0 

London: 11.6 

Paris: 8.6 

Barcelona: 0.8-60.2 

Helsinki: 1.1-35.7 

London: 3.4-37.6 

Paris: 0.4-82.3 

NR 0-14 yrs: 

3.2% (-0.2, 6.8), best cumulative lag 

15-64: 

0.2% (-2.2, 2.6), best cumulative lag 

 5-20 
Standardized 

Excess Risk 

(95% CI)a 

Study Population Mean Concentration 

(ppb) SO2 Range (ppb) SO2 Upper 

Percentile (ppb)

Dab et al. (1996) 

Paris, France 

Jan 1987-Sep 1992 

Hospital admissions 

for asthma from 27 

hospitals 

All year: 

24-h avg: 11.2 

1-h max: 22.5 

Warm season 

24-h avg: 7.6 

1-h max: 16.1 

Cold season: 

24-h avg: 15.1 

1-h max: 29.4 

NR 99th: 

All year: 

24-h avg: 50.0 

1-h max: 87.5 

Warm: 

24-h avg: 18.5 

1-h max: 50.3 

Cold: 

24-h avg: 56.0 

1-h max: 100.9 

All ages: 

1.8% (0.1, 3.6), lag 2 

Anderson et al. (1998) 

London, UK 

Apr 1987-Feb 1992 

All hospital 

admissions for 

asthma 

24-h avg: 12.0 (SD 4.4) 3.4-37.6 75th: 14.3 

90th: 17.3 

95th: 19.5 

All ages: 7.4% (3.2, 11.7), lag 0-3 

0-14: 5.4% (0.8, 10.3), lag 0-3 

15-64: -1.8% (-6.9, 3.4), lag 0-2 

65+: 8.2% (-1.9.19.3), lag 0-3 

Walters et al. (1994) 

Birmingham, UK 

Jan 1988-Dec 1990 

All hospital 

admissions for 

asthma 

24-h avg: 

All year: 14.7 

Spring: 16.1 

Summer: 14.2 

Autumn: 15.4 

Winter: 12.9 

NR Max: 47.5 All ages: 

Summer: 0.4% (-2.8, 9.2), lag 0 

Winter: 0.7% (-2.2, 1.6), lag 0 

AUSTRALIA 

Barnett et al. (2005) 

Multicity, Australia/New 

Zealand 

Auckland, Brisbane, 

Canberra, Christchurch, 

Melbourne, Perth, 

Sydney 

Jan 1998-Dec 2001 

All hospital 

admissions for 

asthma 

24-h avg: 

Auckland: 4.3 

Brisbane: 1.8 

Christchurch: 2.8 

Sydney: 0.9 

NA in Canberra, 

Melbourne, and Perth 

1-h max: 

Brisbane: 7.6 

Christchurch: 10.1 

Sydney: 3.7 

NA in Auckland, 

Canberra, Melbourne, 

and Perth 

24-h avg: 

Auckland: 0-24.3 

Brisbane: 0-8.2 

Christchurch: 0-11.9

Sydney: 0-3.9 

1-h max: 

Brisbane: 0-46.5 

Christchurch: 0.1-

42.1 

Sydney: 0.1-20.2 

NR 1-4 yrs: 28.1% (-27.8, 125.5), lag 0-1 

5-14: 27.2% (-34.7, 147.3), lag 0-1 

Petroeschevsky et al. 

(2001) 

Brisbane, Australia 

Jan 1987-Dec 1994 

13,246 hospital 

admissions for 

asthma 

24-h avg: 4.1 

1-h max: 9.2 

NR NR All ages: -5.9% (-12.4, 1.1), lag 2 

0-14: 8.0% (-2.9, 20.1), lag 0 

15-64: -21.6% (-34.4, -6.2), lag 0 

LATIN AMERICA 

Gouveia and Fletcher 

(2000) 

São Paulo, Brazil 

Nov 1992-Sep 1994 

All hospital 

admissions for 

asthma 

24-h avg: 6.9 (SD 3.4) 1.2-22.9 75th: 8.3 

95th: 13.5 

<5 yrs: 10.4% (-1.9, 24.2), lag 2 

ASIA 

Wong et al. (1999b) 

Hong Kong 

Jan 1994-Dec 1995 

Hospital admissions 

for asthma from 12 

hospitals 

24-h avg: 6.4 1.0-25.7 75th: 9.4 All ages: 4.6% (-0.5, 9.9), lag 0 

Lee et al. (2006b) 

Hong Kong 

Jan 1997-Dec 2002 

26,663 hospital 

admissions for 

asthma 

24-h avg: 6.6 (SD 4.0) NR 75th: 8.2 <18 yrs: -3.7% (-6.7, -0.6), lag 0 

 5-21 
Standardized 

Excess Risk 

(95% CI)a 

Study Population Mean Concentration 

(ppb) SO2 Range (ppb) SO2 Upper 

Percentile (ppb)

Ko et al. (2007b) 

Hong Kong 

Jan 2000-Dec 2005 

69,716 hospital 

admissions for 

asthma from 15 

hospitals 

24-h avg: 

All year: 7.2 (SD 4.9) 

<20°C: 6.9 (SD 3.8) 

≥20°C: 7.3 (SD 5.4) 

All year: 1.1-45.6 

<20°C: 1.3-28.8 

≥20°C: 1.1-45.6 

75th: 

All year: 8.6 

<20°C: 21.3 

≥20°C: 22.5 

All ages: 1.1% (-1.6, 3.7), lag 0-3 

0-14: Not statistically significant 

(estimates NR) 

15-65: 4.8% (0.3, 9.4), lag 0-3 

65+: Not statistically significant (estimates 

NR) 

Tsai et al. (2006) 

Kaohsiung, Taiwan 

Jan 1996-Dec 2003 

17,682 hospital 

admissions for 

asthma from 63 

hospitals 

24-h avg: 9.49 0.92-31.33 75th: 12.16 SO2 alone (lag 0-2): 

≥25°C: 3.1% (-7.5, 14.8) 

<25°C: 34.5% (12.9, 60.3) 

PM10 adjusted (lag 0-2): 

≥25°C: -1.2% (-11.5, 10.2) 

<25°C: 4.7% (-13.2, 26.5) 

NO2 adjusted (lag 0-2): 

≥25°C: -5.6% (-16.2. 6.7) 

<25°C: -41.2% (-53.0, -26.8) 

CO adjusted (lag 0-2): 

≥25°C: -15.0% (-24.9, -3.8) 

<25°C: 6.3% (4.7, 8.1) 

O3 adjusted (lag 0-2): 

≥25°C: 9.7% (-1.7, 22.2) 

<25°C: 36.0% (14.2, 62.2) 

 5-22 
_________________ANNEXES_________________ 
Annex A. Literature Selection 

This Annex includes detailed information on the methods used to identify and select studies, and on 

frameworks for evaluating scientific evidence relative to causality determination. While the overarching 

framework is outlined in the introduction to Chapter 1, this Annex provides supporting information for 

that framework, including excerpts from decision frameworks or criteria developed by other 

organizations. 

A.1. Literature Search and Retrieval 

Literature searches are conducted continuously, to identify studies published since the last review. 

The current review includes studies published subsequent to the 1982 AQCD for SOX (EPA, 1982). 

Search strategies are iteratively modified in an effort to optimize the identification of pertinent 

publications. Additional publications are identified for inclusion in several ways: review of prepublication tables of contents for journals in which relevant papers may be published; independent 

identification of relevant literature by expert authors; and identification by the public and CASAC during 

the external review process. Generally, only information that has undergone scientific peer review and has 

been published, or accepted for publication, in the open literature is considered. Studies identified are 

further evaluated by EPA staff and outside experts to determine if they merit inclusion. Criteria used for 

study selection are summarized below. 

A.2. General Criteria for Study Selection 

In assessing the scientific quality and relevance of epidemiologic and animal or human 

toxicological studies, the following considerations have been taken into account. 

 Were the study populations adequately selected and are they sufficiently well defined to 

allow for meaningful comparisons between study groups? 

 Are the statistical analyses appropriate, properly performed, and properly interpreted? 

 Are likely covariates (i.e., potential confounders or effect modifiers) adequately controlled 

or taken into account in the study design and statistical analysis? 

 Are the reported findings internally consistent, biologically plausible, and coherent with 

other known facts? 

 To what extent are the aerometric data, exposure, or dose metrics of adequate quality and 

sufficiently representative to serve as indicators of exposure to ambient SO2? 

Consideration of these issues informs our judgments on the relative quality of individual studies and 

allows us to focus the assessment on the most pertinent studies. The following two sections describe 

criteria for selecting specific types of studies. 

A.2.1. Criteria for Selecting Epidemiologic Studies 

In selecting epidemiologic studies for this assessment, EPA considered whether a given study 

contains information on (1) associations with measured SOX concentrations using short- or long-term 

 A-1 
exposures at or near ambient levels of SOX, (2) health effects of specific SOX species or indicators related 

to SOX sources (e.g., combustion-related particles), (3) health endpoints and populations not previously 

extensively researched, (4) multiple pollutant analyses and other approaches to address issues related to 

potential confounding and modification of effects, and/or (5) important methodological issues (e.g., lag of 

effects, model specifications, thresholds, mortality displacement) related to interpretation of the health 

evidence. Among the epidemiologic studies, particular emphasis has been placed on those most relevant 

to reviews of the NAAQS. Specifically, studies conducted in the U.S. or Canada may be discussed in 

more detail than those from other geographic regions. Particular emphasis has been placed on: (1) recent 

multicity studies that employed standardized methodological analyses for evaluating effects of SOX and 

that provide overall estimates for effects based on combined analyses of information pooled across 

multiple cities, (2) recent studies that provide quantitative effect estimates for populations of interest, and 

(3) studies that consider SOX as a component of a complex mixture of air pollutants. 

Not all studies were accorded equal weight in the overall interpretive assessment of evidence 

regarding SO2-associated health effects. Among studies with adequate control for confounding, increasing 

scientific weight is accorded in proportion to the precision of their effect estimates. Small-scale studies 

without a wide range of exposures generally produce less precise estimates compared to larger studies 

with a broad exposure gradient. For time-series studies, the size of the study, as indicated by the duration 

of the study period and total number of events, and the variability of SO2 exposures are important 

components that help to determine the precision of the health effect estimates. In evaluating the 

epidemiologic evidence in this chapter, more weight is accorded to estimates from studies with narrow 

confidence bands. 

The goal of what was a balanced and objective evaluation that summarizes, interprets, and 

synthesizes the most important studies and issues in the epidemiologic database pertaining to SOX

exposure, illustrated by using newly created or previously published summary tables and figures. For each 

study presented, the quality of the exposure and outcome data, as well as the quality of the statistical 

analysis methodology, are discussed. The discussion incorporates the magnitude and statistical strengths 

of observed associations between SO2 exposure and health outcomes. 

A.2.2. Criteria for Selecting Animal and Human Toxicological Studies 

Criteria for the selection of research evaluating animal toxicological or controlled human exposure 

studies included a focus on those studies conducted at levels within about an order of a magnitude of 

ambient SO2 concentrations and those studies that approximated expected human exposure conditions in 

terms of concentration and duration. Studies that elucidate mechanisms of action and/or susceptibility, 

particularly if the studies were conducted under atmospherically relevant conditions, were emphasized 

whenever possible. For controlled human exposure studies, emphasis was placed on studies that (1) 

investigated potentially susceptible populations such as asthmatics, particularly studies that compared 

responses in susceptible individuals with those in age-matched healthy controls; (2) addressed issues such 

as concentration-response or time-course of responses; (3) investigated exposure to SO2 separately and in 

combination with other pollutants such as O3 and NO2; (4) included control exposures to filtered air; and 

(5) had sufficient statistical power to assess findings. 

 A-2 
Figure A-1. Selection process for studies included in the ISA. Studies are categorized into: 

informative, highly informative, and policy-relevant. 

 A-3 
A.3. Other Approaches to the Causal Determination 

The following sections include excerpts from several reports that document approaches for the 

causality, or related decision-making processes. These sections provide supplementary documentation of 

approaches that are similar in nature to EPA’s causal decision framework. 

A.3.1. Surgeon General’s Report: The Health Consequences of 

Smoking 

The Surgeon General’s Report (CDC, 2004) evaluated the health effects of smoking; building upon 

the first Surgeon General’s report published in 1964 (PHS, 1964). It also updated the methodology for 

evaluating evidence that was first presented in the 1964 report. The 2004 report acknowledged the 

effectiveness of the previous methodology, but standardizes the language surrounding causality of 

associations. 

The Surgeon General’s Reports on Smoking played a central role in the translation of scientific 

evidence into policy, conveying succinctly the link between smoking and a health effect. Specifically, the 

report stated: 

The statement that an exposure “causes” a disease in humans represents a 

serious claim, but one that carries with it the possibility of prevention. 

Causal determinations may also carry substantial economic implications 

for society and for those who might be held responsible for the exposure 

or for achieving its prevention. 

To address the issue of identifying causality, the 2004 report provided the following summary of 

the earlier 1964 report: 

When a relationship or an association between smoking...and some 

condition in the host was noted, the significance of the association was 

assessed. 

The characterization of the assessment called for a specific term. ...The 

word cause is the one in general usage in connection with matters 

considered in this study, and it is capable of conveying the notion of a 

significant, effectual relationship between an agent and an associated 

disorder or disease in the host. 

No member was so naive as to insist upon mono-etiology in pathological 

processes or in vital phenomena. All were thoroughly aware... that the 

end results are the net effect of many actions and counteractions. 

Granted that these complexities were recognized, it is to be noted clearly 

that the Committee’s considered decision to use the words “a cause,” or 

“a major cause,” or “a significant cause,” or “a causal association” in 

certain conclusions about smoking and health affirms their conviction 

(CDC, 2004) 

 A-4 
The 2004 report created uniformly labeled conclusions that were used throughout the document. The 

following excerpts from the report also include a description of the methodology and the judgments used 

to reach a conclusion: 

Terminology of Conclusions and Causal Claims 

Evidence is sufficient to infer a causal relationship. 

Evidence is suggestive but not sufficient to infer a causal relationship. 

Evidence is inadequate to infer the presence or absence of a causal 

relationship (which encompasses evidence that is sparse, of poor quality, 

or conflicting). 

Evidence is suggestive of no causal relationship. 

For this report, the summary conclusions regarding causality are 

expressed in this four-level classification. Use of these classifications 

should not constrain the process of causal inference, but rather bring 

consistency across chapters and reports, and greater clarity as to what the 

final conclusions are actually saying. As shown in Table 1.1 [see original 

document], without a uniform classification the precise nature of the 

final judgment may not always be obvious, particularly when the 

judgment is that the evidence falls below the “sufficient” category. 

Experience has shown that the “suggestive” category is often an 

uncomfortable one for scientists, since scientific culture is such that any 

evidence that falls short of causal proof is typically deemed inadequate to 

make a causal determination. However, it is very useful to distinguish 

between evidence that is truly inadequate versus that which just falls 

short of sufficiency. 

There is no category beyond “suggestive of no causal relationship” as it 

is extraordinarily difficult to prove the complete absence of a causal 

association. At best, “negative” evidence is suggestive, either strongly or 

weakly. In instances where this category is used, the strength of evidence 

for no relationship will be indicated in the body of the text. In this new 

framework, conclusions regarding causality will be followed by a section 

on implications. This section will separate the issue of causal inference 

from recommendations for research, policies, or other actions that might 

arise from the causal conclusions. This section will assume a public 

health perspective, focusing on the population consequences of using or 

not using tobacco and also a scientific perspective, proposing further 

research directions. The proportion of cases in the population as a result 

of exposure (the population attributable risk), along with the total 

prevalence and seriousness of a disease, are more relevant for deciding 

on actions than the relative risk estimates typically used for etiologic 

determinations. In past reports, the failure to sharply separate issues of 

inference from policy issues resulted in inferential statements that were 

sometimes qualified with terms for action. For example, based on the 

evidence available in 1964, the first Surgeon General’s report on 

 A-5 
smoking and health contained the following statement about the 

relationship between cardiovascular diseases and smoking: 

It is established that male cigarette smokers have a higher death rate from 

coronary artery disease than non-smoking males. Although the causative 

role of cigarette smoking in deaths from coronary disease is not proven, 

the Committee considers it more prudent from the public health 

viewpoint to assume that the established association has causative 

meaning, than to suspend judgment until no uncertainty remains (CDC, 

2004) 

Using this framework, this conclusion would now be expressed 

differently, probably placing it in the “suggestive” category and making 

it clear that although it falls short of proving causation, this evidence still 

makes causation more likely than not. The original statement makes it 

clear that the 1964 committee judged that the evidence fell short of 

proving causality but was sufficient to justify public health action. In this 

report, the rationale and recommendations for action will be placed in the 

implications section, separate from the causal conclusions. This 

separation of inferential from action-related statements clarifies the 

degree to which policy recommendations are driven by the strength of 

the evidence and by the public health consequences acting to reduce 

exposure. In addition, this separation appropriately reflects the 

differences between the processes and goals of causal inference and 

decision making. 

A.3.2. EPA: Guidelines for Carcinogen Risk Assessment 

The EPA Guidelines for Carcinogen Risk Assessment, published in 2005 (EPA, 2005), was an 

update to the previous risk assessment document published in 1986. This document served to guide EPA 

staff and public about the Agency’s risk assessment development and methodology. In the 1986 

Guidelines, a step-wise approach was used to evaluate the scientific findings. However, this newer 

document was similar to the Surgeon General’s Report on Smoking in that it used single integrative step 

after assessing all of the individual lines of evidence. Five standard descriptors were used to evaluate the 

weight of evidence: 

1. Carcinogenic to Humans 

2. Likely to Be Carcinogenic to Humans 

3. Suggestive Evidence of Carcinogenic Potential 

4. Inadequate Information to Assess Carcinogenic Potential 

5. Not Likely to Be Carcinogenic to Humans. 

The 2005 Guidelines recommend that a separate narrative be prepared on the weight of evidence 

and the descriptor. The Guidelines further recommend that the descriptors should only be used in the 

context of a weight-of-evidence discussion. 

 A-6 
The following excerpt describes how a weight of evidence narrative should be developed and how 

a descriptor should be selected (EPA, 2005): 

The weight of the evidence should be presented as a narrative laying out 

the complexity of information that is essential to understanding the 

hazard and its dependence on the quality, quantity, and type(s) of data 

available, as well as the circumstances of exposure or the traits of an 

exposed population that may be required for expression of cancer. For 

example, the narrative can clearly state to what extent the determination 

was based on data from human exposure, from animal experiments, from 

some combination of the two, or from other data. Similarly, information 

on mode of action can specify to what extent the data are from in vivo or 

in vitro exposures or based on similarities to other chemicals. The extent 

to which an agent’s mode of action occurs only on reaching a minimum 

dose or a minimum duration should also be presented. A hazard might 

also be expressed disproportionately in individuals possessing a specific 

gene; such characterizations may follow from a better understanding of 

the human genome. Furthermore, route of exposure should be used to 

qualify a hazard if, for example, an agent is not absorbed by some routes. 

Similarly, a hazard can be attributable to exposures during a susceptible 

lifestage on the basis of our understanding of human development. 

The weight of evidence narrative should highlight: 

 the quality and quantity of the data; 

 all key decisions and the basis for these major decisions; and 

 any data, analyses, or assumptions that are unusual for or new to EPA. 

To capture this complexity, a weight of evidence narrative generally 

includes 

 conclusions about human carcinogenic potential (choice of descriptor(s), described 

below) 

 a summary of the key evidence supporting these conclusions (for each descriptor 

used), including information on the type(s) of data (human and/or animal, in vivo 

and/or in vitro) used to support the conclusion(s) 

 available information on the epidemiologic or experimental conditions that 

characterize expression of carcinogenicity (e.g., if carcinogenicity is possible only by 

one exposure route or only above a certain human exposure level), 

 a summary of potential modes of action and how they reinforce the conclusions, 

 indications of any susceptible populations or lifestages, when available, and 

 a summary of the key default options invoked when the available information is 

inconclusive. 

To provide some measure of clarity and consistency in an otherwise freeform narrative, the weight of evidence descriptors are included in the 

first sentence of the narrative. Choosing a descriptor is a matter of 

judgment and cannot be reduced to a formula. Each descriptor may be 

applicable to a wide variety of potential data sets and weights of 

evidence. These descriptors and narratives are intended to permit 

sufficient flexibility to accommodate new scientific understanding and 

 A-7 
new testing methods as they are developed and accepted by the scientific 

community and the public. Descriptors represent points along a 

continuum of evidence; consequently, there are gradations and borderline 

cases that are clarified by the full narrative. Descriptors, as well as an 

introductory paragraph, are a short summary of the complete narrative 

that preserves the complexity that is an essential part of the hazard 

characterization. Users of these cancer guidelines and of the risk 

assessments that result from the use of these cancer guidelines 

should consider the entire range of information included in the 

narrative rather than focusing simply on the descriptor. 

In borderline cases, the narrative explains the case for choosing one 

descriptor and discusses the arguments for considering but not choosing 

another. For example, between “suggestive” and “likely” or between 

“suggestive” and “inadequate,” the explanation clearly communicates the 

information needed to consider appropriately the agent’s carcinogenic 

potential in subsequent decisions. 

Multiple descriptors can be used for a single agent, for example, when 

carcinogenesis is dose-or route-dependent. For example, if an agent 

causes point-of-contact tumors by one exposure route but adequate 

testing is negative by another route, then the agent could be described as 

likely to be carcinogenic by the first route but not likely to be 

carcinogenic by the second. Another example is when the mode of action 

is sufficiently understood to conclude that a key event in tumor 

development would not occur below a certain dose range. In this case, 

the agent could be described as likely to be carcinogenic above a certain 

dose range but not likely to be carcinogenic below that range. 

Descriptors can be selected for an agent that has not been tested in a 

cancer bioassay if sufficient other information, e.g., toxicokinetic and 

mode of action information, is available to make a strong, convincing, 

and logical case through scientific inference. For example, if an agent is 

one of a well-defined class of agents that are understood to operate 

through a common mode of action and if that agent has the same mode of 

action, then in the narrative the untested agent would have the same 

descriptor as the class. Another example is when an untested agent’s 

effects are understood to be caused by a human metabolite, in which case 

in the narrative the untested agent could have the same descriptor as the 

metabolite. As new testing methods are developed and used, assessments 

may increasingly be based on inferences from toxicokinetic and mode of 

action information in the absence of tumor studies in animals or humans. 

When a well-studied agent produces tumors only at a point of initial 

contact, the descriptor generally applies only to the exposure route 

producing tumors unless the mode of action is relevant to other routes. 

The rationale for this conclusion would be explained in the narrative. 

When tumors occur at a site other than the point of initial contact, the 

descriptor generally applies to all exposure routes that have not been 

adequately tested at sufficient doses. An exception occurs when there is 

 A-8 
convincing information, e.g., toxicokinetic data that absorption does not 

occur by another route. 

When the response differs qualitatively as well as quantitatively with 

dose, this information should be part of the characterization of the 

hazard. In some cases reaching a certain dose range can be a 

precondition for effects to occur, as when cancer is secondary to another 

toxic effect that appears only above a certain dose. In other cases 

exposure duration can be a precondition for hazard if effects occur only 

after exposure is sustained for a certain duration. These considerations 

differ from the issues of relative absorption or potency at different dose 

levels because they may represent a discontinuity in a dose-response 

function. 

When multiple bioassays are inconclusive, mode of action data are likely 

to hold the key to resolution of the more appropriate descriptor. When 

bioassays are few, further bioassays to replicate a study’s results or to 

investigate the potential for effects in another sex, strain, or species may 

be useful. 

When there are few pertinent data, the descriptor makes a statement 

about the database, for example, “Inadequate Information to Assess 

Carcinogenic Potential,” or a database that provides “Suggestive 

Evidence of Carcinogenic Potential.” With more information, the 

descriptor expresses a conclusion about the agent’s carcinogenic 

potential to humans. If the conclusion is positive, the agent could be 

described as “Likely to Be Carcinogenic to Humans” or, with strong 

evidence, “Carcinogenic to Humans.” If the conclusion is negative, the 

agent could be described as “Not Likely to Be Carcinogenic to Humans.” 

Although the term “likely” can have a probabilistic connotation in other 

contexts, its use as a weight of evidence descriptor does not correspond 

to a quantifiable probability of whether the chemical is carcinogenic. 

This is because the data that support cancer assessments generally are not 

suitable for numerical calculations of the probability that an agent is a 

carcinogen. Other health agencies have expressed a comparable weight 

of evidence using terms such as “Reasonably Anticipated to Be a Human 

Carcinogen” (NTP) or “Probably Carcinogenic to Humans” (IARC). 

1989). 

A.3.3. Improving the NAS/IOM Presumptive Disability Decision-Making 

Process for Veterans Report 

A recent publication by the Institute of Medicine also provided foundation for the causality 

framework adapted in this ISA (IOM, 2007). The Committee on Evaluation of the Presumptive Disability 

Decision-Making Process for Veterans was charged by the Veterans Association to describe how 

presumptive decisions are made for veterans with health conditions arising from military service 

currently, as well as recommendations for how such decisions could be made in the future. The committee 

proposed a multiple-element approach that includes a quantification of the extent of disease attributable to 

 A-9 
an exposure. This process involved a review of all relevant data to decide the strength of evidence for 

causation, using one of four categories: 

 Sufficient: the evidence is sufficient to conclude that a causal relationship exists. 

 Equipoise and Above: the evidence is sufficient to conclude that a causal relationship is at 

least as likely as not, but not sufficient to conclude that a causal relationship exists. 

 Below Equipoise: the evidence is not sufficient to conclude that a causal relationship is at 

least as likely as not, or is not sufficient to make a scientifically informed judgment. 

 Against: the evidence suggests the lack of a causal relationship. 

The following is an excerpt from the report and describes these four categories in detail: 

In light of the categorizations used by other health organizations and 

agencies as well as considering the particular challenges of the 

presumptive disability decision-making process, we propose a four-level 

categorization of the strength of the overall evidence for or against a 

causal relationship from exposure to disease. 

We use the term “equipoise” to refer to the point at which the evidence is 

in balance between favoring and not favoring causation. The term 

“equipoise” is widely used in the biomedical literature, is a concept 

familiar to those concerned with evidence-based decision-making and is 

used in VA processes for rating purposes as well as being a familiar term 

in the veterans’ community. 

Below we elaborate on the four-level categorization which the 

Committee recommends. 

Sufficient 

If the overall evidence for a causal relationship is categorized as 

Sufficient, then it should be scientifically compelling. It might include: 

 replicated and consistent evidence of a causal association: that is, evidence of an 

association from several high-quality epidemiologic studies that cannot be explained 

by plausible noncausal alternatives (e.g., chance, bias, or confounding) 

 evidence of causation from animal studies and mechanistic knowledge 

 compelling evidence from animal studies and strong mechanistic evidence from 

studies in exposed humans, consistent with (i.e., not contradicted by) the 

epidemiologic evidence. 

Using the Bayesian framework to illustrate the evidential support and the 

resulting state of communal scientific opinion needed for reaching the 

Sufficient category (and the lower categories that follow), consider again 

the causal diagram in Figure A-2. In this model, used to help clarify 

matters conceptually, the observed association between exposure and 

health is the result of: (1) measured confounding, parameterized by α; (2) 

the causal relation, parameterized by β; and (3) other, unmeasured 

sources such as bias or unmeasured confounding, parameterized by γ. 

The belief of interest, after all the evidence has been weighed, is in the 

size of the causal parameter β. Thus, for decision making, what matters is 

how strongly the evidence supports the proposition that β is above 0. As 

 A-10 
it is extremely unlikely that the types of exposures considered for 

presumptions reduce the risk of developing disease, we exclude values of 

β below 0. If we consider the evidence as supporting degrees of belief 

about the size of β, and we have a posterior distribution over the possible 

size of β, then a posterior like Figure A-2 illustrates a belief state that 

might result when the evidence for causation is considered Sufficient. 

As the “mass” over a positive effect (the area under the curve to the right 

of the zero) vastly “outweighs” the small mass over no effect (zero), the 

evidence is considered sufficient to conclude that the association is 

causal. Put another way, even though the scientific community might be 

uncertain as to the size of β, after weighing all the evidence, it is highly 

confident that the probability that β is greater than zero is substantial; 

that is, that exposure causes disease. 

Equipoise and Above 

To be categorized as Equipoise and Above, the scientific community 

should categorize the overall evidence as making it more confident in the 

existence of a causal relationship than in the non-existence of a causal 

relationship, but not sufficient to conclude causation. 

For example, if there are several high-quality epidemiologic studies, the 

preponderance of which show evidence of an association that cannot 

readily be explained by plausible noncausal alternatives (e.g., chance, 

bias, or confounding), and the causal relationship is consistent with the 

animal evidence and biological knowledge, then the overall evidence 

might be categorized as Equipoise and Above. Alternatively, if there is 

strong evidence from animal studies or mechanistic evidence, not 

contradicted by human or other evidence, then the overall evidence 

might be categorized as Equipoise and Above. Equipoise is a common 

term employed by VA and the courts in deciding disability claims (see 

Appendix D [see original document]). 

 A-11 
Source: IOM (2008).

Figure A-2. Focusing on unmeasured confounders/covariates, or other sources of spurious 

association from bias. 

Source: IOM (2008).

Figure A-3. Example posterior distribution for the determination of Sufficient. 

Again, using the Bayesian model to illustrate the idea of Equipoise and 

Figure A-4 shows a posterior probability distribution that is an example 

of belief compatible with the category Equipoise and Above. 

 A-12 
Source: IOM (2007).

Figure A-4. Example posterior distribution for the determination of Equipoise and Above. 

In this figure, unlike the one for evidence classified as Sufficient, there is 

considerable mass over zero, which means that the scientific community 

has considerable uncertainty as to whether exposure causes disease at all; 

that is, whether β is greater than zero. At least half of the mass is to the 

right of the zero, however, so the community judges causation to be at 

least as likely as not, after they have seen and combined all the evidence 

available. 

Below Equipoise 

To be categorized as Below Equipoise, the overall evidence for a causal 

relationship should either be judged not to make causation at least as 

likely as not, or not sufficient to make a scientifically informed 

judgment. 

This might occur: 

 when the human evidence is consistent in showing an association, but the evidence is 

limited by the inability to rule out chance, bias, or confounding with confidence, and 

animal or mechanistic evidence is weak 

 when animal evidence suggests a causal relationship, but human and mechanistic 

 evidence is weak or inconsistent 

 when mechanistic evidence is suggestive but animal and human evidence is weak or 

inconsistent 

 when the evidence base is very thin. 

Against 

To be categorized as Against, the overall evidence should favor belief 

that there is no causal relationship from exposure to disease. For 

example, if there is human evidence from multiple studies covering the 

full range of exposures encountered by humans that are consistent in 

showing no causal association, or there are is animal or mechanistic 

evidence supporting the lack of a causal relationship, and combining all 

of the evidence results in a posterior resembling Source: IOM (2008). 

 A-13 
Figure A-5 then the scientific community should categorize the evidence 

as Against causation. 

Source: IOM (2007).

Figure A-5. Example posterior distribution for the determination of Against. 

A.3.4. National Acid Precipitation Assessment Program Guidelines 

The following guidelines in the form of questions were developed and published in 1991 by the 

Oversight Review Board for the National Acid Precipitation Assessment Program (Washington, 1991) to 

assist scientists in formulating presentations of research results to be used in policy decision processes. 

Is the statement sound? Have the central issues been clearly identified? 

Does each statement contain the distilled essence of present scientific 

and technical understanding of the phenomenon or process to which it 

applies? Is the statement consistent with all relevant evidence – evidence 

developed either through NAPAP research or through analysis of 

research conducted outside of NAPAP? Is the statement contradicted by 

any important evidence developed through research inside or outside of 

NAPAP? Have apparent contradictions or interpretations of available 

evidence been considered in formulating the statement of principal 

findings? 

Is the statement directional and, where appropriate, quantitative? Does 

the statement correctly quantify both the direction and magnitude of 

trends and relationships in the phenomenon or process to which the 

statement is relevant? When possible, is a range of uncertainty given for 

each quantitative result? Have various sources of uncertainty been 

identified and quantified, for example, does the statement include or 

acknowledge errors in actual measurements, standard errors of estimate, 

possible biases in the availability of data, extrapolation of results beyond 

the mathematical, geographical, or temporal relevancy of available 

information, etc. In short, are there numbers in the statement? Are the 

numbers correct? Are the numbers relevant to the general meaning of the 

statement? 

 A-14 
Is the degree of certainty or uncertainty of the statement indicated 

clearly? Have appropriate statistical tests been applied to the data used in 

drawing the conclusion set forth in the statement? If the statement is 

based on a mathematical or novel conceptual model, has the model or 

concept been validated? Does the statement describe the model or 

concept on which it is based and the degree of validity of that model or 

concept? 

Is the statement correct without qualification? Are there limitations of 

time, space, or other special circumstances in which the statement is 

true? If the statement is true only in some circumstances, are these 

limitations described adequately and briefly? 

Is the statement clear and unambiguous? Are the words and phrases used 

in the statement understandable by the decision makers of our society? Is 

the statement free of specialized jargon? Will too many people 

misunderstand its meaning? 

Is the statement as concise as it can be made without risk of 

misunderstanding? Are there any excess words, phrases, or ideas in the 

statement which are not necessary to communicate the meaning of the 

statement? Are there so many caveats in the statement that the statement 

itself is trivial, confusing, or ambiguous? 

Is the statement free of scientific or other biases or implications of 

societal value judgments? Is the statement free of influence by specific 

schools of scientific thought? Is the statement also free of words, 

phrases, or concepts that have political, economic, ideological, religious, 

moral, or other personal-, agency-, or organization-specific values, 

overtones, or implications? Does the choice of how the statement is 

expressed rather than its specific words suggest underlying biases or 

value judgments? Is the tone impartial and free of special pleading? If 

societal value judgments have been discussed, have these judgments 

been identified as such and described both clearly and objectively? 

Have societal implications been described objectively? Consideration of 

alternative courses of action and their consequences inherently involves 

judgments of their feasibility and the importance of effects. For this 

reason, it is important to ask if a reasonable range of alternative policies 

or courses of action have been evaluated? Have societal implications of 

alternative courses of action been stated in the following general form? 

“If this [particular option] were adopted then that [particular outcome] 

would be expected.” 

Have the professional biases of authors and reviewers been described 

openly? Acknowledgment of potential sources of bias is important so 

that readers can judge for themselves the credibility of reports and 

assessments. 

 A-15 
A.3.5. IARC Guidelines for Scientific Review and Evaluation 

Categories 

The following is excerpted from the International Agency for Research on Cancer Monographs on 

the evaluation of carcinogenic risks to humans (IARC, 2006) 

The available studies are summarized by the Working Group, with 

particular regard to the qualitative aspects discussed below. In general, 

numerical findings are indicated as they appear in the original report; 

units are converted when necessary for easier comparison. The Working 

Group may conduct additional analyses of the published data and use 

them in their assessment of the evidence; the results of such 

supplementary analyses are given in square brackets. When an important 

aspect of a study that directly impinges on its interpretation should be 

brought to the attention of the reader, a Working Group comment is 

given in square brackets. 

The scope of the IARC Monographs program has expanded beyond 

chemicals to include complex mixtures, occupational exposures, physical 

and biological agents, lifestyle factors and other potentially carcinogenic 

exposures. Over time, the structure of a Monograph has evolved to 

include the following sections: 

1. Exposure data 

2. Studies of cancer in humans 

3. Studies of cancer in experimental animals 

4. Mechanistic and other relevant data 

5. Summary 

6. Evaluation and rationale 

In addition, a section of General Remarks at the front of the volume 

discusses the reasons the agents were scheduled for evaluation and some 

key issues the Working Group encountered during the meeting. 

This part of the Preamble discusses the types of evidence considered and 

summarized in each section of a Monograph, followed by the scientific 

criteria that guide the evaluations. 

Evaluation and rationale 

Evaluations of the strength of the evidence for carcinogenicity arising 

from human and experimental animal data are made, using standard 

terms. The strength of the mechanistic evidence is also characterized. 

 A-16 
It is recognized that the criteria for these evaluations, described below, 

cannot encompass all of the factors that may be relevant to an evaluation 

of carcinogenicity. In considering all of the relevant scientific data, the 

Working Group may assign the agent to a higher or lower category than 

a strict interpretation of these criteria would indicate. 

These categories refer only to the strength of the evidence that an 

exposure is carcinogenic and not to the extent of its carcinogenic activity 

(potency). A classification may change as new information becomes 

available. 

An evaluation of the degree of evidence is limited to the materials tested, 

as defined physically, chemically or biologically. When the agents 

evaluated are considered by the Working Group to be sufficiently closely 

related, they may be grouped together for the purpose of a single 

evaluation of the degree of evidence. 

(a) Carcinogenicity in humans 

The evidence relevant to carcinogenicity from studies in humans is 

classified into one of the following categories: 

Sufficient evidence of carcinogenicity: The Working Group considers 

that a causal relationship has been established between exposure to the 

agent and human cancer. That is, a positive relationship has been 

observed between the exposure and cancer in studies in which chance, 

bias and confounding could be ruled out with reasonable confidence. A 

statement that there is sufficient evidence is followed by a separate 

sentence that identifies the target organ(s) or tissue(s) where an increased 

risk of cancer was observed in humans. Identification of a specific target 

organ or tissue does not preclude the possibility that the agent may cause 

cancer at other sites. 

Limited evidence of carcinogenicity: A positive association has been 

observed between exposure to the agent and cancer for which a causal 

interpretation is considered by the Working Group to be credible, but 

chance, bias or confounding could not be ruled out with reasonable 

confidence. 

Inadequate evidence of carcinogenicity: The available studies are of 

insufficient quality, consistency or statistical power to permit a 

conclusion regarding the presence or absence of a causal association 

between exposure and cancer, or no data on cancer in humans are 

available. 

Evidence suggesting lack of carcinogenicity: There are several adequate 

studies covering the full range of levels of exposure that humans are 

known to encounter, which are mutually consistent in not showing a 

positive association between exposure to the agent and any studied 

cancer at any observed level of exposure. The results from these studies 

alone or combined should have narrow confidence intervals with an 

 A-17 
upper limit close to the null value (e.g. a relative risk of 1.0). Bias and 

confounding should be ruled out with reasonable confidence, and the 

studies should have an adequate length of follow-up. A conclusion of 

evidence suggesting lack of carcinogenicity is inevitably limited to the 

cancer sites, conditions and levels of exposure, and length of observation 

covered by the available studies. In addition, the possibility of a very 

small risk at the levels of exposure studied can never be excluded. 

In some instances, the above categories may be used to classify the 

degree of evidence related to carcinogenicity in specific organs or 

tissues. 

When the available epidemiologic studies pertain to a mixture, process, 

occupation or industry, the Working Group seeks to identify the specific 

agent considered most likely to be responsible for any excess risk. The 

evaluation is focused as narrowly as the available data on exposure and 

other aspects permit. 

(b) Carcinogenicity in experimental animals 

Carcinogenicity in experimental animals can be evaluated using 

conventional bioassays, bioassays that employ genetically modified 

animals, and other in-vivo bioassays that focus on one or more of the 

critical stages of carcinogenesis. In the absence of data from 

conventional long-term bioassays or from assays with neoplasia as the 

end-point, consistently positive results in several models that address 

several stages in the multistage process of carcinogenesis should be 

considered in evaluating the degree of evidence of carcinogenicity in 

experimental animals. 

The evidence relevant to carcinogenicity in experimental animals is 

classified into one of the following categories: 

Sufficient evidence of carcinogenicity: The Working Group considers 

that a causal relationship has been established between the agent and an 

increased incidence of malignant neoplasms or of an appropriate 

combination of benign and malignant neoplasms in (a) two or more 

species of animals or (b) two or more independent studies in one species 

carried out at different times or in different laboratories or under 

different protocols. An increased incidence of tumors in both sexes of a 

single species in a well-conducted study, ideally conducted under Good 

Laboratory Practices, can also provide sufficient evidence. 

A single study in one species and sex might be considered to provide 

sufficient evidence of carcinogenicity when malignant neoplasms occur 

to an unusual degree with regard to incidence, site, type of tumor or age 

at onset, or when there are strong findings of tumors at multiple sites. 

Limited evidence of carcinogenicity: The data suggest a carcinogenic 

effect but are limited for making a definitive evaluation because, e.g. (a) 

the evidence of carcinogenicity is restricted to a single experiment; (b) 

 A-18 
there are unresolved questions regarding the adequacy of the design, 

conduct or interpretation of the studies; (c) the agent increases the 

incidence only of benign neoplasms or lesions of uncertain neoplastic 

potential; or (d) the evidence of carcinogenicity is restricted to studies 

that demonstrate only promoting activity in a narrow range of tissues or 

organs. 

Inadequate evidence of carcinogenicity: The studies cannot be 

interpreted as showing either the presence or absence of a carcinogenic 

effect because of major qualitative or quantitative limitations, or no data 

on cancer in experimental animals are available. 

Evidence suggesting lack of carcinogenicity: Adequate studies involving 

at least two species are available which show that, within the limits of the 

tests used, the agent is not carcinogenic. A conclusion of evidence 

suggesting lack of carcinogenicity is inevitably limited to the species, 

tumor sites, age at exposure, and conditions and levels of exposure 

studied. 

(c) Mechanistic and other relevant data 

Mechanistic and other evidence judged to be relevant to an evaluation of 

carcinogenicity and of sufficient importance to affect the overall 

evaluation is highlighted. This may include data on preneoplastic lesions, 

tumor pathology, genetic and related effects, structure–activity 

relationships, metabolism and toxicokinetics, physicochemical 

parameters and analogous biological agents. 

The strength of the evidence that any carcinogenic effect observed is due 

to a particular mechanism is evaluated, using terms such as ‘weak,’ 

‘moderate’ or ‘strong.’ The Working Group then assesses whether that 

particular mechanism is likely to be operative in humans. The strongest 

indications that a particular mechanism operates in humans derive from 

data on humans or biological specimens obtained from exposed humans. 

The data may be considered to be especially relevant if they show that 

the agent in question has caused changes in exposed humans that are on 

the causal pathway to carcinogenesis. Such data may, however, never 

become available, because it is at least conceivable that certain 

compounds may be kept from human use solely on the basis of evidence 

of their toxicity and/or carcinogenicity in experimental systems. 

The conclusion that a mechanism operates in experimental animals is 

strengthened by findings of consistent results in different experimental 

systems, by the demonstration of biological plausibility and by coherence 

of the overall database. Strong support can be obtained from studies that 

challenge the hypothesized mechanism experimentally, by demonstrating 

that the suppression of key mechanistic processes leads to the 

suppression of tumor development. The Working Group considers 

whether multiple mechanisms might contribute to tumor development, 

whether different mechanisms might operate in different dose ranges, 

whether separate mechanisms might operate in humans and experimental 

 A-19 
animals and whether a unique mechanism might operate in a susceptible 

group. The possible contribution of alternative mechanisms must be 

considered before concluding that tumors observed in experimental 

animals are not relevant to humans. An uneven level of experimental 

support for different mechanisms may reflect that disproportionate 

resources have been focused on investigating a favored mechanism. 

For complex exposures, including occupational and industrial exposures, 

the chemical composition and the potential contribution of carcinogens 

known to be present are considered by the Working Group in its overall 

evaluation of human carcinogenicity. The Working Group also 

determines the extent to which the materials tested in experimental 

systems are related to those to which humans are exposed. 

(d) Overall evaluation 

Finally, the body of evidence is considered as a whole, in order to reach 

an overall evaluation of the carcinogenicity of the agent to humans. 

An evaluation may be made for a group of agents that have been 

evaluated by the Working Group. In addition, when supporting data 

indicate that other related agents, for which there is no direct evidence of 

their capacity to induce cancer in humans or in animals, may also be 

carcinogenic, a statement describing the rationale for this conclusion is 

added to the evaluation narrative; an additional evaluation may be made 

for this broader group of agents if the strength of the evidence warrants 

it. 

The agent is described according to the wording of one of the following 

categories, and the designated group is given. The categorization of an 

agent is a matter of scientific judgement that reflects the strength of the 

evidence derived from studies in humans and in experimental animals 

and from mechanistic and other relevant data. 

Group 1: The agent is carcinogenic to humans. 

This category is used when there is sufficient evidence of carcinogenicity 

in humans. Exceptionally, an agent may be placed in this category when 

evidence of carcinogenicity in humans is less than sufficient but there is 

sufficient evidence of carcinogenicity in experimental animals and strong 

evidence in exposed humans that the agent acts through a relevant 

mechanism of carcinogenicity. 

Group 2. 

This category includes agents for which, at one extreme, the degree of 

evidence of carcinogenicity in humans is almost sufficient, as well as 

those for which, at the other extreme, there are no human data but for 

which there is evidence of carcinogenicity in experimental animals. 

Agents are assigned to either Group 2A (probably carcinogenic to 

humans) or Group 2B (possibly carcinogenic to humans) on the basis of 

 A-20 
epidemiologic and experimental evidence of carcinogenicity and 

mechanistic and other relevant data. The terms probably carcinogenic

and possibly carcinogenic have no quantitative significance and are used 

simply as descriptors of different levels of evidence of human 

carcinogenicity, with probably carcinogenic signifying a higher level of 

evidence than possibly carcinogenic. 

Group 2A: The agent is probably carcinogenic to humans. 

This category is used when there is limited evidence of carcinogenicity in 

humans and sufficient evidence of carcinogenicity in experimental 

animals. In some cases, an agent may be classified in this category when 

there is inadequate evidence of carcinogenicity in humans and sufficient 

evidence of carcinogenicity in experimental animals and strong evidence 

that the carcinogenesis is mediated by a mechanism that also operates in 

humans. Exceptionally, an agent may be classified in this category solely 

on the basis of limited evidence of carcinogenicity in humans. An agent 

may be assigned to this category if it clearly belongs, based on 

mechanistic considerations, to a class of agents for which one or more 

members have been classified in Group 1 or Group 2A. 

Group 2B: The agent is possibly carcinogenic to humans. 

This category is used for agents for which there is limited evidence of 

carcinogenicity in humans and less than sufficient evidence of 

carcinogenicity in experimental animals. It may also be used when there 

is inadequate evidence of carcinogenicity in humans but there is 

sufficient evidence of carcinogenicity in experimental animals. In some 

instances, an agent for which there is inadequate evidence of 

carcinogenicity in humans and less than sufficient evidence of 

carcinogenicity in experimental animals together with supporting 

evidence from mechanistic and other relevant data may be placed in this 

group. An agent may be classified in this category solely on the basis of 

strong evidence from mechanistic and other relevant data. 

Group 3: The agent is not classifiable as to its carcinogenicity to humans. 

This category is used most commonly for agents for which the evidence 

of carcinogenicity is inadequate in humans and inadequate or limited in 

experimental animals. 

Exceptionally, agents for which the evidence of carcinogenicity is 

inadequate in humans but sufficient in experimental animals may be 

placed in this category when there is strong evidence that the mechanism 

of carcinogenicity in experimental animals does not operate in humans. 

Agents that do not fall into any other group are also placed in this 

category. 

An evaluation in Group 3 is not a determination of non-carcinogenicity 

or overall safety. It often means that further research is needed, 

 A-21 
especially when exposures are widespread or the cancer data are 

consistent with differing interpretations. 

Group 4: The agent is probably not carcinogenic to humans. 

This category is used for agents for which there is evidence suggesting 

lack of carcinogenicity in humans and in experimental animals. In some 

instances, agents for which there is inadequate evidence of 

carcinogenicity in humans but evidence suggesting lack of 

carcinogenicity in experimental animals, consistently and strongly 

supported by a broad range of mechanistic and other relevant data, may 

be classified in this group. 

(e) Rationale 

The reasoning that the Working Group used to reach its evaluation is 

presented and discussed. This section integrates the major findings from 

studies of cancer in humans, studies of cancer in experimental animals, 

and mechanistic and other relevant data. It includes concise statements of 

the principal line(s) of argument that emerged, the conclusions of the 

Working Group on the strength of the evidence for each group of studies, 

citations to indicate which studies were pivotal to these conclusions, and 

an explanation of the reasoning of the Working Group in weighing data 

and making evaluations. When there are significant differences of 

scientific interpretation among Working Group Members, a brief 

summary of the alternative interpretations is provided, together with their 

scientific rationale and an indication of the relative degree of support for 

each alternative. 

A.3.6. NTP: Report on Carcinogens 

The criteria for listing an agent, substance, mixture, or exposure circumstance in the National 

Toxicology Program’s Report on Carcinogens (NTP, 2005) as follows: 

Known to Be Human Carcinogen: 

There is sufficient evidence of carcinogenicity from studies in humans*, 

which indicates a causal relationship between exposure to the agent, 

substance, or mixture, and human cancer. 

Reasonably Anticipated to Be Human Carcinogen: 

There is limited evidence of carcinogenicity from studies in humans*, 

which indicates that causal interpretation is credible, but that alternative 

explanations, such as chance, bias, or confounding factors, could not 

adequately be excluded, 

or 

 A-22 
there is sufficient evidence of carcinogenicity from studies in 

experimental animals, which indicates there is an increased incidence of 

malignant and/or a combination of malignant and benign tumors (1) in 

multiple species or at multiple tissue sites, or (2) by multiple routes of 

exposure, or (3) to an unusual degree with regard to incidence, site, or 

type of tumor, or age at onset, 

or 

there is less than sufficient evidence of carcinogenicity in humans or 

laboratory animals; however, the agent, substance, or mixture belongs to 

a well-defined, structurally related class of substances whose members 

are listed in a previous Report on Carcinogens as either known to be a 

human carcinogen or reasonably anticipated to be a human carcinogen, 

or there is convincing relevant information that the agent acts through 

mechanisms indicating it would likely cause cancer in humans. 

Conclusions regarding carcinogenicity in humans or experimental 

animals are based on scientific judgment, with consideration given to all 

relevant information. Relevant information includes, but is not limited to, 

dose response, route of exposure, chemical structure, metabolism, 

pharmacokinetics, sensitive sub-populations, genetic effects, or other 

data relating to mechanism of action or factors that may be unique to a 

given substance. For example, there may be substances for which there is 

evidence of carcinogenicity in laboratory animals, but there are 

compelling data indicating that the agent acts through mechanisms which 

do not operate in humans and would therefore not reasonably be 

anticipated to cause cancer in humans. 

*This evidence can include traditional cancer epidemiology studies, data 

from clinical studies, and/or data derived from the study of tissues or 

cells from humans exposed to the substance in question that can be 

useful for evaluating whether a relevant cancer mechanism is operating 

in people. 

 A-23 
Annex B. Additional Information on the 

Atmospheric Chemistry of SOX

B.1. Introduction 

SO2 is chiefly but not exclusively primary in origin. Primary SO2 is emitted directly from sources, 

whereas secondary SO2 is formed as a product of atmospheric reactions. Secondary SO2 is produced by 

the photochemical oxidation of reduced sulfur compounds such as dimethyl sulfide (DMS) (CH3-S-CH3), 

hydrogen sulfide (H2S), carbon disulfide (CS2), carbonyl sulfide (OCS), methyl mercaptan (CH3-S-H), 

and dimethyl disulfide (CH3-S-S-CH3) which are all mainly biogenic in origin. Their sources are 

discussed in Section B.3. Table B-1 lists the atmospheric lifetimes of reduced sulfur species with respect 

to reaction with various oxidants. Except for OCS, which is lost mainly by photolysis (τ~6 months), these 

species are lost mainly by reaction with OH and NO3 radicals. Because OCS is relatively long-lived in the 

troposphere, it can be transported upwards into the stratosphere. 

Table B-1. Atmospheric lifetimes of SO2 and reduced sulfur species with respect to reaction with 

OH, NO3, and Cl radicals. 

OH NO3 Cl 

Compound 

K X 1012 Τ K X 1012 T K X 1012 T 

SO2 1.6 7.2d NA NA 

CH3-S-CH3 5.0 2.3 d 1.0 1.1-h 400 29 d 

H2S 4.7 2.2 d NA 74 157 d 

CS2 1.2 9.6 d < 0.0004 > 116 d < 0.004 NR 

OCS 0.0019 17 y < 0.0001 > 1.3 y < 0.0001 NR 

CH3-S-H 33 8.4 h 0.89 1.2 h 200 58 d 

CH3-S-S-CH3 230 1.2 h 0.53 2.1-h NA 

NA = Reaction rate coefficient not available. 

NR = Rate coefficient too low to be relevant as an atmospheric loss mechanism. Rate coefficients were calculated at 298 K and 1 atmosphere. 

yr = year 

h = hour 

OH = 1 × 106/cm3 

NO3 = 2.5 × 108/cm3 

Cl = 1 × 103/cm3. 1Rate coefficients were taken from JPL Chemical Kinetics Evaluation No. 14 (JPL2003) 

Source: Seinfeld and Pandis (1998). 

Crutzen (1976) proposed that OCS oxidation serves as the major source of sulfate (SO4

2-) in the 

stratospheric aerosol layer sometimes referred to the “Junge layer,” (Junge et al., 1961) during periods 

when volcanic plumes do not reach the stratosphere. However, the flux of OCS into the stratosphere is 

probably not sufficient to maintain this stratospheric aerosol layer. Myhre et al. (2004) proposed instead 

 B-1 
that SO2 transported upwards from the troposphere is the most likely source, as the upward flux of OCS is 

too small to sustain observed SO4

2- loadings in the Junge layer. In addition, in situ measurements of the 

isotopic composition of sulfur (S) do not match those of OCS (Leung et al. 2002). Reaction with NO3

radicals at night most likely represents the major loss process for DMS and methyl mercaptan. The 

mechanisms for the oxidation of DMS are still not completely understood. Initial attack by NO3 and OH 

radicals involves H atom abstraction, with a smaller branch leading to OH addition to the S atom. The OH 

addition branch increases in importance as temperatures decrease, becoming the major pathway below 

temperatures of 285 K (Ravishankara, 1997). The adduct may either decompose to form methane sulfonic 

acid (MSA), or undergo further reactions in the main pathway, to yield dimethyl sulfoxide (Barnes et al., 

1991). Following H atom abstraction from DMS, the main reaction products include MSA and SO2. The 

ratio of MSA to SO2 is strongly temperature dependent, varying from about 0.1 in tropical waters to about 

0.4 in Antarctic waters (Seinfeld and Pandis, 1998). Excess SO4

2- (over that expected from the SO42- in 

seawater) in marine aerosol is related mainly to the production of SO2 from the oxidation of DMS. 

Transformations among atmospheric S compounds are summarized in Figure B-1 

Source: Adapted from Berresheim et al. (1995). 

Figure B-1. Transformations of sulfur compounds in the atmosphere. 

B.1.1. Multiphase Chemical Processes Involving SOX and Halogens 

Chemical transformations involving inorganic halogenated compounds effect changes in the 

multiphase cycling of SOX in ways analogous to their effects on NOX. Oxidation of dimethylsulfide 

(CH3)2S by BrO produces dimethylsulfoxide (CH3)2SO (Barnes et al., 1991; Toumi, 1994), and oxidation 

by atomic chloride leads to formation of SO2 (Keene et al., 1996). Dimethylsulfoxide and SO2 are 

precursors for methanesulfonic acid (CH3SO3H) and H2SO4. In the MBL, virtually all H2SO4 and 

 B-2 
CH3SO3H vapor condenses onto existing aerosols or cloud droplets, which subsequently evaporate, 

thereby contributing to aerosol growth and acidification. Unlike CH3SO3H, H2SO4 also has the potential 

to produce new particles (Korhonen et al., 1999; Kulmala et al., 2000), which in marine regions is thought 

to occur primarily in the free troposphere. Saiz-Lopez et al. (2004) estimated that observed levels of 

bromine oxide (BrO) at Mace Head Atmospheric Research Station in Ireland, would oxidize (CH3)2S 

about six times faster than OH and thereby substantially increase production rates of H2SO4 and other 

condensible S species in the MBL. Sulfur dioxide is also scavenged by deliquesced aerosols and oxidized 

to H2SO4 in the aqueous phase by several strongly pH-dependent pathways (Chameides and Stelson, 

1992; Keene et al., 1998; Vogt et al., 1996). Model calculations indicate that oxidation of S(IV) by O3

dominates in fresh, alkaline sea salt aerosols, whereas oxidation by hypohalous acids (primarily HOCl) 

dominates in moderately acidic solutions. Additional particulate non-sea salt (nss) SO4

2- is generated by 

SO2 oxidation in cloud droplets (Clegg and Toumi, 1998). Ion-balance calculations indicate that most nss 

SO4

2- in short-lived (two to 48 h) sea salt size fractions accumulates in acidic aerosol solutions and/or in 

acidic aerosols processed through clouds (Keene et al., 2004). The production, cycling, and associated 

radiative effects of S-containing aerosols in marine and coastal air are regulated in part by chemical 

transformations involving inorganic halogens (Von Glasow et al., 2002). These transformations include: 

dry-deposition fluxes of nss SO4

2- in marine air dominated, naturally, by the sea salt size fractions 

(Huebert et al., 1996; Turekian et al., 2001); HCl phase partitioning that regulates sea salt pH and 

associated pH-dependent pathways for S(IV) oxidation (Keene et al., 2002; Pszenny et al., 2004); and 

potentially important oxidative reactions with reactive halogens for (CH3)2S and S(IV). However, both 

the absolute magnitudes and relative importance of these processes in MBL S cycling are poorly 

understood. 

Table B-2. Relative contributions of various reactions to the total S(IV) oxidation rate within a 

sunlit cloud, 10 min after cloud formation. 

Reaction % of Total a % of Total b

GAS PHASE 

 OH + SO2 3.5 3.1 

AQUEOUS PHASE 

 O3 + HSO3

− 0.6 0.7 

 O3 + SO3

2− 7.0 8.2 

 H2O2 + SO3

2− 78.4 82.1 

 CH3OOH + HSO3

− 0.1 0.1 

 HNO4 + HSO3

− 9.0 4.4 

 HOONO + HSO3

− < 0.1 < 0.1 

 HSO5

− + HSO3− 1.2 < 0.1 

 SO5

− + SO32− < 0.1 < 0.1 

 HSO5

− + Fe2+ 0.6 

a In the absence of transition metals. Bin the presence of iron and copper ions. 

Source: Adapted from Warneck (1999). 

Iodine (I) chemistry has been linked to ultrafine particle bursts at Mace Head (O’Dowd et al., 1999; 

O’Dowd et al., 2002). Observed bursts coincide with elevated concentrations of IO and are characterized 

 B-3 
by particle concentrations increasing from background levels to up to 300,000/cm3

 on a time scale of 

seconds to minutes. This newly identified source of marine aerosol would provide additional aerosol 

surface area for condensation of SOX and thereby presumably diminish the potential for nucleation 

pathways involving H2SO4. However, a subsequent investigation in polluted air along the New England, 

USA coast found no correlation between periods of nanoparticle growth and corresponding concentrations 

of I oxides (Fehsenfeld et al., 2006). The potential importance of I chemistry in aerosol nucleation and its 

associated influence on sulfur cycling remain highly uncertain. 

B.1.2. Mechanisms for the Aqueous Phase Formation of Sulfate 

Warneck (1999) constructed a box model describing the chemistry of the oxidation of SO2 and NO2

including the interactions of N and S species and minor processes in sunlit cumulus clouds. The relative 

contributions of different reactions to the oxidation of S(IV) species to S(VI) and NO2 to NO3

-

 10 min 

after cloud formation are given in Table B-2. The two columns show the relative contributions with and 

without transition metal ions. As can be seen from Table B-2, SO2 within a cloud (gas + cloud drops) is 

oxidized mainly by H2O2 in the aqueous phase, while the gas-phase oxidation by OH radicals is small by 

comparison. A much smaller contribution in the aqueous phase is made by methyl hydroperoxide 

(CH3OOH) because it is formed mainly in the gas phase and its Henry’s Law constant is several orders of 

magnitude smaller that of H2O2. After H2O2, HNO4 is the major contributor to S(IV) oxidation. The pH 

dependence of the oxidation rate of S(IV) in the presence of transition metal ions is illustrated in Figure 

B-2. 

Source: Seinfeld and Pandis (1998). 

Figure B-2. Comparison of aqueous-phase oxidation paths. The rate of conversion of S(IV) to 

S(VI) is shown as a function of pH. Conditions assumed are: [SO2(g)] = 5 ppb; [NO2(g)] 

= 1 ppb; [H2O2(g)] = 1 ppb; [O3(g)] = 50 ppb; [Fe(III)(aq)] = 0.3 μM; [Mn(II)(aq)] = 0.3 μM. 

 B-4 
B.1.3. Multiphase Chemical Processes Involving SOX and NH3

The phase partitioning of ammonia (NH3) with deliquesced aerosol solutions is controlled primarily 

by the thermodynamic properties of the system expressed as follows: 

where KH and Kb are the temperature-dependent Henry’s Law and dissociation constants (62 M/atm and 

1.8 × 10-5 M), respectively, for NH3, and Kw is the ion product of water (1.0 × 10-14 M) (Chameides, 

1984). It is evident that for a given amount of NHX (NH3 + particulate NH4

+

) in the system, increasing 

aqueous concentrations of particulate H+

 will shift the partitioning of NH3 towards the condensed phase. 

Consequently, under the more polluted conditions characterized by higher concentrations of acidic sulfate 

aerosol, ratios of gaseous NH3 to particulate NH4

+

 decrease (Smith et al., 2007). It also follows that in 

marine air, where aerosol acidity varies substantially as a function of particle size, NH3 partitions 

preferentially to the more acidic sub-micron size fractions where KH and Kb are the temperaturedependent Henry’s Law and dissociation constants (62 M/atm and 1.8 × 10-5 M), respectively, for NH3, 

and Kw is the ion product of water (1.0 × 10-14 M) (Chameides 1984). It is evident that for a given amount 

of NHX (NH3 + particulate NH4

+

) in the system, increasing aqueous concentrations of particulate H+ will 

shift the partitioning of NH3 towards the condensed phase. Consequently, under the more polluted 

conditions characterized by higher concentrations of acidic sulfate aerosol, ratios of gaseous NH3 to 

particulate NH4

+

 decrease (Smith et al., 2007). Under these conditions, the roughly equal partitioning of 

total NH3 between the gas and particulate phases sustains substantial dry-deposition fluxes of total NH3 to 

the coastal ocean (median of 10.7 μmol//m2

/day). In contrast, heavily polluted air transported from the 

industrialized midwestern U.S. contains concentrations of nss SO4

2- and total NH3 that are about a factor 

of 3 greater, based on median values. Under these conditions, most total NH3 (> 85%) partitions to the 

highly acidic sulfate aerosol size fractions and, consequently, the median dry-deposition flux of total NH3

is 30% lower than that under the cleaner northerly flow regime. The relatively longer atmospheric lifetime 

of total NH3 against dry deposition under more polluted conditions implies that, on average, total NH3

would accumulate to higher atmospheric concentrations under these conditions and also be subject to 

atmospheric transport over longer distances. Consequently, the importance of NHX removal via wet 

deposition would also increase. Because of the inherently sporadic character of precipitation, we might 

expect greater heterogeneity in NH3 deposition fields and any potential responses in sensitive ecosystems 

downwind of major S-emission regions. 

B.2. Transport of SOX in the Atmosphere 

Crutzen and Gidel (1983), Gidel (1983), and Chatfield and Crutzen (1984) hypothesized that 

convective clouds played an important role in rapid atmospheric vertical transport of trace species and 

first tested simple parameterizations of convective transport in atmospheric chemical models. At nearly 

the same time, evidence was shown of venting the boundary layer by shallow, fair weather cumulus 

clouds (Greenhut et al., 1984; 1986). Field experiments were conducted in 1985 which resulted in 

verification of the hypothesis that deep convective clouds are instrumental in atmospheric transport of 

trace constituents (Dickerson et al., 1987). Once pollutants are lofted to the middle and upper troposphere, 

they typically have a much longer chemical lifetime and with the generally stronger winds at these 

altitudes, they can be transported large distances from their source regions. 

 B-5 
B.3. Emissions of SO2

As can be seen from Table B-3, emissions of SO2 are due mainly to the combustion of fossil fuels 

by electrical utilities and industry. Transportation related sources make only a minor contribution. As a 

result, most SO2 emissions originate from point sources. Since S is a volatile component of fuels, it is 

almost quantitatively released during combustion, and emissions can be calculated on the basis of the 

sulfur content of fuels to greater accuracy than for other pollutants such as NOX or primary PM. 

Table B-3 Emissions of NOX, NH3, and SO2 in the U.S. by source and category, 2002.

2002 Emissions (Tg/yr) NOX1 NH3

2 SO2

Total all Sources 23.19 4.08 16.87

Fuel Combustion Total 9.11 0.02 14.47

Fuel Combustion Electrical Utilities 5.16 < 0.01 11.31

 Coal 4.50 < 0.01 10.70

 Bituminous 2.90 8.04

 Subbituminous 1.42 2.14

 Anthracite & Lignite 0.18 0.51

 Other < 0.01 

 Oil 0.14 < 0.01 0.38

 Residual 0.13 0.36

 Distillate 0.01 0.01

 Gas 0.30 < 0.01 0.01

 Natural 0.29 

 Process 0.01 

 Other 0.05 < 0.01 0.21

 Internal Combustion 0.17 < 0.01 0.01

Fuel Combustion Industrial 3.15 < 0.01 2.53

 Coal 0.49 < 0.01 1.26

 Bituminous 0.25 0.70

 Subbituminous 0.07 0.10

 Anthracite & Lignite 0.04 0.13

 Other 0.13 0.33

Oil 0.19 < 0.01 0.59

 Residual 0.09 0.40 

 Distillate 0.09 0.16 

 Other 0.01 0.02 

 Gas 1.16 < 0.01 0.52 

 Natural 0.92 

 Process 0.24 

 Other < 0.01 

 Other 0.16 < 0.01 0.15 

 Wood/Bark Waste 0.11 

 Liquid Waste 0.01 

 Other 0.04 

 Internal Combustion 1.15 < 0.01 0.01 

Fuel Combustion Other 0.80 < 0.01 0.63 

Commercial/Institutional Coal 0.04 < 0.01 0.16 

Commercial/Institutional Oil 0.08 < 0.01 0.28 

Commercial/Institutional Gas 0.25 < 0.01 0.02 

 Misc. Fuel Combustion (Exc. Residential) 0.03 < 0.01 0.01 

 Residential Wood 0.03 < 0.01

 Residential Other 0.36 0.16

 Distillate Oil 0.06 0.15

 Bituminous/Subbituminous 0.26 < 0.01

 Other 0.04 < 0.01

Industrial Process Total 1.10 0.21 1.54 

Chemical & Allied Product Mfg 0.12 0.02 0.36

 Organic Chemical Mfg 0.02 < 0.01 0.01

 Inorganic Chemical Mfg 0.01 < 0.01 0.18

 Sulfur Compounds 0.17

2002 Emissions (Tg/yr) NOX1 NH3

2 SO2

 Other 0.02

 Polymer & Resin Mfg < 0.01 < 0.01 < 0.01

 Agricultural Chemical Mfg 0.05 0.02 0.05

 Ammonium Nitrate/Urea Mfg. < 0.01

 Other 0.02

 Paint, Varnish, Lacquer, Enamel Mfg 0.00 0.00

 Pharmaceutical Mfg 0.00 0.00

 Other Chemical Mfg 0.03 < 0.01 0.12

Metals Processing 0.09 < 0.01 0.30

 Non-Ferrous Metals Processing 0.01 < 0.01 0.17

 Copper 0.04

 Lead 0.07

 Zinc 0.01

 Other < 0.01

 Ferrous Metals Processing 0.07 < 0.01 0.11

 Metals Processing 0.01 < 0.01 0.02

Petroleum & Related Industries 0.16 < 0.01 .38

 Oil & Gas Production 0.07 < 0.01 0.11

 Natural Gas 0.11

 Other 0.01

 Petroleum Refineries & Related Industries 0.05 < 0.01 0.26

 Fluid Catalytic Cracking Units < 0.01 0.16

 Other < 0.01 0.07

 Asphalt Manufacturing 0.04 0.01

Other Industrial Processes 0.54 0.05 0.46

 Agriculture, Food, & Kindred Products 0.01 < 0.01 0.01

 Textiles, Leather, & Apparel Products < 0.01 < 0.01 < 0.01

 Wood, Pulp & Paper, & Publishing Products 0.09 < 0.01 0.10

 Rubber & Miscellaneous Plastic Products < 0.01 < 0.01 < 0.01

 Mineral Products 0.42 < 0.01 0.33

 Cement Mfg 0.24 0.19

 Glass Mfg 0.01

 Other 0.10 0.09

 Machinery Products < 0.01 < 0.01 < 0.01

 Electronic Equipment < 0.01 < 0.01 < 0.01

 Transportation Equipment < 0.01 < 0.01

 Miscellaneous Industrial Processes 0.01 0.05 0.02

Solvent Utilization 0.01 < 0.01 < 0.01

 Degreasing < 0.01 < 0.01 < 0.01

 Graphic Arts < 0.01 < 0.01 < 0.01

 Dry Cleaning < 0.01 < 0.01 < 0.01

 Surface Coating < 0.01 < 0.01 < 0.01

 Other Industrial < 0.01 < 0.01 < 0.01

 Nonindustrial < 0.01

 Solvent Utilization Nec < 0.01

Storage & Transport < 0.01 < 0.01 0.01

 Bulk Terminals & Plants < 0.01 < 0.01 < 0.01

 Petroleum & Petroleum Product Storage < 0.01 < 0.01 < 0.01

 Petroleum & Petroleum Product Transport < 0.01 < 0.01 < 0.01

 Service Stations: Stage II < 0.01 < 0.01

 Organic Chemical Storage < 0.01 < 0.01 < 0.01

 B-6 
2002 Emissions (Tg/yr) NOX1 NH3

2 SO2

 Organic Chemical Transport 0.01 < 0.01

 Inorganic Chemical Storage < 0.01 < 0.01 < 0.01

 Inorganic Chemical Transport < 0.01 < 0.01

 Bulk Materials Storage 0.01 < 0.01 < 0.01

Waste Disposal & Recycling 0.17 0.14 0.03

 Incineration 0.06 < 0.01 0.02

 Industrial 

 Other < 0.01

 Open Burning 0.10 < 0.01 < 0.01

 Industrial < 0.01

 Land Clearing Debris 

 Other < 0.01

 Public Operating Treatment Works < 0.01 0.14 < 0.01

 Industrial Waste Water < 0.01 < 0.01 < 0.01

 Treatment, Storage, And Disposal Facility < 0.01 < 0.01 < 0.01

 Landfills < 0.01 < 0.01 < 0.01

 Industrial < 0.01

 Other < 0.01

Other < 0.01 < 0.01 < 0.01

Transportation Total 12.58 0.32 0.76 

Highway Vehicles 8.09 0.32 0.30

 Light-Duty Gas Vehicles & Motorcycles 2.38 0.20 0.10

 Light-Duty Gas Vehicles 2.36 0.10

 Motorcycles 0.02 0.00

 Light-Duty Gas Trucks 1.54 0.10 0.07

 Light-Duty Gas Trucks 1 1.07 0.05

 Light-Duty Gas Trucks 2 0.47 0.02

 Heavy-Duty Gas Vehicles 0.44 < 0.01 0.01 

 Diesels 3.73 < 0.01 0.12 

 Heavy-Duty Diesel Vehicles 3.71 

 Light-Duty Diesel Trucks 0.01 

 Light-Duty Diesel Vehicles 0.01 

Off-Highway 4.49 < 0.01 0.46 

 Non-Road Gasoline 0.23 < 0.01 0.01 

 Recreational 0.01 

 Construction 0.01 

 Industrial 0.01 

 Lawn & Garden 0.10 

 Farm 0.01 

2002 Emissions (Tg/yr) NOX1 NH3

2 SO2

 Light Commercial 0.04 

 Logging < 0.01

 Airport Service < 0.01

 Railway Maintenance < 0.01

 Recreational Marine Vessels 0.05 

 Non-Road Diesel 1.76 < 0.01 0.22

 Recreational 0.00

 Construction 0.84

 Industrial 0.15

 Lawn & Garden 0.05

 Farm 0.57

 Light Commercial 0.08

 Logging 0.02

 Airport Service 0.01

 Railway Maintenance < 0.01

 Recreational Marine Vessels 0.03

 Aircraft 0.09 0.01

 Marine Vessels 1.11 0.18

 Diesel 1.11

 Residual Oil 

 Other 

 Railroads 0.98 0.05

 Other 0.32 < 0.01 0.00

 Liquefied Petroleum Gas 0.29

 Compressed Natural Gas 0.04

Miscellaneous 0.39 3.53 0.10

 Agriculture & Forestry < 0.01 3.45 < 0.01

 Agricultural Crops < 0.01

 Agricultural Livestock 2.66

 Other Combustion 0.08 0.10

 Health Services 

 Cooling Towers 

 Fugitive Dust 

 Other 

 Natural Sources 3.10 0.03 

1 Emissions are expressed in terms of NO2. 2 Natural emissions of non-methane volatile organic compounds, carbon monoxide, 

and oxides. 

Note: Subcategory values may not sum to category totals due to rounding. 

Source: (EPA, 2006a) 

The major natural sources of SO2 are volcanoes, biomass burning, and DMS oxidation over the 

oceans. SO2 constitutes a relatively minor fraction (0.005% by volume) of volcanic emissions (Holland, 

1978). The ratio of H2S to SO2 is highly variable in volcanic gases. It is typically much less than one, as 

in the Mt. St. Helens’ eruption (Turco et al. 1983). However, in addition to being degassed from magma, 

H2S can be produced if ground waters, especially those containing organic matter, come into contact with 

volcanic gases. In this case, the ratio of H2S to SO2 can be greater than one. H2S produced this way would 

more likely be emitted through side vents than through eruption columns (Pinto et al., 1989). Primary 

particulate SO4

2- is a component of marine aerosol and is also produced by wind erosion of surface soils. 

Volcanic sources of SO2 in the U.S. are limited to the Pacific Northwest, Alaska, and Hawaii. Since 

1980, the Mt. St. Helens volcano in the Washington Cascade Range (46.20oN, 122.18oW, summit 2549 m 

asl) has been a variable source of SO2. Its major effects came in the explosive eruptions of 1980, which 

primarily affected the Northwest. The Augustine volcano near the mouth of the Cook Inlet in 

southwestern Alaska (59.363oN, 153.43°W, summit 1252 m asl) has had variable SO2 emission since its 

last major eruptions in 1986. Volcanoes in the Kamchatka peninsula of eastern Siberia do not significantly 

affect surface SO2 concentrations in northwestern North America. The most serious effects in the U.S. 

from volcanic SO2 occur on the island of Hawaii. Nearly continuous venting of SO2 from Mauna Loa and 

Kilauea produces SO2 in such large amounts that > 100 km downwind of the island SO2 concentrations 

can exceed 30 ppb (Thornton and Bandy, 1993). Depending on wind direction, the west coast of Hawaii 

 B-7 
(Kona region) has had significant deleterious effects from SO2 and acidic sulfate aerosols for the past 

decade. 

Emissions of SO2 from burning vegetation are generally in the range of 1 to 2% of the biomass 

burned (e.g., Levine and Pinto, 1998). Gaseous emissions are mainly in the form of SO2 with much 

smaller amounts of H2S and OCS. The ratio of gaseous N to S emissions is about 14, very close to their 

ratio in plant tissue (Andrea et al., 1991). Gaseous emissions are mainly in the form of SO2 with much 

smaller amounts of H2S and OCS. The ratio of reduced N and S species such as NH3 and H2S to their 

more oxidized forms, such as NO and SO2, increases from flaming to smoldering phases of combustion, 

as emissions of reduced species are favored by lower temperatures and reduced O2 availability. 

Emissions of reduced S species are associated typically with marine organisms living either in 

pelagic or coastal zones and with anaerobic bacteria in marshes and estuaries. Mechanisms for their 

oxidation were discussed in Section B.1. Emissions of DMS from marine plankton represent the largest 

single source of reduced S species to the atmosphere (e.g., Berresheim et al., 1995). 

However, it should be noted that reduced S species are also produced by industry. For example, 

DMS is used in petroleum refining and in petrochemical production processes to control the formation of 

coke and carbon monoxide. In addition, it is used to control dusting in steel mills. It is also used in a 

range of organic syntheses. It also has a use as a food flavoring component. It can also be oxidized by 

natural or artificial means to dimethyl sulfoxide (DMSO), which has several important solvent properties. 

B.4. Methods Used to Calculate SOx and Chemical 

Interactions in the Atmosphere 

Atmospheric chemistry and transport models are the major tools used to calculate the relations 

among O3, other oxidants, and their precursors, the transport and transformation of air toxics, the 

production of secondary organic aerosol, the evolution of the particle size distribution, and the production 

and deposition of pollutants affecting ecosystems. Chemical transport models are driven by emissions 

inventories for primary species such as the precursors for O3 and PM and by meteorological fields 

produced by other numerical models. Emissions of precursor compounds can be divided into 

anthropogenic and natural source categories. Natural sources can be further divided into biotic 

(vegetation, microbes, animals) and abiotic (biomass burning, lightning) categories. However, the 

distinction between natural sources and anthropogenic sources is often difficult to make as human 

activities affect directly, or indirectly, emissions from what would have been considered natural sources 

during the preindustrial era. Emissions from plants and animals used in agriculture have been referred to 

as anthropogenic or natural in different applications. Wildfire emissions may be considered to be natural, 

except that forest management practices may have led to the buildup of fuels on the forest floor, thereby 

altering the frequency and severity of forest fires. Needed meteorological quantities such as winds and 

temperatures are taken from operational analyses, reanalyses, or circulation models. In most cases, these 

are off-line analyses, i.e., they are not modified by radiatively active species such as O3 and particles 

generated by the model. 

A brief overview of atmospheric chemistry-transport models is given in Section B.5. Uncertainties 

in emissions estimates have also been discussed in AQCD for PM (EPA, 1996). Chemistry-transport 

model evaluation and an evaluation of the reliability of emissions inventories are also presented in 

Section B.5. 

 B-8 
B.5. Chemical-transport Models 

Atmospheric chemical transport models (CTMs) have been developed for application over a wide 

range of spatial scales ranging from neighborhood to global. Regional scale CTMs are used: (1) to obtain 

better understanding of the processes controlling the formation, transport, and destruction of gas and 

particle phase criteria and hazardous air pollutants; (2) to understand the relations between O3

concentrations and concentrations of its precursors such as NOX and VOCs, the factors leading to acid 

deposition, and hence to possible damage to ecosystems; and (3) to understand relations among the 

concentration patterns of various pollutants that may exert adverse health effects. CTMs are also used for 

determining control strategies for O3 precursors. However, this application has met with varying degrees 

of success because of the highly nonlinear relations between O3 and emissions of its precursors, and 

uncertainties in emissions, parameterizations of transport, and chemical production and loss terms. 

Uncertainties in meteorological variables and emissions can be large enough to lead to significant errors 

in developing control strategies (e.g., Russell and Dennis, 2000; Sillman, 1995). 

Global scale CTMs are used to address issues associated with climate change, stratospheric O3

depletion, and to provide boundary conditions for regional scale models. CTMs include mathematical 

(and often simplified) descriptions of atmospheric transport, the transfer of solar radiation through the 

atmosphere, chemical reactions, and removal to the surface by turbulent motions and precipitation for 

pollutants emitted into the model domain. Their upper boundaries extend anywhere from the top of the 

mixing layer to the mesopause (about 80 km in height), to obtain more realistic boundary conditions for 

problems involving stratospheric dynamics. There is a trade-off between the size of the modeling domain 

and the grid resolution used in the CTM that is imposed by computational resources. 

There are two major formulations of CTMs in current use. In the first approach, grid-based, or 

Eulerian, air quality models, the region to be modeled (the modeling domain) is subdivided into a threedimensional array of grid cells. Spatial derivatives in the species continuity equations are cast in terms of 

finite-differences. A system of equations for the concentrations of all the chemical species in the model 

are solved numerically at each grid point. Time dependent continuity (mass conservation) equations are 

solved for each species including terms for transport, chemical production and destruction, and emissions 

and deposition (if relevant), in each cell. Chemical processes are simulated with ordinary differential 

equations, and transport processes are simulated with partial differential equations. Because of a number 

of factors such as the different time scales inherent in different processes, the coupled, nonlinear nature of 

the chemical process terms, and computer storage limitations, all of the terms in the equations are not 

solved simultaneously in three dimensions. Instead, operator splitting, in which terms in the continuity 

equation involving individual processes are solved sequentially, is used. In the second CTM formulation, 

trajectory or Lagrangian models, a large number of hypothetical air parcels are specified as following 

wind trajectories. In these models, the original system of partial differential equations is transformed into 

a system of ordinary differential equations. 

A less common approach is to use a hybrid Lagrangian/Eulerian model, in which certain aspects of 

atmospheric chemistry and transport are treated with a Lagrangian approach and others are treaded in an 

Eulerian manner (Stein et al., 2000). Each approach has its advantages and disadvantages. The Eulerian 

approach is more general in that it includes processes that mix air parcels and allows integrations to be 

carried out for long periods during which individual air parcels lose their identity. There are, however, 

techniques for including the effects of mixing in Lagrangian models such as FLEXPART (e.g., Zanis et 

al., 2003), ATTILA (Reithmeier and Sausen, 2002), and CLaMS (McKenna et al., 2002). 

B.5.1. Regional Scale Chemical-Transport Models 

Major modeling efforts within the EPA center on the Community Multiscale Air Quality modeling 

system (CMAQ) (Byun and Ching, 1999; Byun and Schere, 2006). A number of other modeling platforms 

 B-9 
using Lagrangian and Eulerian frameworks have been reviewed in the 1996 AQCD for O3 (U.S. EPA, 

1997) and in Russell and Dennis (2000). The capabilities of a number of CTMs designed to study localand regional-scale air pollution problems are summarized by Russell and Dennis (2000). Evaluations of 

the performance of CMAQ are given in Arnold et al. (2003), Eder and Yu (2006), and Fuentes and Raftery 

(2005). The domain of CMAQ can extend from several hundred km to the hemispherical scale. In 

addition, both of these classes of models allow the resolution of the calculations over specified areas to 

vary. CMAQ is most often driven by the MM5 mesoscale meteorological model (Seaman, 2000), though 

it may be driven by other meteorological models (e.g., WRF, RAMS). Simulations of O3 episodes over 

regional domains have been performed with a horizontal resolution as low as 1 km, and smaller 

calculations over limited domains have been accomplished at even finer scales. However, simulations at 

such high resolutions require better parameterizations of meteorological processes such as boundary layer 

fluxes, deep convection and clouds (Seaman, 2000), and finer-scale emissions. Finer spatial resolution is 

necessary to resolve features such as urban heat island circulations; sea, bay, and land breezes; mountain 

and valley breezes, and the nocturnal low-level jet. 

The most common approach to setting up the horizontal domain is to nest a finer grid within a 

larger domain of coarser resolution. However, there are other strategies such as the stretched grid (e.g., 

Fox-Rabinovitz et al., 2002) and the adaptive grid. In a stretched grid, the grid’s resolution continuously 

varies throughout the domain, thereby eliminating any potential problems with the sudden change from 

one resolution to another at the boundary. Caution should be exercised in using such a formulation, 

because certain parameterizations that are valid on a relatively coarse grid scale (such as convection) may 

not be valid on finer scales. Adaptive grids are not fixed at the start of the simulation, but instead adapt to 

the needs of the simulation as it evolves. They have the advantage that they can resolve processes at 

relevant spatial scales. However, they can be very slow if the situation to be modeled is complex. 

Additionally, if adaptive grids are used for separate meteorological, emissions, and photochemical 

models, there is no reason a priori why the resolution of each grid should match, and the gains realized 

from increased resolution in one model will be wasted in the transition to another model. The use of finer 

horizontal resolution in CTMs will necessitate finer-scale inventories of land use and better knowledge of 

the exact paths of roads, locations of factories, and, in general, better methods for locating sources and 

estimating their emissions. 

The vertical resolution of these CTMs is variable, and usually configured to have higher resolution 

near the surface and decreasing aloft. Because the height of the boundary layer is of critical importance in 

simulations of air quality, improved resolution of the boundary layer height would likely improve air 

quality simulations. Additionally, current CTMs do not adequately resolve fine scale features such as the 

nocturnal low-level jet. 

CTMs require time-dependent, three-dimensional wind fields for the period of simulation. The 

winds may be either generated by a model using initial fields alone or with four-dimensional data 

assimilation to improve the model’s performance; fields (i.e., model equations can be updated periodically 

or “nudged,” to bring results into agreement with observations. Modeling efforts typically focus on 

simulations of several days’ duration, the typical time scale for individual O3 episodes, but there have 

been several attempts at modeling longer periods. For example, Kasibhatla and Chameides (2000) 

simulated a four-month period from May to September of 1995 using MAQSIP. The current trend in 

modeling applications is towards annual simulations. This trend is driven in part by the need to better 

understand observations of periods of high wintertime PM (e.g., Blanchard et al., 2002) and the need to 

simulate O3 episodes occurring outside of summer. 

Chemical kinetics mechanisms (a set of chemical reactions) representing the important reactions 

occurring in the atmosphere are used in CTMs to estimate the rates of chemical formation and destruction 

of each pollutant simulated as a function of time. Unfortunately, chemical mechanisms that explicitly treat 

the reactions of each individual reactive species are too computationally demanding to be incorporated 

into CTMs. For example, a master chemical mechanism includes approximately 10,500 reactions 

involving 3603 chemical species (Jenkin et al., 2003). Instead, “lumped” mechanisms, that group 

compounds of similar chemistry together, are used. The chemical mechanisms used in existing 

 B-10 
photochemical O3 models contain significant uncertainties that may limit the accuracy of their 

predictions; the accuracy of each of these mechanisms is also limited by missing chemistry. Because of 

different approaches to the lumping of organic compounds into surrogate groups, chemical mechanisms 

can produce somewhat different results under similar conditions. The CB-IV chemical mechanism (Gery 

et al., 1989), the RADM II mechanism (Stockwell et al., 1990), the SAPRC (e.g., (Carter, 1990; Wang et 

al., 2000a; b) and the RACM mechanism (Stockwell et al. 1997) can be used in CMAQ. Jimenez et al. 

(2003b) provide brief descriptions of the features of the main mechanisms in use and they compared 

concentrations of several key species predicted by seven chemical mechanisms in a box model simulation 

over 24 h. The avg deviation from the avg of all mechanism predictions for O3 and NO over the daylight 

period was less than 20%, and was 10% for NO2 for all mechanisms. However, much larger deviations 

were found for HNO3, PAN, HO2, H2O2, C2H4, and C5H8 (isoprene). An analysis for OH radicals was not 

presented. The large deviations shown for most species imply differences between the calculated lifetimes 

of atmospheric species and the assignment of model simulations to either NOX-limited or radical quantity 

limited regimes between mechanisms. Gross and Stockwell (2003) found small differences between 

mechanisms for clean conditions, with differences becoming more significant for polluted conditions, 

especially for NO2 and organic peroxy radicals. They caution modelers to consider carefully the 

mechanisms they are using. Faraji et al. (2008) found differences of 40% in peak 1-h O3 in the HoustonGalveston-Brazoria area between simulations using SAPRAC and CB4. They attributed differences in 

predicted O3 concentrations to differences in the mechanisms of oxidation of aromatic hydrocarbons. 

CMAQ and other CTMs (e.g., PM-CAMX) incorporate processes and interactions of aerosol-phase 

chemistry (Mebust et al., 2003). There have also been several attempts to study the feedbacks of 

chemistry on atmospheric dynamics using meteorological models, like MM5 (e.g., Grell et al., 2000; Liu 

et al., 2001a; Liu et al., 2001b; Lu et al., 1997; Park et al., 2001b). This coupling is necessary to simulate 

accurately feedbacks such as may be caused by the heavy aerosol loading found in forest fire plumes (Lu 

et al., 1997; Park et al., 2001b), or in heavily polluted areas. Photolysis rates in CMAQ can now be 

calculated interactively with model produced O3, NO2, and aerosol fields (Binkowski et al., 2007). 

Spatial and temporal characterizations of anthropogenic and biogenic precursor emissions must be 

specified as inputs to a CTM. Emissions inventories have been compiled on grids of varying resolution 

for many hydrocarbons, aldehydes, ketones, CO, NH3, and NOX. Emissions inventories for many species 

require the application of some algorithm for calculating the dependence of emissions on physical 

variables such as temperature and to convert the inventories into formatted emission files required by a 

CTM. For example, preprocessing of emissions data for CMAQ is done by the Spare-Matrix Operator 

Kernel Emissions (SMOKE) system. For many species, information concerning the temporal variability 

of emissions is lacking, so long-term (e.g., annual or O3-season) averages are used in short-term, episodic 

simulations. Annual emissions estimates are often modified by the emissions model to produce emissions 

more characteristic of the time of day and season. Significant errors in emissions can occur if an 

inappropriate time dependence or a default profile is used. Additional complexity arises in model 

calculations because different chemical mechanisms are based on different species, and inventories 

constructed for use with another mechanism must be adjusted to reflect these differences. This problem 

also complicates comparisons of the outputs of these models because one chemical mechanism may 

produce some species not present in another mechanism yet neither may agree with the measurements. 

In addition to wet deposition, dry deposition (the removal of chemical species from the atmosphere 

by interaction with ground-level surfaces) is an important removal process for pollutants on both urban 

and regional scales and must be included in CTMs. The general approach used in most models is the 

resistance in series method, in which where dry deposition is parameterized with a vd , which is 

represented as vd = (ra + rb + rc)

-1 where ra, rb, and rc represent the resistance due to atmospheric 

turbulence, transport in the fluid sublayer very near the elements of surface such as leaves or soil, and the 

resistance to uptake of the surface itself. This approach works for a range of substances, although it is 

inappropriate for species with substantial emissions from the surface or for species whose deposition to 

the surface depends on its concentration at the surface itself. The approach is also modified somewhat for 

aerosols: the terms rb and rc are replaced with a surface vd to account for gravitational settling. In their 

 B-11 
review, Wesely and Hicks (2000) point out several shortcomings of current knowledge of dry deposition. 

Among those shortcomings are difficulties in representing dry deposition over varying terrain where 

horizontal advection plays a significant role in determining the magnitude of ra and difficulties in 

adequately determining a vd for extremely stable conditions such as those occurring at night (e.g., Mahrt, 

1998). Under the best of conditions, when a model is exercised over a relatively small area where dry 

deposition measurements have been made, models still commonly show uncertainties at least as large as 

± 30% (e.g., Brook et al., 1996; Massman et al., 1994; Padro, 1996). Wesely and Hicks (2000) state that 

an important result of these comparisons is that the current level of sophistication of most dry deposition 

models is relatively low, and that deposition estimates therefore must rely heavily on empirical data. Still 

larger uncertainties exist when the surface features in the built environment are not well known or when 

the surface comprises a patchwork of different surface types, as is common in the eastern U.S.. 

The initial conditions, i.e., the concentration fields of all species computed by a model, and the 

boundary conditions, i.e., the concentrations of species along the horizontal and upper boundaries of the 

model domain throughout the simulation must be specified at the beginning of the simulation. It would be 

best to specify initial and boundary conditions according to observations. However, data for vertical 

profiles of most species of interest are sparse. The results of model simulations over larger, preferably 

global, domains can also be used. As may be expected, the influence of boundary conditions depends on 

the lifetime of the species under consideration and the time scales for transport from the boundaries to the 

interior of the model domain (Liu et al., 2001a; Liu et al., 2001b). 

Each of the model components described above has an associated uncertainty, and the relative 

importance of these uncertainties varies with the modeling application. The largest errors in 

photochemical modeling are still thought to arise from the meteorological and emissions inputs to the 

model (Russell and Dennis, 2000). Within the model itself, horizontal advection algorithms are still 

thought to be significant source of uncertainty (e.g., Chock and Winkler, 1994), though more recently, 

those errors are thought to have been reduced (e.g., Odman and Ingram, 1996). There are also indications 

that problems with mass conservation continue to be present in photochemical and meteorological models 

(e.g.,(Odman and Russel, 1999); these can result in significant simulation errors. The effects of errors in 

initial conditions can be minimized by including several days “spin-up” time in a simulation to allow the 

model to be driven by emitted species before the simulation of the period of interest begins. 

While the effects of poorly specified boundary conditions propagate through the model’s domain, 

the effects of these errors remain undetermined. Because many meteorological processes occur on spatial 

scales which are smaller than the model grid spacing (either horizontally or vertically) and thus are not 

calculated explicitly, parameterizations of these processes must be used and these introduce additional 

uncertainty. 

Uncertainty also arises in modeling the chemistry of O3 formation because it is highly nonlinear 

with respect to NOX concentrations. Thus, the volume of the grid cell into which emissions are injected is 

important because the nature of O3 chemistry (i.e., O3 production or titration) depends in a complicated 

way on the concentrations of the precursors and the OH radical as noted earlier. The use of ever-finer grid 

spacing allows regions of O3 titration to be more clearly separated from regions of O3 production. The use 

of grid spacing fine enough to resolve the chemistry in individual power-plant plumes is too demanding 

of computer resources for this to be attempted in most simulations. Instead, parameterizations of the 

effects of sub-grid-scale processes such as these must be developed; otherwise serious errors can result if 

emissions are allowed to mix through an excessively large grid volume before the chemistry step in a 

model calculation is performed. In light of the significant differences between atmospheric chemistry 

taking place inside and outside of a power plant plume (Ryerson et al., 1998; Sillman, 2000), inclusion of 

a separate, meteorological module for treating large, tight plumes is necessary. Because the 

photochemistry of O3 and many other atmospheric species is nonlinear, emissions correctly modeled in a 

tight plume may be incorrectly modeled in a more dilute plume. Fortunately, it appears that the chemical 

mechanism used to follow a plume’s development need not be as detailed as that used to simulate the rest 

of the domain, as the inorganic reactions are the most important in the plume see (e.g., Kumar and 

Russell, 1996). The need to include explicitly plume-in-grid chemistry only down to the level of the 

 B-12 
smallest grid disappears if one uses the adaptive grid approach mentioned previously, though such grids 

are more computationally intensive. The differences in simulations are significant because they can lead 

to significant differences in the calculated sensitivity of O3 to its precursors (e.g., Sillman, 1995). 

Because the chemical production and loss terms in the continuity equations for individual species 

are coupled, the chemical calculations must be performed iteratively until calculated concentrations 

converge to within some preset criterion. The number of iterations and the convergence criteria chosen 

also can introduce error. 

B.5.2. Intra-urban Scale Dispersion Modeling 

The grid spacing for regional applications of air quality CTMs, typically between 1 and 12 km2, is 

usually too coarse to resolve spatial variations on urban neighborhood scales. The link between regional 

scale models and the population exposure models described in Annex C.2 is made with the classical 

dispersion models. Several models could be used to simulate concentration fields near local sources, each 

with its own set of strengths and weaknesses. For example, AERMOD is a steady-state plume model 

formulated by a committee of the American Meteorological Society and EPA as a replacement to the ISC3 

dispersion model. (Technical information on the current version of AERMOD, including model 

evaluation databases, can be found at http://www.epa.gov/scram001/dispersion_prefrec.htm.) In the stable 

boundary layer (SBL), steady-state dispersion models like AERMOD assume the distributions of 

pollutant concentrations to be Gaussian in both the vertical and horizontal dimensions. In the convective 

boundary layer, the horizontal distribution is also assumed to be Gaussian, but the vertical distribution is 

described with a bi-Gaussian probability density function (pdf). Dispersion of emissions from line sources 

like highways is treated as the sum of emissions from a series of contiguous rectangular area or volume 

sources. AERMOD has provisions to be applied to flat and complex terrain, and with multiple source 

types including point, area, and volume sources in both urban and rural settings with air dispersion 

algorithms based on planetary boundary layer turbulence structure and scaling concepts. A large number 

of applications have been carried out and evaluated for both surface and elevated sources, and simple and 

complex terrain in rural and urban areas. However, AERMOD like similar models was not designed to 

treat the formation and dispersal of secondary products. 

B.5.3. Global-scale CTMs 

The importance of global transport of O3 and O3 precursors and their contribution to regional O3

levels in the U.S. is slowly becoming apparent. There are presently on the order of 20 three-dimensional 

global models that have been developed by various groups to address problems in tropospheric chemistry. 

These models resolve synoptic meteorology, O3-NOX-CO-hydrocarbon photochemistry, have 

parameterizations for wet and dry deposition, and parameterize sub-grid scale vertical mixing processes 

such as convection. Global models have proven useful for testing and advancing scientific understanding 

beyond what is possible with observations alone. For example, they can calculate quantities of interest 

that cannot be measured directly, such as the export of pollution from one continent to the global 

atmosphere or the response of the atmosphere to future perturbations to anthropogenic emissions. 

Global simulations are typically conducted at a horizontal resolution of about 200 km2

. Simulations 

of the effects of transport from long-range transport link multiple horizontal resolutions from the global to 

the local scale. Finer resolution will only improve scientific understanding to the extent that the governing 

processes are more accurately described at that scale. Consequently, there is a critical need for 

observations at the appropriate scales to evaluate the scientific understanding represented by the models. 

 B-13 
During the recent IPCC-AR4 tropospheric chemistry study coordinated by the European Union 

project Atmospheric Composition Change: the European Network of excellence (ACCENT), 26 

atmospheric CTMs were used to estimate the impacts of three emissions scenarios on global atmospheric 

composition, climate, and air quality in 2030 (Dentener et al., 2006a). All models were required to use 

anthropogenic emissions developed at IIASA (Dentener et al., 2005) and GFED version 1 biomass 

burning emissions (Van der Werf et al., 2003) as described in Stevenson et al. (2006). The base 

simulations from these models were evaluated against a suite of present-day observations. Most relevant 

to this assessment report are the evaluations with O3 and NO2, and for N and sulfur deposition (Dentener 

et al., 2006a; Stevenson et al., 2006; van Noije et al., 2006); see Figure B-3, sulfate deposition. 

Source: Dentener et al. (2006a). 

Figure B-3. Sulfate wet deposition (mg S/m2/yr) of the mean model versus measurements for the 

National Atmospheric Deposition Program (NADP) network. Dashed lines indicate 

factor of 2. The gray line is the result of a linear regression fitting through 0. 

B.5.4. Modeling the Effects of Convection 

The effects of deep convection can be simulated using cloud-resolving models, or in regional or 

global models in which the convection is parameterized. The Goddard Cumulus Ensemble (GCE) model 

(Tao and Simpson, 1993) has been used by Pickering et al. (1991; 1992; 1993; 1996), Scala et al. (1990), 

and Stenchikov et al. (1996) in the analysis of convective transport of trace gases. The cloud model is 

nonhydrostatic and contains a detailed representation of cloud microphysical processes. Two- and threedimensional versions of the model have been applied in transport analyses. The initial conditions for the 

model are usually from a sounding of temperature, water vapor and winds representative of the region of 

storm development. Model-generated wind fields can be used to perform air parcel trajectory analyses 

and tracer advection calculations. 

 B-14 
B.5.5. CTM Evaluation 

The comparison of model predictions with ambient measurements represents a critical task for 

establishing the accuracy of photochemical models and evaluating their ability to serve as the basis for 

making effective control strategy decisions. The evaluation of a model’s performance, or its adequacy to 

perform the tasks for which it was designed can only be conducted within the context of measurement 

errors and artifacts. Not only are there analytical problems, but there are also problems in assessing the 

representativeness of monitors at ground level for comparison with model values which represent 

typically an average over the volume of a grid box. 

Evaluations of CMAQ are given in Arnold et al. (2003) and Fuentes and Raftery (2005). 

Discrepancies between model predictions and observations can be used to point out gaps in current 

understanding of atmospheric chemistry and to spur improvements in parameterizations of atmospheric 

chemical and physical processes. Model evaluation does not merely involve a straightforward comparison 

between model predictions and the concentration field of the pollutant of interest. Such comparisons may 

not be meaningful because it is difficult to determine if agreement between model predictions and 

observations truly represents an accurate treatment of physical and chemical processes in the CTM or the 

effects of compensating errors in complex model routines. Ideally, each of the model components 

(emissions inventories, chemical mechanism, meteorological driver) should be evaluated individually. 

However, this is rarely done in practice. 

B.6. Sampling and Analysis of SOX

B.6.1. Sampling and Analysis for SO2

SO2 molecules absorb ultraviolet (UV) light at one wavelength and emit UV light at longer 

wavelengths. This process is known as fluorescence, and involves the excitation of the SO2 molecule to a 

higher energy (singlet) electronic state. Once excited, the molecule decays non-radiatively to a lower 

energy electronic state from which it then decays to the original, or ground, electronic state by emitting a 

photon of light at a longer wavelength (i.e., lower energy) than the original, incident photon. The process 

can be summarized by the following equations 

12 →ν+ 2 *SOhSO 

2 22 → + hSO*SO ν 

where SO2* represents the excited state of SO2, hv1, and hv2 represent the energy of the excitation and 

fluorescence photons, respectively, and hv2 < hv1. The intensity of the emitted light is proportional to the 

number of SO2 molecules in the sample gas. 

In commercial analyzers, light from a high intensity UV lamp passes through a bandwidth filter, 

allowing only photons with wavelengths around the SO2 absorption peak (near 214 nm) to enter the 

optical chamber. The light passing through the source bandwidth filter is collimated (to improve image 

quality) using a UV lens and passes through the optical chamber, where it is detected on the opposite side 

of the chamber by the reference detector. A photomultiplier tube (PMT) is offset from and placed 

perpendicular to the light path to detect the SO2 fluorescence. Since the SO2 fluorescence (330 nm) is at a 

wavelength that is different from the excitation wavelength, an optical bandwidth filter is placed in front 

of the PMT to filter out any stray light from the UV lamp. A lens is located between the filter and the 

 B-15 
PMT to focus the fluorescence onto the active area of the detector and optimize the fluorescence signal. 

The LOD for a non-trace level SO2 analyzer is 10 ppb (CFR, 2006). The SO2 measurement method is 

subject to both positive and negative interference. 

B.6.1.1. Other Techniques for Measuring SO2

A more sensitive SO2 measurement method than the UV-fluorescence method was reported by 

Thornton et al. (2002), who reported use of an atmospheric pressure ionization mass spectrometer. The 

high measurement precision and instrument sensitivity were achieved by adding isotopically labeled SO2

(

34S16O2) continuously to the manifold as an internal standard. Field studies showed that the method 

precision was better than 10% and the limit of detection was less than 1 ppt for a sampling interval of 1s. 

SO2 can be measured by LIF at around 220 nm (Matsumi et al., 2005). Because the laser 

wavelength is alternately tuned to an SO2 absorption peak at 220.6 and trough at 220.2 nm, and the 

difference signal at the two wavelengths is used to extract the SO2 concentration, the technique eliminates 

interference from either absorption or fluorescence by other species and has high sensitivity (5 ppt in 60 

sec). SO2 can also be measured by the same DOAS instrument that can measure NO2. 

Photoacoustic techniques have been employed for SO2 detection, but they generally have detection 

limits suitable only for source monitoring (Gondal, 1997; Gondal and Mastromarino, 2001). 

Chemical Ionization Mass Spectroscopy (CIMS) utilizes ionization via chemical reactions in the 

gas phase to determine an unknown sample’s mass spectrum and identity. High sensitivity (10 ppt or 

better) has been achieved with uncertainty of ~15% when a charcoal scrubber is used for zeroing and the 

sensitivity is measured with isotopically labeled 34SO2 (Hanke et al., 2003; Hennigan et al., 2006; Huey et 

al., 2004). 

B.6.2. Sampling and Analysis for SO4

2-, NO3, and NH4+ 

Sulfate is commonly present in PM2.5. Most PM2.5 samplers have a size-separation device to 

separate particles so that only those particles approximately 2.5 μm or less are collected on the sample 

filter. Air is drawn through the sample filter at a controlled flow rate by a pump located downstream of the 

sample filter. The systems have two critical flow rate components for the capture of fine particulate: (1) 

the flow of air through the sampler must be at a flow rate that ensures that the size cut at 2.5 μm occurs; 

and (2) the flow rate must be optimized to capture the desired amount of particulate loading with respect 

to the analytical method detection limits. 

When using the system described above to collect SO4

2- sampling artifacts can occur because of: 

(1) positive sampling artifact for SO4

2-, nitrate (NO3), and particulate ammonium due to chemical 

reaction; and (2) negative sampling artifact for NO3 and ammonium (NH4

+

) due to the decomposition and 

evaporation. 

There are two major PM speciation ambient air-monitoring networks in the U.S.: the Speciation 

Trend Network (STN), and the Interagency Monitoring of Protected Visual Environments (IMPROVE) 

network. The current STN samplers include three filters: (1) Teflon for equilibrated mass and elemental 

analysis including elemental sulfur; (2) a HNO3 denuded nylon filter for ion analysis including NO3 and 

SO4

2-, (3) a quartz-fiber filter for elemental and organic carbon. The IMPROVE sampler, which collects 

two 24-h samples per week, simultaneously collects one sample of PM10 on a Teflon filter, and three 

samples of PM2.5 on Teflon, nylon, and quartz filters. PM2.5 mass concentrations are determined 

gravimetrically from the PM2.5 Teflon filter sample. The PM2.5 Teflon filter sample is also used to 

determine concentrations of selected elements. The PM2.5 nylon filter sample, which is preceded by a 

denuder to remove acidic gases, is analyzed to determine NO3 and sulfate aerosol concentrations. Finally, 

the PM2.5 quartz filter sample is analyzed for OC and EC using the thermal-optical reflectance (TOR) 

 B-16 
method. The STN and the IMPROVE networks represent a major advance in the measurement of NO3, 

because the combination of a denuder (coated with either sodium carbonate [Na2CO3] or MgO) to remove 

HNO3 vapor and a Nylon filter to adsorb HNO3 vapor volatilizing from the collected ammonium nitrate 

particles overcomes the loss of NO3 from Teflon filters. 

The extent to which sampling artifacts for particulate NH4

+

 have been adequately addressed in the 

current networks is not clear. Recently, new denuder-filter sampling systems have been developed to 

measure SO4

2-, NO3, and NH4+

 with an adequate correction of NH4

+

 sampling artifacts. The denuder-filter 

system, Chembcomb Model 3500 speciation sampling cartridge developed by Rupprecht & Patashnick 

Co, Inc. could be used to collect NO3, SO4

2-, and NH4+

 simultaneously. The sampling system contains a 

single-nozzle size-selective inlet, two honeycomb denuders, the aerosol filter and two backup filters 

(Keck and Wittmaack, 2005). The first denuder in the system is coated with 0.5% Na2CO3 and 1% 

glycerol and collects acid gases such as HCl, SO2, HONO, and HNO3. The second denuder is coated with 

0.5% phosphoric acid in methanol for collecting NH3. Backup filters collect the gases behind denuded 

filters. The backup filters are coated with the same solutions as the denuders. A similar system based on 

the same principle was applied by Possanzini et al. (1999). The system contains two NaCl-coated annular 

denuders followed by other two denuders coated with Na2CO3/glycerol and citric acid, respectively. This 

configuration was adopted to remove HNO3 quantitatively on the first NaCl denuder. The third and fourth 

denuders remove SO2 and NH3, respectively. A polyethylene cyclone and a two-stage filter holder 

containing three filters is placed downstream of the denuders. Aerosol fine particles are collected on a 

Teflon membrane. A backup nylon filter and a subsequent citric acid impregnated filter paper collect 

dissociation products (HNO3 and NH3) of ammonium nitrate evaporated from the filtered particulate 

matter. 

Several traditional and new methods could be used to quantify elemental S collected on filters: 

energy dispersive X-ray fluorescence, synchrotron induced X-ray fluorescence, proton induced X-ray 

emission (PIXE), total reflection X-ray fluorescence, and scanning electron microscopy. Energy 

dispersive X-ray fluorescence (EDXRF)(Method IO-3.3, EPA, 1997b; see 2004 PM CD for details) and 

PIXE are the most commonly used methods. Since sample filters often contain very small amounts of 

particle deposits, preference is given to methods that can accommodate small sample sizes and require 

little or no sample preparation or operator time after the samples are placed into the analyzer. X-ray 

fluorescence (XRF) meets these needs and leaves the sample intact after analysis so it can be submitted 

for additional examinations by other methods as needed. To obtain the greatest efficiency and sensitivity, 

XRF typically places the filters in a vacuum which may cause volatile compounds (nitrates and organics) 

to evaporate. As a result, species that can volatilize such as ammonium nitrate and certain organic 

compounds can be lost during the analysis. The effects of this volatilization are important if the PTFE 

filter is to be subjected to subsequent analyses of volatile species. 

Polyatomic ions such as SO4

2-, NO3, and NH4+

 are quantified by methods such as ion 

chromatography (IC) (an alternative method commonly used for NH4

+

 analysis is automated colorimetry). 

All ion analysis methods require a fraction of the filter to be extracted in deionized distilled water for 

SO4

2- and Na2CO3/NaHCO3 solution for NO3 and then filtered to remove insoluble residues prior to 

analysis. The extraction volume should be as small as possible to avoid over-diluting the solution and 

inhibiting the detection of the desired constituents at levels typical of those found in ambient PM2.5 

samples. During analysis, the sample extract passes through an ion-exchange column which separates the 

ions in time for individual quantification, usually by an electroconductivity detector. The ions are 

identified by their elution/retention times and are quantified by the conductivity peak area or peak height. 

In a side-by-side comparison of two of the major aerosol monitoring techniques (Hains et al., 

2007), PM2.5 mass and major contributing species were well correlated among the different methods with 

correlation coefficients in excess of 0.8. Agreement for mass, SO4

2-, OC, TC, and NH4+

 was good while 

that for NO3 and BC was weaker. Based on reported uncertainties, however, even daily concentrations of 

PM2.5 mass and major contributing species were often significantly different at the 95% confidence level. 

Greater values of PM2.5 mass and individual species were generally reported from Speciation Trends 

Network methods than from the Desert Research Institute Sequential Filter Samplers. These differences 

 B-17 
can only be partially accounted for by known random errors. The authors concluded that the current 

uncertainty estimates used in the STN network may underestimate the actual uncertainty. 

The reaction of SO2 (and other acid gases) with basic sites on glass fiber filters or with basic coarse 

particles on the filter leads to the formation of SO4

2- (or other nonvolatile salts, e.g., NO3, chloride). These 

positive artifacts lead to the overestimation of total mass, and SO4

2-, and probably also NO3

concentrations. These problems were largely overcome by changing to quartz fiber or Teflon filters and 

by the separate collection of PM2.5. However, the possible reaction of acidic gases with basic coarse 

particles remains a possibility, especially with PM10 and PM10-2.5 measurements. These positive artifacts 

could be effectively eliminated by removing acidic gases in the sampling line with denuders coated with 

NaCl or Na2CO3. 

Positive sampling artifacts also occur during measurement of particulate NH4

+

. The reaction of NH3

with acidic particles (e.g. 2NH3 + H2SO4 → (NH4)2SO4), either during sampling or during transportation, 

storage, and equilibration could lead to an overestimation of particulate NH4

+

 concentrations. Techniques 

have been developed to overcome this problem: using a denuder to remove NH3 during sampling and to 

protect the collected PM from NH3 (Brauer et al., 1991; Keck and Wittmaack, 2006; 1988b; Koutrakis et 

al., 1988a; Possanzini et al., 1999; Suh et al., 1992; Winberry et al., 1999). Hydrogen fluoride, citric acid, 

and phosphorous acids have been used as coating materials for the NH3 denuder. Positive artifacts for 

particulate NH4

+

 can also be observed during sample handling due to contamination. No chemical 

analysis method, no matter how accurate or precise, can adequately represent atmospheric concentrations 

if the filters to which these methods are applied are improperly handled. Ammonia is emitted directly 

from human sweat, breath and smoking. It can then react with acidic aerosols on the filter to form 

ammonium sulfate, ammonium bisulfate and ammonium nitrate if the filter was not properly handled 

(Sutton et al., 2000). Therefore, it is important to keep filters away from NH3 sources, such as human 

breath, to minimize neutralization of the acidic compounds. Also, when filters are handled, preferably in a 

glove box, the analyst should wear gloves that are antistatic and powder-free to act as an effective 

contamination barrier. 

Continuous methods for the quantification of aerosol S compounds first remove gaseous S (e.g., 

SO2, H2S) from the sample stream by a diffusion tube denuder followed by the analysis of particulate S 

(Cobourn et al., 1978; Durham et al., 1978; Huntzicker et al., 1978; Tanner et al., 1980). Another 

approach is to measure total S and gaseous S separately by alternately removing particles from the sample 

stream. Particulate S is obtained as the difference between the total and gaseous S (Kittelson et al., 1978). 

The total S content is measured by a flame photometric detector (FPD) by introducing the sampling 

stream into a fuel-rich, hydrogen-air flame (e.g., Farwell and Rasmussen, 1976; Stevens et al., 1969) that 

reduces sulfur compounds and measures the intensity of the chemiluminescence from electronically 

excited sulfur molecules (S2*). Because the formation of S2* requires two S atoms, the intensity of the 

chemiluminescence is theoretically proportional to the square of the concentration of molecules that 

contain a single S atom. In practice, the exponent is between 1 and 2 and depends on the S compound 

being analyzed (Dagnall et al., 1967; Stevens et al., 1971). Calibrations are performed using both particles 

and gases as standards. The FPD can also be replaced by a chemiluminescent reaction with O3 that 

minimizes the potential for interference and provides a faster response time (Benner and Stedman, 1989; 

1990). Capabilities added to the basic system include in situ thermal analysis and sulfuric acid speciation 

(Cobourn et al., 1978; Cobourn and Husar, 1982; Huntzicker et al., 1978; Tanner et al., 1980). 

Sensitivities for particulate S as low as 0.1 μg/m3

, with time resolution ranging from 1 to 30 min, have 

been reported. Continuous measurements of particulate S content have also been obtained by on-line XRF 

analysis with resolution of 30 min or less (Jaklevic et al., 1981). During a field-intercomparison study of 

five different S instruments, Camp et al. (1982) reported four out of five FPD systems agreed to within 

± 5% during a 1-week sampling period. 

 B-18 
Annex C. Modeling Human Exposure 

C.1. Introduction 

Predictive (or prognostic) exposure modeling studies are assessments that start from emissions and 

demographic information and explicitly consider the physical and chemical processes of environmental 

and microenvironmental transport and fate, in conjunction with human activities, to estimate inhalation 

intake and uptake. This type of comprehensive modeling effort, although conducted for other pollutants, 

has not previously been undertaken for SO2

1

. Previous efforts for SO2 exposure assessment have relied on 

statistical (diagnostic) analyses that have been reported using data obtained in various field exposure 

studies. However, existing prognostic modeling systems for the assessment of inhalation exposures can in 

principle be directly applied to, or adapted for, SO2 studies; specifically, such systems include APEX, 

SHEDS, and MENTOR-1A, to be discussed in the following sections. Nevertheless, it should be 

mentioned that such applications will be constrained by data limitations, such as the degree of ambient 

concentration characterization (e.g., concentrations at the local level) and quantitative information on 

indoor sources and sinks. 

Predictive models of human exposure to ambient air pollutants such as SO2 can be classified and 

differentiated based upon a variety of attributes. For example, exposure models can be classified as: 

 models of potential (typically maximum) outdoor exposure versus models of actual 

exposures (the latter including locally modified microenvironmental exposures, both 

outdoor and indoor); 

 Population Based Exposure Models (PBEM) versus Individual Based Exposure Models 

(IBEM); 

 deterministic versus probabilistic (or statistical) exposure models; and 

 observation-driven versus mechanistic air quality models (see Section C.4 for discussions 

about the construction, uses and limitations of this class of mathematical models. 

Some points should be made regarding terminology and essential concepts in exposure modeling, 

before proceeding to the overview of specific developments reported in the current research literature: 

First, it must be understood that there is significant variation in the definitions of many of the terms 

used in the exposure modeling literature; indeed, the science of exposure modeling is a rapidly evolving 

field and the development of a standard and commonly accepted terminology is an ongoing process (e.g., 

WHO, 2004).

Second, it should also be mentioned that, very often, procedures that are called exposure modeling, 

exposure estimation, etc. in the scientific literature, may in fact refer to only a sub-set of the complete set 

of steps or components required for a comprehensive exposure assessment. For example, certain selfidentified exposure modeling studies focus solely on refining the sub-regional or local spatio-temporal 

dynamics of pollutant concentrations (starting from raw data representing monitor observations or 

regional grid-based model estimates). Though not exposure studies per se, such efforts have value and are 

included in the discussion of the next sub-section, as they provide potentially useful tools that can be used 

in a complete exposure assessment. On the other hand, formulations which are self-identified as exposure 

models but actually focus only on ambient air quality predictions, such as chemical-transport models, are 

not included in the discussion that follows. 

Third, the process of modeling human exposures to ambient pollutants is very often identified 

explicitly with population-based modeling, while models describing the specific mechanisms affecting the 

 

1

 OAQPS is proposing to apply the APEX exposure model to SO2 as part of this NAAQS review. 

 C-1 
exposure of an actual individual (at specific locations) to an air contaminant (or to a group of cooccurring gas and/or aerosol phase pollutants) are usually associated with studies focusing specifically on 

indoor air chemistry modeling.

Finally the concept of microenvironments, introduced in earlier sections of this document, should 

be clarified further, as it is critical in developing procedures for exposure modeling.In the past, 

microenvironments have typically been defined as individual or aggregate locations (and sometimes even 

as activities taking place within a location) where a homogeneous concentration of the pollutant is 

encountered. Thus a microenvironment has often been identified with an ideal (i.e. perfectly mixed) 

compartment of classical compartmental modeling. More recent and general definitions view the 

microenvironment as a control volume, either indoors or outdoors, that can be fully characterized by a set 

of either mechanistic or phenomenological governing equations, when appropriate parameters are 

available, given necessary initial and boundary conditions.The boundary conditions typically would 

reflect interactions with ambient air and with other microenvironments. The parameterizations of the 

governing equations generally include the information on attributes of sources and sinks within each 

microenvironment. This type of general definition allows for the concentration within a microenvironment 

to be non-homogeneous (non-uniform), provided its spatial profile and mixing properties can be fully 

predicted or characterized.By adopting this definition, the number of microenvironments used in a study 

is kept manageable, but variability in concentrations in each of the microenvironments can still be taken 

into account. Microenvironments typically used to determine exposure include indoor residential 

microenvironments, other indoor locations (typically occupational microenvironments), outdoors near 

roadways, other outdoor locations, and in-vehicles. Outdoor locations near roadways are segregated from 

other outdoor locations (and can be further classified into street canyons, vicinities of intersections, etc.) 

because emissions from automobiles alter local concentrations significantly compared to background 

outdoor levels.Indoor residential microenvironments (kitchen, bedroom, living room, etc. or aggregate 

home microenvironment) are typically separated from other indoor locations because of the time spent 

there and potential differences between the residential environment and the work/public environment.

Once the actual individual and relevant activities and locations (for Individual Based Modeling), or 

the sample population and associated spatial (geographical) domain (for Population Based Modeling) 

have been defined along with the temporal framework of the analysis (time period and resolution), the 

comprehensive modeling of individual/population exposure to SO2 (and related pollutants) will in general 

require seven steps (or components, as some of them do not have to be performed in sequence) that are 

listed below. This list represents a composite based on approaches and frameworks described in the 

literature over the last twenty-five years (WHO). 2005; EPA, 1992; 1997a; Georgopoulos and Lioy, 1994; 

Georgopoulos et al., 2005; Georgopoulos and Lioy, 2006; Ott, 1982; Price et al., 2003) as well as on the 

structure of various inhalation exposure models that have been used in the past or in current studies to 

specifically assess inhalation exposures. Figure C-1, adapted from Georgopoulos et al. (2005), 

schematically depicts the sequence of steps summarized here. 

1) Estimation of the background or ambient levels of both SO2 and related pollutants. This is 

done through either (or a combination of): 

a) multivariate spatio-temporal analysis of fixed monitor data, or 

b) emissions-based, photochemical, air quality modeling (typically with a regional, gridbased model such as Models-3/CMAQ or CAMX) applied in a coarse resolution mode. 

2) Estimation of local outdoor pollutant levels of both SO2 and related pollutants. These levels 

could typically characterize the ambient air of either an administrative unit (such as a census 

tract, a municipality, a county, etc.) or a conveniently defined grid cell of an urban scale air 

quality model. Again, this may involve either (or a combination of): 

 C-2 
a) spatio-temporal statistical analysis of monitor data, or 

b) application of either an intra-urban scale dispersion model (such as AERMOD or 

CALPUFF) or an urban multi-scale, grid based model (such as CMAQ or CAMX) at its 

highest resolution (typically around 2-4 km), or 

c) correction of the estimates of the regional model using some scheme that adjusts for 

observations and/or for subgrid chemistry and mixing processes. 

3) Characterization of relevant attributes of the individuals or populations under study 

(residence and work locations, occupation, housing data, income, education, age, gender, 

race, weight, and other physiological characteristics). For Population Based Exposure 

Modeling (PBEM) one can either: 

a) select a fixed-size sample population of virtual individuals in a way that statistically 

reproduces essential demographics (age, gender, race, occupation, income, education) of 

the administrative population unit used in the assessment (e.g., a sample of 500 people is 

typically used to represent the demographics of a given census tract, whereas a sample of 

about 10,000 may be needed to represent the demographics of a county), or 

b) divide the population-of interest into a set of cohorts representing selected subpopulations 

where the cohort is defined by characteristics known to influence exposure. 

4) Development of activity event (or exposure event) sequences for each member of the sample 

population (actual or virtual) or for each cohort for the exposure period. This could utilize:

a) study-specific information, if available 

b) existing databases based on composites of questionnaire information from past studies 

c) time-activity databases, typically in a format compatible with EPA’s Consolidated Human 

Activity Database (McCurdy et al., 2000) 

5) Estimation of levels and temporal profiles of both SO2 and related pollutants in various 

outdoor and indoor microenvironments such as street canyons, roadway intersections, parks, 

residences, offices, restaurants, vehicles, etc.This is done through either: 

a) linear regression of available observational data sets, 

b) simple mass balance models (with linear transformation and sinks) over the volume (or a 

portion of the volume) of the microenvironment, 

c) lumped (nonlinear) gas or gas/aerosol chemistry models, or 

d) detailed combined chemistry and Computational Fluid Dynamics modeling. 

6) Calculation of appropriate inhalation rates for the members of the sample population, 

combining the physiological attributes of the (actual or virtual) study subjects and the 

activities pursued during the individual exposure events. 

 C-3 
7) Calculation of target tissue dose through biologically based modeling estimation (specifically, 

respiratory dosimetry modeling in the case of SO2 and related reactive pollutants) if sufficient 

information is available. 

Figure C-1. Schematic description of a general framework identifying the processes (steps or 

components) involved in assessing inhalation exposures and doses for individuals 

and populations. In general terms, existing comprehensive exposure modeling 

systems such as SHEDS, APEX, and MENTOR-1A follow this framework. 

Implementation of the above framework for comprehensive exposure modeling has benefited 

significantly from recent advances and expanded availability of computational technologies such as 

Relational Database Management Systems (RDBMS) and Geographic Information Systems (GIS) 

(Georgopoulos et al., 2005; Purushothaman and Georgopoulos, 1997; 1999b; a). 

In fact, only relatively recently comprehensive, predictive, inhalation exposure modeling studies 

for O3, PM, and various air toxics, have attempted to address/incorporate all the components of the 

general framework described here.In practice, the majority of past exposure modeling studies have either 

incorporated only subsets of these components or treated some of them in a simplified manner, often 

focusing on the importance of specific factors affecting exposure. Of course, depending on the objective 

of a particular modeling study, implementation of only a limited number of steps may be necessary. For 

example, in a regulatory setting, when comparing the relative effectiveness of emission control strategies, 

the focus can be on expected changes in ambient levels (corresponding to those observed at NAAQS 

monitors) in relation to the density of nearby populations. The outdoor levels of pollutants, in conjunction 

with basic demographic information, can thus be used to calculate upper bounds of population exposures 

associated with ambient air (as opposed to total exposures that would include contributions from indoor 

 C-4 
sources) useful in comparing alternative control strategies. Though the metrics derived would not be 

quantitative indicators of actual human exposures, they can serve as surrogates of population exposures 

associated with outdoor air, and thus aid in regulatory decision making concerning pollutant standards and 

in studying the efficacy of emission control strategies. This approach has been used in studies performing 

comparative evaluations of regional and local emissions reduction strategies in the eastern U.S. (Foley et 

al., 2003; Georgopoulos et al., 1997; Purushothaman and Georgopoulos, 1997). 

C.2. Population Exposure Models: Their Evolution and 

Current Status 

Existing comprehensive inhalation exposure models consider the trajectories of individual human 

subjects (actual or virtual), or of appropriately defined cohorts, in space and time as sequences of 

exposure events.In these sequences, each event is defined by time, a geographic location, a 

microenvironment, and the activity of the subject. EPA offices (OAQPS and NERL) have supported the 

most comprehensive efforts in developing models implementing this general concept (see, e.g., Johnson, 

2002). These families of models are the result: National Exposure Model and Probabilistic National 

Exposure Model (NEM/pNEM, Whitfield et al., 1997) Hazardous Air Pollutant Exposure Model 

(HAPEM, Rosenbaum, 2005); Simulation of Human Exposure and Dose System (SHEDS, Burke et al., 

2001); Air Pollutants Exposure Model (APEX, EPA, 2006b); and Modeling Environment for Total Risk 

Studies (MENTOR, Georgopoulos et al., 2005; Georgopoulos and Lioy, 2006).European efforts have 

produced some formulations with similar general attributes as the above U.S. models but, generally, 

involving simplifications in some of their components. Examples of European models addressing 

exposures to photochemical oxidants (specifically, O3) include the Air Pollution Exposure Model 

(AirPEx, Freijer et al., 1998), which basically replicates the pNEM approach and has been applied to the 

Netherlands, and the Air Quality Information System Model (AirQUIS, Clench-Aas et al., 1999). 

The NEM/pNEM, SHEDS, APEX, and MENTOR for One-Atmosphere studies (MENTOR-1A) 

families of models provide exposure estimates defined by concentration and breathing rate for each 

individual exposure event, and then average these estimates over periods typically ranging from one hour 

to one year. These models allow simulation of certain aspects of the variability and uncertainty in the 

principal factors affecting exposure. An alternative approach is taken by the HAPEM family of models 

that typically provide annual avg exposure estimates based on the quantity of time spent per year in each 

combination of geographic locations and microenvironments. The NEM, SHEDS, APEX, and MENTORtype models are therefore expected to be more appropriate for pollutants with complex chemistry such as 

SO2, and could provide useful information for enhancing related health assessments. 

More specifically, regarding the consideration of population demographics and activity patterns: 

 pNEM divides the population of interest into representative cohorts based on the 

combinations of demographic characteristics (age, gender, and employment), home/work 

district, residential cooking fuel and replicate number, and then assigns an activity diary 

record from the CHAD to each cohort according to demographic characteristic, season, 

day-type (weekday/weekend) and temperature. 

 HAPEM6 divides the population of interest into demographic groups based on age, gender 

and race, and then for each demographic group/day-type (weekday/weekend) combination, 

selects multiple activity patterns randomly (with replacement) from CHAD and combines 

them to find the averaged annual time allocations for group members in each census tract 

for different day types. 

 SHEDS, APEX, and MENTOR-1A generate population demographic files, which contain a 

user-defined number of person records for each census tract of the population based on 

proportions of characteristic variables (age, gender, employment, and housing) obtained 

 C-5 
from the population of interest, and then assign a matching activity diary record from 

CHAD to each individual record of the population based on the characteristic variables. 

It should be mentioned that, in the formulations of these models, workers may commute 

from one census tract to another census tract for work. So, with the specification of 

commuting patterns, the variation of exposure concentrations due to commuting between 

different census tracts can be captured. 

The conceptual approach originated by the SHEDS models was modified and expanded for use in 

the development of MENTOR-1A. Flexibility was incorporated into this modeling system, such as the 

option of including detailed indoor chemistry and other relevant microenvironmental processes, and 

providing interactive linking with CHAD for consistent definition of population characteristics and 

activity events (Georgopoulos et al., 2005). 

Table C-1. The Essential Attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A 

pNEM HAPEM APEX SHEDS MENTOR-1A 

Exposure Estimate Hourly averaged Annual averaged Hourly averaged Activity event based Activity event based 

Characterization of the 

High-End Exposures 

Yes No Yes Yes Yes 

Typical Spatial 

Scale/Resolution 

Urban areas/Census 

tract level 

Ranging from urban to 

national/ Census tract 

level 

Urban area/Census tract 

level 

Urban areas/Census 

tract level 

Multiscale/ Census tract 

level 

Temporal 

Scale/Resolution 

Annual / 

one hour 

Annual / 

one hour 

Annual / 

one hour 

Annual / 

event based 

Annual / 

activity event based time 

step 

Population Activity 

Patterns Assembly 

Top-down approach Top-down approach Bottom-up “personoriented” approach Bottom-up “personoriented” approach Bottom-up “personoriented” approach 

Microenvironment 

Concentration Estimation 

Non-steady-state and 

steady-state mass 

balance equations (hardcoded) 

Linear relationship 

method (hard-coded) 

Non-steady-state mass 

balance and linear 

regression (flexibility of 

selecting algorithms) 

Steady-state mass 

balance equation 

(residential) and linear 

regression (nonresidential) (hard-coded) 

Non-steady-state mass 

balance equation with 

indoor air chemistry 

module or regression 

methods (flexibility of 

selecting algorithms) 

Microenvironmental (ME) 

Factors 

Random samples from 

probability distributions 

Random samples from 

probability distributions 

Random samples from 

probability distributions 

Random samples from 

probability distributions 

Random samples from 

probability distributions 

Specification of Indoor 

Source Emissions 

Yes (gas-stove, tobacco 

smoking) 

Available; set to zero in 

HAPEM6 

Yes (multiple sources 

defined by the user) 

Yes (gas-stove, tobacco 

smoking, other sources) 

Yes (multiple sources 

defined by the user) 

Commuting Patterns Yes Yes Yes Yes Yes 

Exposure Routes Inhalation Inhalation Inhalation Inhalation Multiple (optional) 

Potential Dose 

Calculation 

Yes No Yes Yes Yes 

Physiologically Based 

Dose 

No No No Yes Yes 

Variability/Uncertainty Yes No Yes Yes Yes (Various “Tools”) 

The essential attributes of the pNEM, HAPEM, APEX, SHEDS, and MENTOR-1A models are 

elaborated in Table C-1. 

 C-6 
NEM/pNEM implementations have been extensively applied to O3 studies in the 1980s and 1990s.

The historical evolution of the pNEM family of models of OAQPS started with the introduction of the 

first NEM model in the 1980s (Biller et al., 1981).The first such implementations of pNEM/O3 in the 

1980s used a regression-based relationship to estimate indoor O3 concentrations from outdoor 

concentrations. The second generation of pNEM/O3 was developed in 1992 and included a simple mass 

balance model to estimate indoor O3 concentrations. A report by Johnson (2002) describes this version of 

pNEM/O3 and summarizes the results of an initial application of the model to 10 cities. Subsequent 

enhancements to pNEM/O3 and its input databases included revisions to the methods used to estimate 

equivalent ventilation rates, to determine commuting patterns, and to adjust ambient O3 levels to simulate 

attainment of proposed NAAQS. During the mid-1990s, the EPA applied updated versions of pNEM/O3 to 

three different population groups in selected cities:(1) the general population of urban residents, (2) 

outdoor workers, and (3) children who tend to spend more time outdoors than the average child. This 

version of pNEM/O3 used a revised probabilistic mass balance model to determine O3 concentrations over 

one-h periods in indoor and in-vehicle microenvironments (Johnson, 2002).

In recent years, pNEM has been replaced by (or “evolved to”) the APEX. APEX differs from 

earlier pNEM models in that the probabilistic features of the model are incorporated into a Monte Carlo 

framework (U.S. EPA, 2006; Langstaff, 2007). Like SHEDS and MENTOR-1A, instead of dividing the 

population-of-interest into a set of cohorts, APEX generates individuals as if they were being randomly 

sampled from the population. APEX provides each generated individual with a demographic profile that 

specifies values for all parameters required by the model. The values are selected from distributions and 

databases that are specific to the age, gender, and other specifications stated in the demographic profile.

The EPA has applied APEX to the study of exposures to O3 and other criteria pollutants; APEX can be 

modified and used for the estimation of SO2 exposures, if required. 

Reconfiguration of APEX for use with SO2 or other pollutants would require significant literature 

review, data analysis, and modeling efforts. Necessary steps include determining spatial scope and 

resolution of the model; generating input files for activity data, air quality and temperature data; and 

developing definitions for microenvironments and pollutant-microenvironment modeling parameters 

(penetration and proximity factors, indoor source emissions rates, decay rates, etc.) (ICF Consulting, 

2005). To take full advantage of the probabilistic capabilities of APEX, distributions of model input 

parameters should be used wherever possible. 

C.3. Ambient Concentrations of SO2 and Related 

Air Pollutants 

As mentioned earlier, background and regional outdoor concentrations of pollutants over a study 

domain may be estimated through emissions-based mechanistic modeling, through ambient data based 

modeling, or through a combination of both. Emissions-based models calculate the spatio-temporal fields 

of the pollutant concentrations using precursor emissions and meteorological conditions as inputs and 

using numerical representations of transformation reactions to drive outputs. The ambient data based 

models typically calculate spatial or spatio-temporal distributions of the pollutant through the use of 

interpolation schemes, based on either deterministic or stochastic models for allocating monitor station 

observations to the nodes of a virtual regular grid covering the region of interest. The geostatistical 

technique of kriging provides various standard procedures for generating an interpolated spatial 

distribution for a given time, from data at a set of discrete points. Kriging approaches were evaluated by 

Georgopoulos et al. (1997) in relation to the calculation of local ambient O3 concentrations for exposure 

assessment purposes, using either monitor observations or regional/urban photochemical model outputs.It 

was found that kriging is severely limited by the nonstationary character of the concentration patterns of 

reactive pollutants; so the advantages of this method in other fields of geophysics do not apply here. The 

 C-7 
above study showed that the appropriate semivariograms had to be hour-specific, complicating the 

automated reapplication of any purely spatial interpolation over an extended time period. 

Spatio-temporal distributions of pollutant concentrations such as O3, PM, and various air toxics 

have alternatively been obtained using methods of the Spatio-Temporal Random Field (STRF) theory 

(Christakos and Hristopulos, 1998). The STRF approach interpolates monitor data in both space and time 

simultaneously. This method can thus analyze information on temporal trends which cannot be 

incorporated directly in purely spatial interpolation methods such as standard kriging. Furthermore, the 

STRF method can optimize the use of data which are not uniformly sampled in either space or time. STRF 

was further extended within the Bayesian Maximum Entropy (BME) framework and applied to O3

interpolation studies (Christakos and Hristopulos, 1998; Christakos and Kolovos, 1999; Christakos, 

2000).It should be noted that these studies formulate an over-arching scheme for linking air quality with 

population dose and health effects; however, they are limited by the fact that they do not include any 

microenvironmental effects. MENTOR has incorporated STRF/BME methods as one of the steps for 

performing a comprehensive analysis of exposure to O3 and PM (Georgopoulos et al., 2005). 

The issue of subgrid variability (SGV) from the perspective of interpreting and evaluating the 

outcomes of grid-based, multiscale, photochemical air quality simulation models is discussed in Ching et 

al. (2006), who suggest a framework that can provide for qualitative judgments on model performance 

based on comparing observations to the grid predictions and its SGV distribution. From the perspective of 

Population Exposure Modeling, the most feasible/practical approach for treating subgrid variability of 

local concentrations is probably through 1)the identification and proper characterization of an adequate 

number of outdoor microenvironments (potentially related to different types of land use within the urban 

area as well as to proximity to different types of roadways) and 2)then, concentrations in these 

microenvironments will have to be adjusted from the corresponding local background ambient 

concentrations through either regression of empirical data or various types of local atmospheric 

dispersion/transformation models. This is discussed further in the next section. 

C.4. Characterization of Microenvironmental 

Concentrations 

Once the background and local ambient spatio-temporal concentration patterns have been derived, 

microenvironments that can represent either outdoor or indoor settings when individuals come in contact 

with the contaminant of concern (e.g., SO2) must be characterized. This process can involve modeling of 

various local sources and sinks, and interrelationships between ambient and microenvironmental 

concentration levels. Three general approaches have been used in the past to model microenvironmental 

concentrations: 

 Empirical (typically linear regression) fitting of data from studies relating ambient/local 

and microenvironmental concentration levels to develop analytical relationships. 

 Parameterized mass balance modeling over, or within, the volume of the 

microenvironment. This type of modeling has ranged from very simple formulations, i.e. 

from models assuming ideal (homogeneous) mixing within the microenvironment (or 

specified portions of it) and only linear physicochemical transformations (including sources 

and sinks), to models incorporating analytical solutions of idealized dispersion 

formulations (such as Gaussian plumes), to models that take into account aspects of 

complex multiphase chemical and physical interactions and nonidealities in mixing. 

 Detailed Computational Fluid Dynamics (CFD) modeling of the outdoor or indoor 

microenvironment, employing either a Direct Numerical Simulation (DNS) approach, a 

Reynolds Averaged Numerical Simulation (RANS) approach, or a Large Eddy Simulation 

 C-8 
(LES) approach, the latter typically for outdoor situations (see, e.g., Chang and Meroney, 

2003; Chang, 2006; Milner et al., 2005). 

Parameterized mass balance modeling is the approach currently preferred for exposure modeling 

for populations. As discussed earlier, the simplest microenvironmental setting corresponds to a 

homogeneously mixed compartment, in contact with possibly both outdoor/local environments as well as 

other microenvironments. The air quality of this idealized microenvironment is affected mainly by the 

following processes: 

 Transport processes: These can include advection/convection and dispersion that are 

affected by local processes and obstacles such as vehicle induced turbulence, street 

canyons, building structures, etc. 

 Sources and sinks: These can include local outdoor emissions, indoor emissions, surface 

deposition, etc. 

 Transformation processes: These can include local outdoor as well as indoor gas and 

aerosol phase chemistry, such as formation of secondary organic and inorganic aerosols. 

Exposure modeling also requires information on activity patterns to determine time spent in various 

microenvironments and estimates of inhalation rates to characterize dose.The next two subsections 

describe recent work done in these areas. 

C.4.1. Characterization of Activity Events 

An important development in inhalation exposure modeling has been the consolidation of existing 

information on activity event sequences in the Consolidated Human Activity Database (CHAD) 

(McCurdy et al., 2000; McCurdy, 2000).Indeed, most recent exposure models are designed (or have been 

re-designed) to obtain such information from CHAD which incorporates 24-h time/activity data 

developed from numerous surveys. The surveys include probability-based recall studies conducted by 

EPA and the California Air Resources Board, as well as real-time diary studies conducted in individual 

U.S. metropolitan areas using both probability-based and volunteer subject panels. All ages of both 

genders are represented in CHAD. The data for each subject consist of one or more days of sequential 

activities, in which each activity is defined by start time, duration, activity type (140 categories), and 

microenvironment classification (110 categories). Activities vary from one min to one h in duration, with 

longer activities being subdivided into clock-hour durations to facilitate exposure modeling. A distribution 

of values for the ratio of oxygen uptake rate to body mass (referred to as metabolic equivalents or METs) 

is provided for each activity type listed in CHAD.The forms and parameters of these distributions were 

determined through an extensive review of the exercise and nutrition literature. The primary source of 

distributional data was Ainsworth et al. (1993), a compendium developed specifically to facilitate the 

coding of physical activities and to promote comparability across studies.

C.4.2. Characterization of Inhalation Intake and Uptake 

Use of the information in CHAD provides a rational way for incorporating realistic intakes into 

exposure models by linking inhalation rates to activity information. As mentioned earlier, each cohort of 

the pNEM-type models, or each (virtual or actual) individual of the SHEDS, MENTOR, APEX, and 

HAPEM models, is assigned an exposure event sequence derived from activity diary data. Each exposure 

event is typically defined by a start time, a duration, assignments to a geographic location and 

microenvironment, and an indication of activity level.The most recent versions of the above models have 

defined activity levels using the activity classification coding scheme incorporated into CHAD. A 

probabilistic module within these models converts the activity classification code of each exposure event 

to an energy expenditure rate, which in turn is converted into an estimate of oxygen uptake rate. The 

 C-9 
oxygen uptake rate is then converted into an estimate of total ventilation rate (V ̇ E) expressed in 

Liters/min. Johnson (2001) reviewed briefly the physiological principles incorporated into the algorithms 

used in pNEM to convert each activity classification code to an oxygen uptake rate and describes the 

additional steps required to convert oxygen uptake to (V ̇ E). 

McCurdy (1997; 2000) has recommended that the ventilation rate should be estimated as a function 

of energy expenditure rate. The energy expended by an individual during a particular activity can be 

expressed as EE = (MET)(RMR) in which EE is the average energy expenditure rate (kcal/min) during 

the activity and RMR is the resting metabolic rate of the individual expressed in terms of number of 

energy units expended per unit of time (kcal/min). MET (the metabolic equivalent of tasks) is a ratio 

specific to the activity and is dimensionless.If RMR is specified for an individual, then the above 

equation requires only an activity-specific estimate of MET to produce an estimate of the energy 

expenditure rate for a given activity. McCurdy et al. (2000) developed distributions of MET for the 

activity classifications appearing in the CHAD database. 

An issue that should be mentioned in closing is that of evaluating comprehensive prognostic 

exposure modeling studies, for either individuals or populations, with field data. Although databases that 

would be adequate for performing a comprehensive evaluation are not expected to be available any time 

soon, there have been a number of studies, reviewed in earlier sections of this chapter, which can be used 

to start building the necessary information base. Some of these studies report field observations of 

personal, indoor, and outdoor levels and have also developed simple semi-empirical personal exposure 

models that were parameterized using the observational data and regression techniques. 

In conclusion, though existing inhalation exposure modeling systems have evolved considerably in 

recent years, limitations of available modeling methods and data in relation to potential SO2 studies 

should be taken into account. Existing prognostic modeling systems for inhalation exposure can in 

principle be directly applied to, or adapted for, SO2 studies; APEX, SHEDS, and MENTOR-1A are 

candidates. However, such applications would be constrained by data limitations such as ambient 

characterization at the local scale and by lack of quantitative information for indoor sources and sinks. 

 C-10 
Annex D. Controlled Human Exposure 

This Annex summarizes the findings of human clinical studies that have been published since the 

previous review. Descriptions of older studies were presented in the 1994 Supplement to the Second 

Addendum to the 1982 AQCD for Sulfur Oxides (U.S. EPA, 1994a), and are not described in great detail 

in this document. 

Table D-1. Effects of medications on SO2-induced changes in lung function among human 

subjects. 

Study Concentration Duration Subjects Exposure Status Effects 

Bigby and 

Boushey 

(1993) 

0.25 – 8.0 ppm 4 min 10 asthmatics Increasing concentrations of SO2 during voluntary 

eucapnic hyperpnea (20 L/min) preceded by 

administration of the anti-inflammatory agent 

nedocromil sodium (baseline, placebo, 2 mg, 4 

mg, 8 mg). 

Treatment with nedocromil sodium significantly 

increased the concentration of SO2 required to 

produce an 8 unit increase in sRaw. Increasing the 

dose of nedocromil sodium from 2 mg to 8 mg did 

not significantly affect the response. 

Lazarus 

et al. 

(1997) 

0.25 – 8.0 ppm 4 min 12 asthmatics Subjects exposed via mouthpiece to filtered air 

and increasing concentrations of SO2 during 

eucapnic hyperventilation (20 L/min). Exposures 

occurred following pretreatment with a 

leukotriene receptor antagonist (zafirlukast 20 

mg) or placebo. 

Compared with placebo, zafirlukast significantly 

increased the SO2 concentration required to 

produce an 8 unit increase in sRaw. This effect was 

observed with challenges occurring both at 2 and 

10 h following treatment. 

Field et al. 

(1996) 

0.25 – 8.0 ppm 3 min 31 asthmatics Increasing concentrations of SO2 (including clean 

air exposure) in an exposure chamber during 

voluntary eucapnic hyperpnea (35 L/min) 

preceded by administration of placebo, the 

anticholinergic bronchodilator ipratropium 

bromide (15 subjects), morphine (opioid agonist) 

(15 subjects), or the anti-inflammatory agent 

indomethacin (16 subjects). 

Both ipratropium bromide and morphine reduced 

the responsiveness to SO2, significantly increasing 

the SO2 concentration required to reduce specific 

airway conductance by 35%. Similarly, 

indomethacin was observed to attenuate airway 

responsiveness to SO2, however, this effect was 

smaller than what was observed with either 

ipratropium bromide or morphine. 

Gong 

et al. 

(1996) 

0.75 ppm 10 min 10 asthmatics Subjects exposed to SO2 or clean air in a 

chamber while performing light exercise (29 

L/min) at 1, 12, 18, and 24 h after pretreatment 

with salmeterol xinafoate (β2-adrenergic agonist) 

or placebo (each subject exposed 4 times). 

Observed a significant protective effect of 

salmeterol xinafoate at 1 and 12-h post-dosing. 

Following exercise/SO2 exposure at 1, 12, 18, and 

24 h, FEV1 decreased (versus preexposure) by 7, 

12, 25, and 26%, respectively. Exercise with SO2

resulted in an approximate 26% decrease in FEV1

at all time points with placebo. 

Gong 

et al. 

(2001) 

0.75 ppm 10 min 11 asthmatics Exposure to SO2 or clean air following 3 days of 

treatment with montelukast (leukotriene receptor 

antagonist) or placebo (each subject exposed 4 

times). Exposures conducted in an exposure 

chamber during moderate levels of exercise (35 

L/min). 

Reported a statistically significant SO2-induced 

increase in eosinophil count in induced sputum. 

Measures of lung function (FEV1 and sRaw), as 

well as respiratory symptoms and eosinophil count 

all showed significant improvement after 

pretreatment with montelukast. 

 

 D-1 
Table D-2. Summary of new studies of controlled human exposure to SO2.

Study Concentration Duration Subjects Exposure Status Effects 

Trenga 

et al. 

(2001) 

0.1, 0.25 ppm 10 min 17 asthmatics SO2-sensitive asthmatics exposed to 

SO2 via mouthpiece while performing 

mild to moderate levels of exercise. 

Exposures preceded by 45 min exposures to filtered air or O3 (0.12 ppm), 

with or without pretreatment with 

dietary antioxidants. 

Exposure to O3 slightly increased bronchial 

responsiveness to SO2 as measured by FEV1 and peak 

expiratory flow. Pretreatment with dietary antioxidants 

was shown to have a protective effect on respiratory response, particularly among individuals with greater 

sensitivity to SO2. 

Devalia 

et al. 

(1994) 

0.2 ppm 6 h 10 asthmatics Exposures to filtered air, as well as 

0.2 ppm SO2 and 0.4 ppm NO2, 

conducted separately and in 

combination in an exposure chamber 

(subjects at rest). All subjects 

sensitive to inhaled house dust mite 

antigen. 

Neither SO2 nor NO2, alone or in combination, significantly affected FEV1. The combination of SO2 and NO2

significantly reduced the amount of inhaled allergen 

(60.5% change, p = 0.015) required to produce a 20% 

decrease in FEV1 (PD20FEV1). Both SO2 and NO2 alone 

reduced PD20FEV1, but this reduction was not statistically significant (32.2% (p = 0.506), and 41.2% 

(p = 0.125), respectively). 

Rusznak 

et al. 

(1996) 

0.2 ppm 6 h 13 asthmatics Exposures to filtered air and a combination of 0.2 ppm SO2 and 0.4 ppm 

NO2 in an exposure chamber (subjects at rest). All subjects sensitive to 

inhaled house dust mite antigen. 

Confirmed findings of Devalia et al. and further observed 

that the combination of SO2 and NO2 enhanced airway 

responsiveness to an inhaled allergen up to 48 h postexposure (maximal response at 24 h). 

Tunnicliffe 

et al. 

(2001) 

0.2 ppm 1 h 12 healthy adults, 

12 asthmatics 

Exposures (head dome) at rest to 

filtered air and 0.2 ppm SO2. 

Among healthy subjects, an SO2-induced increase in 

heart rate variability (total power) was observed, while a 

reduction in heart rate variability with SO2 versus air was 

observed in asthmatics. 

Tunnicliffe 

et al. 

(2003) 

0.2 ppm 1 h 12 healthy adults, 

12 asthmatics 

Exposures (head dome) at rest to 

filtered air and 0.2 ppm SO2. 

Exposures to SO2 at 0.2 ppm did not have a significant 

effect on lung function, respiratory symptoms, markers of 

inflammation, or antioxidant levels in healthy adults or 

mild asthmatics. 

Routledge 

et al. 

(2006) 

0.2 ppm 1 h 20 older adults with 

coronary artery 

disease (age 52-74), 

20 healthy older 

adults (age 56-75) 

Exposures (head dome) at rest to 

filtered air, as well as 0.2 ppm SO2

and ultra-fine carbon particles (50 

μg/m3), separately and in 

combination. 

In healthy subjects, exposure to SO2 alone significantly 

decreased heart rate variability 4 h post-exposure 

compared to clean air. No effect was observed in 

subjects with coronary artery disease. The combination 

of SO2 and carbon particles did not affect heart rate 

variability in either group. SO2 was not observed to affect 

markers of inflammation or coagulation. 

Nowak 

et al. 

(1997) 

0.25 – 2.0 ppm 3 min 786 adults Mouthpiece exposures to filtered air 

and increasing concentrations of SO2

during eucapnic hyperventilation (40 

L/min). 

Among individuals who were not hyperresponsive to 

methacholine, less than 1% were found to be hyperresponsive to SO2. However, more than 22% of the individuals who were hyperresponsive to methacholine were 

also hyperresponsive to SO2. Individuals were considered hyperresponsive to SO2 when exposure resulted in 

a 20% or greater decrease in FEV1 versus baseline. 

Trenga 

et al. 

(1999) 

0.5 ppm 10 min 47 asthmatics Subjects exposed to SO2 via 

mouthpeice while performing light to 

moderate levels of exercise. 

An SO2-induced decrease in FEV1 of at least 8% was 

observed in 53% of the subjects (range 8-44%). 

Increases in respiratory symptoms were significantly 

associated with decreases in FEV1. Among SO2-sensitive 

subjects, severity of asthma (as defined by medication 

use) was not a significant predictor of the level of 

response. It is not clear whether the response was 

adjusted for the effects of exercise in clean air. 

Winterton 

et al. 

(2001) 

0.5 ppm 10 min 62 asthmatics Subjects exposed to SO2 via 

mouthpiece while performing light to 

moderate levels of exercise. 

Subjects who experienced at least at 12% decrease in 

FEV1 following exposure were considered to be sensitive 

to SO2. Out of 58 subjects who were genotyped for the 

polymorphism at position -308 in the promoter region of 

TNF-α, 21% (N: 12) were sensitive to SO2. Sensitivity to 

SO2 was found to be associated with the homozygous 

wild type allele (GG) (12 of 12 responders vs. 28 of 46 

subjects who were not responsive to SO2). 

Gong et al. 

(1995) 

0.5, 1.0 ppm 10 min 14 asthmatics Exposure to SO2 and filtered air were 

conducted in an exposure chamber 

during low, moderate, and heavy 

levels of exercise (target ventilation 

ranges of 20-29, 30-39, and 40-49 

L/min). 

For the average individual, increasing SO2 concentration 

resulted in a significant decrement in lung function (decrease in FEV1 and increase in sRaw) as well as a significant increase in respiratory symptoms. Increasing SO2

conc. had a greater effect on lung function and 

respiratory symptoms than increasing level of exercise. 

 D-2 
Annex E. Toxicological Studies 

This Annex summarizes the findings of animal toxicology studies that have been published since 

the previous review. Descriptions of older studies were presented in the 1982 AQCD for Sulfur Oxides 

(U.S. EPA, 1982), and are not described in great detail in this document. 

Table E-1. Respiratory System – Effects of SO2 on lung function. 

Study Concentration Duration Species Effects 

ACUTE AND SUBACUTE 

Lewis and 

Kirchner 

(1984) 

10 or 30 ppm 

(26.2 or 78.6 

mg/m3); 

intratracheal 

5 min Mongrel dog; male and female; age and weight NR; 

N = 5-15 /group 

Initial transient bronchoconstriction approximately 10 min in duration 

followed by a gradual change in pulmonary mechanics (43% increase 

in airway resistance and 30% decrease in dynamic compliance) 4 hrs 

following 30 ppm but not 10 ppm SO2. 

Barthélemy 

et al. (1988) 

0.5 or 5 ppm 

(1.3 or 13.1 

mg/m3); 

intratracheal 

45 min Rabbit; sex NR; adult; 

mean 2.0 kg; N = 5-9/ 

group; rabbits were 

mechanically ventilated 

Lung resistance increased by 16% and 50% in response to 0.5 and 

5 ppm SO2, respectively. Bivagotomy had no effect on 5 ppm SO2-

induced increases in lung resistance. Reflex bronchoconstrictive 

response to phenyldiguanide (intravenously administered) was eliminated by exposure to SO2 but SO2 had no effect on lung resistance 

induced by intravenously- administered histamine. Authors concluded 

that (1) vagal reflex is not responsible for SO2-induced increase in lung 

resistance at 45 min; (2) transient alteration in tracheobronchial wall 

following SO2 exposure may have reduced accessibility of airway 

nervous receptors to phenyldiguanide. 

Amdur et al. 

(1983) 

~1 ppm 

(2.62 mg/m3); head 

only 

1 h Hartley guinea pig; male; 

age NR; 200-300 g; N = 

8-23/group 

An 11% increase in pulmonary resistance and 12% decrease in 

dynamic compliance were observed. Neither effect persisted into the 1 

h period following exposure. No effects were observed for breathing 

frequency, tidal volume, or min volume. 

Conner et 

al. (1985) 

1 ppm (2.62 

mg/m3); nose only 

3 h/day for 6 days; 

animals evaluated up to 

48 h post-exposure 

Hartley guinea pig; male; 

age NR; 250-320 g; N = 

≤ 18 group/time point 

No effect was observed on residual volume, functional reserve 

capacity, vital capacity, total lung capacity, respiratory frequency, tidal 

volume, pulmonary resistance, pulmonary compliance, diffusing 

capacity for CO or alveolar volume at 1 or 48 h after last exposure. 

SUBCHRONIC AND CHRONIC 

Douglas et 

al. (1994) 

5 ppm 

(13.1 mg/m3); 

whole body 

2 h/day for 13 wks from 

birth 

New Zealand White rabbit; 

male and female; 1 day 

old; weight NR; N = 3-4/ 

group; immunized against 

Alternaria tenuis 

No effects on lung resistance, dynamic compliance, transpulmonary 

pressure, tidal volume, respiration rate, or min volume. 

Scanlon 

et al. (1987) 

15 or 50 ppm 

(39.3 or 

131 mg/m3); 

intratracheal exposure 

2 h/day, 4 or 5 days/wk, for 

5 mos (low dose group) or 

10-11 mos (high dose 

group); authors stated that 

physiological changes 

were observed within 5 

mos; 7-9 mo recovery 

period 

Mongrel dog; adult; sex 

NR; 10-20 kg; 

N = 3-4/group (3 hyperresponsive, 3 hyporesponsive, and 1 avg 

responsive) 

At 15 ppm, there was no clinical evidence of bronchitis; pulmonary 

resistance increased by 35-38% in 2 of 3 dogs, and dynamic lung 

compliance decreased in 1 of 3 dogs, but physiological changes were 

not significant for the group as a whole. At 50 ppm, cough and mucus 

hypersecretion were observed; symptoms ceased during the recovery 

period. Pulmonary resistance increased by 56% during the treatment 

period and an additional 28% during the recovery period for a total 

increase of 99%; dynamic lung compliance decreased in 2 of 4 dogs 

and increased in 1 of 4 dogs during treatment but there were no 

significant changes in the group as a whole. Authors considered 

15 ppm to be the lower limit of exposure that failed to produce 

physiological changes. 

Smith et al. 

(1989) 

1 ppm 

(2.62 mg/m3); 

whole body 

5 h/day, 5 days/wk for 4 

mos 

Sprague-Dawley rat; 

male; young adult; initial 

weight NR; 

N = 12-15/data point 

Physiological tests were conducted in anesthetized animals both 

during spontaneous breathing and during paralysis. SO2 exposure 

resulted in an 11% decrease in residual volume and reduced 

quasistatic compliance in paralyzed animals. Authors noted that 

because residual volume was decreased only in paralyzed rats and 

the magnitude of effect was very small, it may have been due to 

chance. Quasistatic compliance values were observed to be very high 

in controls, and may have accounted for effect in the treatment group. 

 E-3 
Table E-2. Respiratory System – Inflammatory responses following SO2 exposure. 

Study Concentration Duration Species Effects 

ACUTE/SUBACUTE/SUBCHRONIC 

Clarke et al. 

(2000) 

10 ppm 

(26.2 mg/m3); nose 

only 

4 h Outbred Swiss mouse; 

female; age, weight 

NR; 

N = 10/experimental 

value 

No evidence was seen of inflammatory response in terms of total cell number, 

lymphocyte/polymorphonuclear leukocytes differentials, or total protein level 

taken from BAL fluid. 

Meng et al. 

(2005a) 

5.35, 10.7, or 

21.4 ppm (14, 28, or 

56 mg/m3); whole 

body 

4 h/day for 7 days Kunming albino 

mouse; male; age NR; 

18-22 g; 

N = 10/group 

In lung tissue, in vivo SO2 exposure (low, mid concentrations) significantly 

elevated levels of the pro-inflammatory cytokines interleukin-6 and tumor 

necrosis factor-α, but did not affect levels of the anti-inflammatory cytokine 

transforming growth factor-β1. In serum, the only effect observed was a 

low-dose elevation of tumor necrosis factor-α. 

LangleyEvans et al. 

(1996) 

5, 50, or 100 ppm 

(13.1, 131, or 

262 mg/m3); whole 

body 

5 h/day for 7-28 

days 

Wistar rat; male; 7 wks 

old; weight NR; 

N = 4-5/treatment 

group, 8 controls 

No lung injury was observed and evidence of inflammatory response was only 

observed in the 100 ppm group. A 4-fold increase in BAL fluid leukocyte 

numbers was observed in the 100 ppm group at day 14; the increase lessened 

at days 21 and 28 but remained higher than controls. The number of 

macrophages in BAL fluid was increased at day 28 in the 100 ppm group. 

Neutrophil numbers were 120 times higher than controls at day 14 in the 

100 ppm group but returned to normal by day 21. Blood neutrophils were 

depleted in rats exposed to 50 ppm on days 7-21 but were increased in rats 

exposed to 5 ppm (significant) and 100 ppm (non-significant) at day 14. Lung 

epithelial permeability was not affected. 

Conner et 

al. (1989) 

1 ppm (2.62 mg/m3); 

nose only 

3 h/day for 1-5 

days; bronchoalveolar lavage performed each day 

Hartley guinea pig; 

male; age NR; 

250-320 g; N = 4 

No change in numbers of total cells and neutrophils, protein levels or enzyme 

activity in lavage fluid following SO2 exposure. 

Park et al. 

(2001a) 

0.1 ppm 

(0.26 mg/m3); whole 

body; with and 

without exposure to 

ovalbumin 

5 h/day for 5 days Dunkin-Hartley guinea 

pig; male; age NR; 

250-350 g; N = 

7-12/group 

After bronchial challenge, the ovalbumin/SO2-exposed group had significantly 

increased eosinophil counts in BAL fluid compared with all other groups, 

including the SO2 group. The bronchial and lung tissue of this group showed 

infiltration of inflammatory cells, bronchiolar epithelial damage, and mucus and 

cell plug in the lumen. 

Li et al. 

(2007) 

2 ppm (5.24 mg/m3) 

with and without 

exposure to 

ovalbumin 

1 h/day for 7 days Wistar rat; male; age 

NR 

Increased number of inflammatory cells in BAL fluid, increased levels of 

MUC5AC and ICAM-1 and an enhanced histopathological response compared 

with those treated with ovalbumin or SO2 alone. 

 

 E-4 
Table E-3. Respiratory System – Effects of SO2 exposure on airway responsiveness and allergic 

sensitization. 

Study Concentration Duration Species Effects 

ANTIGEN SENSITIZATION / ALLERGIC REACTIONS – ACUTE / SUBACUTE 

Abraham 

et al. (1981) 

5 ppm (13.1 mg/m3); 

head only 

4 h Sheep; sex and age 

NR; mean weight 38 

± 7 kg; N = 7/group 

Acute exposure to 5 ppm SO2 did not produce significant airway changes 

(pulmonary resistance, static compliance, dynamic compliance, tidal volume, 

breathing frequency) in either normal or allergic (sensitized to Ascaris suum 

antigen) sheep, nor increase airway reactivity (measured as pulmonary 

resistance increase after aerosolized carbachol provocation) in normal sheep. 

However, 5 ppm SO2 did significantly increased airway reactivity in allergic 

sheep, which have antigen-induced airway responses similar to humans with 

allergic airway disease; may model airway responses to SO2 in a sensitive 

human subpopulation. 

Park et al. 

(2001a) 

0.1 ppm (0.26 mg/m3); 

whole body; with and 

without exposure to 

ovalbumin 

5 h/day for 5 days Dunkin-Hartley 

guinea pig; male; age 

NR; 250-350 g; N = 

7-12/group 

After bronchial challenge, the ovalbumin/SO2-exposed group had significantly 

increased enhanced pause (an indicator of airway obstruction) compared with 

all other groups, including the SO2 group. Authors concluded that low level 

SO2 may enhance the development of ovalbumin-induced asthmatic reactions 

in guinea pigs. 

Riedel et al. 

(1988) 

0.1, 4.3, or 16.6 ppm 

(0, 0.26, 11.3, or 

43.5 mg/m3); whole 

body; animals were 

sensitized to ovalbumin on the last 3 days 

of exposure. 

8 h/day for 5 days Perlbright-White 

Guinea pig; female; 

age NR; 300-350 g; N 

= 5 or 6/group 

(14 controls) 

Bronchial provocation with ovalbumin was conducted every other day for 2 

wks, starting at 1 wk after last exposure. Numbers of animals displaying 

symptoms of bronchial obstruction after ovalbumin provocation increased in 

all SO2 groups compared to air-exposed groups. Anti-ovalbumin antibodies 

(IgG total and IgG1) were increased in BAL fluid and serum of SO2-exposed 

animals compared to air-exposed controls; statistical significance attained for 

IgG total in BAL fluid at ≥ 4.3 ppm SO2 and in serum at all SO2 concentrations. Results indicate that subacute exposure to even low concentrations 

of SO2 can potentiate allergic sensitization of the airway. 

ANTIGEN SENSITIZATION / ALLERGIC REACTIONS – SUBCHRONIC 

Kitabatake 

et al. (1992; 

1995) 

5 ppm (13.1 mg/m3); 

whole body; sensitized 

with Candida albicans 

on day 1 and wk 4 

4 h/day, 5 days/wk, 

6 wks 

Hartley guinea pig; 

male; age NR; 

~200 g; 

N = 12/group 

Respiratory challenge to Candida albicans was conducted 2 wks after last 

exposure. At 15 h after challenge an increased number of SO2-exposed 

animals displayed prolonged expiration, inspiration, or both. Authors 

concluded that SO2 exposure increased dyspneic symptoms. 

GENERAL BRONCHIAL REACTIVITY STUDIES – ACUTE 

Amdur et al. 

(1988) 

1 ppm 1 h Guinea pig; N=8 Airway responsiveness to acetylcholine was measured 2 h following SO2

exposure. No changes were observed. 

Douglas 

et al. (1994) 

5 ppm (13.1 mg/m3); 

whole body 

2 h New Zealand White 

rabbit; sex NR; apparently 3 mos old; 

2.2-3.1 kg; N=6/group

No effect on airway responsiveness to inhaled histamine, as measured by 

provocation concentrations of histamine required to increase pulmonary 

resistance by 50% and decrease dynamic compliance by 35%. 

Lewis and 

Kirchner 

(1984) 

10 or 30 ppm (26.2 or 

78.6 mg/m3); intratracheal 

5 min; a second 

exposure was 

conducted 20 days 

later, after 

exposure to the 

antiallergic drug 

Mongrel dog; male 

and female; age and 

weight NR; 

N = 5-15/group 

No effect was observed at 10 ppm. At 30 ppm hyperresponsiveness and 

hypersensitivity to aerosolized methacholine and 5-hydroxytryptamine was 

observed for up to 24 h following exposure. Twenty days later, pretreatment 

with aerosolized 4% Wy-41,195 or disodium cromoglycate (antiallergic drugs) 

at high doses lessened the methacholine-induced hypersensitivity observed 

after exposure to 30 ppm SO2. Calculations used to determine hyperresponsive and hyperreactivity were not clear. 

GENERAL BRONCHIAL REACTIVITY STUDIES – CHRONIC 

Scanlon 

et al. (1987) 

15 or 50 ppm (39.3 or 

131 mg/m3); intratracheal 

2 h/day, 4 or 5 

days/wk for 5 mos 

(low dose group) 

or 10-11 mos (high 

dose group); physiological changes 

observed within 5 

mos; 7-9 mo 

recovery period. 

Mongrel dog; adult; 

sex NR; 10-20 kg; 

N = 3-4/ group 

(3 hyperresponsive, 

3 hyporesponsive, 

and 1 avg responsive) 

Bronchial reactivity in response to inhaled histamine or methacholine was not 

affected in either treatment group, as determined by the concentration of 

histamine or methacholine required to double pulmonary resistance or the 

concentrations required to decrease dynamic compliance by 65% (ED65). 

 E-5 
Table E-4. Respiratory System – Effects of SO2 layered on metallic or carbonaceous particles. 

Study SO2 Metal or Carbon Effects 

Lam et al. (1982) 

Hartley guinea pig; 

male; age 

NR;240-300 g; 

N = 7-16/group 

Exposure: 3 h 

 

0 or ~1 ppm 

(2.6 mg/m3); whole 

body 

Zinc oxide: 

0.8, 2.7, 6.0, or 7.8 mg/m3

(0.05 micron projected area 

diameter, GSD 2.0) (sulfate, 

sulfite, and sulfur trioxide 

detected) 

Vital capacity: No effect with exposure to 7.8 mg/m3 zinc oxide alone and 2.7 mg/m3 zinc 

oxide in combination with SO2, but decreased with exposure to 0.8 and 6.0 mg/m3 zinc 

oxide in combination with SO2. 

Total lung capacity: No effect with exposure to 7.8 mg/m3 zinc oxide alone, but 

decreased with exposure to 6.0 mg/m3 zinc oxide in combination with SO2. 

Diffusion capacity for CO and ratio of diffusion capacity for CO to total lung capacity or 

alveolar volume: No effect with exposure to 7.8 mg/m3 zinc oxide alone, but decreased 

with exposure to 2.7 and 6.0 mg/m3 zinc oxide in combination with SO2. 

Alveolar volume: No effect with exposure to 7.8 mg/m3 zinc oxide alone, but decreased 

with exposure to 6.0 mg/m3 zinc oxide in combination with SO2. 

~1 ppm 

(2.6 mg/m3); head 

only 

Zinc oxide: 0 mg/m3 

 

Pulmonary function: SO2 exposure alone resulted in an 11% increase in resistance and 

12% decrease in compliance. 

0 ppm Zinc oxide: ~1-2 mg/m3

(0.05 micron projected area 

diameter, GSD 2.0); mixed 

at 24 °C and 30% RH 

Pulmonary function: Zinc oxide exposure alone resulted in a 9% decrease in compliance 

that persisted 1 h after exposure. 

Zinc oxide: ~1-2 mg/m3; 

mixed at 24 °C and 30% 

RH 

Pulmonary function: A 12% decrease in compliance and decreased tidal volume that 

persisted 1 h after exposure, and decreased min volume. There was no evidence of new 

compound formation. Authors concluded that effects on tidal volume and min volume 

most likely represented an additive effect. 

Zinc oxide: ~1-2 mg/m3; 

mixed at 480 °C and 30% 

RH 

Pulmonary function: A 12% decrease in compliance and decreased tidal volume that 

persisted 1 h after exposure and a 12% increase in resistance and decreased min 

volume. There was no evidence of new compound formation. 

 Zinc oxide: ~1-2 mg/m3; 

mixed at 480 °C and 80% 

RH with addition of water 

vapor downstream 

Pulmonary function: A 13% decrease in compliance that persisted 1 h after exposure and 

a 29% increase in resistance. Sulfite formation was observed. 

Amdur et al. (1983) 

Hartley guinea pig; 

male; age NR; 

200-300 g; 

N = 8-23/group 

Exposure: 1 h 

~1 ppm 

(2.6 mg/m3); head 

only 

Zinc oxide: ~1-2 mg/m3; 

mixed at 480 °C and 30% 

RH with addition of water 

vapor during mixing. 

Pulmonary function: A 19% increase in resistance that persisted 1 h after exposure, 

decreased tidal volume immediately after exposure, and a 26% decrease in compliance 

1 h after exposure. Sulfate, sulfite, and sulfur trioxide formation was observed. 

Chen et al. (1991) 

Hartley guinea pig; 

male; age 

NR;275-375 g; 

N = 8/group 

Exposure: 1h 

1.10-1.25 ppm 

(2.9-3.3 mg/m3); 

head only 

Copper oxide: 

0 or 1.16-2.70 mg/m3

(< 0.1 micron) 

Pulmonary resistance: Increased 32-47% during exposure and at 1 and 2 h 

postexposure when SO2 and copper oxide were mixed at 37 °C, a condition that resulted 

in formation of 0.36 μmol/m3 sulfite on the copper oxide particles. No effect was 

observed when the compounds were mixed at 1411 °C, a condition that led to the 

formation of sulfate on the copper oxide particles. 

Dynamic lung compliance: No effect when mixed under conditions that led to the 

formation of either sulfate or sulfite on particles. 

1.02 ppm (2.7 

mg/m3); head only 

Zinc oxide: 

0 mg/m3 

0 2.76 mg/m3 (0.05 micron 

median diameter, GSD 2.0) 

1.10 ppm 

(2.9 mg/m3) 

0.87 mg/m3

Chen et al. (1992) 

Hartley guinea pig; 

male; age NR; 

290-410 g; 

N = 6-9/group 

Exposure: 1h 

1.08 ppm 

(2.8 mg/m3) 

2.34 mg/m3

Baseline pulmonary resistance at 2 h following exposure: No effect in any group. 

AHR to acetylcholine: No effect with exposure to SO2 or zinc oxide alone; compared to 

furnace controls (3% argon). Hyperresponsiveness increased in both groups exposed to 

SO2-layered zinc oxide particles. 

Jakab et al. (1996) 

Swiss mouse; 

10 ppm (26.2 

mg/m3); nose only 

0 mg/m3 AM Fc receptor-mediated phagocytosis of sheep red blood cells at 3 days after 

exposure: Dose-dependent reductions in AM phagocytosis were observed at each 

 E-6 
Study SO2 Metal or Carbon Effects 

0 ppm Carbon black: 10 mg/m3

(0.3 micron, GSD 2.7) 

5 ppm 

(13.1 mg/m3) 

10 mg/m3 (formed 6 μg 

sulfate at 85% humidity) 

10 ppm 

(26.2 mg/m3) 

10 mg/m3 (formed 13.7 μg 

sulfate at 85% humidity) 

female; 5 wks old; 

20-23 g; N = 

5/group 

Exposure: 4 h 

20 ppm 

(52.4 mg/m3) 

10 mg/m3 (formed 48.7 μg 

sulfate at 85% humidity) 

concentration of SO2 mixed with carbon black aerosol at 85% relative humidity, the only 

conditions under which SO2 significantly chemisorbed to carbon black aerosol and 

oxidized to sulfate. AM phagocytic activity was reduced somewhat immediately after 

exposure (Day 0) to carbon black and 10 or 20 ppm SO2, was minimal on Days 1 and 3, 

began increasing on Day 7, and was fully recovered by Day 14. No effects were 

observed with exposure to SO2 or carbon black alone. The data indicate that 

environmentally relevant respirable carbon particles can act as effective vectors for 

delivering toxic amounts of acid SO4

2- to distal parts of the lung. 

10 ppm 

(26.2 mg/m3); 

nose only 

0 mg/m3

0 ppm Carbon black: 10 mg/m3

(10% humidity) 

0 ppm Carbon black: 10 mg/m3 in 

85% humidity to generate 8 

μg/m3 acid sulfate 

10 ppm 

(26.2 mg/m3) 

Carbon black: 10 mg/m3 in 

10% humidity to generate 

41 μg/m3 acid sulfate 

10 ppm 

(26.2 mg/m3) 

Carbon black: 10 mg/m3 in 

85% humidity to generate 

137 μg/m3 acid sulfate 

Clarke et al. (2000) 

Outbred Swiss 

mouse; female; 

age and weight 

NR; 

N = 10 or 12/ 

experimental value. 

Exposure: 4 h once 

or for 4, 5, or 6 

days 

1 ppm 

(2.62 mg/m3) 

Carbon black: 1 mg/m3 in 

85% humidity to generate 

20 μg/m3 acid sulfate 

Inflammatory response after a single 4 h exposure: There was no effect on total cell 

number, lymphocyte/PMN differentials, or total protein levels in BAL fluid in any group. 

AM Fc receptor-mediated phagocytosis after a single 4 h exposure: Suppressed by acid 

sulfate coated particles (at ~140 μg/m3) at 1, 3, and 7 days postexposure; values 

returned to normal by Day 14. 

Intrapulmonary bactericidal activity toward Staphylococcus aureus: Decreased by a 

single 4 h exposure to sulfate coated particles (at ~140 μg/m3) at 1 and 3 days 

postexposure, with recovery by day 7. Suppression was also observed after 5 and 6 

days of repeated exposure to ~20 μg/m3 sulfate-coated particles, a condition more 

relevant to potential ambient human exposures. 

Conner et al. 

(1985) 

Hartley guinea pig; 

male; age NR; 

250-320 g; 

N = 5-18/group/ 

time point 

Exposure: 3 h/day 

for 6 days; 

Animals evaluated 

for up to 72 h 

following exposure 

1 ppm 

(2.6 mg/m3); nose 

only 

Zinc oxide: 0 or 6 mg/m3

(0.05 micron projected area 

diameter, GSD 2.0) 

Right lung to body weight ratio: No effect of SO2 alone. Increased at 48 h in group 

exposed to zinc oxide/SO2. 

Right lung wet to dry weight ratio: No effect of SO2 alone. Increased at 1 h after exposure 

in zinc oxide/SO2 group. 

Lung morphology: No lesions were observed in SO2 group. In group exposed to zinc 

oxide/SO2 there was an increased incidence of alveolar duct inflammation consisting of 

interstitial cellular infiltrate, increased numbers of macrophages, and replacement of 

squamous alveolar epithelium with cuboidal cells. Frequency and severity of lesions 

were greatest immediately following exposure and by 72 h following exposure, lesions 

were mild and infrequent. 

Tracheal secretory cell concentration: No effects with either exposure. 

Epithelial permeability: No effects with either exposure scenario. 

DNA synthesis (3H-thymidine uptake) terminal bronchial cells: Unaffected by SO2. 

Increased at 24 and 72 h after exposure to zinc oxide/SO2. 

Lung volumes: Unaffected by SO2 exposure. Functional reserve capacity, vital capacity, 

and total lung capacity were decreased from 1 to 72 h following exposure to zinc 

oxide/SO2. 

Diffusion capacity for CO: Unaffected by SO2 exposure. Decreased by ~40-50% from 

1 to 24 h following zinc oxide/SO2 exposure. 

Alveolar volume: Unaffected by SO2 exposure. Decreased by ~10% from 1 to 24 h 

following exposure to zinc oxide/SO2. 

Pulmonary mechanics: Respiratory frequency, tidal volume, pulmonary resistance, 

pulmonary compliance were unaffected by either exposure. 

Author conclusion: Changes were identical to those reported in a previous study in which 

guinea pigs were exposed to zinc oxide alone. Sulfur compounds deposited on surface 

are less important than zinc oxide particle. 

 E-7 
Study SO2 Metal or Carbon Effects 

Amdur et al. (1988) 

Guinea pig, sex, 

age, and weight 

NR; 

N = 8-9/group 

Exposure: 3 h/day 

for 5 days 

Pulmonary diffusing capacity: No effect with exposure to 1 ppm SO2 or 2.5 mg/m3 zinc 

oxide alone (data not shown by authors). Significant and dose related decreases on 

exposure days 4 and 5 at 7 μg/m3 sulfate (20% less than control) and days 2-5 at 

11 μg/m3 sulfate (up to 40% less than control). 

Exposure: 1 h 

1 ppm 

(2.6 mg/m3); head 

only 

Zinc oxide: 1 or 2.5 mg/m3

(0.05 micron CMD, GSD 

2.0) 

Sulfate was generated at 

7 and 11 μg/m3 at each 

respective dose; sulfuric 

acid level was reported at 

21 and 33 μg/m3 at each 

respective dose. Bronchial sensitivity to acetylcholine: No effect of 1 ppm SO2 or 2.5 mg/m3 zinc oxide 

alone. Increased with SO2 administered in combination with either zinc oxide dose. The 

authors noted that responses were similar to those produced by 200 μg/m3 sulfuric acid 

of similar particle size, thus indicating the importance of surface layer. 

Shami et al. (1985) 

Fischer-344 rat; 

male and female; 

18-19 wks old, 

weight NR; N = 

2/sex/group at 

each evaluation 

time period 

Exposure: 2 h/day 

for 4 days, followed 

by 2 days without 

exposure, followed 

by 5 more days of 

exposure; animals 

were evaluated for 

up to 28 days 

following exposure 

5 ppm (13 mg/m3); 

nose only 

22 mg/m3 gallium oxide (0.2 

micron volume median 

diameter, GSD not reported), with and without 

addition of 7 mg/m3 B[c]P 

Tracheal and large airways morphology: No effects observed with coexposure to gallium 

oxide and SO2. 

Pulmonary morphology: Increased numbers of non-ciliated cells in terminal bronchial 

epithelium were observed in the SO2/gallium oxide/benzo(a)pyrene group. Mild 

peribronchial and perivascular mononuclear inflammatory cell infiltrate, small 

hyperplastic epithelial cells in alveoli, and alveolar septal hypertrophy were observed in 

the SO2/gallium oxide group, with and without B[c]P; effects were more prominent with 

B[c]P exposure. 

Cell proliferation (3H-thymidine uptake) in trachea and large airways: Increased on days 

1 and 14 in SO2/ gallium oxide group; basal cells primarily labeled. Increased on day 8 In 

the SO2/gallium oxide/B[c]P group. 

Cell proliferation (3H-thymidine uptake) in terminal bronchioles: Increased on day 14 in 

SO2/gallium oxide group; Clara cells primarily labeled. Increased on day 11 in the 

SO2/gallium oxide/B[c]P group. 

Types of 3H-thymidine-labeled cells in the alveolar region: Type 2 cells were primarily 

labeled in the alveolar region through 14 days of exposure in the SO2/gallium oxide 

group. Labeling was increased in Type II, Type I, and endothelial cells on day 8 in the 

SO2/gallium oxide/B[c]P group. 

Wolff et al. (1989) 

F344/Crl rat; male 

and female; 

10-11 wks old; 

weight NR; N = 

6/sex/group 

Exposure: 2 h/d, 

5 d/wk, 4 wks 

5 ppm (13 mg/m3); 

nose only 

Gallium oxide: 0 or 

27 mg/m3 (~0.20 micron 

MMD, GSD ~1.5-2), with 

and without 7.5 mg/m3 of 

1-NP and B[c]P 

Pulmonary particle clearance: No effect was observed with exposure to SO2 alone; 

clearance was slowed only by gallium oxide, with or without coexposure to SO2 or the 

other compounds; SO2 in combination with the PAH had no effect on clearance rate. 

Authors concluded that toxicity was dominated by gallium oxide. 

 E-8 
Table E-5. Respiratory System – Effects of mixtures containing SO2 and O3. 

Study SO2 O3 Effects 

ACUTE/SUBACUTE 

Abraham et al. (1986) 

Sheep; sex NR; adult; 23-50 kg; 

N = 6 

Exposure: 5 h/day for 3 days 

3 ppm 

(7.9 mg/m3); head 

only 

0.3 ppm Tracheal mucus velocity: Decreased by 40% immediately after exposure and 25% 

at 24 h postexposure to the mixture of the 2 compounds. The effects of either 

compound alone were NR. 

Ciliary beat frequency: No effect 

CHRONIC/SUBCHRONIC 

Aranyi et al. (1983) 

CD1 mouse; female; 3-4 wks old; weight 

NR; 

N = 360/group total 

(14-154/group in each assay) 

Exposure: 5 h/day, 5 days/wk for up to 

103 days 

5.0 ppm 

(13.2 mg/m3) in 

addition to 

1.04 mg/m3

ammonium 

sulfate; whole 

body 

0.2 mg/m3

(0.10 ppm) 

Mortality rate after Streptococcus aerosol challenge: Increased in groups exposed 

to O3 alone and mixture of O3, SO2, and ammonium sulfate. 

Alveolar macrophage bactericidal activity towards inhaled K. pneumoniae: 

Increased trend (non-significant) in O3 group but significantly increased in mixture 

group. 

Counts, viability, and ATP levels in cells obtained by pulmonary lavage: No effect 

of either treatment. 

Raub et al. (1983) 

Golden hamster; male; age NR; ~105 g; N 

= 14 or 15/group; mild emphysema was 

induced in some animals by intratracheal 

administration of elastase 

Exposure: 23 h/day, 7 days/wk, for 4 wks 

1 ppm 

(2.62 mg/m3); 

whole body 

1 ppm in 

addition to 

3 ppm 

trans-2-butene

Lung volumes: End expiratory volume, residual volume, total lung capacity and 

vital capacity were unaffected in the mixture versus air exposure group in normal 

or emphysematous hamsters. 

Respiratory system compliance: Unaffected in the mixture versus air exposure 

group in normal or emphysematous hamsters. 

Distribution of ventilation (N2 washout slope): The N2 slope decreased in the 

mixture versus air exposure group in both normal and emphysematous hamsters. 

Diffusion capacity for CO: Significantly increased in the mixture versus 

air-exposed normal animals. Although the text reported an increase in the mixture 

versus air-exposed emphysematous animals, Figure 3 of the study indicated that 

the effect was very small and did not obtain statistical significance. Significantly 

lower in emphysematous versus normal hamsters exposed to the mixture. The 

authors noted a significant interaction between exposure to the mixture and 

emphysema. 

Histopathology: Inflammatory lesions were found in the lungs of emphysematous 

hamsters exposed to air or the mixture. Hyperplasia incidence was higher in 

emphysema hamsters exposed to the mixture versus air. Inflammatory lesions 

were similar in emphysematous hamsters exposed to air or the mixture. Data were 

not shown for histopathology data. 

Overall author conclusion: Animals with impaired lung function may have 

decreased capacity to compensate for the pulmonary insult caused by exposure to 

a complex pollutant mixture. 

 

 E-9 
Table E-6. Respiratory System – Effects of SO2 and sulfate mixtures. 

Study SO2 Sulfate Effects 

ACUTE 

Mannix et al. (1982) 

Sprague Dawley rat, male, age NR, 

~200 g, N = 8/group 

4 h exposure 

5 ppm 

(13.1 mg/m3); nose 

only 

Sulfate aerosol 1.5 

(0.5 micron MMAD, 

GSD 1.6) 

Lung clearance of radiolabeled tracer particles: No significant effect was 

observed with the mixture of the two compounds at 80-85% humidity. 

CHRONIC/SUBCHRONIC 

1 ppm 

(2.62 mg/m3); whole 

body 

0 

0 (NH4)2SO4 

0.5 mg/m3

(MMAD = 0.42-0.44 ± 

0.04 micron, GSD 

2.2-2.6) 

Smith et al. (1989) 

Sprague-Dawley rat, male, young 

adult, initial weight NR, N = 12-15/data 

point 

Exposure: 5 h/day, 5 days/wk for 4 or 

8 mos; half the animals in the 8-mo 

group were allowed to recover for 

3 mos. 

1 ppm (2.62 mg/m3) 0.5 mg/m3

Morphological observations at 4 mos exposure in “normal” rats: 

Bronchiolar epithelial hyperplasia and increased numbers of non-ciliated 

epithelial cells were observed in rats exposed to either compound alone 

but coexposure to both compounds did not magnify the effects. An 

increase in alveolar chord length was observed in the (NH4)2SO4 group 

and no further changes were observed with coexposure to SO2. 

Morphological observations at 4 mos exposure in rats treated with 

elastase to induce an emphysema-like condition: Bronchiolar epithelial 

hyperplasia was decreased in groups exposed to either compound alone 

or the mixture of the two compounds. A decrease in alveolar chord length 

was observed in the (NH4)2SO4 group and no further changes were 

observed with coexposure to SO2. 

Morphological observations at 8 mos exposure in “normal” rats: An 

increase in non-ciliated epithelial cells and alveolar birefringence (an 

indication of alveolar interstitial fibrosis) was observed only in the group 

exposed to (NH4)2SO4. 

Morphological observations at 8 mos exposure in rats treated with 

elastase: An increase in lung volume per body weight and emphysema 

incidence was observed in groups treated with either compound alone or 

in combination; alveolar chord length was increased only in the group 

exposed to the mixture of compounds. 

Morphological observations at 12 mos exposure in normal rats: Increased 

alveolar chord length was observed only in the (NH4)2SO4 group. 

Morphological observations at 12 mos exposure in rats treated with 

elastase: An increase in absolute lung volume was observed only in the 

group treated with the mixture of both compounds. 

Lung function effects at 4 mos exposure in normal rats: A decrease in 

residual volume was observed in the SO2 group and decreased 

quasistatic compliance was observed in the SO2 group and in the 

(NH4)2SO4 group, but the effects were not observed with the mixture. 

Lung function effects at 4 mos exposure in elastase-treated rats: Ratio of 

residual volume/total lung capacity and N2 washout was decreased in the 

SO2 group and in the (NH4)2SO4 group, but the effects were not observed 

with the mixture. 

Overall conclusions: In general, pollutant effects were minimal and 

transient, and appeared obscured or repressed in elastase-treated 

groups; (NH4)2SO4 was more bioactive than SO2, with little evidence of 

mixture additivity (in several instances, effects seen with one or both 

pollutants individually were not seen with the mixture). 

 E-10 
Table E-7. Respiratory System – Effects of actual or simulated air pollution mixtures. 

Study Exposed Control Effects 

ACUTE / SUBACTUTE 

Mautz et al. (1988) 

Sprague-Dawley rat, 

male, age NR, 240-280 

g, N = 6-9/group 

Exposure: 4 h 

Air pollutant mixture at full concentration (tested in 2 studies): 

0.35 ppm O3, 1.3 ppm NO2, 

2.5 ppm (6.6 mg/m3) SO2, 10 μg/m3

manganese sulfate, 500 μg/m3

ferric sulfate, 500 μg/m3 ammonium sulfate, 500 μg/m3 carbon 

aerosol. Mixture also tested 1⁄2 and 

1⁄4 concentrations. For aerosols: 

MMAD = 0.3-0.48 micron with 

GSD: 2.6-4.6. Nose-only exposure. 

Compounds formed: sulfate, nitrate, hydrogen ion, nitric acid. 

Clean air Breathing pattern: Effect of full concentration mixture in 2 studies – increased 

breathing frequency, trend or significant decrease in tidal volume, decreased or 

unaffected O2 consumption, increased or unaffected ventilation equivalent for O2. 

Effect of half concentration mixture – increased min ventilation. Quarter 

concentration – no significant effects. 

Histopathology: Full concentration – area of type 1 parenchymal lung lesions 

increased in 1 of 2 experiments; area of type 2 parenchymal lung lesions were 

increased in both experiments. Effects were equivalent to those observed with O3

exposure alone. Half and quarter concentrations – no effects. 

Mucociliary clearance: No effect on early or late clearance of 85Kr-labeled 

polystyrene particles. 

Nasal epithelial injury (measured by tritiated thymidine uptake): No effect at any 

concentration. 

Phalen and Kleinman 

(1987) 

Sprague-Dawley rat, 

male, age NR, 

200-225 g, N = 

5-13/group/ 

time period 

Exposure: 4 h/day for 7 

or 21 days 

2.55 ppm (6.7 mg/m3) SO2, 

0.3 ppm O3, 1.2 ppm NOx, 150 

μg/m3 ferric oxide, 130 μg/m3 nitric 

acid, 2.0 μM/m3 hydrogen ion, and 

500 μg/m3 total Fe3+, Mn2+, and 

NH4

2+ combined; nose only 

Purified air Bronchoalveolar epithelial permeability to 99mTc-diethylenetriaminepentaacetate: No 

effect at either time period. 

Nasal mucosal permeability to 99mTc-diethylenetriaminepentaacetate: No effect at 

either time period. 

Macrophage rosette formation: Decreased (indicating damage to Fc receptors) for 

up to 4 days after 7- or 21-day exposure; magnitude of effect greater following 

21-day exposure. By day 4 post-exposure, numbers began increasing and by day 7 

were equivalent to control values. 

Macrophage phagocytic activity: In rats exposed for 7 days, decreased activity was 

observed for 2 days post-exposure. No effects observed after 21-day exposure. 

SUBCHRONIC / CHRONIC 

Saldiva et al. (1992) 

Wistar rat, male, 2 mos 

old, weight NR, N = 

14-30/group 

Exposure: 6 mos 

Urban air: São Paulo, mean levels 

of air pollutants measured 200 m 

from the police station where rats 

were kept: 29.05 μg/m3

(0.011 ppm) SO2; 1.25 ppm CO, 

11.08 ppb O3, 35.18 μg/m3 particulates 

Rural air: Atibaia, 

an agricultural 

town 50 km from 

São Paulo was 

considered the 

control; air pollutant levels were not 

measured 

Death: 37 of 69 São Paulo rats died before study end; autopsy of 10 animals 

identified pneumonia as the cause of death; 10/56 Atibaia animals died. 

Respiratory mechanics: Nasal resistance was higher in Atibaia animals. 

No differences were observed for pulmonary resistance or dynamic lung elastance. 

Mucus properties: São Paulo animals’ tracheal mucus output was lower, relative 

speed of tracheal mucus was slower, ratio between viscosity and elasticity was 

higher for nasal mucus, and rigidity of tracheal mucus was increased. 

Bronchoalveolar lavage: In lavage fluid from São Paulo animals, there were 

increased numbers of cells, lymphocytes, polymorphonuclear cells. 

Histochemical evaluation: Hyperplasia was observed in respiratory epithelium of 

rats housed in São Paulo. 

Ultrastructural studies: Animals housed in São Paulo had a higher frequency of cilia 

abnormalities including composite cilia, microtubular defect, vesiculation, and decreased microvelocity of luminal membrane. 

Lemos et al. (1994) 

Rats from the same 

cohort as Saldiva et al. 

(1992). N = 15/group 

Exposure: 6 mos 

Urban air: São Paulo, mean levels 

of pollutants measured 200 m from 

police station where rats were kept: 

29.05 μg/m3 (0.011 ppm) SO2; 1.25 

ppm CO, 11.08 pb O3, 35.18 μg/m3

particulates 

Rural air: Atibaia, 

agricultural town 

50 km from São 

Paulo, considered 

control; air pollutant levels not 

measured 

Nasal passage pathology: Rats housed in São Paulo had increased nasal 

epithelium volume, larger amounts of mucosubstances stored in epithelium, and 

more acidic mucus secretions in lamina propria glands. 

Pereira et al. (1995) 

4 groups of rats 

housed: 3 mos in São 

Paulo, 3 mos in São 

Paulo followed by 3 

mos in Atibaia, 3 mos in 

Atibaia, or 6 mos at 

Atibaia. 

Wistar rats, male, 1.0-

1.5 mos old, weight 

NR, N = 30/group 

Urban air: São Paulo, levels of air 

pollutants measured were: ~8-50 

μg/m3 (0.003-0.019 ppm) SO2, 

~0.1-0.45 ppm nitrogen dioxide, 

~4.8-7 ppm carbon monoxide, and 

~50-120 μg/m3 PM 

Rural air: Atibaia, 

an agricultural 

town 50 km from 

São Paulo was 

considered the 

control; air pollutant levels were not 

measured 

Lung responsiveness to methacholine: Increased respiratory system elastance 

resulting from increased sensitivity to methacholine in rats housed in São Paulo for 

3 mos compared to all the other groups. No exposure-related effects were observed 

for respiratory system resistance. 

 E-11 
Table E-8. Effects of meteorological conditions on SO2 effects. 

Study SO2 Condition Effects 

Barthélemy et al. (1988) 

Rabbit, sex NR, adult, mean 2.0 kg, N = 

5-10/group; animals were mechanically 

ventilated 

Exposure: 45 min 

0.5 or 5 ppm (1.31 

or 13.1 mg/m3); 

intratracheal 

Drop in air 

temperature from 

38 °C to 15 °C 

Lung resistance: Exposure to cool air for 20 min resulted in a ~54% 

mean increase in lung resistance. Exposure to SO2 for 20 min 

increased lung resistance by 16% at 0.5 ppm and 50% at 5 ppm. The 

difference in lung resistance from warm to cold air was halved (27%) 

by exposure to 0.5 ppm and was not significant at 5 ppm. The authors 

concluded that transient alteration in tracheobronchial wall following 

SO2 exposure may have reduced accessibility of airway nervous 

receptors to cold air. 

Hälinen et al. (2000a) 

Duncan-Hartley guinea pig, male, age and 

weight NR, N = 7-12/group, mechanically 

ventilated; animals were hyperventilated during 

cold air and SO2 exposure to simulate exercise. 

In pre-exposure period: 15-min exposure to 

warm humid air, 10-min exposure to cold dry 

air, and 15-min exposure to warm humid air. In 

the SO2 exposure period: 10-min exposures to 

each SO2 concentration in cold dry air or with 

cold dry air alone were preceded and followed 

by 15-min exposures to warm humid air 

1.0, 2.5, or 5 ppm 

(2.62, 6.55, or 

13.1 mg/m3); 

apparently 

intratracheal 

Drop in 

intratracheal 

temperatures 

from ~35.5 °C to 

~27 °C 

Peak expiratory flow: Percent decreases were significantly greater with 

exposures to SO2 in dry air at concentrations of 1.0 ppm (~32.7%) and 

2.5 ppm (~35.6%) than with exposure to cold dry air (~27%); decrease 

at 5 ppm SO2 in cold dry air (~25.3%) was similar to that with cold dry 

air. The effects did not persist following exposures. 

Tidal volume: Percent decreases were significantly greater with 

exposure to SO2 in cold dry air at concentrations of 1.0 ppm (~22.4%) 

and 2.5 ppm (~28.3%) than with exposure to cold dry air (~18.1%); 

decrease at 5 ppm SO2 in cold dry air (~17.8%) was similar to that of 

cold dry air. The effects did not persist following exposures. 

Bronchoalveolar lavage: The clean dry air group had significantly more 

macrophages, lymphocytes, and increased protein concentration in 

lavage than the warm humid air control. The cold dry air + SO2 group 

had fewer macrophages than the clean dry air group and higher 

protein concentration than controls. 

Histopathology: Increased incidence of eosinophilic infiltration within 

and below tracheal epithelium with exposure to cold dry air or SO2 in 

cold dry air. 

Hälinen et al. (2000b) 

Duncan-Hartley guinea pig, male, age and 

weight NR, N = 8-9/group, mechanically 

ventilated; animals were hyperventilated during 

cold air and SO2 exposure to simulate exercise 

Exposure: 60 min 

1 ppm 

(2.62 mg/m3); 

apparently 

intratracheal 

Drop in 

intratracheal 

temperatures 

from ~37 °C to 

~26 °C 

Peak expiratory flow: Non-significant decreases compared to baseline 

(4.5-10.8%) at 10 and 20 min of exposure to cold dry air. With 

exposure to SO2 in cold dry air: decreased significantly (11.4%, i.e., 

bronchoconstriction) compared to baseline at 10 min of exposure but 

recovered from 20 to 60 min of exposure. The effect with SO2

exposure was not statistically significant compared to that of cold dry 

air alone. 

Tidal volume: Decreased from baseline throughout most of the 

exposure period with cold dry air or SO2 in cold dry air; response with 

SO2 was more shallow than that of cold dry air alone, but statistical 

significance compared to cold dry air was obtained only at 60 min of 

exposure. 

Bronchoalveolar lavage: Decreased neutrophil numbers in the SO2

group compared to the warm humid air group but no significant 

difference compared to the cold dry air group. 

Histopathology: No effect in lung or tracheobronchial airway. 

General conclusions: Functional effects on the lower respiratory tract 

were weaker than in the previous study with 10-min exposures 

(Hälinen et al., 2000a). 

 

 E-12 
Table E-9. Cardiovascular effects of SO2 and metabolites. 

Study Conc. Duration Species Effects 

IN VITRO EXPOSURE 

Nie and 

Meng (2005) 

Bisulfite/sulfite, 1:3 

molar/molar, 10 

μM 

NR Ventricular myocytes isolated 

from Wistar rat; adult; 

200-300 g; N = 8 

Effects of the 10 μM bisulfite/sulfite mixture on sodium current included 

a shift of steady state inactivation curve to a more positive potential, a 

shift of the time-dependent recovery from inactivation curve to the left, 

accelerated recovery, and shortened inactivation and activation time 

constants. It was concluded the bisulfite/sulfite mixture stimulated 

cardiac sodium channels. 

Nie and 

Meng (2006) 

Bisulfite/sulfite, 1:3 

molar/molar, 10 

μM 

NR Ventricular myocytes isolated 

from 200-300 g; N = 8 

Effects of the 10 μM bisulfite/sulfite mixture on voltage-dependent 

L-type calcium currents included a shift of steady-state activation and 

inactivation to more positive potentials, accelerated recovery from 

inactivation, and shortened fast and slow time inactivation constants. 

Authors stated that their results suggested the possibility of cardiac 

injury following SO2 inhalation. 

ACUTE / SUBACUTE 

Hälinen 

et al. 

(2000a) 

1.0, 2.5, or 5 ppm 

(2.62, 6.55, or 

13.1 mg/m3) in 

cold dry air; 

apparently intratracheal 

In pre-exposure period: 15 min exposure 

to warm humid air, 10 

min to cold dry air, 

and 15 min to warm 

humid air. In exposure 

period: 10 min 

exposures to each 

SO2 concentration or 

cold dry air were preceded and followed by 

15 min exposures to 

warm, humid air. 

Duncan-Hartley guinea pig; 

male; age and weight NR; N 

= 7-12/ group, mechanically 

ventilated; animals were 

hyperventilated during cold 

air and SO2 exposure to 

simulate exercise 

Arterial blood pressure increased transiently during exposure to 5 ppm 

SO2 in cold dry air. No analyses were done to determine if the effects 

on blood pressure were caused by exposure to cold air or SO2. 

Hälinen 

et al. 

(2000b) 

1 ppm (2.62 

mg/m3) in cold dry 

air; apparently 

intratracheal 

60 min Duncan-Hartley guinea pig; 

male; age and weight NR; 

N = 8-9/group, mechanically 

ventilated; animals were 

hyperventilated during cold 

air and SO2 exposure to 

simulate exercise 

Blood pressure and heart rate increased similarly with exposure to cold 

dry air or SO2 in cold dry air. Blood pressure generally increased during 

the first 10-20 min of exposure and remained steady from that point 

forward. The increase in heart rate was gradual. No analyses were 

done to determine if the effects on blood pressure were caused by 

exposure to cold air or SO2. 

Nadziejko 

et al. (2004) 

1 ppm 

(2.62 mg/m3); 

nose only 

4 h F344 rat, male, 18 mos old, 

weight NR, N = 20 

(crossover design) 

SO2 exposure had no effect on spontaneous arrythmia frequency in 

aged rats. Authors urged caution in the interpretation of effects 

because occurrence of arrhythmias in aged rats was sporadic and 

variable from day to day. 

Meng et al. 

(2003a) 

10, 20, or 40 ppm 

(26.2, 52.4 or 

105 mg/m3); whole 

body 

6 h Wistar rat; male; 7-8 wks old; 

180-200 g; 

N = 10/group 

A dose-related decrease in blood pressure was observed at ≥ 20 ppm. 

Meng et al. 

(2003a) 

10, 20, or 40 ppm 

(26.2, 52.4, or 

104.8 mg/m3); 

whole body 

6 h/day for 7 days Wistar rat, male; 7-8 wks old; 

180-200 g; 

N = 10/group 

Dose-related decreases in blood pressure were observed on exposure 

day 3 in the 10 ppm group, exposure days 2-6 in the 20 ppm group, 

and all exposure days in the 40 ppm group. The authors noted possible 

adaptive mechanism in the low but not the high dose group. 

LangleyEvans et al. 

(1996) 

5, 50, or 100 ppm 

(13.1, 131, or 262 

mg/m3); whole 

body 

5 h/day for 7-28 days Wistar rat; male; 7 wks old; 

weight NR; N = 4-5/treatment 

group, 8 controls 

GSH was depleted in the heart at 5 and 100 ppm. At 50 ppm, GSH 

level decreased in heart at 7 days and returned to normal by 14 days. 

No effects were observed for other GSH-related enzymes. Injury and 

inflammation were not assessed in heart, but assessment in lung 

revealed no effect. 

Meng et al. 

(2003b) 

8.4, 21, or 43 ppm 

(22, 56, or 112 

mg/m3); whole 

body 

6 h/day for 7 days Kunming albino mouse; male 

and female; 5 wks old; 

19 ± 2 g; 

N = 10/sex/group 

Changes observed in heart (concentrations of effect) included: lower 

SOD activity in males and females (≥ 8.4 ppm), higher TBARS level in 

males and females (≥ 8.4 ppm), lower GPx activity in males (8.4 and 

21 ppm; also 43 ppm according to text) and lower GSH level in males 

(43 ppm). Authors concluded that SO2 induced oxidative damage in 

hearts of mice. 

Wu and 

Meng (2003) 

8.4, 24.4, or 

56.5 ppm (22, 64, 

or 148 mg/m3); 

whole body 

6 h/day for 7 days Kunming-strain mouse; 

male; age NR; 18-20 g; 

N = 10/group 

GSH, GST, and G6PD activities were decreased in the heart at 56.5 

ppm. 

 E-13 
Table E-10. Hematological effects of SO2. 

Study Concentration Duration Species Effects 

ACUTE/SUBACUTE 

Baskurt 

(1988) 

0.87 ppm 

(2.36 mg/m3); whole 

body 

24 h Swiss Albino rat; male; 

age NR; 250-300 g; 

N = 51, 50 

Effects of SO2 exposure included increased hematocrit, sulfhemoglobin and 

osmotic fragility and decreased whole blood and packed cell viscosities. RBC 

number, hemoglobin, mean corpuscular volume, mean corpuscular 

hemoglobin concentration, and plasma viscosity were not significantly 

altered. 

SUBCHRONIC 

Gümüşlü 

et al. 

(1998) 

10 ppm 

(26.2 mg/m3); whole 

body 

1 h/day, 7 days/wk 

for 8 wks 

Swiss-Albino rat; male; 

2.5-3.0 mos old; weight 

NR; N = 30 (14 controls, 

16 treated) 

Decreased CuZn SOD activity, increased GPx and GST activity, and 

increased TBARS formation were observed in RBC of treated rats. No 

significant effect on G6PD or catalase levels was observed. 

Yargiçoğlu 

et al. 

(2001) 

10 ppm (26.2 

mg/m3); whole body 

1 h/day, 7 days/wk 

for 6 wks 

Albino rat, male; 3, 12, 

and 24 mos old; mean 

weight 213-448 g; 

N = 10/group 

Enzyme and GSH activity (GPx, catalase, GSH, and GST) were increased 

and CuZn SOD activity was decreased in RBCs of all experimental groups 

compared to controls. RBCs in older rats had lower levels of all antioxidant 

enzymes and increased TBARS activity compared to younger rats. 

LangleyEvans 

et al. 

(1997; 

2007) 

100 ppm (286 

mg/m3); whole body. 

Units were initially 

reported as μg/m3

but were corrected 

per correspondence 

w/author. 

5 h/day for 28 days Wistar rat, male; 7 wks 

old; weight NR; 

N = 4-16 

Dams were fed diets containing casein at 180 [control], 120, 90, or 60 g/kg 

during pregnancy and their offspring were exposed to air or SO2 as adults. In 

blood of offspring, SO2 exposure significantly reduced the numbers of 

circulating total leukocytes and lymphocytes in the 180 and 120 g/kg dietary 

groups; neutrophils numbers were not affected in any group. GSH levels in 

the 180 and 60 g/kg (but not the two intermediate) dietary groups were 

reduced by SO2 exposure. 

Etlik et al. 

(1995) 

10 ppm 

(26.2 mg/m3); whole 

body 

1 h/day for 30 days Guinea pig; sex and age 

NR; 250-450 g; 

N = 12/group 

SO2 exposure resulted in RBC membrane lipoperoxidation (elevated levels of 

malonyldialdehyde) and other oxidative damage (elevated osmotic fragility 

ratios and levels of methemoglobin and sulfhemoglobin). All effects significantly (p < 0.05) mitigated by injections of Vitamin E+C three times per wk. 

Ağar et al. 

(2000) 

10 ppm 

(26.2 mg/m3); whole 

body 

1 h/day, 7 days/ 

wk for 6 wks 

Swiss Albino rat; male; 3 

mos old; weight NR; N = 

10/group in 4 groups 

RBC parameters were monitored in non-diabetic rats, non-diabetic rats 

exposed to SO2, alloxan-induced diabetic rats, and diabetic rats exposed to 

SO2. In both non-diabetic and diabetic rats exposed to SO2, levels of GPx, 

catalase, GSH, GST, and TBARS were elevated in RBC while those of SOD 

were reduced. 

Etlik et al. 

(1997) 

10 ppm 

(26.2 mg/m3); whole 

body 

1 h/day for 45 days Rat; sex and age NR; 

214-222 g; 

N = 6-8/group 

SO2 exposure significantly elevated levels of methemoglobin, sulfhemoglobin 

and malonyldialdehyde, the latter of which was substantially reversed by 

Vitamin E+C treatment. RBC osmotic fragility was increased by SO2, and 

again partially mitigated by Vitamin E+C. SO2 elevated RBC, white blood cell, 

hemoglobin and hematocrit values, but not mean corpuscular volume, mean 

corpuscular hemoglobin or mean corpuscular hemoglobin concentration. 

Vitamin E+C exposure did not affect these parameters. 

 

 E-14 
Table E-11. Carcinogenic effects of SO2. 

Study Concentration Duration Species Effects 

PULMONARY EFFECTS 

Gunnison 

et al. (1988) 

0, 10, or 30 ppm (0, 

26.2, or 78.6 

mg/m3) SO2 (whole 

body) ± 1 mg B[a]P 

0, 100 or 400 ppm 

W, or [400 ppm W 

+ 40 ppm Mo] in a 

low-Mo diet, 

± B[a]P (See 

Effects column) ± B[a]P 

SO2: 21 wk, 5 

day/wk (minus 

holidays), 6 h/day 

High W, low Mo 

diet: 21 wk, 7 

day/wk 

B[a]P: 15 wk, once 

per wk starting wk 4 

Rat, 

Sprague-Dawley; 

male; 9 wk old; 

~315- 340 g; 

N = 20-74/group 

Purpose was to investigate carcinogenic/cocarcinogenic effects of SO2 inhalation or 

dietary-induced high levels of systemic sulfite/bisulfite in conjunction with tracheal 

installation of B[a]P. High drinking water levels of W in conjunction with low-Mo feed 

induce sulfite oxidase deficiency in rats, and thus high systemic levels of sulfite and 

bisulfite (at 0, 100 or 400 ppm W, mean plasma sulfite was 0, 0 or 44 μM, while mean 

tracheal sulfite + bisulfite was 33, 69 or 550 nmol/g wet wt). Mortality in B[a]P groups 

(~50% after ~380-430 d) was due almost exclusively to SQCA of the respiratory tract; 

survival rate was excellent for other groups (~50% mortality after ~620-700 d). Results 

indicate no SQCA was induced in any of the SO2 inhalation or systemic sulfite + bisulfite 

groups, nor were incidences in the B[a]P groups enhanced by such coexposures. This 

lack of cocarcinogenicity does not support the hypothesis that SO2 exposure could 

elevate systemic sulfite/bisulfite, generating GSSO3H, which would inhibit GST and 

reduce intracellular GSH, thus interfering with a major detoxication pathway for B[a]P 

and enhancing its carcinogenicity. Authors note that due to the high incidence of animals 

with tumors in the two B[a]P only groups (65/72 and 63/72), cocarcinogenicity of SO2 or 

sulfite + bisulfite could only have been demonstrated by shortening of tumor induction 

time and/or increased rate of SQCA appearance—neither were observed. 

Ohyama 

et al. (1999) 

0, 0.2 mL C, or 

(0.2 mL DEP+C 

± [4 ppm (10.48 

mg/m3) SO2 or 

6 ppm (11.28 

mg/m3) NO2 or 

4 ppm SO2 + 6 

ppm NO2]; whole 

body) 

[Note: 0.2 mL 

CBP = 1mg; 0.2 mL 

DEcCBP =1 mg 

CBP + 2.5 mg 

DEP] 

SO2 and/or NO2: 

10 mo, 16 h/day 

CBP or DEcCBP: 

4 wk, once/wk by 

intratracheal 

infusion 

Rat, SPF 

F344/Jcl; male; 

6 wk old; wt NR; 

N = 23-30/group 

in 6 groups 

Purpose was to study effects of DEP on rat lung tumorigenesis and possible tumor 

promoting effects of SO2 or NO2 singly or together. Alveolar hyperplasia and adenoma 

were significantly (p < 0.01-0.05) increased over controls in the CBP group, but not the 

DEcCBP group. This was ascribed to induction of alveolitis and AM infiltration (a tumor 

response specific to rat and of questionable relevance to humans) in the former group, 

but apparently prevented by DEP in the latter. Alveolar bronchiolization near small 

hyaline masses of deposited DEcCBP was observed in all DEcCBP groups, the masses 

presumably allowing long-term exposure to DEP extracts by contacted alveolar 

epithelium. DNA adducts were found only in the three gas-exposed groups. Discounting 

the CBA group, elevated alveolar hyperplasia was seen only in the DEcCBP + NO2

group, and elevated incidences of alveolar adenoma in the DEcCBP + SO2 and 

particularly the DEcCBP + NO2 groups; neither effect was observed with coexposure to 

both gases—speculated by the authors to perhaps result from inhibition of the stronger 

NO2 promotion by HSO3

-

. Thus, SO2 appears to have weaker capacity than NO2 for 

promoting tumor induction (and perhaps genotoxicity) by DEP extract, and may 

antagonize such effects by NO2 during coexposure of the gases. 

Ito et al. 

(1997) 

0, C, or (25 mg 

SPM+C ± [4 ppm 

(10.48 mg/m3) SO2

or 6 ppm (11.28 

mg/m3) NO2 or 4 

ppm SO2 + 6 ppm 

NO2]); whole body 

SO2 and/or NO2: 

11 mo, 16 h/day 

C ± SPM: 4 wk, 

once/wk by intratracheal injection 

Rat, SPF Fisher 

344; male; 5 wk 

old; wt NR; 

N = 5/group in 6 

groups 

Purpose was to study effects of Tokyo air SPM, with or without coexposure to SO2 or 

NO2 or their combination, on the development of proliferative lesions of PEC. PEC 

hyperplasia was significantly (p < 05) increased by exposure to SPM, but coexposure to 

either gas or their mixture was without additional effect. No PEC papillomas were 

observed in control groups, while a few were seen in the SPM groups, irrespective of gas 

coexposures. Thus, SO2 demonstrated no tumor promotion or cocarcinogenic properties. 

[Study did not describe the nature of the carbon (C) used.] 

Heinrich 

et al. (1989) 

0 or [10 ppm (26.2 

mg/m3) SO2 + 

5 ppm (9.4 mg/m3) 

NO2] ± [3 or 

6 mg/kg bw of 

DEN]; exposure to 

gases whole body 

SO2 + NO2: 6, 10.5, 

15, or 18 mo, 5 

day/wk, 19 h/day 

DEN: once by 

s.c. injection, ~2 wk 

after the start of 

inhalation exposure 

Hamster, Syrian 

golden; both 

sexes; 10 wk old; 

bw NR; N = 

40/sex per each 

of 12 exposure 

groups 

The principle focus of this large study was to examine whether two inhaled diesel-exhaust emission preparations (± particulates) could enhance the tumorigenesis of injected 

DEN. Ancillary aim was to see whether inhalation of the irritant SO2+NO2 mixture could 

cause similar enhancement of DEN tumorigenicity. Gas mixture exposure did not affect 

bw gain, but slightly shortened survival times (although significantly only for females). 

Apart from effects attributed to DEN, serial sacrifices showed progressive increases in 

ciliated tracheal cell aberrations and in number of tracheal mucosal cells. In the lung, gas 

mixture-related effects were limited to a progressing alveolar lesion involving lining with 

bronchiolar epithelium and the presence of some pigment-containing AM, and to a mild, 

diffuse thickening of the alveolar septa. SO2+NO2 exposure did not by itself elevate 

tumor rate in the upper respiratory tract, nor did it enhance increases induced by DEN. 

Thus the mixture appeared to have no tumor inducing or promoting effects. 

NONPULMONARY EFFECTS 

Klein et al. 

(1989) 

0 or 6 ppm (0 or 

15.72 mg/m3) SO2, 

± 0.2 ppm (600 

μg/m3); whole 

body; NDMA 

20 mo, 5 day/wk, 

4 h/day 

Rat, 

Sprague-Dawley; 

female; age and 

wt NR; N = 

36/group in 4 relevant groups 

This is a preliminary report for observations after 20 mo (800 h inhalation in 200 

exposures, with calculated inhaled cumulative doses of 77 mg SO2 and 2-3 mg NDMA 

per rat). The effects of NDMA ± SO2 inhalation were studied. Group mortality was as 

follows: control (3/36), SO2 (5/36), NDMA (4/36), NDMA + SO2 (7/36). The only tumors 

observed were nasal: control (0), SO2 (0), NDMA (1), NDMA + SO2 (3). No observable 

parameters, including body wt gain, were affected by the additional SO2 exposure; 

assessment of tumor incidence effects could not yet be performed. 

 E-15 
Table E-12. Nervous system effects of SO2 and metabolites. 

Study Concentration Duration Species Effects 

IN VITRO / EX VIVO 

Du and 

Meng 

(2004a) 

1, 10, 50, or 100 

μM SO2 derivatives 

(1:3, NaHSO3 to 

Na2SO3) 

Not specified Wistar rat; sex NR; 

6-12 days old; weight 

and number NR; typical 

observations made on 

60 isolated 

hippocampal neurons 

per concentration 

Exposure to SO2 derivatives (sulfite, bisulfite) reversibly increased the 

amplitude of potassium channel TOCs in a dose-dependent and voltage-dependent manner. Compared to controls, 10 μM SO2 shifted inactivation 

of depolarization toward more positive potentials without significantly affecting 

the activation process. By increasing maximal TOC conductance and delaying 

TOC inactivation, micromolar concentrations of SO2 derivatives may increase 

the excitability of hippocampal neurons and thus contribute to the enhanced 

neuronal activity associated with SO2 intoxication. 

Du and 

Meng 

(2004b) 

1 or 10 μM SO2

derivatives 

(1:3, NaHSO3 to 

Na2SO3) 

2-4 min Wistar rat; both sexes; 

10-15 days old; weight 

and number NR; N = 

6-13 isolated dorsal 

root ganglion neurons 

averaged per endpoint 

Maximum sodium current amplitudes for both TTX-S and TTX-R channels were 

increased by exposure to SO2 derivatives (10 or 1 μM, respectively), with 

amplitudes diminished at more negative evoking potentials and enhanced at 

less negative or positive potentials. SO2 derivatives (a) slowed both current 

activation and inactivation for both types of sodium channels; (b) shifted 

activation currents to more positive potentials, increasing threshold voltages for 

action potential generation and contributing to reduced neuron excitability; and 

(c) caused even larger counteracting positive shifts in inactivation voltages 

tending to increase dorsal root ganglion neuron excitability. On balance, the 

data suggest micromolar concentrations of sulfite/bisulfite can increase the 

excitability of dorsal root ganglion neurons, providing a basis for SO2-

associated neurotoxicity. 

Du and 

Meng 

(2006) 

0.01, 0.1, 0.5, or 1 

μM SO2 derivatives 

(1:3, NaHSO3 to 

Na2SO3) 

Not specified, but 

brief (“added to the 

external solution 

just before each 

experiment”) 

Wistar rat; both sexes; 

10-15 days old; weight 

and number NR; N = 

6-15 isolated dorsal 

root ganglion neurons 

averaged per endpoint 

In isolated dorsal root ganglion neurons, SO2 derivatives increased HVA-ICa

amplitudes in a concentration- and depolarizing voltage-dependent manner 

(EC50 was ~0.4 μM) by altering Ca channel properties. This effect was partially 

reversible by SO2 derivative washout, and was PKI-inhibitable, indicating 

involvement of PKA and secondary messengers. Additionally, exposure caused 

a positive shift in reversal potential. SO2 derivatives also delayed activation and 

inactivation of Ca channels, but the latter was more pronounced, thus overall 

prolonging action potential duration and increasing HVA-ICa. Exposure also 

slowed the fast component and accelerated the slow component of recovery 

from Ca channel inactivation. Thus, ≤ 1 μM sulfite/bisulfite caused prolonged 

opening and altered properties of Ca channels, elevated HVA-ICa, and 

abnormal Ca signaling with neuronal cell injury. Authors speculate these effects 

may correlate with SO2 inhalation toxicity, perhaps leading to abnormal 

regulation via peripheral neuron Ca channels of nociceptive impulse 

transmission. 

ACUTE / SUBACUTE / SUBCHRONIC 

Wu and 

Meng 

(2003) 

8.4, 24.4, or 

56.5 ppm (22, 64, 

or 148 mg/m3); 

whole body 

6 h/day for 7 days Kunming-strain mouse; 

male; age NR; 

18-20 g; N = 10/group 

Decreased glutathione, G6PD, and GST activities were observed in the brain 

at 24.4 and 56.5 ppm. 

Haider et al. 

(1981) 

10 ppm (26.2 

mg/m3); whole body 

1 h/day for 21 or 

24 days 

Guinea pig; sex NR; 

adult; 250-500 g; 

N = 12/group 

(6/subgroup) 

The effects of SO2 exposure on lipid profiles, lipid peroxidation and lipase 

activity in three regions of the brain (cerebral hemisphere, CH; cerebellum, CB; 

brain stem, BS) were examined. Significant (p < 0.001-0.05) findings include 

reductions in total lipids (CH, BS; also CB, but nonsignificant) and free fatty 

acids (CH, CB, BS). PL were elevated in CH, but reduced in CB; Chol was 

elevated in CH, but reduced in CB and BS; and esterified fatty acids were 

elevated in CB, but reduced in CH and BS. Levels of malonaldehyde and 

lipase activity were elevated in all regions. Results indicate that subacute brief 

exposures to SO2 can lead to degradation of brain lipids, with the exact nature 

of the lipid alterations dependent upon brain region. 

 E-16 
Study Concentration Duration Species Effects 

Haider et al. 

(1982) 

10 ppm (26.2 

mg/m3); whole body 

1 h/day for 

30 days 

Charles Foster rat; 

male; adult; 

150-200 g; 

N = 12/group 

(6/subgroup) 

The effects of SO2 exposure on lipid profiles, lipid peroxidation and lipase 

activity in three regions of the brain (cerebral hemisphere, CH; cerebellum, CB; 

brain stem, BS) were examined. Significant (p < 0.001-0.05) findings include 

reductions in total lipids (CH, BS, CB), while PL were elevated only in CB. Chol 

was elevated in CH and CB, but not BS; and gangliosides were elevated in CB 

and BS, but reduced in CH. Lipid peroxidation (malonaldehyde formation) was 

elevated in whole brain and all regions (although nonsignificantly in BS), as 

was lipase activity in CH, the only tissue examined. Despite regional 

differences in PL and Chol changes, Chol/PL ratios were elevated in all three 

brain regions (again nonsignificantly in BS). Results are somewhat different 

than those seen in guinea pig (Haider et al., 1981), but again suggest that 

subacute brief exposures to SO2 can lead to degradation of brain lipids, with 

the exact nature of the lipid alterations dependent upon brain region. 

Haider and 

Hasan 

(1984) 

10 ppm (26.2 

mg/m3) SO2

alternated with 

20 ppm 

(14.7 mg/m3) H2S; 

whole body 

1 h/day for 

30 days (alternating SO2 or H2S) 

Guinea pig; sex and 

age NR; 250-400 g; 

N = 18/group in 2 

groups (6/group in 

some subgroups) 

The effects of alternating SO2 + H2S exposure on lipid profiles, lipid 

peroxidation and lipase activity in four regions of the brain (cerebral 

hemisphere, CH; basal ganglia, BG; cerebellum, CB; brain stem, BS) and in 

the spinal cord (SC) were examined. Significant (p < 0.001-0.05) findings 

include reductions in total lipids and Chol, and elevated lipid peroxidation 

(malonaldehyde formation) and lipase activity, in all brain regions and SC. 

Chol/PL ratios were also reduced in all tissues (but nonsignificantly in BG and 

CB). For other parameters (PL, free fatty acids, esterified fatty acids, and 

gangliosides), changes were observed in most tissues but were region-specific. 

Results indicate that subacute brief, alternating exposures to SO2 or H2S lead 

to degradation of brain lipids, again with the exact nature of the lipid alterations 

dependent upon brain/spinal cord region. Additionally, some of the effects 

observed for this mixture vary from those seen with SO2 alone (Haider et al., 

1981; 1982). 

Ağar et al. 

(2000) 

10 ppm (26.2 

mg/m3) (± iv alloxan 

to induce 

experimental type 1 

diabetes); whole 

body 

1 h/day, 7 days/wk 

for 6 wks 

Swiss albino rat; male; 

3 mos old; weight NR; 

N = 10/group in 

4 groups 

In retina tissue, exposure elevated SOD activity and reduced GPx and catalase 

activities. TBARS were elevated only in non-diabetic rats exposed to SO2. In 

brain tissue, exposure elevated SOD and reduced GPx activities in both 

non-diabetics and diabetics, while catalase activities were not affected; TBARS 

were elevated in both non-diabetics and diabetics. With respect to VEPs, 

exposure prolonged latencies in 4 of 5 VEP components in non-diabetics and 5 

of 5 in diabetics, while reducing virtually all peak-to-peak amplitudes in 

non-diabetics and diabetics. For many endpoints, SO2 effects were additive to 

those resulting from the induced diabetic condition. In summary, brain and 

retinal anti-oxidant and lipid peroxidation status, as well as neuro-visual 

performance were affected by subchronic exposure to brief periods of 10 ppm 

SO2, and these effects were exacerbated by a diabetic condition. 

SUBCHRONIC / CHRONIC 

Küçükatay 

et al. (2003) 

10 ppm (26.2 

mg/m3) (± iv alloxan 

to induce 

experimental type 1 

diabetes); whole 

body 

1 h/day, 7 days/wk 

for 6 wks 

Rat; male; 3 mos old; 

weight not reported; N 

= 10/group in 4 groups 

In brain tissue, SO2 exposure elevated SOD and reduced GPx activities in both 

non-diabetics and diabetics, while catalase activities were not affected; TBARS 

were elevated in both non-diabetics and diabetics. With respect to afferent 

peripheral nerve pathways (SEPs), exposure prolonged latencies in 4 of 4 SEP 

components in both non-diabetics and diabetics; also altered were some 

inter-peak latencies (non-diabetics and diabetics) and some peak-to-peak 

amplitudes (non-diabetics only). In some cases, SO2 effects were additive to 

those resulting from the induced diabetic condition. In summary, brain 

anti-oxidant and lipid peroxidation status, as well as afferent peripheral nerve 

pathways, were affected by subchronic exposure to 10 ppm SO2, and these 

effects were exacerbated by a diabetic condition. Authors suggest that SO2

exposure could potentiate the incidence and/or severity of diabetes. 

 E-17 
Study Concentration Duration Species Effects 

Yargiçoğlu 

et al. (1999) 

10 ppm (26.2 

mg/m3); whole body 

1 h/day, 7 days/wk 

for 6 wks 

Swiss albino rat; male; 

3, 12, or 24 mos old; 

weight NR; N = 

10/group in 6 groups 

Effects of aging ± SO2 exposure on levels of lipid peroxidation (TBARS), 

antioxidant enzyme status (catalase, GPx, SOD), and afferent peripheral nerve 

pathways (SEPs) were monitored in the brain of young (Y, 3 mo), middle-aged 

(M, 12 mo) and old (O, 24 mo) rats. In addition to age-related changes, SO2

exposure significantly (p < 0.0001-0.02) elevated TBARS and SOD, while 

reducing GPx (Y, M, O); catalase levels were not affected. Of 4 monitored SEP 

component peaks, SO2 significantly (p < 0.01-0.05) prolonged latencies in 

groups Y (4/4) and M (1/4), but not in O (0/4). Peak-to-peak amplitudes were 

decreased in Y, (2/3) and increased in M (1/3), but not affected in O (0/3). 

Taken together, these data indicate that subchronic exposure to brief periods of 

10 ppm SO2 can impact afferent peripheral nerve pathways and the lipid 

peroxidation and antioxidant enzyme status of the brain. 

Kilic (2003) 10 ppm 

(26.2 mg/m3); whole 

body 

1 h/day, 7 days/wk 

for 6 wks 

Swiss albino rat; male; 

3, 12, or 24 mos old; 

weight NR; N = 

10/group in 6 groups 

Effects of aging ± SO2 exposure on levels of lipid peroxidation (TBARS), 

antioxidant enzyme status (catalase, GPx, SOD), and visual system function 

(VEPs) were monitored in the brain and eye (retina and lens) of young (Y, 3 

mo), middle-aged (M, 12 mo) and old (O, 24 mo) rats. In addition to age-related 

changes, SO2 exposure significantly (p < 0.0001-0.04) elevated TBARS in 

brain and lens (Y, M, O), and in retina (Y); reduced GPx in brain (Y) and lens 

(Y, M, O); reduced catalase in retina (Y, M, O); and elevated SOD in brain (Y, 

M), retina (Y, M, O) and lens (M, O). Of 5 monitored VEP component peaks, 

SO2 prolonged latencies in groups Y (4/5), M (3/5) and O (1/5). Taken together, 

these data indicate that subchronic exposure to brief periods of 10 ppm SO2

can impact the visual system and the lipid peroxidation and antioxidant enzyme 

status of the brain and eye. 

NEUROBEHAVIOR 

Petruzzi 

et al. (1996) 

5, 12, or 30 ppm 

(13.1, 31.4, or 

78.6 mg/m3); whole 

body 

Near continuous 

(80% of time) 

exposure from 

9 days before 

mating through the 

12-14th day of 

pregnancy 

CD-1 mouse, adult 

male and female 

parental animals were 

exposed (N = 

10/group/sex) 

(N = 8 litters/group, 

fostered by unexposed 

dams at birth) were 

evaluated at 2-18 days 

of age; adult male 

offspring also 

evaluated (N = 

8/group) 

Adults: Observation of behavior outside the exposure chamber on exposure 

days 3, 6, and 9 revealed dose-related increases in digging and decreases in 

grooming by females in the 30 ppm group on exposure day 9; non-dose related 

increases were observed for crossing and wall rearing by females in the 

30 ppm group on exposure day 9. Observance of behaviors in 2 breeding 

pairs/group in the 12 and 30 ppm groups revealed increased rearing and social 

interaction in the 30 ppm group shortly after the start of exposure, followed by 

return to baseline levels; effects were generally of greater magnitude in males. 

 

 E-18 
Table E-13. Reproductive and developmental effects of SO2. 

Study Concentration Duration Species Effects 

Meng and 

Bai (2004) 

8.4, 21, or 43 ppm 

(22, 56, or 112 

mg/m3); whole body 

6 h/day for 7 days Kunming albino mouse; 

male; 5 wks old; 19 ± 2 

g; N = 10/group 

Changes observed in mouse testes (concentrations of effects) included 

decreased activities of SOD (43 ppm, possibly at 21 ppm according to text) 

and GPx (≥ 21 ppm), increased catalase activity (8.4 and 21 ppm), decreased 

GSH level (≥ 21 ppm), and increased TBARS levels (≥ 8.4 ppm). The authors 

concluded that SO2 can induce oxidative damage in testes of mice. 

Gunnison 

et al. 

(1987) 

10 or 30 ppm (26.2 

or 78.6 mg/m3); 

whole body 

6 h/day, 

~5 days/wk for 21 

wks (total of 99 

days) 

Sprague-Dawley CD 

rat; male; 8 wks old; 

weight NR; 

N = 70/group in 

3 groups (inhalation 

series) 

No significant (p < 0.05) effect on testes histopathology was found, although 

there was a very slight and probably biologically insignificant increase in 

relative testes weight. (0.61 ± 0.02 vs. 0.56 ± 0.02, % body weight). 

Singh 

(1989) 

32 or 65 ppm (83.8 

or 170 mg/m3); whole 

body 

Gestation day 7-18 CD-1 mouse dams 

were exposed; 

numbers of dams 

exposed and offspring 

evaluated not indicated 

No significant effects were observed for number of live pups born/litter. Pup 

birth weight was lower at 65 ppm. Righting and negative geotaxis reflexes 

were delayed at both concentrations. 

Petruzzi 

et al. 

(1996) 

5, 12, or 30 ppm 

(13.1, 31.4, or 

78.6 mg/m3); whole 

body 

Near continuous 

(80% of time) 

exposure from 

9 days before 

mating through the 

12-14th day of 

pregnancy 

CD-1 mouse; adult 

male and female 

parental animals were 

exposed 

(N = 10/group/sex) and 

male and female 

offspring 

(N = 8 litters/group, 

fostered by unexposed 

dams at birth) were 

evaluated at 2-18 days 

of age; adult male 

offspring also evaluated 

(N = 8/group) 

Decreased food and water intake were observed in parental males and 

females of the 12 and 30 ppm groups at the start of mating (exposure days 

9-13). No effects observed for mating or successful pregnancies. There were 

no effects on litter sizes, sex ratio, or neonatal mortality (data not shown by 

authors). No effects observed for birth weight, postnatal body weight gain, 

somatic and neurobehavioral development (e.g., eyelid and ear opening, 

incisor eruption, and reflex development); no postnatal developmental data 

were shown by authors. No effects observed in passive avoidance testing of 

adult males. 

Fiore et al. 

(1998) 

5, 12, or 30 ppm 

(13.1, 31.4, or 

78.6 mg/m3); whole 

body 

Near continuous 

(90% of time) 

exposure from 

9 days before 

mating through the 

14th day of 

pregnancy 

CD-1 mouse; adult 

male and female 

parental animals were 

exposed and adult 

male offspring (fostered 

by unexposed dams at 

birth) were evaluated at 

~120 days of age, N = 

11-12 offspring/group 

In 20-min encounters with unexposed males, prenatally-exposed males compared to controls displayed (dose(s) of effect, time of testing effect observed) 

increased duration of self grooming (5 ppm, 15-20 min), decreased frequency 

and duration of tail rattling (≥ 5 ppm at 5-10 min and 12 ppm at 10-15 min), 

and decreased duration of defensive postures (≥ 12 ppm, 0-5 min). Authors 

also noted a non-significant decrease in freezing (apparently at all dose 

levels) and non-significant increases in social exploration (apparently at all 

doses) and rearing (apparently at ≥ 12 ppm). 

Douglas 

et al. 

(1994) 

5 ppm (13.1 mg/m3); 

whole body 

2 h/day for 13 wks New Zealand White 

rabbit; male and 

female; 

N = 3-4/group; 

1-day-old; immunized 

against Alternaria 

tenuis 

Following subchronic exposure beginning in the neonatal period, there were 

no effects on lung resistance, dynamic compliance, transpulmonary pressure, 

tidal volume, respiration rate, or min volume. 

 

 E-19 
Table E-14. Endocrine system effects of SO2. 

Study Concentration Duration Species Effects 

Lovati et 

al. (1996) 

5 or 10 ppm (13.1 or 

26.2 mg/m3); whole 

body 

24 h/day for 

15 days 

Sprague-Dawley CD rat; 

male; age NR; 250-275 

g; 

N = 9/subgroup in 9 

subgroups 

Subjects were rats fed standard diet (normal) or high cholesterol diet, and rats 

with streptozotocin-induced diabetes fed standard diet. In diabetic rats, there 

was no effect on glucose levels. Exposure to ≥ 5 ppm lowered plasma insulin 

level in normal and hypercholesterolemic diet groups, but elevated it 

(non-significantly) in diabetic rats. In each rat model, inhalation of SO2 at 

levels without overt effects affected insulin levels. Specific effects varied 

according to diet or diabetes. 

Ağar et al. 

(2000) 

10 ppm (26.2 

mg/m3); whole body 

1 h/day, 7 days/wk 

for 6 wks 

Swiss Albino rat; male; 3 

mos old; weight NR; N = 

10/group 

Effects were compared in non-diabetic rats and rats with alloxan-induced 

diabetes. SO2 increased blood glucose in diabetic and non-diabetic rats. 

Küçükatay 

et al. 

(2003) 

10 ppm (26.2 

mg/m3); whole body 

1 h/day, 7 days/wk 

for 6 wks 

Rat, male; 3 mos old; 

weight NR; 

N = 10/group in 4 groups

Effects were compared in normal rats and rats with alloxan-induced diabetes. 

SO2 elevated blood glucose levels in both non-diabetics and diabetics. 

 

Table E-15. Liver and gastrointestinal effects of SO2. 

Study Concentration Duration Species Effects 

SUBACUTE / SUBCHRONIC 

Meng et al. 

(2003c) 

7.86, 20, or 40 ppm (22, 

56, or 112 mg/m3) per 

author conversion; 

whole body 

6 h/day for 7 days Kunming albino mouse, 

male and female; 5 wks 

old; 19 ± 2 g; 

N = 6/sex/subgroup 

Effects observed in stomach (concentration of effect) included: 

increase in SOD activity (7.86 ppm, males only) and TBARS 

level (≥ 7.86 ppm) and decreases in SOD (≥ 20 ppm, males 

only) and GPx activities (≥ 20 ppm, males only) and GSH level 

(40 ppm). Effects observed in intestine were increases in 

catalase activity (≥ 20 ppm in males, 40 ppm in females) and 

TBARS level (≥ 20 ppm) and decreases in SOD (≥ 7.86 ppm) 

and GPx (≥ 20 ppm) activities and GSH level (≥ 7.86 ppm). 

Wu and Meng 

(2003) 

8.4, 24.4, or 56.5 ppm 

(22, 64, or 148 mg/m3); 

whole body 

6 h/day for 7 days Kunming-strain mouse; 

male; age NR; 18-20 g; 

N = 10/group 

No effects were observed in the liver at 8.4 or 24.4 ppm. GST 

and G6PD activities and GSH level were decreased at 56.5 ppm. 

Bai and Meng 

(2005b) 

5.35, 10.70, or 

21.40 ppm (14, 28, or 56 

mg/m3); whole body 

6 h/day for 7 days Wistar rat; male; age NR; 

180-200 g; 

N = 6/group in 4 groups 

Significant and concentration-dependent changes in mRNA (mid 

and high concentrations) and protein expression (all 

concentrations) included increases for bax and p53 

apoptosis-promoting genes, and decrease for bcl-2

apoptosis-repressing gene. Authors speculated potential impact 

on human apoptosis-deficient diseases. 

Qin and Meng 

(2005) 

5.35, 10.70, 

or 21.40 ppm (14, 28, or 

56 mg/m3); whole body 

6 h/day for 7 days Wistar rat; male; age NR; 

180-200 g; 

N = 6/group in 4 groups 

SO2 caused significant concentration-dependent reductions in 

liver enzyme activities and gene expression for CYP1A1 and 

CYP1A2. Effects were seen at the mid and high concentrations 

(only high for CYP1A1 enzyme activity), but not the low. Authors 

speculate that underlying mechanisms may involve oxidative 

stress and/or cytokine release, and may represent an adaptive 

response to minimize cell damage. 

 E-20 
Study Concentration Duration Species Effects 

Lovati et al. 

(1996) 

5 or 10 ppm (13.1 or 

26.2 mg/m3); whole 

body 

24 h/day for 

15 days 

Sprague-Dawley CD rat; 

male; age NR; 

250-275 g; 

N = 9/subgroup 

Subjects were rats fed standard diet (normal) or high cholesterol 

diet, and rats with streptozotocin-induced diabetes fed standard 

diet. SO2 (≥ 5 ppm) elevated plasma triglycerides in normal and 

hypercholesterolemic groups, while 10 ppm lowered plasma high 

density lipoprotein cholesterol in hypercholesterolemic rats. In 

diabetic rats, 10 ppm SO2 lowered triglycerides and free fatty 

acids without affecting high density lipoprotein cholesterol or total 

cholesterol. In the liver, SO2 elevated triglycerides in normal and 

hypercholesterolemic groups (at 10 ppm), but lowered it in 

diabetic rats (at ≥ 5 ppm); esterified cholesterol was elevated in 

normal rats (at 10 ppm), but lowered in diabetic rats (at ≥ 5ppm), 

and free cholesterol was unchanged in all groups. In normal rats, 

triglycerides secretion rate was inhibited by 10 ppm SO2. SO2

caused several changes in plasma apolipoprotein composition in 

normal and hypercholesterolemic groups, but not in diabetic rats. 

Leukotriene parameters were not affected. Thus, in each rat 

model, inhalation of SO2 at levels without overt effects affected 

plasma and tissue lipid content. Specific effects varied according 

to diet or diabetes. 

Langley-Evans 

et al. (1996) 

5, 50, or 100 ppm (13.1, 

131, or 262 mg/m3); 

whole body 

5 h/day for 

7-28 days 

Wistar rat; male; 7 wks old; 

weight NR; 

N = 4-5/treatment group, 

8 controls 

GSH was depleted in the liver at 5 and 100 ppm but not at 

50 ppm. With respect to GSH-related enzymes, exposure to 

5 ppm decreased GRed and GST activity in the liver. Exposure to 

50 ppm did not affect liver GST, but decreased liver GRed and 

GPx. 

Langley-Evans 

et al. (1997); 

Langley-Evans 

(2007) 

100 ppm (286 mg/m3); 

whole body 

Units were incorrectly 

reported as μg/m3 in the 

study but were corrected 

according to information 

provided by study author 

5 h/day for 

28 days 

Wistar rat; male; 7 wks old; 

weight NR; N = 4-16 

Adult rats exposed to air or SO2 were born to dams fed diets with 

varying casein contents (180 [control], 120, 90 or 60 g/kg) during 

gestation. In the liver, SO2 exposure elevated GSH level in the 

120 g/kg dietary group but lowered it in the 60 g/kg dietary group. 

SO2 did not affect liver GST in any group. SO2 increased GCS 

levels in the 180 and 90 g/kg groups, GPx in the 60 g/kg group, 

and GRed in the 120 and 90 g/kg groups. This study provides 

information for an extremely high concentration level but is being 

acknowledged here with the unit corrected to verify that a 

low-concentration level study was not missed. 

Gunnison et al. 

(1987) 

10 or 30 ppm (26.2 or 

78.6 mg/m3); whole 

body 

6 h/day, 

~5 days/wk for 

21 wks (total of 

99 days) 

Sprague-Dawley CD rat; 

male; 8 wks old; weight 

NR; N = 70/group in 

3 groups (inhalation series)

No effects on relative liver weight or histopathology were found. 

Ağar et al. (2000) 10 ppm (26.2 mg/m3); 

whole body 

1 h/day, 7 days/wk 

for 6 wks 

Swiss Albino rat; male; 

3 mos old; weight NR; 

N = 10/group 

Effects were compared in non-diabetic rats, non-diabetic rats 

exposed to SO2, alloxan-induced diabetic rats, and diabetic rats 

exposed to SO2. SO2 increased blood glucose in all groups, but 

did not affect total cholesterol, high density lipoprotein 

cholesterol, low density lipoprotein cholesterol, very low density 

lipoprotein cholesterol, or triglyceride levels in either normal or 

diabetic rats. 

Küçükatay et al. 

(2003) 

10 ppm (26.2 mg/m3); 

whole body 

1 h/day, 7 days/wk 

for 6 wks 

Rat; male; 3 mos old; 

weight NR; N = 10/group in 

4 groups 

Effects compared in normal rats and rats with alloxan-induced 

diabetes. Among the significant effects observed, SO2 exposure 

enhanced the body weight loss seen in the diabetic group, but 

did not affect body weight gain in the control group. SO2 elevated 

blood glucose levels in both controls and diabetics, but lowered 

triglycerides only in diabetics. Cholesterol parameters were not 

affected. 

 

 E-21 
Table E-16. Renal effects of SO2. 

Study Concentration Duration Species Effects 

Wu and Meng 

(2003) 

8.4, 24.4, or 56.5 ppm 

(22, 64, or 148 mg/m3) 

6 h/day for 7 days Kunming-strain mouse; 

male; age NR; 18-20 g; 

N = 10/group 

GST was decreased in the kidney at 24.4, or 56.5 ppm (64 and 

148 mg/m3) and G6PD activity was decreased at 56.5 ppm 

(148 mg/m3). Kidney GSH levels were reduced at all exposure 

levels. 

Langley-Evans 

et al. (1996) 

5, 50, or 100 ppm (13.1, 

131, or 262 mg/m3) 

5 h/day for 7-28 

days 

Wistar rat; male; 7 wks 

old; weight NR; 

N = 4-5/treatment group, 

8 controls 

GSH was depleted in the kidney in the 5 and 100 ppm groups but 

not in the 50 ppm group. No effects were observed for other 

GSH-related enzymes. 

 

Table E-17. Respiratory System – Effect of SO2 on morphology. 

Study Concentration Duration Species Effects 

ACUTE / SUBACUTE 

Conner 

et al. 

(1985) 

1 ppm (2.6 

mg/m3); nose 

only 

3 h/day for 6 days; 

animals evaluated 

for up to 72 h 

following exposure 

Hartley guinea pig; male; 

age NR; 250-320 g; 

N = 14/group/time point 

In combined group of SO2-exposed animals and furnace gas controls, no 

alveolar lesions were observed. 

SUBCHRONIC / CHRONIC 

Wolff 

et al. 

(1989) 

5 ppm (13 

mg/m3); nose 

only 

2 h/day, 5 days/wk 

for 4 wks 

F344/Crl rat; male and 

female; 10-11 wks old; 

weight NR; 

N = 3/sex/ group 

No nasal or pulmonary lesions. 

Smith 

et al. 

(1989) 

1 ppm (2.62 

mg/m3); whole 

body 

5 h/day, 5 days/wk 

for 4 or 8 mos; half 

the animals in the 

8-mo group were 

allowed to recover 

for 3 mos. 

Sprague-Dawley rat; male; 

young adult; initial weight 

NR; N = 12-15/data point 

At 4 mos of SO2 exposure, increases were observed for incidence of bronchial 

epithelial hyperplasia (80 vs. 40% in controls) and numbers of nonciliated 

epithelial cells (31.1 vs. 27.7% in controls); neither effect persisted past 4 mos 

of exposure. 

Gunnison 

et al., 

(1987) 

10 and 30 ppm 6 h/day and 5 

day/wk for 21 wks 

Sprague-Dawley CD rat; 

8 week of age 

Mild epithelial hyperplasia in the trachea and larger bronchi, mucoid 

degeneration and desquamation of epithelium of the larger bronchi. 

 

 E-22 
Table E-18. Respiratory System – Effects of SO2 exposure on host lung defenses. 

Study Concentration Duration Species Effects 

CLEARANCE – SUBCHRONIC 

Wolff et al. 

(1989) 

5 ppm (13.1 

mg/m3); nose only 

2 h/day, 5 days/wk 

for 4 wks 

F344/Crl rat; male and 

female; 10-11 wks old; 

weight NR; N = 

6/sex/group 

There was no effect on pulmonary clearance of radiolabeled aluminosilicate 

particles (MMAD 1.0 micron). 

IMMUNE RESPONSES – ACUTE / SUBACUTE 

Jakab et al. 

(1996) 

10 ppm (26.2 

mg/m3); nose only 

4 h Specific pathogen-free 

white Swiss mouse; female; 5 wks old; 20-23 

g; 

N = 5/ group 

No effect was observed on in situ Fc receptor-mediated phagocytosis of sheep 

red blood cells by AM, which was assessed 3 days after exposure to SO2. 

Clarke et al. 

(2000) 

10 ppm (26.2 

mg/m3) SO2; nose 

only 

4 h Outbred Swiss mouse; 

female; age and 

weight NR; 

N = 10/experimental 

value 

No effect on in situ AM phagocytosis (data not shown) or on intrapulmonary 

bactericidal activity toward Staphylococcus aureus. 

AzoulayDupuis et al. 

(1982) 

10 ppm (26.2 

mg/m3); whole 

body 

24 h, 1 wk, 2 wks, 

or 3 wks 

OF1 mouse; female; 

age NR; mean 20.6 g; 

N = 768 (32/group) 

Respiratory challenge with Klebsiella pneumoniae resulted in increased 

mortality and decreased survival time in the 1, 2, and 3 wk SO2 exposure 

groups compared to controls. Differences did not correlate with exposure 

length. 

EX VIVO 

Blanquart 

et al. (1995) 

0, 5,10, 20, 30, or 

50 ppm (0, 13.1, 

26.2, 52.4, or 

131 mg/m3) SO2

 Fauve de Bourgogne 

rabbit; 1 mo old; 

tracheal epithelium 

explants 

Relative to control cultures, cell viability was not reduced at 5 and 10 ppm, but 

was at 30 ppm (~70%) and 50 ppm (~60%). Ciliary beat frequency was 

significantly reduced (p < 0.05) at 10-30 ppm, and was correlated with swollen 

mitochondria and depletion of cellular ATP, as well as with blebbing of ciliated 

or microvilli-covered cells and with aggregation and flattening of cilia. 

Riechelmann 

et al. (1995) 

2.9, 5.7, 8.6, 11.5, 

or 14.3 ppm 

(7.5, 15, 22.5, 30, 

or 37.5 mg/m3); ex 

vivo exposure of 

trachea 

30 min Guinea pig; sex, age, 

and weight NR; 

N = 4-8/group 

No remarkable morphologic abnormalities in the tracheal mucociliary system 

of the 2.9 ppm group, though slight vacuolization, rare membrane blebs, and 

slightly widened intercellular spaces were observed. Abnormalities in the 5.7 

and 8.6 ppm groups were similar and included loosened contact to the basal 

membrane, extensive intracellular edema and vacuolization, swollen 

mitochondria, polypoid extrusions and huge blebs in the cell membrane and 

ciliary membrane, widened intercellular space, and disrupted tight junctions. 

Additional abnormalities in the 11.5 and 14.3 ppm groups included marked 

epithelial sloughing, occasionally disrupted cell membranes and microtubules, 

and frequently disrupted ciliary membranes. Tracheal mucociliary activity was 

significantly decreased in all exposure groups (from 8.7 ± 1.0 Hz [controls] to 

4.0 ± 1.1, 3.4 ± 2.7, 1.8 ± 2.2, 1.5 ± 1.8, and 2.0 ± 1.2 Hz in the 7.5, 15, 22.5, 

30, and 37.5 mg/m3 groups, respectively). 

Knorst et al. 

(1994) 

2.5, 5.0, 7.5, 10.0, 

or 12.5 ppm (6.6, 

13.1, 19.7, 26.2, or 

32.8 mg/m3); ex 

vivo exposure of 

trachea 

30 min Guinea pig; sex, age, 

and weight NR; 

N = 4-7/group 

63% decrease in tracheal mucociliary activity at 2.5 ppm with dose-dependent 

decrease to 81% at 7.5 ppm; higher concentrations did not further decrease 

mucociliary activity. Ciliary beat frequency decreased by 45% at 5.0 ppm with 

dose-dependent decrease to 72% at 12.5 ppm. All reductions are relative to 

baseline values; no effect on controls for either parameter. 

 E-23 
Table E-19. Genotoxic effects of SO2 and metabolites. 

Study Concentration Duration Species/System Effects 

 “POINT MUTATION” 1 IN VITRO 

Pool-Zobel 

et al. 

(1990) 

0 or 50 ppm (131 

mg/m3) SO2 or 

the equivalent 

agar concentration of SO3

2-, 

15 μg/ml) 

48 h Rat, Sprague-Dawley, 

female, liver enzyme 

preparations 

In vitro induction of reverse mutation in cultures of S. typhimurium strain TA98 was not 

affected by incubating the bacterial-B[a]P-liver S9 enzyme activation system in the 

presence of SO2/sulfite. An ancillary finding from the 0 μg B[a]P control exposures is that 

SO2/sulfite itself did not appear mutagenic. 

CYTOGENETIC AND DNA DAMAGE2 IN VITRO 

Pool et al. 

(1988b) 

0, 20, 50 or 

200 ppm (0, 

52.4, 131 or 524 

mg/m3) SO2 

0, 0.1, 0.2 or 0.4 

mM SO3

2- 

0 or 2.5 μmol 

HSO3

-

 per 

microtiter plate 

well 

0, 0.1, 0.2 or 0.4 

mM SO4

2- 

0 or 10 μmol 

MgSO4 per tube 

1-24 h Hamster, Syrian 

golden; fetal lung cells 

(FHLC, gestational 

day 15) 

Rat, Sprague- Dawley; 

male; age NR; ~200g, 

hepatocytes 

Chinese hamster 

ovary cell line 

transformed by SV40, 

CO60 cells 

Precinorm U (human 

serum standard) 

Toxicity and genotoxicity of SO2, sulfite/bisulfite and sulfate (also NO2/NOX) were variously 

assessed in several in vitro test systems. It was noted that medium pH remained stable at 

[SO2] ≤ 200 ppm. Precinorm LDH activity was substantially inhibited by 50 ppm SO2 after 

1-3 h, and by 0.1 mM sulfite ion almost immediately, but not by 0.1 mM sulfate ion; AST was 

modestly inhibited after 5 h by 200 ppm SO2; other monitored enzymes were not affected. 

While trypan blue exclusion was not affected, SO2 cytotoxicity to FHLC was demonstrated 

at 20 ppm by reduced plating efficiency; at 50 ppm, enzyme activity leaked into culture 

medium was reduced only for AP and especially LDH (not other enzymes). 200 ppm SO2

did not induce DNA damage (single-strand breaks) by itself in either FHLC or rat hepatocytes, but did somewhat reduce that induced by AMMN. In hepatocytes, incubation with 

MgSO4 also caused a small reduction in AMMN-induced DNA damage. A 1 h exposure to 

200 ppm SO2 did not induce selective amplification of SV40 DNA in CO60 cells, nor affect 

that induced by DMBA or B[a]P. However, while also not affecting induction by DMBA or 

B[a]P, HSO3

-

 added directly to the medium for 24 h did induce SV40 DNA amplification on 

its own. Authors appear to suggest this might result from arrest of cells in mid-S phase, 

which leads to DNA amplification. Thus, principal findings include inhibition of LDH by SO2

or sulfite that could impair the cellular energy system; such an impairment could be responsible (possibly along with SO4

2- conjugation of reactive intermediates) for the observed 

inhibition of AMMN-induced DNA damage by SO2. Further, SO2 does not appear by itself to 

induce DNA damage. 

Shi and 

Mao 

(1994) 

3 mM SO3

2- 40 min 

(test tube 

reactions) 

dG or DNA Test tube reaction mixtures that caused sulfite to oxidize to sulfur trioxide radical resulted in 

the hydroxylation of dG (8-OHdG) and the generation of DNA double strand breaks. 

Shi (1994) 5 mM SO3

2- (as 

Na2SO3) 

1.5 h 

(test tube 

reaction) 

Dg Test tube reaction of sulfite ion with H2O2 shown to generate hydroxyl radicals capable of 

hydroxylating dG to the DNA damage marker, 8-OHdG. Furthermore, incubation of sulfite 

with nitrite or various transition metal ions was shown to generate sulfur trioxide anion 

radical. 

CYTOGENETIC AND DNA DAMAGE2 ACUTE / SUBACUTE 

Ruan et al. 

(2003) 

0 ppm SO2 (+ 0 

or 8 mg/kg bw 

SSO) or 10.7 

ppm (28 mg/m3) 

SO2 (+ 0, 2, 4, 6 

or 8 mg/kg bw 

SSO); whole 

body 

± SSO ip on 

days 1-3; 

then SO2 for 

5 day (Days 

4-8), 

6 h/day 

Kunming mouse; male 

and female; ~6 wk old; 

20-25 g; N = 

6/sex/conc. 

Subacute inhalation of 10.7 ppm (28 mg/m3) SO2 induced a significant (p < 0.001) 10-fold 

increase in mouse bone marrow MNPCE, which was partially mitigated in dose-dependent 

fashion by pretreatment with SSO, a complex natural anti-oxidant substance. SO2 exposure 

also resulted in organ:bw ratios that increased for liver and kidney, decreased for lung and 

spleen, and remained unchanged for heart. Such ratio changes were largely mitigated by 

SSO pretreatment. 

Meng 

et al. 

(2002) 

0, 5.35, 10.7, 

21.4, or 32.1 

ppm (0, 14, 28, 

56, or 84 mg/m3) 

SO2; whole body 

4 h/day for 

7 days 

Kunming mouse; male 

and female; ~6 wk old; 

20-25 g; N = 

10/sex/conc. 

In vivo exposure caused significantly (p < 0.01-0.001) increased frequencies of bone 

marrow MNPCE similarly in both sexes at all concentrations in a dose-dependent manner, 

and with only minimal cytotoxicity at the 3 highest concentrations. The level of MNPCE (%) 

even at the low SO2 conc. was triple that of the control value. Thus, subacute inhalation of 

SO2 at noncytotoxic concentrations (though still notably higher than most human 

exposures) was clastogenic in mice. 

 E-24 
Study Concentration Duration Species/System Effects 

Meng 

et al. 

(2005b) 

0, 5.35, 10.7, 

21.4, or 32.1 

ppm (0, 14, 28, 

56, or 84 mg/m3) 

SO2; whole body 

6 h/day for 

7 days 

Kunming mouse; male 

and female; ~5 wk old; 

18-20 g; N = 

6/sex/conc. 

Following in vivo exposure to SO2, it was shown by the single cell gel electrophoresis 

(comet) assay that such exposure induced significant (p < .001-.05) dose-dependent DNA 

damage (presumed mostly to be single-strand breaks and alkali-labile sites) in cells isolated 

from brain, lung, liver, intestine, kidney, spleen, and testicle, as well as in lymphocytes, and 

beginning at the lowest concentration (except male intestine—lowest response at 10.7 ppm 

[28 mg/m3]). Results demonstrate that SO2 can cause systemic DNA damage in many 

organs, not just the lung. Authors note that potential occupational exposures and the fact 

that the obligate nose-breathing mouse removes ~95% of inhaled SO2 in its nasal passages 

make this experimental concentration range relevant to possible human exposures. 

Pool et al. 

(1988a) 

0 or 50 ppm 

(131 mg/m3) SO2

24 h/day, 

7 day/wk, 

for 2 wks 

Rat, Sprague-Dawley; 

female; 4 mo old; wt 

NR; N = 5/group 

Assessments were conducted on isolated primary lung and liver cells, or on blood serum. In 

vivo SO2 exposure did not affect viability (trypan blue exclusion) of cells either immediately 

after isolation or after 1 h incubation with 1% DMSO (used for enzyme leakage assays). In 

contrast to controls, hepatocytes from SO2-exposed rats released no LDH activity into 

DMSO-medium after 1 h, and AST activity was reduced. Other enzyme (AP, ALT, GT) 

activity releases were not affected in lung cells, and none were in hepatocytes. In blood 

serum, the only effect was a marked increase in LDH activity. The only significant 

(p < 0.001- 0.01) exposure effects on lung or liver activities (in x 9000 g supernatants of cell 

homogenates) of xenobiotic metabolizing enzymes (AHH, NDMA-D, GST) were elevated 

NDMA-D in the liver and reduced GST in the lung. Single-strand DNA breakage induced by 

three nitroso compounds (AMMN, NDMA, NMBzA) was reduced in hepatocytes from 

SO2-exposed rats. Authors discuss possible mechanisms for the observed effects, and note 

they are similar to in vitro effects reported elsewhere (Pool et al., 1988). 

CYTOGENETIC AND DNA DAMAGE2 SUBCHRONIC / CHRONIC 

Ohyama 

et al. 

(1999) 

0, 0.2 mL C, or 

(0.2 mL DEP+C 

±[4 ppm (10.48 

mg/m3) SO2 or 

6 ppm (11.28 

mg/m3) NO2 or 

4 ppm SO2+ 6 

ppm NO2]); 

whole body 

[Note: 0.2 mL 

C=1 mg; 0.2 mL 

DEcCBP =1 mg 

C + 2.5 mg DEP] 

SO2 and/or 

NO2: 

16 h/day for 

10 mos 

C or 

DEP+C: 

4 wk, 

once/wk by 

intratracheal 

infusion 

Rat, SPF F344/Jcl; 

male; 6 wk old; wt NR; 

N = 23-30/group in 6 

groups 

Purpose was to study effects of DEP on rat lung tumorigenesis and possible tumor 

promoting effects of SO2 or NO2 singly or together. (See Table E-11 for tumor-related 

effects.) DEP extract-DNA adducts were found only in the three gas-exposed groups. 

Chromatograms revealed two different adducts, one of which appears somewhat more 

abundant with SO2 coexposure, the other substantially more so with NO2; combined 

coexposure of both gases with DEP+C produced an adduct chromatogram appearing to be 

a composite of those for the individual gases. Thus, SO2 and NO2 appear capable of 

promoting the genotoxicity of DEP extract, though perhaps not in identical fashion. 

1Encompasses classical mutant selection assays based upon growth conditions under which mutants (or prototrophic revertants), but not the wild type (or auxotrophic) population 

treated with the test agent, can successfully grow (e.g., “Ames test,” CHO/HGRPT or mouse lymphoma L5178Y/TK mammalian cell systems, various yeast and Drosophila systems, 

etc.); while most viable mutation events detected in these assays are typically “point” mutations (DNA base substitutions, small deletions or frameshifts, etc.), some may involve larger 

losses/rearrangements of genetic material. 

2Encompasses CA, induction of MN or SCE, aneuploidy/polyploidy, DNA adduct and crosslink formation, DNA strand breakage, etc. 

 E-25 
Table E-20. Respiratory System – Effects of SO2 and metabolites on biochemistry. 

Study Concentration Duration Species Effects 

IN VITRO—PRIMARY / NONPRIMARY 

Li and Meng. 

(2007) 

0.1 μM-1mM NaHSO3 and Na2SO3

1:3 

4 h, followed by harvest at 0-

24 h 

BEP2D cell line of 

human bronchial 

epithelial cells 

 Increased mRNA and protein levels of MUC5AC and IL13 

Menzel et al. 

(1986) 

0, 0.1, 2, 20, or 40 mM (0, 4, 80, 

800, or 1600 μg/mL) SO3

2- 

~1 min - 96 h Rat, 

Sprague-Dawley; 

200-250g; sex, 

age, and n NR; 

lung cells and 

liver cells. 

Human 

lung-derived cell 

line, A549 

This study focused on intracellular covalent reactions of 

sulfite with primarily proteinaceous sulfhydryl compounds 

in cells isolated from rat lung and rat liver (for some 

comparative purposes), as well as in the human 

lung-derived cell line, A549. Sulfitolysis of protein disulfide 

bonds results in formation of cysteine S-sulfonate, and 

sulfitolysis of GSSG in formation of GSSO3H. The latter 

was formed in dose-dependent fashion upon the addition 

of sulfite to A549 cells. In addition to fibronectin and 

albumin, this study identified a third sulfite-binding protein 

in rat lung cytosol. GSSO3H was shown to be a potent 

competitive inhibitor of GST in rat lung, liver and A549 

cells. Results suggest that SO2 could affect the detoxication of PAHs and other xenobiotics via formation of 

GSSO3H and subsequent inhibition of GST and enzymatic 

conjugation of GSH with reactive electrophiles. 

OXIDATION AND ANTIOXIDANT DEFENSES – (SUBACUTE / SUBCHRONIC) 

Meng et al. 

(2003b) 

8.4, 21, or 43 ppm (22, 56, or 112 

mg/m3); whole body 

6 h per day for 7 days Kunming albino 

mouse; male and 

female; 5 wks old; 

19 ± 2 g; N = 

10/sex/group 

Changes observed in lung tissue (concentrations of effect) 

included higher SOD activity in males (8.4 ppm) and 

females (8.4 and 21 ppm), lower SOD activity in males (21 

and 43 ppm) and females (43 ppm), increased GPx 

activity in males and females (8.4 ppm), decreased GPx 

activity in males and females (≥ 21 ppm), decreased 

catalase activity in males (43 ppm), decreased reduced 

GSH level in males and females (≥ 8.4 ppm), increased 

TBARS level in males (≥ 8.4 ppm) and females 

≥ 21 ppm). Authors concluded that SO2 induced oxidative 

damage in lungs of mice. 

Wu and 

Meng (2003) 

8.4, 24.4, or 56.5 ppm (22, 64, or 

148 mg/m3); whole body 

6 h/day for 7 days Kunming-strain 

mouse; male; age 

NR; 18-20 g; 

N = 10/group 

Glucose-6-phosphate dehydrogenase and GST activity 

were decreased in lung at 24.4 and 56.5 ppm. Lung GSH 

levels were reduced in the 8.44 and 56.5 ppm exposure 

groups. Administration of buckthorn seed oil increased 

GST and decreased TBARS activity compared to mice 

exposed to SO2 alone. 

LangleyEvans et al. 

(1996) 

5, 50, or 100 ppm 

(13.1, 131, or 262 mg/m3); 

whole body 

5 h/day for 7-28 days Wistar rat; male; 7 

wks old; weight 

NR; 

N = 4-5/treatment 

group, 8 controls 

In the 5 and 100 ppm groups, GSH in BAL fluid decreased 

at 7 days and increased at 21 days; at 28 days GSH 

returned to normal in the 5 ppm group and further 

increased in the 100 ppm group. GSH was depleted in the 

lung, at 5 and 100 ppm but not at 50 ppm. With respect to 

GSH-related enzymes, exposure to 5 ppm lowered GCS, 

GPx, GST, and GRed activity in the lung. Effects in the 

100 ppm group were similar to the 5 ppm group, except 

that lung GPx was not reduced. Exposure to 50 ppm did 

not affect lung GST, but reduced the number of 

inflammatory cells in circulation and decreased GCS, GPx, 

GRed, and GT in the lung. Authors concluded that 

sulfitolysis of glutathione disulphide occurs in vivo during 

SO2 exposure and that SO2 is a potent glutathione 

depleting agent, even in the absence of pulmonary injury. 

 E-26 
Study Concentration Duration Species Effects 

Gümüşlü 

et al. (2001) 

10 ppm (26.2 mg/m3); whole body 1 h/day, 7 days/wk for 6 wks Swiss albino rat; 

male; 

3, 12, or 24 mos 

old; 210-450 g; N 

= 9-11/group in 6 

groups 

Effects of age on SO2-induced oxidative effects in lung 

tissue were observed in young (3-mo-old), middle aged 

(12-mo-old), and old (24-mo old) rats. SO2 exposure 

significantly elevated TBARS, SOD, GPx, and GST in all 

age groups; reduced catalase in young and middle-aged 

rats, but did not affect catalase in old rats. In rats not 

exposed to SO2, SOD, GPx and GST increased with age 

and catalase decreased with age. General observations in 

SO2-exposed animals were increases in SOD, GPx, and 

TBARS with age. The authors noted that while lipid 

peroxidation increased with age, relative TBARS increases 

in response to SO2 were inversely correlated with age (i.e., 

largest percent increase seen in young rats). 

LangleyEvans et al. 

(1997; 2007) 

~101 ppm (by study author 

calculations 286 mg/m3); whole 

body 

Note: The study mistakenly listed 

units of μg/m3 and it was verified 

with the authors that the units 

should have been listed as mg/m3. 

5 h/day for 28 days Wistar rat, male, 

7 wks old, weight 

NR, N = 4-16 

This study explored the effects of maternal diet protein 

restriction during gestation on offspring lung enzyme 

responses after SO2 exposure in adulthood. Adult offspring 

representing different maternal dietary concentrations of 

casein (180 [control], 120, 90 or 60 g/kg) were exposed 

either to air or SO2. GSH levels in BAL fluid and the lung 

were not affected either by maternal diet or SO2 exposure. 

In the lung GRed and GT were not affected by SO2 in any 

maternal diet group; GPx was reduced only in the 120 g/kg 

maternal diet group; GCS was elevated in the 180 and 60 

g/kg groups; and GST was reduced in the 180, 120 and 90 

g/kg groups (to the level seen in both the air- and 

SO2-exposed 60 g/kg maternal diet groups). This study 

does not provide information relevant to ambient 

exposures, but is being mentioned in this table to record 

that a low-concentration level study was not overlooked. 

DIFFERENTIAL GENE EXPRESSION - SUBACUTE 

Qin and 

Meng (2005) 

5.35, 10.70, or 21.40 ppm (14, 28, 

or 56 mg/m3); whole body 

6 h/day for 7 days Wistar rat; male; 

age NR; 

180-200 g; N = 

6/group in 

4 groups 

Repeated acute exposure caused significant (p < 

0.001-0.05) concentration-dependent reductions in 

enzyme activities and gene expression in the lung for both 

CYP1A1 and CYP1A2. Effects were seen at the mid and 

high concentrations, but not the low. Authors speculate 

that underlying mechanisms may involve oxidative stress 

and/or cytokine release, and may represent an adaptive 

response to minimize cell damage. 

Bai and Meng 

(2005a) 

5.35, 10.70, or 21.40 ppm (14, 28, 

or 56 mg/m3); whole body 

6 h/day for 7 days Wistar rat; male; 

age NR; 180-200 

g; N = 6/group in 

4 groups 

SO2 exposure caused significant concentration-dependent 

changes in the mRNA (mid and high concentrations) and 

protein expression (all concentrations in lung, but 

statistical significance not indicated) of apoptosis-related 

genes: increases for bax and p53 apoptosis-promoting 

genes, and decreases for the apoptosis-repressing gene 

bcl-2. Caspase-3 activity (occurring early in apoptosis 

process) was also increased at the mid and high 

concentration. 

 E-27 
Table E-21. Lymphatic system effects of SO2 and SO2 mixtures. 

Study Concentration Duration Species Effects 

SUBCHRONIC / CHRONIC 

Smith et al. 

(1989) 

1 ppm 

(2.62 mg/m3); 

whole body 

5 h/day, 5 

days/wk for 

4 mos 

Sprague- Dawley rat; male; 

young adult; initial weight 

NR; N=12-15/data point 

No significant effects were reported for spleen weight or mitogen-induced 

activation of peripheral blood lymphocytes or spleen cells (data not shown by 

authors). 

Aranyi et al. 

(1983) 

5.0 ppm (13.2 

mg/m3) 

SO2 + 1.04 

mg/m3

ammonium sulfate + 0.10 ppm 

(0.2 mg/m3) O3; 

whole body 

5 h/day, 5 

days/wk for 

up to 103 

days 

CD1 mouse; female; 

3-4 wks old; weight NR; N = 

360/group total 

(14-154/group in each 

assay) 

Cytostasis of MBL-2 leukemia target cells by peritoneal macrophage was 

increased in groups exposed to O3 alone or a mixture of the three compounds 

but was significantly higher with the mixture than with O3 alone at a 

macrophage:target cell ratio of 10:1; no significant effects were observed with 

macrophage:target cell ratio of 20:1. Reduction in splenic lymphocyte 

blastogenesis in response to phytohemagglutinin and concanavalin A occurred 

after exposure to O3 alone, but increased response occurred after exposure to 

the mixture; no response to alloantigen occurred after exposure to O3 alone but 

increased response occurred after exposure to mixture; there were no effects on 

S. typhosa lipopolysaccharide with either exposure scenario. 

 E-28 
Annex F. Epidemiologic Studies 

This Annex summarizes the findings of epidemiologic studies that have been published since the 

previous review. Descriptions of older studies were presented in the 1982 AQCD for Sulfur Oxides (U.S. 

EPA, 1982), and are not described in great detail in this document. 

Table F-1. Short-term exposure to SO2 and respiratory morbidity in field/panel studies. 

Study Method Pollutant Data Findings 

UNITED STATES 

Adamkiewicz et al. 

(2004) 

Steubenville, OH 

Period of Study: 

Sep 2000-Dec 2000 

Panel study of 29 non-smoking adults 

(median age 70.7 yrs) in Steubenville, 

OH. Participants provided breath 

samples weekly to determine the 

association between air pollution levels 

and eNO. ANOVA and GLM used in 

analysis. 

24-h avg SO2: 12.5 ppb 

Max: 50.9; 25th: 5.4 

75th: 16.9; IQR: 11.5 

1-h max SO2: 14.8 ppb 

Max: 233.9; 25th: 3.7 

75th: 15.2; IQR: 11.5 

Copollutants: 

NO2 NO O3 PM2.5

No associations were observed with SO2. 

Single pollutant models 

Effect (95% Cl) per IQR increase in SO2: 

Current hour: 0.09 (-0.22, 0.41) 

24-h moving avg: 0.24 (-0.61, 1.10) 

Delfino et al. 

(2003a) 

Los Angeles, CA 

Period of Study: 

Nov 1999-Jan 2000 

Panel study of 22 Hispanic children with 

asthma aged 10 to 16 yrs. Participants 

performed twice-daily PEF 

measurements and filled out symptom 

diaries. Analyses of symptoms conducted 

using GEE with exchangeable 

correlation. Linear mixed model used for 

PEF analyses. GEE models controlled 

for respiratory infections (data available 

for 20 subjects) and temperature. 

1-h max SO2: 7.0 ppb 

(SD 4.0); IQR: 4.0 

8-h max SO2: 4.6 ppb 

(SD 3.0); IQR: 2.5 

Copollutants: 

O3 (r = −0.19) 

NO2 (r = 0.89) 

CO (r = 0.69) 

PM10 (r = 0.73) 

EC (r = 0.87) 

OC (r = 0.83) 

VOCs 

None of the VOCs or gaseous pollutants associated with PEF. 

Current-day, but not previous-day, SO2 concentrations associated 

with symptom score >1 and >2. 

OR for symptom score >1 per IQR increase in SO2: 1-h max SO2: 

Lag 0: 1.31 (1.10, 1.55); Lag 1: 1.11 (0.91, 1.36) 

8-h max SO2: Lag 0: 1.23 (1.06, 1.41); Lag 1: 1.11 (0.97, 1.28) 

OR for symptom score >2 per IQR increase in SO2: 1-h max SO2: 

Lag 0: 1.37 (0.87, 2.18); Lag 1: 0.76 (0.35, 1.64) 

8-h max SO2: Lag 0: 1.36 (1.08, 1.71); Lag 1: 0.91 (0.51, 1.60) 

Mortimer et al. 

(2002) 

Eight urban areas: 

Baltimore, MD; 

Bronx, NY; Chicago, 

IL; Cleveland, OH; 

Detroit, MI; East 

Harlem, NY; St. 

Louis, MO; 

Washington, DC 

Period of Study: 

Jun-Aug 1993 

Panel study of 846 asthmatic children 4-9 

yrs from the National Cooperative InnerCity Asthma Study (NCICAS). Study 

children either had physician-diagnosed 

asthma and symptoms in the past 

12 mos or respiratory symptoms consistent with asthma that lasted more than 6 

wks during the previous yr. Respiratory 

symptoms recorded in daily diary and 

included cough, chest tightness, and 

wheeze. Mixed effects models and GEE 

models used to evaluate the effect of air 

pollutants on PEF and respiratory symptoms. Models adjusted for day of study, 

previous 12-h avg temperature, urban 

area, diary number, rain in the past 24 h. 

3-h avg SO2

(8 a.m.-11 a.m.) for all 

8 areas (shown in figure): 

22 ppb 

Avg intradiary range: 

53 ppb 

Copollutants: 

O3 (r = 0.29) 

NO2 PM10

None of pollutants associated with evening PEF or evening 

symptoms. Using single-pollutant model, SO2 had little effect on 

morning PEF (data not shown). Significant associations between 

moving avg of 1- to 2-day lag of SO2 and incidence of morning 

asthma symptoms. 

OR for morning symptoms associated with 20 ppb increase in 3-h 

avg SO2 concentration (Lag 1-2 day): 

8 urban areas: Single-pollutant model: 1.19 (1.06, 1.35) 

SO2 with O3 model: 1.18 (1.05, 1.33) 

7 urban areas: Single-pollutant model: 1.22 (1.07, 1.40) 

SO2 with O3 and NO2 model:1.19 (1.04, 1.37) 

3 urban areas: Single-pollutant model: 1.32 (1.03, 1.70) 

SO2 with O3, NO2, and PM10 model:1.23 (0.94, 1.62) 

 F-1 
Study Method Pollutant Data Findings 

Neas et al. (1995) 

Uniontown, PA 

Period of Study: 

Summer 1990 

Panel study of 83 fourth-fifth graders in 

Uniontown, Pennsylvania. Participants 

reported twice-daily PEF and presence of 

cold, cough, or wheeze. During summer 

of 1990, there were 3,582 child-days. 

PEF analyzed with autoregressive linear 

regression model that included a separate intercept for evening measurements, 

trend, temperature and 12-h avg air 

pollutant concentration, weighted by the 

number of hours child spent outdoors 

during the previous 12 h. 

12-h avg SO2: 10.2 ppb 

Max: 44.9; IQR: 11.1 

Daytime 12-h avg SO2

(8 am-8 pm): 14.5 ppb 

Overnight 12-h avg SO2

(8 pm-8 am): 5.9 ppb 

Copollutants: 

PM10 PM2.5 O3

total sulfate particles 

particle-strong acidity 

(r = 0.44) 

Incidence of new evening cough episodes significantly 

associated with the preceding daytime 12-h avg SO2. Mean 

deviation in PEF not associated with SO2. 

Effects associated with 10 ppb increase in 12-h avg SO2: Change 

in mean deviation in PEF: −0.63 L/min (−1.33, 0.07) 

OR for evening cough: 1.19 (1.00, 1.42) 

Concentration weighted by proportion of hours spent outdoors 

during prior 12-h: 

Change in mean deviation in PEF: −1.25 L/min (−2.75, 0.25) 

OR for evening cough: 1.53 (1.07, 2.20) 

Newhouse et al. 

(2004) 

Tulsa, OK 

Period of Study: 

Sep-Oct 2000 

Panel study of 24 patients 9-64 yrs with 

physician-diagnosed asthma. Subjects 

performed twice-daily PEF (morning and 

evening) measurements, and recorded 

medications, symptoms. Simple linear 

regression, forward stepwise multiple 

regression, correlation analysis performed. Multiple regression analyses 

used to develop predictive models for 

other environmental factors. Analyses 

produced complex models with different 

predictor variables for each symptom. 

24-h avg SO2: 0.01 ppm 

Range: 0.00, 0.02 

Copollutants: 

PM2.5 CO O3 pollen 

fungal spores 

Of the atmospheric pollutants, avg and max O3 were most 

significant factors that influenced symptoms. Quantitative results 

not provided for SO2. 

Avg or max SO2 found to be negative predictors of asthma in 

subgroup analyses of women and nonsmokers and rhinitis in all 

patients. Avg SO2 also negative predictor of evening PEF. 

Quantitatively useful effect estimates not provided. 

Ross et al. (2002) 

East Moline, IL 

Period of Study: 

April-Oct 1994 

Panel study of 59 asthmatics 5-49 yrs. 

Analysis based on 40 subjects, due to 

withdrawal or failure to provide requested 

health data. Study assessed the effect of 

single and combined exposures to air 

pollutants and airborne allergens on PEF, 

symptom scores and medication use 

frequency. Multivariate linear-regression 

models with 1st order autoregression 

used for analysis of daily means of mean 

–standardized PEF, symptom scores and 

asthma medication use; logistic regression used for dichotomized data for 

symptom score and medication use, loglinear models for log-transformed symptom scores and medication use 

frequency. 

24-h avg SO2: 

3.4 ppb (SD 3.1) 

Median: 2.8 

IQR: 2.4 

Range: 0, 27.3 

Copollutants: 

PM10 O3 NO2 pollen fungi 

No associations observed with SO2. 

No effect estimates provided. 

 F-2 
Study Method Pollutant Data Findings 

Schildcrout et al. 

(2006) 

Albuquerque, NM; 

Baltimore MD; 

Boston MA; Denver, 

CO; San Diego, CA; 

Seattle, WA; St. 

Louis, MO; Toronto, 

Ontario, Canada 

Period of Study : 

Nov 1993-Sept 1995 

Meta-analysis of 8 panel studies with 990 

children of the Childhood Asthma 

Management Program (CAMP), during 

the 22-mos prerandomization phase to 

investigate effects of criteria pollutants on 

asthma exacerbations (daily symptoms 

and use of rescue inhalers). Poisson 

regression and logistic regression 

models used in analyses. Within city 

models controlled for day of wk, ethnicity, 

annual family income, flexible functions 

of age and log-transformed sensitivity to 

the methacholine challenge using natural 

splines with knots fixed at 25th, 50th, and 

75th percentiles. Also controlled for 

confounding due to seasonal factors. All 

city-specific estimates included in 

calculations of study-wide effects except 

Albuquerque where SO2 data were not 

collected. 

24-h avg SO2: Median 

(10th, 25th, 75th, 90th 

percentile): 

Albuquerque: NA 

Baltimore: 6.7 ppb 

(3.2, 4.7, 9.8, 14.2) 

Boston: 5.8 ppb 

(2.7, 3.7, 9.1, 14.1) 

Denver: 4.4 ppb 

(1.2, 2.5, 6.7, 9.5) 

San Diego: 2.2 ppb 

(1.2, 1.7, 3.1, 4.4) 

Seattle: 6.0 ppb 

(3.7, 4.7, 7.5, 9.5) 

St. Louis: 7.4 ppb 

(3.9, 5.3, 10.7, 13.6) 

Toronto: 2.5 ppb 

(0.2, 1.0, 4.8, 8.8) 

Copollutants: 

O3 (−0.03 ≤ r ≤ 0.44) 

NO2 (0.23 ≤ r ≤ 0.68) 

PM10 (0.31 ≤ r ≤ 0.65) 

CO (0.19 ≤ r ≤ 0.67) 

All SO2 lags positively related to increased risk of asthma 

symptoms, but only the 3-day moving avg was statistically 

significant. Stronger associations observed for CO and NO2. 

Data analyzed using 2-pollutant models based on the sum of the 

2 within-subject pollutant effects, which were intended to provide 

insight into the increased risk of asthma symptoms associated 

with simultaneous shift in 2-pollutants. In 2-pollutant models with 

CO, NO2, and PM10, the SO2 effect estimates remained robust. 

SO2 not associated with rescue inhaler use rates. 

OR for daily symptoms associated with 10 ppb increase withinsubject 24-h avg SO2 concentration: 

Lag 0: 1.06 (0.99, 1.13) 

Lag 1: 1.05 (0.95, 1.16) 

Lag 2: 1.06 (0.99, 1.12) 

3-day moving sum : 1.04 (1.00, 1.08) 

Rate ratio for number of rescue inhaler used associated with 

10 ppb increase within-subject concentration of SO2: 

Lag 0: 1.01 (0.97, 1.06) 

Lag 1: 1.01 (0.97, 1.06) 

Lag 2: 1.04 (0.99, 1.09) 

3-day moving sum: 1.02 (0.99, 1.05) 

Results for 2-pollutant models shown in figure. 

Schwartz et al. 

(1994) 

Kingston-Harriman, 

TN (Apr-Aug 1986); 

Portage, WI; (AprAug 1987); 

St. Louis, MO; (AprAug 1986); 

Steubenville, OH; 

(Apr-Aug 1987); 

Topeka, KS (AprAug 1988) 

Watertown, MA 

(Apr-Aug 1985); 

Longitudinal study of 1,844 children in 

grades 2-5 from the Six Cities Study to 

examine the effects of PM and SOx on 

respiratory health. Daily diaries 

completed by parents, recording 

symptoms, such as cough, chest pain, 

phlegm, wheeze, sore throat, and fever. 

Logistic regression models adjusting for 

aurocorrelation were used for the 

analysis. To examine possible nonlinearity in the relationship, smooth 

functions of the air pollution variables 

were fit using GAM and the significance 

of the deviation from linearity was tested. 

24-h avg SO2: 

Median: 4.1 ppb 

IQR: 1.4, 8.2 

Max: 81.9 

Copolllutants: 

O3 (r = −0.09) 

NO2 (r = 0.51) 

PM10 (r = 0.53) 

PM2.5 (r = 0.55) 

PM2.5 sulfur (r = 0.50) 

H+ (r = 0.23) 

SO2 associated with incidence of cough and LRS. Local smooth 

showed increased cough incidence for only above a 4-day avg of 

20 ppb (less than 5% of data). Test for nonlinearity was 

significant (p = 0.002). No increase in incidence of LRS was seen 

until 24-h avg SO2 concentrations exceeded 22 ppb. 

ORs for cough and LRS related to were substantially reduced 

after adjustment for PM10, suggesting the SO2 associations might 

be confounded by particles. 

OR for cough incidence associated with 10 ppb increase in 4-day 

avg SO2 concentration: 

Single-pollutant model: 1.15 (1.02, 1.31) 

SO2 with PM10 model: 1.08 (0.93, 1.25) 

SO2 with O3 model: 1.15 (1.01, 1.31) 

SO2 with NO2 model: 1.09 (0.94, 1.30) 

OR for LRS associated with 10 ppb increase in 24-h avg SO2

concentration: 

Single-pollutant model: 1.28 (1.13, 1.46) 

SO2 with PM10 model: Not presented. Stated as not statistically 

significant. 

 F-3 
Study Method Pollutant Data Findings 

EUROPE 

Boezen et al. (1998) 

Amsterdam and 

Meppel, the 

Netherlands 

Winter of 1993-1994 

Panel study of 189 adults (48-73 yrs) w/ 

and w/out chronic respiratory symptoms 

in urban and rural areas to investigate 

whether BHR and PEF variability can be 

used to identify subjects who are 

susceptible to air pollution. Spirometry 

and methacholine challenge were 

performed and subjects with a fall in 

FEV1 of 20% or greater were considered 

BHR. Subjects performed twice-daily 

peak flow for 3 mos. A subject’s basal 

PEF variability calculated over an 8-day 

period with low air pollution. PEF 

variability expressed as (highest PEFlowest PEF/mean) or amplitude % mean 

PEF. After calculation of daily PEF 

variability, number of days where the 

amplitude % mean was greater than 5% 

was determined. This resulted in 2 

groups of subjects; those with amplitude 

% mean PEF of 5% or less every day in 

the 8-day period, and those with an 

amplitude % mean PEF greater than 5% 

on at least 1 day. Effects of air pollutants 

on prevalence of symptoms assessed 

with logistic regression models that 

adjusted for autocorrelation of the 

residuals, daily min temp, time trend and 

weekends/holidays. 

24-h avg SO2

Urban 

Mean: 11.8 μg/m3

Range: 2.7, 33.5 

Rural 

Mean: 8.2 

Range: 0.8, 41.5 

Copollutants: 

PM10 BS NO2

No association between SO2 and respiratory symptoms in 

subjects with no BHR, BHR at a cumulative dose of methacholine 

≤ 2.0 mg or ≤ 1.0 mg. In subjects with amplitude % mean PEF > 

5% any day and those with amplitude % mean PEF > 5% for > 

33% of days, SO2 was associated with the prevalence of phlegm.

Odds ratio (per 40 μg/m3 SO2) 

Subjects with no BHR 

URS: 0.86 (0.73, 1.03). LRS: 1.15 (0.90, 1.46) 

Cough: 1.01 (0.84, 1.21). Phlegm: 1.01 (0.86, 1.20) 

BHR at cumulative dose of methacholine ≤ 2.0 mg: 

URS: 1.11 (0.78, 1.56). LRS: 1.03 (0.72, 1.47) 

Cough: 0.89 (0.66, 1.19). Phlegm: 1.03 (0.78, 1.37) 

BHR at cumulative dose of methacholine ≤ 1.0 mg: 

URS: 1.02 (0.65, 1.61). LRS: 0.96 (0.63, 1.47) 

Cough: 0.96 (0.64, 1.44). Phlegm: 1.00 (0.68, 1.46) 

Amplitude % mean PEF ≤ 5%: 

URS: 0.82 (0.62, 1.08). LRS: 1.38 (0.93, 2.03) 

Cough: 0.72 (0.52, 0.98). Phlegm: 0.79 (0.59, 1.05) 

Amplitude % mean PEF > 5%, any day: 

URS: 1.04 (0.88, 1.23). LRS: 1.14 (0.96, 1.36) 

Cough: 1.07 (0.90, 1.26). Phlegm: 1.23 (1.05, 1.43) 

Amplitude % mean PEF > 5%, > 33% of days: 

URS: 1.10 (0.85, 1.41). LRS: 1.14 (0.91, 1.42) 

Cough: 1.14 (0.89, 1.47). Phlegm: 1.36 (1.14, 1.63) 

 F-4 
Study Method Pollutant Data Findings 

Boezen et al. (1999) 

Amsterdam and 

Meppel (urban and 

rural), the 

Netherlands 

Period of Study: 

3 winters of 1992-

1995 

Panel study of 632 children (7 to 11 yrs) 

living in rural and urban areas of the 

Netherlands, to investigate whether 

children with bronchial hyperresponsiveness (BHR) and relatively high serum 

concentrations of total IgE were susceptible to air pollution. 459 children had 

complete data. Methacholine challenge 

performed to determine BHR. Serum 

total IgE higher than the median (60kU/L) 

were defined as relatively high. Peak flow 

was measured twice daily and lower and 

URS were recorded daily for 3 mos. 

Association between symptoms and air 

pollutants assessed using logistic 

regression that adjusted for daily min 

temp, linear, quadratic and cubic time 

trend, weekends and holidays, and 

incidence of influenza. Examined 0, 1, 2 

Lags and 5 day mean of air pollutants. 

1992-9: 

Urban areasMean: 22.5 μg/m3, 

Range: 1.4, 61.3 

Rural areasMean: 9.8 

Range: 1.3, 34.2 

1993-4: 

Urban areasMean: 11.8, 

Range: 2.7, 33.5 

Rural areasMean: 8.2, 

Range: 0.8, 41.5 

1994-5: 

Urban areasMean: 8.3, 

Range: 0.6, 24.4 

Rural areasMean: 4.3, 

Range: 0.5, 17.0 

Copollutants: 

PM10 BS NO2

For children with BHR and relatively high serum total IgE, the 

prevalence of LRS was associated with increases in PM10, BS, 

SO2, and NO2. In the group with no BHR and relatively low IgE, 

and the group with BHR and low IgE, there was no consistent 

association between air pollutants with symptoms or decreased 

PEF. In children with no BHR but relatively high serum total IgE, 

there was a 28% to 149% increase in the prevalence of LRS per 

40 μg/m3 SO2. 

Odds ratio (per 40 μg/m3 SO2) 

Children with BHR and relatively high IgE (N = 121): 

LRS: Lag 0: 1.45 (1.13, 1.85) 

Lag 1: 1.41 (1.09, 1.82) . 5-day mean: 2.25 (1.42, 3.55) 

URS: Lag 0: 1.17 (0.99, 1.38). Lag 1: 1.06 (0.90, 1.25) 

>10% morning PEF decrease 

Lag 0: 1.09 (0.89, 1.34) . Lag 1: 1.00 (0.81, 1.23) 

>10% evening PEF decrease 

Lag 0: 1.06 0.86, 1.30). Lag 1: 0.83 (0.68, 1.02) 

No BHR and low IgE (N = 167): 

LRS: Lag 0: 1.12 (0.76, 1.66). Lag 1: 0.61 (0.39, 0.94) 

URS :Lag 0: 1.01 (0.89, 1.13). Lag 1: 1.08 (0.96, 1.22) 

>10 morning PEF decrease 

Lag 0: 1.02 (0.89, 1.16). Lag 1: 1.00 (0.87, 1.15) 

>10% evening PEF decrease 

Lag 0: 1.10 (0.97, 1.25). Lag 1: 1.06 (0.93, 1.21) 

With BHR and low IgE (N = 67): 

LRS: Lag 0: 0.72 (0.41, 1.28). Lag 1: 1.03 (0.56, 1.91) 

URS: Lag 0: 0.82 (0.62, 1.09). Lag 1: 0.84 (0.64, 1.12) 

>10% morning PEF decrease 

Lag 0: 0.74 (0.51, 1.07). Lag 1: 0.96 (0.67, 1.37) 

>10% evening PEF decrease 

Lag 0: 1.23 (0.88, 1.73). Lag 1: 1.32 (0.96, 1.82) 

No BHR and high IgE (N = 104): 

LRS: Lag 0: 1.44 (1.17, 1.77) 

Lag 1: 1.28 (1.00, 1.64). 5-day mean: 2.49 (1.54, 4.04) 

URS: Lag 0: 0.98 (0.84, 1.14). Lag 1: 1.01 0.87, 1.18) 

>10% morning PEF decrease 

Lag 0: 0.92 (0.79, 1.08). Lag 1: 1.03 (0.89, 1.21) 

>10% evening PEF decrease 

Lag 0: 1.00 (0.85, 1.17). Lag 1: 1.05 (0.90, 1.23) 

 F-5 
Study Method Pollutant Data Findings 

Boezen et al. (2005) 

Amsterdam, 

Meppel, Nunspeet, 

The Netherlands 

Period of Study: 

Two winters 1993-

1995 

Panel study of 327 elderly patients (50 to 

70 yrs) to determine susceptibility to air 

pollution by AHR, high total 

immunoglobulin (IgE), and sex. 

Methacholine challenges were performed 

and subjects with greater than or equal to 

20% fall in FEV1 after inhalation of up to 

2.0 mg methacholine were considered 

AHR+. Subjects with total serum IgE > 

20 kU/L were defined as high total IgE 

(IgE+). Twice daily PEF measurements 

and daily symptoms recorded for 3 mos. 

Data analysis performed using logistic 

regression with modeling of first-order 

autocorrelation in the residuals that adjusted for daily min temperature, time 

trend, weekend/ holidays and influenza 

incident for the rural and urban areas and 

the two winters separately. Subjects were 

classified as IgE+ AHR+, IgE+ AHR−, 

IgE− AHR+ or IgE− AHR+. Examined 

effects of pollutants on the same day, 

Lag 1, Lag 2 and the 5-day mean 

concentration of Lag 0 to Lag 4 

preceding that day. Groups that had 

effect estimates for PM10, BS, SO2, and 

NO2 that were outside the 95% CI of the 

effect estimates for the AHR−/IgE−

(control group) were considered to have 

increased susceptibility to air pollution. 

24-h avg SO2 (μg/m3) in 

winter 

Winter 1993/1994 

Urban: 

Mean: 11.8 μg/m3

Median: 10.2 

Range: 2.7, 33.5 

Rural: 

Mean: 8.2 

Median: 4.4 

Range: 0.8, 41.5 

Winter 1994/1995 

Urban: 

Mean: 8.3 

Median: 7.4 

Range: 0.6, 24.4 

Rural: 

Mean: 4.3 

Median: .7 

Range: 0.5, 17.0 

Copollutants: 

PM10 BS NO2 

No consistent associations between the prevalence of LRS or 

>10% fall in evening PEF and air pollution in any of the four 

groups. In the AHR+/IgE+ group, the prevalence of URS was 

associated with SO2 at 1 day lag, and the prevalence of >10% fall 

in morning PEF with SO2 at Lag 1, Lag 2 and 5-day mean (avg of 

Lag 0 to Lag 4). For females who were AHR+/IgE+, the prevalence of >10% fall in PEF was associated with SO2 Lag 1, Lag 2 

and 5-day mean. In subjects with AHR−/IgE+ the prevalence of 

URS was associated with SO2 the previous day and the mean of 

Lag 0 to Lag 4. The effect estimate was outside the 95% CI of 

the estimate for the control group AHR−/IgE−. No consistent 

positive associations found between prevalences of URS, cough 

or >10% fall in morning PEF and air pollutants in subjects with 

AHR+/IgE− or AHR−/IgE−. Based on results of the study, authors 

conclude that subjects with AHR+/IgE+ were the most responsive 

to air pollution. 

No AHR and low IgE (N = 125): 

URS: Lag 0: 0.99 (0.93, 1.05) 

Lag 1: 1.02 (0.97, 1.08). 5-day mean: 0.99 (0.88, 1.12) 

Cough: Lag 0: 1.03 (0.98, 1.08). Lag 1: 0.97 (0.93, 1.02) 

>10% fall in morning PEF. Lag 1: 1.00 (0.92, 1.08) 

No AHR and high IgE (N = 112): 

URS: Lag 0: 0.98 (0.92, 1.03) 

Lag 1: 1.07 (1.01, 1.12). 5-day mean: 1.15 (1.02, 1.29) 

Cough: Lag 0: 1.01 (0.95, 1.07). Lag 1: 1.02 (0.96, 1.08) 

>10% fall in morning PEF . Lag 1: 1.00 (0.92, 1.08) 

With AHR and low IgE (N = 42): 

URS: Lag 0: 1.05 (0.94, 1.17). Lag 1: 1.07 (0.96, 1.19) 

5-day mean: 1.04 (0.83, 1.30) 

Cough: Lag 0: 1.03 (0.95, 1.12). Lag 1: 1.01 (0.93, 1.09) 

>10% fall in morning PEF. Lag 1: 0.99 (0.87, 1.12) 

With AHR and high IgE (N = 48): 

URS: Lag 0: 1.06 (0.97, 1.15) 

Lag 1: 1.13 (1.05, 1.23). 5-day mean: 1.18 (0.99, 1.40) 

Cough: Lag 0: 1.02 (0.94, 1.11). Lag 1: 1.02 (0.94, 1.10) 

>10% fall in morning PEF 

Lag 1: 1.15 (1.04, 1.27). Lag 2 : 1.18 (1.07, 1.30) 

5-day mean: 1.26 (1.07, 1.49) 

With AHR and high IgE, by gender: 

URS, males: Lag 0: 1.09 (0.95, 1.25) 

Lag 1: 1.12 (0.98, 1.28). 5-day mean: 1.62 (1.25, 2.11) 

URS, females: Lag 0: 1.10 (0.97, 1.24) 

Lag 1: 1.12 (0.99, 1.25). 5-day mean: 1.02 (0.82, 1.28) 

>10% fall in morning PEF, males. Lag 1: 1.04 (0.87, 1.25) 

Lag 2: 0.92 (0.77, 1.10. 5-day mean: 0.88 (0.64, 1.21) 

>10% fall in morning PEF, females. Lag 1: 1.18 (1.03, 1.36) 

Lag 2: 1.24 (1.08, 1.42). 5-day mean: 1.31 (1.03, 1.66) 

 F-6 
Study Method Pollutant Data Findings 

Cuijpers et al. 

(1994) 

Maastricht, the 

Netherlands 

Period of Study: 

Nov-Dec 1990 

(baseline) 

Aug 8-16 

(smog episode) 

The effects of exposure to summer smog 

on respiratory health were studied in 535 

children (age unspecified). During a 

smog episode, 212 children were 

randomly chosen to be reexamined for 

lung function and symptoms. Only 112 of 

the children had adequately completed 

summer questionnaires and were used 

for the symptom analysis. Lung function 

measurements made with forced 

oscillation technique were available for 

212 children and valid spirometry was 

available for 208 children. Corrected 

baseline lung function compared using 

paired t test and difference in the prevalence in symptoms during baseline and 

episode compared. 

24-h avg SO2

Baseline 55 μg/m3

Summer episode 23 μg/m3

Copollutants: 

NO2 BS O3 PM10 

Acid aerosol H+

Small decrements in FEV1 and FEF25-75 found in the 212 children 

during the episode compared to baseline. However, there was 

also a significant decrease in resistance parameters. No 

increases observed in the prevalence of acute respiratory 

symptoms. 

Change in lung function and impedance between baseline and 

smog episode: 

FEV1: −0.032 L (SD 0.226), p < = 0.05 

FEF25-75: −0.086 L/s (SD 0.415), p < = 0.01 

Resistance at 8 Hz: −0.47 cm H2O (L/s) 

(SD 1.17), p < = 0.05 

Desqueyroux et al. 

(2002a) 

Paris, France 

Period of Study : 

Oct 1995-Nov 1996 

Panel study of 39 Parisian adults with 

severe COPD (avg age 67 yrs) to 

determine if air pollution affects health 

outcomes. Episodes of exacerbation 

were based on regular physician 

appointments and patient-initiated 

consultations. Exacerbation was 

confirmed as a decrease in “vesicular” 

breath sound, bronchial obstruction, 

tachycardia/arrhythmia, or cyanosis. 

Examined with logistic-regression 

analysis on the basis of GEE. Examined 

lag effects of 0 to 5 days. 

24-h avg SO2 (μg/m3) 

Summer: 

Mean: 7 (SD 5) 

Range: 2, 27 

Winter: 

Mean: 19 (SD 12) 

Range 3, 81 

Copollutants: 

NO2 PM10 O3

No association between episodes of symptom exacerbation and 

SO2, regardless of the lag. 

Mean 24-h avg SO2 (per 10 μg/m3) 

OR on incident episodes of exacerbation of COPD: 

Lag 1: 0.98 (0.64, 1.33). Lag 2: 0.96 (0.66, 1.40) 

Lag 3: 0.91 (0.63, 1.33). Lag 4: 0.89 (0.61, 1.29) 

Lag 5: 0.87 (0.63, 1.20). Lag 1-5: 0.83 (0.47, 1.50) 

Multipollutant model with O3 and SO2

24-h avg SO2: 

Lag 1-5: 0.64 (0.19, 2.19) 

Desqueyroux et al. 

(2002b) 

Paris, France 

Period of Study : 

Nov 1995-Nov 1996 

Panel study of 60 patients with moderate 

to severe physician-diagnosed asthma 

(mean age 55 yrs). Asthma attacks were 

noted by physician at each consultation 

(regular or emergency). Asthmatic 

attacks defined as need to increase 

twofold the dose of beta2 agonist. 

24-h avg SO2 (μg/m3) 

Summer: 7 (SD 5) 

Range: 2, 27 

Winter 19 (SD 12) 

Range: 3, 81 

Copollutants: 

PM10 NO2 O3

No association between asthma attacks and SO2 for any lag or 

season. 

Mean 24-h avg SO2 (per 10 μg/m3).OR on incident of asthma 

attacks: 

Lag 1: 0.98 (0.76, 1.27). Lag 2: 0.92 (0.72, 1.19) 

Lag 3: 1.01 (0.82, 1.23). Lag 4: 1.01 (0.86, 1.19) 

Lag 5: 1.05 (0.85, 1.29). Lag 1-5: 0.99 (0.76, 1.30) 

Forsberg et al. 

(1993) 

Pitea, Northern 

Sweden 

Period of Study: 

Mar to Apr 

Panel study of 31 asthmatic patients (9 to 

71 yrs) to assess relationship between 

daily occurrence of asthma symptoms 

and fluctuations in air pollution and 

meteorological conditions. Subjects 

recorded symptoms (shortness of breath, 

wheezing, cough, phlegm) for 

14 consecutive days. 

24-h avg SO2 (μg/m3) 

Mean: 5.7 

Range: 1.3, 12.9 

Correlations: 

NO2 (r = 0.24) 

BS (r = 0.70) 

No significant association observed with SO2. Positive association between severe shortness of breath and BS. 

Regression coefficient and 90% CI 

Subjects with shortness of breath (N = 28): 

0.0345 (−0.49, 0.118) 

Subjects with 5 or more incident episodes of severe shortness of 

breath (N = 10): −0.0266 (−0.140, 0.087) 

 F-7 
Study Method Pollutant Data Findings 

Higgins et al. (1995) 

United Kingdom 

Study Period: NR 

Panel study of 75 patients with physician 

diagnosed asthma or chronic bronchitis 

(avg age 50, range 18 to 82 yrs) to 

determine if air pollution affects respiratory function and symptoms. Subjects 

asked to keep symptom records and 

perform PEF for 28 days. PEF values 

recorded every 2 h beginning at 02.00 h 

each day. Methacholine challenge 

performed on each subject. Those with 

PM20 FEV1 of < 12.25 μmol were considered as methacholine reactors. PEF 

variability was calculated as the amplitude % Mean: (highest-lowest PEF 

value/mean) ×100. 75 patients had PEF 

records, 62 completed satisfactory 

symptom questionnaires. 

Max 24-h avg SO2

117 μg/m3

Copollutants: 

O3 NO2

The amplitude % mean was significantly associated with 

increasing levels of SO2, on the same day for all subjects and 

among reactors. Mean daily PEF and min PEF associated with 

SO2 among reactors only. Significant associations also observed 

with wheeze and SO2 on the same day, at 24-h lag, and 48-h lag 

for all subjects and meta-choline reactors; and with 

bronchodilator use for all subjects at 24-h lag. 

Regression coefficient per 10 μg/m3 SO2

All subjects: 

Mean PEF (L/min): Same day 0.021 (0.031); 

 24-h Lag 0.003 (0.033); 48-h Lag 0.021 (0.032) 

Minimum PEF(L/min): Same day 0.062 (0.039); 

 24-h Lag −0.048 (0.041); 48-h Lag −0.001 (0.040) 

Amplitude (% mean): Same day: 0.167 (0.072); 

 24-h Lag 0.191 (0.76); 48-h Lag 0.022 (0.075) 

Wheeze: Same day 1.14 (1.03, 1.26); 

 24-h Lag 1.22 (1.09, 1.37); 48-h Lag 1.14 (1.02, 1.27) 

Dyspnoea: Same day 1.03 (0.94, 1.14); 

 24-h Lag 1.07 (0.96, 1.18); 48-h Lag 0.94 (0.85, 1.05) 

Cough: Same day 1.03 (0.95, 1.12); 

 24-h Lag 1.04 (0.95, 1.13); 48-h Lag 1.02 (0.94, 1.12) 

Bronchodilator use: Same day 1.11 (0.97, 1.26); 

 24-h Lag 1.16 (1.01, 1.34); 48-h Lag 1.12 (0.98, 1.27) 

Reactors: 

Mean PEF (l/min): Same day 0.087 (0.054); 

 24-h Lag −0.44 (0.058); 48-h Lag 0.012 (0.057) 

Minimum PEF(L/min): Same day 0.168 (0.071) 

 24-h Lag −0.078 (0.076); 48-h Lag −0.026 (0.075) 

Amplitude (% mean): Same day 0.157 (0.120); 

 24-h Lag 0.083 (0.127); 48-h Lag 0.005 (0.126) 

Wheeze: Same day 1.26 (1.08, 1.47); 

 24-h Lag 1.57 (1.30, 1.89); 48-h Lag 1.24 (1.06, 1.45) 

Dyspnoea: Same day 1.04 (0.90, 1.20); 

 24-h Lag 1.17 (1.00, 1.37); 48-h Lag 1.03 (0.89, 1.20) 

Cough: Same day 1.09 (0.96, 1.24); 

 24-h Lag 1.05 (0.91, 1.20); 48-h Lag 1.00 (0.87, 1.15) 

Bronchodilator use: Same day 1.18 (0.99, 1.42); 

 24-h Lag 1.23 (1.02, 1.50); 48-h Lag 1.31 (1.09, 1.58) 

Hiltermann et al. 

(1998) 

Bilthoven, The 

Netherlands 

Period of Study: 

Jul-Oct 1995 

Panel study of 60 adult (18 to 55 yrs) 

nonsmoking patients with intermittent to 

severe persistent asthma to examine the 

association of summertime air pollution 

(O3 and PM10) with respiratory symptoms, 

medication use and PEF. Subjects were 

followed over 96 days. Twice daily PEF, 

respiratory symptoms, and medication 

use and whether they were exposed to 

environmental tobacco smoke were recorded daily. Analysis controlled for time 

trends, aeroallergens, environmental 

tobacco smoke exposures, day of wk and 

temperature. Examined Lag effects of 0 

to 2 days. 

24-h avg SO2 (μg/m3) 

Mean: 6.2 

Range: 0.1, 16.2 

Correlation with BS 

r = 0.53 

Correlation with 

copollutants: 

O3 (r = 0.30) 

PM10 (r = 0.37) 

NO2 (r = 0.49) 

BS (r = 0.53) 

SO2 not included in the analysis since levels were negligible 

during the study period (< 17 μg/m3) 

Effect estimates not provided. 

 F-8 
Study Method Pollutant Data Findings 

Hoek and 

Brunekreef (1992) 

The Netherlands 

Period of Study: 

Winter only, 1987-

1990 

Panel study of 1,078 children (7 to 11 

yrs) to examine the effects of low-level 

winter air pollution with respiratory 

symptoms. Pulmonary function test were 

performed 6 to 10 times on predetermined days. Parents filled out symptom 

diary that was turned in every 2 wks. 

Symptoms include hoarseness, cough, 

cough with phlegm, wheeze, runny/ 

stuffed nose, aching throat, shortness of 

breath, chest tightness, eye irritation, and 

sneezing. Association of symptom 

prevalence and symptom incidence was 

analyzed using individual regression 

slopes. 

24-h avg SO2 (μg/m3) 

Mean (SD): 14.9 (14.5) 

Range: 0.4, 94.3 

Correlation with 

copollutants: 

NO2 (r = 0.46) 

PM10 (r =0.50) 

SO4

2- (r =0.41) 

NO3

- (r =0.39) 

HONO (r =0.40) 

No association between pulmonary function and SO2

concentration. 

SE and medians of individual regression slopes 

FVC Same day 0.26 (0.21). Lag 1: 0.54 (0.18), p < 0.05 

FEV1 Same day 0.15 (0.20). Lag 1: 0.21 (0.17) 

PEF Same day -0.83 (1.07). Lag 1: -0.54 (0.87) 

MMEF Same day -0.58 (0.53). Lag 1: -0.44 (0.44) 

Odds ratio and 95% confidence interval: 

Cough. Same day 1.10 (0.78, 1.55). Lag 1: 0.80 (0.55, 1.18) 

LRS. Same day 1.25 (0.81, 1.93). Lag 1: 1.73 (1.11, 2.72) 

URS. Same day 1.28 (0.96, 1.70). Lag 1: 1.11 (0.82, 1.51) 

Any respiratory symptoms. 

Same day 0.76 (0.56, 1.03). Lag 1: 1.09 (0.83, 1.44) 

Hoek and 

Brunekreef (1993) 

Wageningen, 

The Netherlands 

Period of Study: 

Winter 1990-1991 

Panel study of 112 children (7 to 12 yrs, 

non-urban) to assess effects of winter air 

pollution pulmonary function and 

respiratory symptoms. Parents filled out 

symptom diary that was turned in every 2 

wks. Pulmonary function test performed 

by technician every 3 wks. Additional 

pulmonary function tests performed when 

SO2 was predicted to be higher than 

125 μg/m3 or NO2 > 90 μg/m3. 

Daily concentrations 

presented in graph; 

Highest 24-h avg conc 

SO2: 105 μg/m3 (air 

pollution episode) 

Copollutants: 

PM10 BS NO2

During the winter episode, pulmonary function of schoolchildren 

was significantly lower than baseline. Significant negative associations between SO2 and FVC, FEV1 and MMEF. No significant 

associations found with prevalence of respiratory symptoms. 

Authors noted that it is not clear which components of episode 

mix responsible for association and that the concentrations of 

acid aerosol and SO2 were too low for direct effects to be likely. 

SO2 moderately correlated with PM10 (r = 0.69) and BS (r = 0.63) 

but not NO2 (r = 0.28). 

Mean of individual regression slopes and SE 

FVC Same day −0.55 (0.10), p < 0.05 

 Lag 1: −0.74 (0.15) p < 0.05. 1 wk −0.94 (0.20) p < 0.05 

FEV1 Same day −0.51 (0.09) p < 0.05 

 Lag 1: −0.21 (−0.63) p < 0.05. 1 wk −0.78 (0.18) p < 0.05 

PEF Same day −0.64 (−0.44) 

 Lag 1: −0.21 (0.63). 1 wk −0.34 (0.81) p < 0.05 

MMEF. Same day −0.54 (0.20) 

 Lag 1: −0.40 (0.29). 1 wk −0.61 (0.37) 

Prevalence of acute respiratory symptoms regression coefficient 

from time-series model and SE 

Cough. Same day 0.02 (0.18); 

 Lag 1: −0.14 (0.19); 1 wk 0.13 (0.76) 

URS. Same day 0.12 (0.16); 

 Lag 1: −0.02 (0.17); 1 wk −0.24 (0.76) 

LRS. Same day 0.06 (0.26); 

 Lag 1: −0.11 (0.29); 1 wk −0.54 (0.92) 

Any respiratory symptoms. Same day 0.01 (0.13); 

 Lag 1: −0.03 (0.13); 1 wk −0.11 (0.60) 

Hoek and 

Brunekreef (1995) 

Deurne and 

Enkhuizen, The 

Netherlands 

Period of Study: 

Mar-Jul 1989 

Panel study of 300 children (7-11 yrs) to 

examine the effects of photochemical air 

pollution on acute respiratory symptoms. 

Occurrence of respiratory symptoms 

recorded by parents in daily diary. 

Symptoms included cough, shortness of 

breath, upper and LRS, throat and eye 

irritation, headache and nausea. 

Association of symptom prevalence and 

incidence assessed using first order 

autoregressive, logistic regression 

model. 

Daily concentration of SO2

< 43 μg/m3 

Copollutants: 

O3 PM10 SO4

2 NO3−

Same day concentrations of SO2 and NO2 not associated with 

symptom prevalence. 

No effect estimates for SO2 provided. 

Just et al (2002) 

Paris, France 

Period of Study: 

1996 

Panel study consisting of 82 medically 

diagnosed asthmatic children, 7-15 yrs 

old, followed for 3 mos (Jan-Mar). 

Examined the association between air 

pollution and asthma symptoms using 

regression analyses based on 

generalized estimating equations (GEE). 

24-h avg (μg/m3): 

11.6 (5.7) 

Copollutants: 

PM BS NO2 O3

SO2 was not analyzed because it was only present at low 

concentrations. 

 F-9 
Study Method Pollutant Data Findings 

Lagorio et al. (2006) 

Rome, Italy 

Period of Study: 

May 24 to June 24, 

1999 and Nov 18 to 

Dec 22, 1999 

Panel study of 29 patients with either 

COPD (N: 11, mean age 67 yrs), asthma 

(N: 11, mean age 33 yrs) or ischemic 

heart disease (N: 7, mean age 63 yrs) to 

evaluate whether daily levels of air 

pollutants have a measurable impact on 

lung function in adults with preexisting 

lung or heart disease. 

24-h avg SO2 (μg/m3) 

Spring mean 4.7 

 SD 1.8 

Winter mean 7.9 

 SD 2.2 

Overall mean 6.4 

 SD: 2.6 

Copollutants: 

PM2.5, PM10-2.5, PM10, Cd, 

Cr, Fe, Ni, Pb, Pt, V, Zn, 

NO2, CO, O3 

Correlation with 

copollutants: 

PM2.5 (r = 0.34) 

PM10-2.5 (r = −0.16) 

PM10 (r = 0.21) 

NO2 (r = 0.01) 

O3 (r = −0.61) 

CO (r = 0.65) 

Because avg 24-h concentrations of SO2 were low and showed 

little variability, SO2 was not considered in the analysis 

Neukirch et al. 

(1998) 

Paris, France 

Period of Study: 

Nov 15, 1992 to 

May 9, 1993 

Panel study of 40 nonsmoking, mild to 

moderate asthmatics (16 to 70 yrs, mean 

46) to examine the short-term effects of 

winter air pollution in asthma symptoms 

and three daily peak flow measurements. 

Patients were followed for 23 wks. Used 

GEE models that controlled for autocorrelation of responses, weather, and time 

trends. Analysis conducted on entire 

study population and for subgroup of 

subjects who took inhaled B2 agonists as 

needed. Assessed air pollution effect on 

both incident and prevalence of symptoms, Z-transformed morning PEF and 

daily PEF variability. 

24-h avg SO2

Mean: 21.7 (13.5) μg/m3

Range: 4.4, 83.8 

Copollutants 

NO2, PM13, BS 

Correlation with 

copollutants: 

NO2 (r = 0.54) 

PM13 (r = 0.83) 

BS (r = 0.89) 

Significant effects on incidence and prevalence of symptoms. 

Effects at Lag days 3-6 and weekly avg exposures. Based on 

group avg PEF of 407 l/min, a 50 μg/m3 increase SO2 caused a 

maximum decrease in morning PEF of 5.5%. 

Odds ratio per 50 μg/m3 SO2. 

All subjects - Incident episodes: 

Wheeze: Lag 5: 1.66 (1.01, 2.70) 

Nocturnal cough: Lag 3: 1.60 (0.98, 2.62); 

 Lag 4: 1.71 (0.86, 3.40); Lag 6: 1.72 (1.16, 2.55) 

Respiratory infections: Lag 3: 3.14 (1.30, 7.59); 

 Lag 4: 2.70 (1.36, 5.37); Lag 5: 2.79 (0.95, 8.21); 

 Wk: 8.52 (1.20, 60.5) 

All subjects - Prevalent episodes: 

Wheeze: Lag 5: 1.35 (1.01, 1.81); 

 Lag 6: 1.39 (1.04, 1.87); Wk: 1.64 (0.91, 2.94) 

Nocturnal cough: Lag 6: 1.34 (1.00, 1.79) 

Shortness of breath: Wk: 1.56 (1.06, 2.32) 

Respiratory infections: Lag 4: 2.40 (1.33, 4.33); 

 Lag 5: 2.72 (1.67, 4.44); Lag 6: 2.94 (1.80, 4.79); 

 Wk: 6.30 (1.31, 30.2) 

Subjects taking B2 agonists - Incident episodes: 

Asthma attacks: Lag 6: 2.19 (0.91, 5.29) 

Wheeze: Lag 5: 1.84 (1.13, 3.00) 

Nocturnal cough: Lag 3: 2.41 (1.47, 3.93); 

 Lag 4: 2.35 (0.88, 6.26); Lag 6: 1.86 (1.14, 3.04) 

Subjects taking B2 agonists - Prevalent episodes: 

Asthma attacks: Lag 5: 1.88 (0.95, 3.73); 

 Lag 6: 2.82 (1.57, 5.07) 

Wheeze: Lag 5: 1.51 (1.02, 2.23); 

 Lag 6: 1.57 (1.06, 2.32) 

Nocturnal cough: Lag 3: 1.73 (1.06, 2.82); 

 Lag 4: 2.28 (1.27, 4.11); Lag 5: 1.91 (1.17, 3.12); 

 Lag 6: 1.91 (1.17, 3.12) 

Shortness of breath: Lag 4: 1.81 (1.22, 2.67); 

 Lag 5: 1.65 (1.11, 2.44); Lag 6: 1.61 (1.20, 2.16); 

 Wk: 3.03 (1.26, 7.33) 

Regression coefficients of the effects and SE (per 1 μg/m3) 

Z-transformed morning PEF: Lag 5: −0.450 (0.138) p = 0.001 

 Lag 6: −0.337 (0.164) p = 0.03 

PEF daily variability, Lag 2: 0.025 (0.013) p = 0.05 

 F-10 
Study Method Pollutant Data Findings 

Peacock et al. 

(2003) 

Southern England 

Period of Study: 

Nov 1, 1996 to Feb 

14, 1997 

Panel study of 177 children (mean age 

10.7 yrs, range 7 to 13) from three 

schools (two urban and 1 rural location) 

to investigate effects of winter air 

pollution on respiratory function. Children 

were followed for 13 wks. Used two 

sources of air pollution in the rural area, 

one that was “locally validated” and the 

other “nationally validated.” 

24-h avg SO2 (ppb) 

Rural 

(nationally validated) 

Mean: 5.1 (4.7) 

Range: 0.0, 35.6 

Rural 

(locally validated) 

Mean: 5.4 (5.1) 

Range: 0.0, 39.1 

Urban 1 

Mean: 6.0 (6.0) 

Range: 0.5, 32.5 

Copollutants: 

O3 NO2 PM10 SO4

No statistically significant association between winter SO2 and 

PEFR, 0.70% decline in PEFR for a 10 ppb increase in the fiveday mean concentration of SO2 (community monitor) 

Regression Coefficient (95% CI) per 1 ppb SO2

Community monitor 

Lag 0: 0.05 (−0.05, 0.16). Lag 1: −0.04 (−0.13, 0.06) 

Lag 2: −0.08 (−0.19, 0.04). Lag 0-4: −0.23 (−0.65, 0.18) 

Local monitor 

Lag 0: −0.01 (−0.10, 0.07). Lag 1: 0.02 (−0.05, 0.10) 

Lag 2: −0.09 (−0.18, 0.01). Lag 0-4: −0.09 (−0.25, 0.07) 

Odds of 20% decrement in PEF below the median – all children 

Lag 0: 0.987 (0.958, 1.017). Lag 1: 1.007 (0.986, 1.030) 

Lag 2: 0.992 (0.963, 1.023). Lag 0-4: 0.972 (0.887, 1.066) 

Odds of 20% decrement in PEF below the median – wheezy 

children 

Lag 0: 0.981 (0.925, 1.041). Lag 1: 0.999 (0.957, 1.042) 

Lag 2: 0.995 (0.939 1.054). Lag 0-4: 1.019 (0.890, 1.167) 

Peters et al. (1996) 

Erfurt and Weimar, 

former German 

Democratic 

Republic; Sokolov, 

Czech Republic 

Period of Study: 

Sep 1990 to June 

1992 

Panel study of 102 adult (32 to 80 yrs) 

and 155 children (7 to 15 yrs) with 

asthma from the former German 

Democratic Republic and Czech 

Republic to investigate the acute effects 

of winter type air pollution on symptoms, 

medication intake and PEF. Used 

regression analyses and distributed Lag 

models. 

Winter 1990/1991 

Erfurt: 

 Mean: 125 μg/m3

 Max: 564 μg/m3

 IQR: 113 μg/m3 

Weimar 

 Mean: 236 μg/m3 

 Max: 1018 μg/m3

 IQR: 207 μg/m3 

Sokolov 

 Mean: 90 μg/m3

 Max: 492 μg/m3

 IQR: 94 μg/m3 

Winter 1991/1992 

Erfurt 

 Mean: 96 μg/m3 

 Max : 462 μg/m3, 

 IQR: 80 μg/m3 

Weimar 

 Mean: 153 μg/m3

 Max: 794 μg/m3

 IQR: 130 μg/m3 

Sokolov 

 Mean: 71 μg/m3

 Max: 383 μg/m3 

 IQR: 66 μg/m3 

Copollutants: TSP, PM10, 

SO4, PSA (particle strong 

acidity) 

5-day mean concentration of SO2 associated with PEF and 

symptoms in children (combined analysis from former German 

Democratic Republic and Czech Republic). 

Correlation coefficient between SO2 and TSP in Erfurt was r = 

0.8, 0.9 during both winters and in Weimar during the first winter. 

Correlation with TSP in Sokolov and in Weimar during the second 

winter was r = 0.4, 0.5. 

Combined analysis for children 

% change in PEF 

Concurrent day 0.18 (−0.44, 0.09) per 133 μg/m3 

5-day mean −0.90 (−1.35, −0.46) per 128 μg/m3

% change in symptom score 

Concurrent day −0.1 (−5.9, 5.7) per 133 μg/m3

5-day mean 14.7 (0.8, 28.6) per 128 μg/m3

Combined analysis for adults 

% change in PEF 

Concurrent day −0.20 (−0.53, 0.12) per 133 μg/m3

5-day mean −0.28 (−0.72, 0.16) per 128 μg/m3

Pikhart et al. (2000) 

Czech Republic 

Period of Study: 

1993-1994 

SAVIAH study of 3045 children by 

questionnaire to determine association of 

SO2 to wheezing. Used ecological and 

multilevel analysis 

Median: 73.9 μg/m3

25th percentile: 63.5 

75th percentile: 95.5 

Copollutant: NO2

Positive association of SO2 with wheezing 

Odds Ratio (95% CI) per 10 μg/m3 increase SO2

Logistic Regression: 

Individual outcome and area exposure: 1.08 (0.98, 1.20). 

Individual outcome and individual exposure: 1.08 (0.98, 1.19). 

Ecological analysis: 1.05 (0.96, 1.16) 

 F-11 
Study Method Pollutant Data Findings 

Pinter et al. (1996) 

Tata Area, Hungary 

Period of Study: 

winter mos between 

Dec 1993-Mar 1994 

Longitudinal (children < 14 yrs) and 

cross-sectional study (9 to 11 yrs) to 

examine air pollution and respiratory 

morbidity in children. In the longitudinal 

prospective study, respiratory morbidity 

was evaluated daily and on a weekly 

basis. In cross-sectional study, 

anthropometric parameters, physical 

status, pulse and blood pressure, lung 

function parameters, eosinophils in the 

nasal smear, hematological 

characteristics and urinary excretion of 

some metabolites were examined and 

measured. Anova and linear regression 

used in analysis. 

Avg SO2 exceeded the 

limit of yearly avg 150 

μg/m3

Daily peaks reached as 

high as 450 μg/m3

No specific values given 

Copollutant: NO2

Significant correlation between SO2 levels and acute daily 

respiratory morbidity, but no correlation with weekly incidence. 

Authors stated that in the cross-sectional study, almost all health 

parameters were impaired but no results were shown. 

Results only provided in graph. No p-values provided. 

Pönka, 1990 

Helsinki, Finland 

1991 

Survey study to compare weekly 

changes in ambient SO2, NO2, and 

temperature and the incidence of 

respiratory diseases, and absenteeism 

for children in day-care centers and 

schools and for adults in the work place 

during a 1-yr period (1987). 

Avg weekly concentration 

of SO2 (μg/m3) 

Mean: 21.1 

SD: 11.7 

Median: 17.0 

Range: 9, 61.5 

Mean of daily max 

Mean: 53 

SD: 20.8 

Median: 48 

Range: 25.9, 130.3 

Copollutant: NO2

Mean SO2 concentration correlated with the incidences of URI 

and tonsillitis reported from health centers. SO2 also correlated 

with absenteeism due to febrile illness among children in day 

care centers and adults. When comparing incidences during the 

low and high levels of SO2, the number of cases of URI and 

tonsillitis reported from health centers increased as well as 

absenteeism. After standardization for temperature, the only 

difference that was statistically significant was the occurrence of 

URI diagnosed at health centers. Frequency of URI was 15% 

higher during high levels of SO2 compared to low. 

Statistical significance of product moment correlation coefficients 

(correlation coefficient) between SO2 and respiratory disease and 

absenteeism 

Respiratory tract infections diagnosed at health centers: 

URI: SO2 arithmetic mean: p < 0.001 (0.553) 

Mean of daily maximums: p = 0.0012 (0.437) 

Tonsillitis: SO2 arithmetic mean: p = 0.0098 (0.355) 

Mean of daily maximums: NS 

Absenteeism due to febrile illness: 

Day care centers: 

SO2 arithmetic mean: p = 0.012 (0.404) 

Mean of daily maximums: p = 0.048 (0.323) 

School children: 

SO2 arithmetic mean: NS. Mean of daily maximums: NS 

Adults: 

SO2 arithmetic mean: p < 0.0001 (0.644) 

Mean of daily maximums: p < 0.0001 (0.604) 

No significant correlation between SO2 and URI, tonsillitis, otitis, 

or LRI in day care center children 

Statistical significance of weekly frequency of respiratory tract 

disease and absenteeism during low and high levels of SO2: 

Respiratory infections diagnosed at health centers: 

URI: SO2 arithmetic Mean: p < 0.001 

Mean of daily max: p = 0.0005 

Tonsillitis: 

SO2 arithmetic mean: 0.0351. SO mean of daily max: NS 

Absenteeism due to febrile illness: 

Day care center children: p = 0.0256 

School children: p = 0.0014. Adults: p = 0.0005 

 F-12 
Study Method Pollutant Data Findings 

Roemer et al. (1993) 

Bennekom and 

Wageningen, 

Netherlands 

Period of Study: 

Winter 1990-1991 

Panel of 73 children (mean age 9.3 yrs, 

range 6 to 12 yrs) with chronic 

respiratory symptoms to investigate 

effects of winter air pollution on lung 

function, symptoms and medication use. 

Subjects performed twice-daily PEF 

measurements, largest of three PEF 

readings used in regression analysis. 

Both incidence and prevalence of 

symptoms analyzed, using logistic 

regression. 

Daily concentrations of 

SO2 shown in graph 

Highest 24-h avg 

concentration SO2: 105 

μg/m3

Copollutants: 

NO2 PM10 BS 

Correlation with 

copollutants: 

NO2 (r = 0.26) 

PM10 (r = 0.65) 

BS (r = 0.63) 

Positive association between incidence of phlegm and runny 

nose with SO2 on the same day. Significant association also 

found between evening PEF and SO2 on, the same day, previous 

day and 1 wk (avg of same day and 6 days before). The use of 

bronchodilators also associated with SO2. 

Mean of individual regression coefficient 

Morning PEF: Same day −0.021 (0.024); 

 Lag 1 −0.024 (0.031); Wk −0.50 (0.069) 

Evening PEF: Same day −0.048 (0.018) p < 0.05; 

 Lag 1 −0.039 (0.021) p < 0.10; Wk −0.110 (0.055) p < 0.05 

Prevalence of symptoms (per 50 μg/m3 SO2) 

Asthma attack: Same day 0.008 (0.012); 

 Lag 1 0.016 (0.011); 1 Wk 0.058 (0.027) p < 0.05 

Wheeze: Same day 0.033 (0.17) p < 0.10; 

 Lag 1 0.042 (0.016) p < 0.05; Wk 0.069 (0.032) p < 0.05 

Waken with symptoms: Same day 0.033 (0.019) p < 0.10; 

 Lag 1 0.032 (0.018) p < 0.10; Wk 0.058 (0.045) 

Shortness of breath: Same day 0.029 (0.016) p < 0.10; 

 Lag 1 0.016 (0.015); Wk 0.044 (0.035) 

Cough: Same day 0.018 (0.025); 

 Lag 1 0.012 (0.023); Wk 0.072 (0.066) 

Runny nose: Same day 0.070 (0.026) p < 0.05; 

 Lag 1 −0.11 (0.025); Wk 0.153 (0.074) p < 0.05 

Phlegm: Same day 0.011 (0.022); 

 Lag 1 0.014 (0.020); Wk −0.005 (0.056) 

Roemer et al. (1998) 

14 European 

Centers: 

Amsterdam, The 

Netherlands; 

Athens, Greece; 

Berlin and Hettstadt, 

Germany; Budapest, 

Hungary; Krakow 

and Katowice, 

Poland; Kuopi, 

Finland; Malmö and 

Umeå, Sweden; 

Oslo, Norway; Pisa, 

Italy; Prague and 

Teplice, Czech 

Republic 

Period of Study: 

Winter 1993-1994 

Multicenter panel study of the acute 

effects of air pollution on respiratory 

health of 2,010 children (aged 6 to 12 

yrs) with chronic respiratory symptoms. 

Results from individual centers were 

reported by Kotesovec et al. (1998), 

Kalandidi et al. (1998), Haluszka et al. 

(1998), Forsberg et al. (1998), ClenchAas et al. (1998), and Beyer et al. (1998). 

Calculated effect estimates of air 

pollution on PEF or the daily prevalence 

of respiratory symptoms and 

bronchodilator use from the panelspecific effect estimates 

Range: 2.7 μg/m3 (Umeå, 

urban), 113.9 μg/m3 

(Prague, urban) 

Copollutants: 

PM10, BS NO2

No clear associations between PM10, BS, SO2, or NO2 and 

morning PEF, evening PEF, prevalence of respiratory symptoms, 

or bronchodilator use could be detected. Previous day PM10 was 

negatively associated with evening PEF, but only in locations 

where BS was high compared to PM10 concentrations. 

No consistent differences in effect estimates between subgroups 

based on urban versus suburban, geographical location or mean 

levels of PM10, BS, SO2, and NO2. Combined effect estimates 

with 95% CI of air pollution on PEF. 

Morning: Lag 0: 0.2 (−0.2, 0.6) 

Lag 1: 0.2 (−0.2, 0.6). Lag 2: 0.6 (0.2, 1.0) 

7-day mean: 0.6 (−1.3, 2.5) 

Afternoon: Lag 0: 0.1 (−0.3, 0.5) 

Lag 1: 0.0 (−0.4, 0.4). Lag 2: 0.1 (−0.4, 0.6) 

7-day mean: 0.2 (−0.5, 0.9) 

 F-13 
Study Method Pollutant Data Findings 

Segala et al. (1998) 

Paris, France 

Period of Study: 

Nov 15, 1992 to 

May 9, 1993 

Panel study of 84 children (7 to 15 yrs) 

with physician diagnosed asthma to examine the effects of winter air pollution on 

childhood asthma. For 25 wks, parents 

recorded the presence or absence of 

asthma attacks, upper or lower respiratory infections with fever, the use of 

supplementary inhaled B2 agonist, the 

severity of symptoms (wheeze, nocturnal 

cough and shortness of breath). Children 

also recorded PEF three times a day. 

GEE models adjusted for age, sex, 

weather and time trend. Investigated 

effects of SO2 at 0 to 6 day Lags. 

SO2 avg (SD): 

21.7 (13.5) μg/m3

Range: 

(4.4, 83.8) μg/m3

Copollutants: 

NO2 PM13 BS 

Correlation with 

copollutants: 

NO2 (r = 0.54) 

PM13 (r = 0.43) 

BS (r = 0.89) 

SO2 associated with both incident and prevalent episodes of 

asthma, use of supplementary beta 2 agonist, incident episodes 

of nocturnal cough, prevalent episodes of shortness of breath 

and respiratory infection. 

OR per 50 μg/m3 SO2 (Only effects at 0 and 1-days lag shown 

below unless statistically significant) 

Incident episodes: Mild asthmatics (N: 43) 

Asthma: Lag 0: OR 2.86 (1.31, 6.27). Lag 1: 2.45 (1.01, 5.92) 

Wheeze: Lag 0: 1.47 (0.90, 2.41). Lag1: 1.27 (0.48, 3.38) 

Nocturnal cough: Lag 3: 1.93 (1.18, 3.15). 

 Lag 4: 2.12 (1.43, 3.13) 

Respiratory infections: Lag 1: 1.52 (0.38, 5.98) 

Prevalent episodes: Mild asthmatics (N: 43) 

Asthma: Lag 0: 1.71 (1.15, 2.53). Lag 1: 1.55 (0.86, 2.78) 

Wheeze: Lag 4: 1.48 (0.90, 2.41) 

Shortness of breath : Lag 1: 1.36 (0.92, 2.01) 

 Lag 2: 1.45 (0.98, 2.14). Lag 3: 1.52 (1.03, 2.25) 

 Lag 4: 1.51 (1.02, 2.24) 

Respiratory infections: Lag 0: 1.58 (0.72, 3.46) 

 Lag 1: 1.91 (0.79, 4.62). Lag 2: 2.13 (0.97, 4.67) 

 Lag 3: 2.09 (1.05, 4.15). Lag 4: 2.05 (1.14, 3.68) 

Beta2 agonist: Lag 4: 1.63 (1.00, 2.66) 

Beta2 agonist: Lag 4: 2.02 (1.02, 4.01). Lag 5: 1.96 (0.99, 3.88) 

Moderate asthmatics (N: 41) 

Statistically significant (only) prevalent episodes: 

Beta2 agonist: Lag 0: 3.67 (1.25, 10.8). Lag 1: 4.60 (2.10, 10.1) 

 Lag 2: 7.01 (3.53, 13.9). Lag 3: 4.74 (1.96, 11.5) 

Søyseth et al. 

(1995a) 

Ardal and Laerdal 

regions, Norway 

Period of Study: 

Winters, 1989-1992 

Cross sectional study of 620 children 

(ages 7 to 13 yrs) to determine whether 

short term exposure to SO2 and fluoride 

on the number of capillary blood 

eosinophils is related to prevalence of 

BHR. Ardal is located in a SO2 emitting 

aluminum smelter and Laerdal is 

nonindustrialized. Parents filled out 

respiratory questionnaires. Clinical 

examinations used skin prick tests and 

spirometry to test for atopy and BHR, 

respectively. Multiple regression and 

logistic regression models used in 

analyses. 

24-h avg SO2 (μg/m3): 

Median: 22.2 

10th: 1.9 

90th: 85.3 

A significant positive association between BHR and SO2 was 

observed in atopic children. Eosinophils and SO2 exposure also 

had a positive correlation. 

Odds ratio for BHR (per 10 μg/m3 SO2) 

Last 24-hours: 1.12 (1.01, 1.24) 

Last 1-30 days: 0.94 (0.73, 1.21) 

Taggart et al. (1996) 

Runcorn and 

Widnes in NW 

England 

Period of Study: 

Jul-Sep 1993 

Panel study of 38 nonsmoking asthma 

subjects (18 to 70 yrs) to investigate the 

relationship between asthmatic BHR and 

pulmonary function (PEF, FEV1, FVC) 

and summertime ambient air pollution. 

Used univariate nested (hierarchical) 

analysis of variance to test hypothesis 

that BHR or spirometry measurements 

varied with air pollution levels. Analysis 

was limited to within-subject variation of 

(BHR, FEV1, or FVC). 

24-h avg SO2 

Max: 103.7 μg/m3

Copollutants: 

NO2, O3, smoke 

Correlation with 

copollutants: 

O3 (r = 0.13) 

NO2 (r = 0.65) 

Smoke (r = 0.48) 

No association between SO2 and FEV1 or FVC. 

Changes in BHR correlated significantly with changes in 24-h 

mean SO2, NO2, and smoke. 

Percentage change in BHR per 10 μg/m3 SO2

24-h mean SO2 −6.3 % (−13.6, 0.6) 

48-h mean −2.9 % (−12.8, 8.2) 

24-h Lag 7.4 % (−4.5, 20.8) 

 F-14 
Study Method Pollutant Data Findings 

Timonen and 

Pekkanen (1997) 

Kuopio (urban and 

suburban), Finland 

Period of Study: 

Winter, 1994 

Panel study of 169 children (7 to 12 yrs) 

with asthma or cough symptoms living in 

urban and suburban areas of Kuopio, 

Finland to determine association 

between air pollution and respiratory 

health. In the urban areas there were 39 

asthmatics and 46 with cough only; in the 

suburban areas there were 35 

asthmatics and 49 with cough who were 

included in the final analysis. Twice daily 

PEF and daily symptoms were recorded 

for 3 mos. First order autoregressive 

models used to assess associations 

between air pollutants and PEF and 

logistic regression models used for 

symptom prevalences and incidences. 

Analysis conducted on daily mean PEF 

deviations. Mean morning or evening 

PEF calculated for each child was 

subtracted from the daily value of 

morning or evening PEF. The daily 

deviations were then averaged to obtain 

daily mean PEF deviation for morning or 

evening PEF. 

24-h avg SO2 (μg/m3) 

Urban area: 

Mean: 6.0 

25th percentile: 2.6 

50th percentile: 3.6 

75th percentile: 7.1 

Max: 32.0 

Copollutants: 

PM10 BS NO2 

Correlation coefficient with 

SO2

PM10 (r = 0.21) 

BS (r = 0.20) 

NO2 (r = 0.22) 

Among children with cough only, morning and evening deviations 

in PEF in the urban panel was negatively associated with SO2. 

SO2 was also associated with an increase in the incidence of 

URS in children with cough only in the urban area. When 

excluding the three highest SO2 days, these effects were no 

longer statistically significant. No associations found between 

SO2 and morning or evening PEF or respiratory symptoms in 

children with cough only in the suburban panel. 

Asthmatic: Lag 0: 0.198 (0.804). Lag 1: 0.382 (0.789) 

Lag 2: 0.648 (0.715). 4-day Mean: 1.39 (1.14) 

Odds ratio (per 10 μg/m3) 

URS: Lag 1: 1.46 (1.07, 2.00). Lag 2: 1.46 (1.14, 1.87) 

4-day Mean: 1.55 (1.08, 2.24) 

Odds ratio when excluded 3 highest SO2 days (no 95% CI 

provided, but effects were not significant) 

Lag 1: 1.13. Lag 2: 1.46. 4-day Mean: 1.12 

Regression coefficient (SE) (per 10 μg/m3 SO2): 

Morning PEF deviations 

Children with cough alone: Lag 0: −0.229 (0.608) 

Lag 1: −1.38 (0.564). Lag 2: −0.683 (0.523) 

4-day Mean: −1.28 (0.633) 

Evening PEF deviations 

Children with cough alone. Lag 0: −1.84 (0.673) 

Lag 1: −0.144 (0.711) . Lag 2: −0.291 (0.613) 

4-day Mean: −0.878 (0.868) 

Asthmatics: Lag 0: 1.28 (0.711). Lag 1: 0.575 (0.727) 

Lag 2: 0.819 (0.642). 4-day Mean: 1.34 (1.05) 

 F-15 
Study Method Pollutant Data Findings 

van der Zee et al. 

(1999) 

The Netherlands: 

Amsterdam and 

Meppel (1993-1994) 

Amsterdam and 

Nunspeet 

(1994-1995) 

Bodegrven/Reeuwijk 

and Rotterdam 

(1992-1993) 

Period of Study: 

3 winters from 1992 

to 1995 

Panel study of 633 children (aged 7 to 

11 yrs) with and without chronic 

respiratory symptoms, living in urban and 

nonurban areas in the Netherlands. 

Volunteers measured daily PEF and 

reported the occurrence of respiratory 

symptoms and bronchodilator use in a 

diary. Association between air pollution 

and decrements in PEF, symptoms and 

bronchodilator use evaluated with logistic 

regression models that adjusted for first 

order autocorrelation, min daily 

temperature, day of wk, time trend, 

incidence of influenza and influenza-like 

illness. 

Median and max 24-h avg 

concentration (μg/m3) 

1992-1993 

Urban 23 (152); Nonurban 

8.9 (43) 

1993-1994 

Urban 11 (34); Nonurban 

5.0 (42) 

1994-1995 

Urban 6.0 (24); Nonurban 

3.6 (17) 

Copollutants: 

PM10 BS 

Sulfate NO2

The correlation between SO2 and PM varied from 0.5 to 0.8 

during first two winters. Correlation with NO2 about 0.50. In the 

urban areas, SO2 was associated with > 10% decrements in 

evening PEF, LRS and use of bronchodilator in children with 

symptoms (n = 142). Most consistent associations found with 

PM10, BS, and sulfate. No association found between SO2 and 

prevalence of URS, cough, phlegm, and > 10% decrements in 

morning PEF. In the nonurban areas, no associations found with 

SO2. In children without symptoms, no consistent associations 

with SO2. Authors concluded that children with symptoms are 

more susceptible to particulate air pollution effects and that use 

of medication for asthma did not prevent the adverse effects of 

PM in children with symptoms. 

Odds ratio (per 40 μg/m3 SO2) 

Children with symptoms: 

Urban areas: 

Evening PEF: Lag 0: 1.32 (0.96, 1.80). 

 Lag 1: 0.83 (0.60, 1.14). Lag 2: 1.67 (1.28, 2.19) 

Symptoms of lower respiratory tract 

 Lag 0: 1.35 (1.01, 1.79). Lag 1: 1.23 (0.93, 1.64) 

Symptoms of upper respiratory tract 

 Lag 0: 0.97 (0.82, 1.14).Lag 1: 1.10 (0.94, 1.28) 

Cough: Lag 0: 0.90 (0.77, 1.05). Lag 1: 1.12 (0.96, 1.30) 

Use of bronchodilator: Lag 0: 0.92 (0.72, 1.18) 

 Lag 1: 1.45 (1.13, 1.86) 

Nonurban areas: 

Evening PEF: Lag 0: 1.20 (0.91, 1.58). Lag 1: 0.89 (0.68, 1.17) 

Symptoms of lower respiratory tract. Lag 0: 0.91 (0.69, 1.19) 

 Lag 1: 0.91 (0.69, 1.22) 

Symptoms of upper respiratory. Lag 0: 0.94 (0.81, 1.09) 

 Lag 1: 0.97 (0.83, 1.13). 5-day Mean: 0.67 (0.47, 0.94) 

Cough: Lag 0: 1.08 (0.94, 1.23). Lag 1: 0.98 (0.85, 1.12) 

Use of bronchodilator. Lag 0: 0.86 (0.59, 1.25) 

 Lag 1: 1.18 (0.80, 1.74) 

Odds ratio (per 40 μg/m3 SO2) 

Children without symptoms: 

Urban areas: 

Evening PEF: Lag 0: 1.13 (0.88, 1.47). Lag 1: 1.16 (0.90, 1.50) 

URS: Lag 0: 0.92 (0.76, 1.11). Lag 1: 1.10 (0.91, 1.34) 

 Lag 2: 0.83 (0.70, 0.99) 

Cough: Lag 0: 0.93 (0.78, 1.11). Lag 1: 1.02 (0.84, 1.23) 

Nonurban areas: 

Evening PEF: Lag 0: 1.10 (0.87, 1.39). Lag 1: 1.07 (0.85, 1.35) 

URS: Lag 0: 1.07 (0.92, 1.25). Lag 1: 0.85 (0.72, 1.00) 

Cough: Lag 0: 0.86 (0.76, 0.97). Lag 1: 0.95 (0.83, 1.08) 

Two-Pollutant Models 

Odds Ratio (95% CI) (per 40 μg/m3 ppm SO2) 

Evening PEF: SO2 + PM10. Lag 0: 1.14 (0.80, 1.61) 

 Lag 1: 0.75 (0.51, 1.09). Lag 2: 1.56 (1.13, 2.13) 

5 Day mean: 1.03 (0.50, 2.10) 

 F-16 
Study Method Pollutant Data Findings 

van der Zee (2000) 

Netherlands, 

3 winters from 

1992 to 1995 

Rotterdam 

1992-1993 

Panel study of 489 adults (aged 50 to 

70 yrs) with and without chronic 

respiratory symptoms, living in urban and 

nonurban areas in the Netherlands. 

Volunteers measured daily PEF and 

reported the occurrence of respiratory 

symptoms and bronchodilator use in a 

diary. Association between air pollution 

and decrements in PEF, symptoms and 

bronchodilator use evaluated with logistic 

regression models that adjusted for first 

order autocorrelation, min daily 

temperature, day of wk, time trend, 

incidence of influenza and influenza-like 

illness. 

Avg (max) conc 

1992/1993: 

Urban 25 (61) μg/m3

1993/1994 

Urban 11 (34) μg/m3

Nonurban 5.0 (42) μg/m3

1994/1995 

Urban 6.0 (24) 

Nonurban 3.6 (17) μg/m3

Copollutants 

PM10 BS Sulfate NO2

Among symptomatic adults (n = 138) living in urban areas, the 

prevalence of >20% decrement in morning PEF was associated 

with SO2. Moreover, there were no associations found with 

prevalence of bronchodilator use, LRS, >10% decrement in 

morning PEF and >10% and >20% decrement in evening PEF. 

In the nonurban areas, there was no consistent association 

between air pollution and respiratory health. In the 

nonsymptomatic adults, no consistent associations observed 

between health effects and air pollutants, but a significant and 

positive association was observed with URS in the nonurban 

area at 1 day Lag. 

Range of Spearman correlation coefficients between 24-h avg 

conc SO2 and copollutants: 

PM10: 0.31, 0.78. BS: 0.21, 0.75 

Sulfate: 0.29, 0.69. NO2: 0.47, 0.51 

Odds ratio (per 40 μg/m3 SO2) symptomatic adults 

In urban areas 

>10% decline in PEF; Morning 

 Lag 0: 0.86 (0.60, 1.23); Lag 1: 0.97 (0.68, 1.39) 

>20% decline in PEF; Morning 

 Lag 0: 1.33 (0.66, 2.71); Lag 1: 1.98 (1.03-3.79) 

LRS: Lag 0: 1.01 (0.84, 1.20)l Lag1: .97 (0.82, 1.16) 

 5-day mean: 0.71 (95% CI: 0.53 to 0.95) 

URS: Lag 0: 1.15 (0.97, 1.37) 

 Lag 1: 1.06 (0.90, 1.26) 

Bronchodilator use: Lag 0: 1.09 (0.93, 1.28) 

 Lag 1: 1.05 (0.89, 1.24). Lag 2: 0.85 (0.72, 0.99) 

In nonurban areas 

>10 % decline in PEF; Morning 

 Lag 0: 79 (0.48, 1.29); Lag 1: 1.08 (0.68, 1.72) 

>20% decline in PEF; Morning 

 Lag 0: 0.79 (0.22, 2.88); Lag 1: 71 (0.13, 4.02) 

LRS: Lag 0: 1.11 (0.94, 1.30); 

Lag 1: 1.04 (0.88, 1.22) 

URS: Lag 0: 0.97 (0.79, 1.20); 

Lag 1: 1.20 (0.98, 1.47) 

Bronchodilator use: Lag 0: 1.04 (0.91, 1.18); 

Lag 1: 1.08 (0.95, 1.22) 

Ward et al. (2002b) 

Birmingham and 

Sandwell, England 

Period of Study: 

Jan-Mar 1997, 

May-Jul 1997 

Children ages 9-yrs old in 5 different 

schools were given a questionnaire and 

administered PEF measurement in 

summer and/or winter. Study used 

bivariate correlation, linear and logistic 

regressions for analysis 

Median, Range (ppb): 

Winter: 5.4 (2-18) 

Summer: 4.7 (2-10) 

Copollutants: NO2 ; O3; 

PM10 ; PM2.5 ; H+; Cl−; HCl; 

HNO3; NH3; NH4+; NO3−; 

SO4

2−

Study does not provide evidence for day-to-day respiratory health 

effects of pollutants. 

Effect size, Cl 

Winter: 

ΔPEF morning (L/min): -0.60 (-2.51, 1.32) 

ΔPEF afternoon: -0.32 (-2.71, 2.04). Cough: 0.92 (0.82, 1.05) 

Ill: 1.09 (1.01, 1.18). SOB: 1.02 (0.93, 1.13) 

Wake: 1.00 (0.91, 1.10). Wheeze: 0.96 (0.85, 1.07) 

Summer: 

ΔPEF morning (L/min): 0.91 (-0.95, 2.78). 

ΔPEF afternoon: -0.89 (-2.61, 0.83). Cough: 1.018 (1.02, 1.15) 

Ill: 1.05 (0.96, 1.14). SOB: 0.98 (0.87, 1.10) 

Wake: 1.00 (0.87, 1.14). Wheeze: 1.05 (0.92, 1.19) 

 F-17 
Study Method Pollutant Data Findings 

Ward et al. (2002a) 

Birmingham and 

Sandwell, England 

Period of Study: 

Jan-Mar 1997 

May-July 1997 

Panel study of 162 children (9 yrs at time 

of enrollment) from two inner city 

locations to investigate the association 

between ambient acid species with PEF 

and symptoms. Daily symptoms and 

twice-daily peak flow measurements 

were recorded over 8 wk periods in the 

summer and winter. 39 of the children 

reported wheezing in the past 12 mos. 

Linear regression used for PEF and 

logistic regression for symptoms. 

24-h avg SO2

Winter: 

Jan 13-Mar 10, 1997 

Median: 5.4 ppb Range: 2, 

18 ppb 

Summer: 

May 19-July 14, 1997 

Median: 4.7 ppb 

Range: 2, 10 ppb 

Copollutants: 

NO2 , O3, PM10 , H+ , Cl− , 

HCl , HNO3 , NH3 , NH4

+ , 

NO3

− , SO42−

SO2 concentrations were 

not related to changes in 

PEF or respiratory 

symptoms 

In the summer, changes in morning PEF were associated with 

SO2 at 3-days lag and the 7-day mean SO2. Prevalence of cough 

associated with SO2 on the same day. In the winter SO2 was only 

associated with symptom of feeling ill on the same day. 24-h avg 

SO2 (per 4.0 ppb in winter; per 2.2 ppb in summer). Data also 

available for 3-,4-, and 7-day Lag 

ΔPEF (L/min) 

Morning: Lag 0-day 

Winter: -0.60 (-2.51, 1.32); Summer: 0.91 (-0.95, 2.78) 

Afternoon: Lag 0-day 

Winter: -0.32 (-2.71, 2.04); Summer: -0.89 (-2.61, 0.83) 

Morning: Lag 1-day 

Winter: 0.08 (-1.67,1.86); Summer: 0.29 (-1.56, 2.14) 

Afternoon: Lag 1-day 

Winter: -0.88 (-2.87, 1.10); Summer: -0.02 (-1.68, 1.65) 

Odds ratio for symptoms 

Cough:Lag 0-day 

Winter: 0.92 (0.81, 1.05); Summer: 1.08 (1.02, 1.15) 

Ill:Lag 0-day 

Winter 1.09 (1.01, 1.18); Summer 1.05 (0.96, 1.14) 

SOB: Lag 0-day 

Winter: 1.02 (0.93, 1.13); Summer: 0.98 (0.87,1.10) 

Cough:Lag 1-day 

Winter: 1.00 (0.87, 1.15); Summer: 1.04 (0.97, 1.11) 

Ill: Lag 1-day 

Winter: 1.03 (0.95, 1.11); Summer: 1.02 (0.94, 1.12) 

SOB: Lag 1-day 

Winter: 1.00 (0.90, 1.09); Summer: 1.00 (0.89, 1.13) 

Wake at night with cough: Lag 0 day 

Winter: 1.00 (0.91, 1.10); Summer: 1.00 (0.87, 1.14) 

Wake at night with cough: Lag 1 day 

Winter: 1.05 (0.96, 1.15); Summer: 1.02 (0.89, 1.16) 

Wheeze: Lag 0 day 

Winter: 0.96, (0.85, 1.07); Summer: 1.05 (0.92, 1.19) 

Wheeze: Lag 1 day 

Winter: 0.96 (0.86, 1.07); Summer: 1.00 (0.88, 1.13 

Summer ΔPEF 2.7 (1.03, 4.38) per 2.2 ppb SO2 

Lag 3 days (p < 0.05) 

Summer ΔPEF 6.83 (0.98, 12.69) per 2.2 ppb SO2 

Lag 0-6 days (p < 0.05) 

LATIN AMERICA 

Pino et al. (2004) 

Santiago, Chile 

Period of Study: 

1995-1997 

Cohort study of 504 infants recruited at 

4 mos of age and followed through the 

first yr of life to determine the association 

between air pollution on wheezing 

bronchitis. 

Mean concentration of SO2

(ppb) 

Mean: 11.6 

SD: 8.1 

Median: 10.0 

Copollutants: 

PM2.5 NO2

No consistent association was found between the 24-h avg SO2

and risk of wheezing bronchitis. However, after a 7-day lag, a 10-

ppb increase in the 24-h avg SO2 was associated with a 21% 

increase in risk of wheezing bronchitis. 

Increase in wheezing bronchitis (95% Cl) per 10 ppb SO2

21% (8, 39%) 

Romieu et al. (1996) 

Mexico City, Mexico 

Period of Study: 

Apr-Jul 1991 

Nov 1991-Feb 1992 

Panel study of 71 mildly asthmatic 

children (5 to 13 yrs) to assess the 

relationship between air pollution and 

childhood asthma exacerbation. Children 

measured PEF three times daily and 

recorded daily symptoms and medication 

use. Examined both incidence and 

prevalence of symptoms. LRS, cough, 

phlegm, wheeze, and/or difficulty 

breathing. 

24-h avg SO2 (ppm) 

Mean: 0.09 

SD: 0.05 

Range: 0.02, 0.20 

Copollutants: 

O3 PM10 PM2.5 NO2

SO2 concentrations were not related to changes in PEF or 

respiratory symptoms. 

ΔPEF per 10-ppb increase in SO2 

0.26 (-0.35, 0.88, 1.01) L/min 

Odds ratio per 10-ppb SO2

Coughing: 0.96 (0.92, 1.01) 

LRS: 0.97 (0.94, 1.01) 

 F-18 
Study Method Pollutant Data Findings 

ASIA 

Chen et al. (1999) 

Three towns in 

Taiwan: 

Sanchun, Taihsi, 

Linyuan 

Period of Study: 

May 1995-Jan 1996 

Cross-sectional panel study of 895 

children (8 to 13 yrs) to evaluate the 

short-term effect of ambient air pollution 

on pulmonary function. Single and 

multipollutant models adjusted for sex, 

height, BMI, community, temperature, 

and rainfall. Examined 1, 2, and 7-day 

lag effects. 

Peak concentrations of 

SO2

Range: 0, 72.4 ppb 

Day-time avg and 1-day 

lag 

Copollutants: 

CO NO3 PM10 (r = 0.68) 

NO2 (r = 0.71) 

Daytime peak SO2 at 2 days lag significantly associated with FVC 

using the single-pollutant model. Association also observed with 

NO2 and CO with FVC. No PM10 effects. Only O3 effects 

significant in multipollutant models.

ΔFVC (mL) daytime avg SO2 

Lag 1: -3.18 (1.80); Lag 2: -2.70 (1.49); Lag 7: 0.61 (2.59) 

Daytime peak SO2 

Lag 1: -0.91 (0.73); Lag 2: -1.27 (0.59), p < 0.05; 

Lag 7: -1.05 (1.29) 

ΔFEV1 (mL) daytime avg SO2 

Lag 1: -1.95 (1.69); Lag 2: -1.12 (1.41); Lag 7: -1.05 (1.29) 

Daytime peak SO2 

Lag 1: -0.57 (0.68); Lag 2: -0.64 (0.56); Lag 7: -1.96 (1.22) 

Jadsri et al. (2006) 

Thailand 

Period of Study: 

1993-1996 

Spatial regression analysis of outpatient 

disease occurrence (respiratory system 

diseases; ICD chapter 10) in 25 

communities in Rayong Province. 

--- 

Copollutants: 

TSP, NOX

During summer, SO2 played a role in adverse health effects after 

taking into account distance between community and health 

providers. During winter, no relationship was found. 

Min et al. (2008) 

Korea 

Panel study consisting of 867 smokers, 

former smokers, and never smokers 20-

86 yrs old. Used linear regression 

analysis, adjusting for age, height, 

gender, and a diagnosis of asthma to 

examine the combined effects of 

cigarette smoking and SO2 on lung 

function. Lung function measurements 

used in this analysis included forced vital 

capacity (FVC), forced expiratory volume 

in 1 sec (FEV1), percent predicted value 

of FVC (FVC % pred), and percent 

predicted value of FEV1 (FEV1 % pred). 

24-h avg (ppm): 0.006 Found a short, marked decrease in FVC and FEV1 in smokers 

after exposure to SO2 that lasted for up to 30 h. 

Study did not provide effect estimates. 

Park et al. (2002) 

Seoul, Korea 

Period of Study: 

Mar 2, 1996 to 

Dec 22, 1999 

Time-series analysis of school 

absenteeism due to illness and air 

pollution in one elementary school in 

Seoul. School located in area with heavy 

traffic. Avg enrollment in 1996 was 1,264.

24-h avg SO2

Mean: 9.19 ppb 

SD: 4.61 

Range: 2.68, 28.11 

Copollutants: 

PM10 

NO2 CO (r = 0.67) O3

SO2, PM10, and O3 associated with illness related school 

absenteeism. SP2 and O3 are protective for non-illness related 

absences. 

Relative risk per IQR SO2 (5.68 ppb) 

Total absences: 1.03 (1.02, 1.05) 

Non-illness related absences: 0.95 (0.92, 0.99) 

Illness related absences: 1.09 (1.07, 1.12) 

2-pollutant model with O3: 1.10 (1.08, 1.13) 

Park et al. (2005a) 

Korea 

Period of Study: 

Mar to June 2002 

Panel study of 69 patients (16 to 75 yrs) 

diagnosed with asthma by bronchial 

challenge or by bronchodilator response. 

Patients recorded twice-daily PE, 

symptoms at the end of each day (cough, 

wheeze, chest tightness, shortness of 

breath, sputum changes and the next 

morning, night awakenings). During the 

study period, 14 Asian dust days were 

identified. GEE and generalized additive 

Poisson regression model used in 

analysis. 

Daily avg SO2

Control Days: 

0.0069 

(0.0019) ppm 

Dust days: 

0.0052 (0.0010) ppm 

Copollutants: 

PM10, NO2, CO, O3

During the dust days, SO2 levels were significantly lower 

compared to control days. SO2 had no significant effect on PEF 

variability or night symptoms. 

Relative risk based on Poisson log-linear regression analysis 

PEF variability (>20%) 0.76 (0.37, 1.56) 

Night symptoms: 0.98 (0.59, 1.51) 

 F-19 
Study Method Pollutant Data Findings 

Xu et al. (1991) 

Beijing, China 

Three areas: 

industrial, 

residential, and 

suburban (control) 

Period of Study: 

Aug 1986 

Cross sectional survey of 1140 adults (40 

to 69 yrs) who had never smoked living 

in three areas of Beijing, to determine 

respiratory health effects of indoor and 

outdoor air pollution. A trained interviewer 

obtained pulmonary function measurements and determined history of 

chest illnesses, respiratory symptoms, 

cigarette smoking, occupational 

exposure, residential history, education 

level, and type of fuel used for cooking 

and heating. 

Annual mean 

concentration of SO2

(μg/m3) 

Residential: 128 

Industrial: 57 

Suburban: 18 

Copollutants: 

 TSP 

An inverse linear association found between Ln outdoor SO2 and 

FEV1 and FVC after adjusting for age, height and sex. 

Regression estimate and standard error 

per Ln SO2 (μg/m3) 

Height-adjusted FEV1 (mL): −35.6 (17.3) 

Height-adjusted FVC (mL): −131.4 (18.8) 

Table F-2. Short-term exposure to SO2 and emergency department visits and hospital 

admissions for respiratory diseases. 

Study Methods Pollutant Data Findings 

UNITED STATES 

Gwynn* et al. (2000) 

Buffalo and Rochester, NY 

U.S. 

Period of Study: 

May 1988-October 1990 

Days: 1,090 

Hospital Admissions 

Outcome(s) (ICD9): 

Respiratory admissions: 

Acute bronchitis/ bronchiolitis 

(466); Pneumonia (480-4860); 

COPD and Asthma (490-493, 

496) 

Age groups analyzed: 6 

Study design: Time-series 

N: 24 

Statistical analyses: Poisson 

regression with GLM and 

GAM 

Covariates: season, day of 

wk, holiday, temperature, 

relative humidity 

Lag: 0-3 days 

24-h avg SO2

(ppb): 

Min: 1.63 

25th: 8.4 

Mean: 12.2 

75th: 15.4 

Max: 37.7 

Copollutants: 

H+ (r = 0.06) 

SO4

2- (r = 0.19) 

PM10 (r = 0.19) 

O3 (r = 0.02) 

NO2 (r = 0.36) 

CO (r = 0.11) 

COH (r = 0.29) 

Significant associations observed between several pollutants and various 

health-effect outcomes make it difficult to discriminate the influence of a 

single-pollutant. This is likely a result of the relatively high intercorrelations 

among the various pollutants, as well as the possible interactive role of 

several pollutants in the reported associations. 

Increment: 25.5, 7.0 ppb 

(Max-Mean; IQR) 

SO2 alone: 

Max-Mean RR 1.096 (t = 3.05) lag 0 

IQR RR 1.025 (t = 3.05) lag 0 

 F-20 
Study Methods Pollutant Data Findings 

Ito et al. (2007) 

New York, NY 

Period of Study: 

Jan 1999-Dec 2002 

ED Visits 

Outcome(s): Asthma 

Study design: Time-series 

Statistical Analysis: Poisson 

GLM 

Age groups analyzed: All 

ages 

Covariates: Adjustment for 

temperature (same day and 

avg lag 1-3), dew point (same 

day and avg lag 1-3) 

# Hospitals: 11 

Lag(s): Avg 0 and 1 day 

All yr 

24-h avg (ppb): 7.8 

(4.6) 

5th: 3 

25th: 5 

50th: 7 

75th: 10 

95th: 17 

Warm mos 

(Apr-Sep) 

24-h avg (ppb): 5.4 

(2.2) 

5th: 3 

25th: 4 

50th: 5 

75th: 7 

95th: 10 

Cold Mos 

(Oct-Mar) 

24-h avg (ppb): 

10.2 (5.1) 

5th: 4 

25th: 6 

50th: 9 

75th: 13 

95th: 19 

Copollutants: 

PM2.5;NO2; O3; CO

In single-pollutant models, NO2 was found to have the most significant 

association with asthma ED visits for all-yr and warm mos. SO2 was 

significantly associated with asthma ED visits for all single-pollutant models 

for all-yr and both the warm and cold mos. In copollutant models for the warm 

mos, NO2 eliminated the association between SO2 and asthma ED visits. 

This result is consistent with the monitor-to-monitor correlations, which 

suggested that NO2 had less exposure error compared to SO2. 

Warm Mos (Apr-Sep) 

(Weather model including smoothing terms for same day temperature and 

avg lag 1-3 day temperature.) 

Relative Risk (95% CI) (per 6 ppb SO2) 1.20 (1.13, 1.28) 

Jaffe et al. (2003) 

3 cities, Ohio, U.S. 

(Cincinnati, Cleveland, 

Columbus) 

Period of Study: 

Jul 1991-Jun 1996 

(June-Aug months only) 

ED Visits 

Outcome(s) (ICD9): Asthma 

(493) 

Age groups analyzed: 5-34 

Study design: Time-series; N: 

4,416 

Statistical analyses: Poisson 

regression using a standard 

GAM approach 

Covariates: City, day of wk, 

wk, yr, min temperature, 

overall trend, dispersion 

parameter 

Season: June to Aug only 

Dose-response investigated: 

Yes 

Statistical package: NR 

Lag: 0-3 days 

24-h avg: 

Cincinnati: 

35.9 (25.1) μg/m3

Range: 1.7, 132 

Cleveland: 

39.4 (25.3) μg/m3

Range: 2.6, 167 

Columbus: 

11.1(8.5) μg/m3

Range: 0, 56.8 

Copollutants: 

Cincinnati: 

PM10 (r = 0.31) 

NO2 (r = 0.07) 

O3 (r = 0.14) 

Cleveland: 

PM10 (r = 0.29) 

NO2 (r = 0.28) 

O3 (r = 0.26) 

Columbus: 

PM10 (r = 0.42) 

NO2 (r = NR) 

O3 (r = 0.22) 

Wide confidence intervals for data from Cleveland and Columbus make these 

data not significant and unstable. Only data for Cincinnati was considered 

statistically significant and demonstrated a concentration response function 

that was positive. 

No multipollutant models were utilized. 

Increment: 50 μg/m3

Cincinnati: 35% (9, 21) lag 2 

Cleveland: 6% (−7, 21) lag 2 

Columbus: 26% (−25, 213) lag 3 

All cities: 12% (1, 23) 

Attributable risk from SO2 

increment: 

Cincinnati: 4.2% 

Cleveland: 0.66% 

Columbus: 2.94% 

 F-21 
Study Methods Pollutant Data Findings 

Lin et al. (2004d) 

New York (Bronx County), 

U.S. 

Period of Study: 6/1991-

12/1993 

Hospital Admissions 

Outcome(s) (ICD9): Asthma 

(493) 

Age groups analyzed: 0-14 

Study design: 

Case-control 

N: 2,629 cases; 2,236 

controls 

Statistical analyses: logistic 

regression 

Covariates: race and ethnicity, 

age, gender, season 

Statistical package: 

Lag: 0,1,2,3, 0-3 

Cases: 

24-h avg: 16.78 

ppb 

50th: 13.72 

Range: 2.88, 

66.35 

Controls: 

24-h avg: 15.57 

ppb 

50th: 13.08 

Range: 2.88, 

66.35 

Quartile 

Concentrations 

(ppb) : 

Q1: 2.88, 8.37 

Q2: 9.37, 13.38 

Q3: 13.5, 20.91 

Q4: 20.21, 66.35 

Odds ratios for risk of hospitalization for asthma increased with each quartile 

of SO2 concentration. 

Lag 1, 2, or 3 all showed a concentration response that was positive for odds 

ratio as each quartile was compared to the total exposure group 

(trend p > 0.001). 

Quartile (24-h avg) 

Q2 OR 1.26 lag 3 

Q3 OR 1.45 lag 3 

Q4 OR 2.16 (1.77, 2.65) lag 3 

Quartile (1-h max) 

Q4 OR 1.86 (1.52, 2.27) lag 3 

For a 4 ppb increase in SO2 

(24-h avg) 

RR 1.07 (1.04, 1.11) 

Michaud et al. (2004) 

Hilo, Hawaii 

Period of Study: 2/21/1997-

5/31/2001 

ED Visits 

Outcome(s) (ICD9): COPD 

(490-496); Asthma (493, 495); 

bronchitis (490, 491), other 

COPD (492, 494, 496) 

Age groups analyzed: All 

Study design: Time-series 

Statistical analyses: 

Exponential regression 

models 

Covariates: temporal 

variables, day of wk, 

meteorology 

Statistical package: Stata, 

SAS 

Lag: 0,1,2,3 days 

1-h max: 

1.92 (12.2) ppb 

Range: 0.0, 447 

24-h avg: 

1.97 (7.12) ppb 

Range: 0.0, 108.5 

Copollutants: PM1

The lack of organic carbon shows the pure SO2 effect uncontaminated by 

vehicle emissions. 

Asthma is associated with vog, but vog is not a major cause of asthma in 

Hawaii. The strongest association was with the mo of the yr. 

Admission for asthma and respiratory conditions was higher in the winter 

compared to the summer, based on admission per day (observational- not 

statistical analysis). 

Increment: 10 ppb 

COPD: RR 1.04 (0.99, 1.09) lag 1; RR 1.04 (1.00, 1.09) lag 2; 

RR 1.07 (1.03, 1.11) lag 3 

Asthma: RR 1.01 (1.00, 1.10) lag 1; RR 1.02 (1.03, 1.12) lag 2; 

RR 1.02 (1.03, 1.12) lag 3 

Bronchitis: RR 1.01 (0.93, 1.13) lag 1; RR 0.99 (0.88, 1.05) lag 2; 

RR 1.01 (1.00, 1.14) lag 3 

Other COPD: RR 1.00 (0.78, 1.23) lag 1; RR 0.96 (0.62, 1.11) lag 2; 

RR 0.98 (0.75, 1.16) lag 3 

 F-22 
Study Methods Pollutant Data Findings 

Moolgavkar* et al. (1997) 

U.S.: Minneapolis-St. Paul; 

Birmingham 

Period of Study: 

Jan 1986-Dec 1991 

Hospital Admissions 

Outcome(s) (ICD9): COPD 

including asthma 

(490-496), Pneumonia 

(480-487) 

Age groups analyzed: 65+ 

Study design: Time-series 

Statistical analyses: Semiparametric Poisson 

regression, GAM 

Covariates: day of wk, 

season, temporal trends, 

temperature 

Statistical package: 

S Plus 

Lag: 0-3 days 

SO2 24-h avg 

(ppb): 

Minneapolis: 

Mean: 4.82 

10th: 1.9 

25th: 2.66 

50th: 4.02 

75th: 6.0 

90th: 8.5 

Birmingham: 

Mean: 6.58 

10th: 2.2 

25th: 3.7 

50th: 6.0 

75th: 8.6 

90th: 11.6 

Copollutants: 

Minneapolis: 

PM10 (r = 0.08) 

NO2 (r = 0.09) 

CO (r = 0.07) 

O3 (r = −0.12) 

Birmingham: 

PM10 (r = 0.17) 

CO (r = 0.16) 

O3 (r = 0.02) 

SO2 with NO2 and PM10 were associated with hospital admissions. Evidence 

of mixture effects was found. No single-pollutant was more important than the 

other for respiratory admissions. Each pollutant was associated with 

admissions except CO. 

Consideration of four pollutants together showed the strongest association 

with O3. No pollutant other than O3 was stable in its association with hospital 

admissions. 

No effects were reported for Birmingham. Positive results were only observed 

in Minneapolis. 

Increment: 3.5 ppb 

Sum of Pneumonia and COPD: 1.6% (−0.1, 3.3) lag 2 

Pneumonia Only 

Minneapolis: 

65+ 0.9% (−1.1, 2.9) lag 2 20 df 

0.5% (−1.5, 2.5) 

lag 2 130 dfS 

Moolgavkar (2000a) 

Multicity, U.S.: Cook, Los 

Angeles, Maricopa County, 

(Phoenix) 

Period of Study: 

1987-1995 

Hospital Admissions 

Outcome(s) (ICD9): COPD 

including asthma (490-496) 

Age groups analyzed: 

0-19, 20-64, 65+ 

(LA only) 

Study design: Time-series 

Statistical analyses: Poisson 

regression, GAM 

Covariates: Day of wk, 

temporal trends, temperature, 

relative humidity 

Lag: 0-5 days 

Cook: 

Median: 6 ppb 

25th: 4 

75th: 8 

Range: 0.5, 36 

Los Angeles: 

Median: 2 ppb 

25th: 1 

75th: 4 

Range: 0, 16 

Maricopa: 

Median: 2 ppb 

25th: 0.5 

75th: 4 

Range: 0, 14 

Copollutants: 

Cook: 

PM10 (r = 0.42) 

CO (r = 0.35) 

NO2 (r = 0.44) 

O3 (r = 0.01) 

Los Angeles: 

PM2.5 (r = 0.42) 

PM10 (r = 0.41) 

CO (r = 0.78) 

NO2 (r = 0.74) 

O3 (r = −0.21) 

Maricopa: 

PM10 (r = 0.11) 

CO (r = 0.53) 

NO2 (r = 0.02) 

O3 (r = −0.37) 

In Los Angeles there was a significant association with hospital admissions 

for COPD. 

SO2 may be acting as a surrogate for other pollutants since heterogeneous 

responses found in different cities are inconsistent with a cause-effect model. 

Increment: 10 ppb 

COPD, >65 yrs 

Cook lag 0: 4.87 (t = 3.18) GAM-100 

LA lag 0: 2.84 (t = 13.32) GAM-30 

LA lag 0: 1.80 (t = 9.60) 

GAM-100 

LA lag 0: 1.78 (t = 7.72) 

NS-100 

 F-23 
Study Methods Pollutant Data Findings 

NY DOH (2006) 

Bronx and Manhattan, NY 

Period of Study: 

Jan 1999-Dec 2000 

ED Visits 

Outcome(s) (ICD9): Asthma 

(493), for infants (466.1 and 

786.09) 

Study design: Time-series 

N: ≈ 36,000 

Statistical Analysis: Poisson 

regression with GLM 

Statistical package: S-Plus 

Age groups analyzed: All 

ages 

Covariates: Season, day-ofwk, temperature 

# Hospitals: 22 

Lag(s): Avg 0- to 4-day lags 

24-h avg (ppm): 

0.011 (0.0072) 

Copollutants: 

PM10 PM2.5 OC 

EC Cr Fe Pb 

Mn Ni Zn H+ 

Sulfate O3

NO2 SO2 

In single-pollutant models, PM2.5, SO2, O3, and NO2 were all found to be 

significantly associated with asthma ED visits in a community, Bronx, with a 

high prevalence of asthma. This association was maintained in both two- and 

three-pollutant models for O3 and SO2. 

Single-Pollutant Models 

Relative Risk: (95% CI) (per 0.011 ppm SO2) 

5-day moving avg 

Manhattan: 0.99 (0.88, 1.12); Bronx: 1.11 (1.06, 1.17) 

Relative Risk: (95% CI) (per 0.0072 ppm SO2) 

Bronx: 1.07 (1.04, 1.11) 

Relative Risk based on Daily Max Hourly SO2: (95% CI) (per 0.0227 ppm 

SO2) 

Manhattan: 0.96 (0.86, 1.07); Bronx: 1.07 (1.03, 1.12) 

Relative Risk:(95% CI) (per 0.0072 ppm SO2)-model excludes temperature 

Manhattan: 0.99 (0.88, 1.11); Bronx: 1.11 (1.06, 1.17) 

Relative Risk: (95% CI) (per 0.0072 ppm SO2)- by Gender 

Manhattan: Male: 0.90 (0.75, 1.07); Female: 1.08 (0.91, 1.29) 

Bronx: Male: 1.08 (1.00, 1.17); Female: 1.14 (1.06, 1.23) 

Relative Risk: (95% CI) (per 0.0072 ppm SO2)-By Age 

Manhattan: 0-4: 0.82 (0.59, 1.15); 5-18: 1.03 (0.77, 1.37); 

19-34: 1.01 (0.76, 1.35); 35-64: 1.04 (0.86, 1.25); 65+: 0.88 (0.57, 1.37) 

Bronx: 0-4: 1.13 (1.01, 1.26); 5-18: 1.03 (0.92, 1.16); 19-34: 1.06 (0.93, 1.21); 

35-64: 1.18 (1.07, 1.30); 65+: 1.12 (0.88, 1.42) 

Two-Pollutant Models 

Relative Risk: (95% CI) (per 0.0072 ppm SO2) 

5-day moving avg 

Manhattan: SO2 + Max 8-h O3: 0.99 (0.88, 1.12); 

SO2 + FRM PM2.5: 0.97 (0.85, 1.11); SO2 + Max PM2.5: 0.98 (0.85, 1.12); 

SO2 + NO2: 1.01 (0.87, 1.16) 

Bronx: SO2 + Max 8-h O3: 1.11 (1.05, 1.17); 

SO2 + FRM PM2.5: 1.11 (1.04, 1.18); SO2 + Max PM2.5: 1.09 (1.03, 1.16); 

SO2 + NO2: 1.11 (1.04, 1.17) 

Norris et al. (1999) 

Seattle, Washington 

Period of Study: 

1995-1996 

ED Visits 

Outcome(s) (ICD9): Asthma 

(493) 

Study design: Time-series 

Statistical Analysis: 

Semiparametric Poisson 

regression model 

Statistical package: S-Plus 

Age groups analyzed: < 18 

Covariates: adjustments for 

day-of-wk indicator variables, 

time trends, temperature, dew 

point temperature 

# Hospitals: 6 

Lag(s): 0, 2 

24-h avg (ppb): 6.0 

(3.0) 

Range: 1.0, 21.0 

1-h max (ppb): 

16.0 (14.0) 

Range: 2.0, 84.0 

Copollutants: 

PM10, Dry light 

scattering, 

NO2,CO, O3 

A significant association was found between asthma emergency department 

visits in children and PM2.5 and CO. Estimates were not found to be different 

between high and low hospital utilization areas. SO2 was negatively 

associated with asthma emergency department visits in high utilization areas, 

and positively associated in low utilization areas. 

Relative Rates (95% CI) (per 3 ppb 24-h avg SO2; per 12 ppb 1-h max SO2) 

High Utilization Areas: 

24-h avg: 0.92 (0.83, 1.03) 

1-h max: 0.99 (0.89, 1.10) 

Low Utilization Areas 

24-h avg: 1.09 (1.00, 1.19) 

1-h max: 1.09 (1.00, 1.19) 

All Areas 

24-h avg: 0.97 (0.91, 1.04) lag 0 

1-h max: 1.02 (0.95, 1.09) lag 2 

 F-24 
Study Methods Pollutant Data Findings 

Peel et al. (2005) 

Atlanta, GA, U.S. 

Period of Study: 

1/93-8/2000 

ED Visits 

Outcome(s) (ICD 9): All 

respiratory (460-6, 477, 

480-6, 480-6, 490-3, 496); 

Asthma (493); COPD (491-2, 

496); Pneumonia (480-486); 

Upper Respiratory Infection 

(460-6, 477) 

Age groups analyzed: All 

Study design: Time-series 

N: ≈ 480,000 

# of Hospitals: 31 

Statistical analyses: Poisson 

Regression, GEE, GLM, and 

GAM (data not shown for 

GAM) 

Covariates: Day of wk, 

hospital entry/exit, holidays, 

time trend; season, 

temperature, dew point 

temperature 

Statistical package: SAS, SPlus 

Lag: 0 to 7 days. 3 day 

moving avgs. 

1-h max: 16.5 

(17.1) ppb 

10th%: 2.0 

90th%: 39.0 

Copollutants: O3

NO2

CO 

PM2.5

Evaluated 

multipollutant 

models (data not 

shown) 

Estimates from distributed lag models (0-13 days) tend to be higher than for 

3-day moving avg. Positive associations for URI and COPD with SO2 were 

noted for unconstrained lags (0-13 days) that covered the previous two 

weeks of exposure. 

Increment: 20 ppb 

All respiratory: 

RR 1.008 (0.997, 1.019) lag 0-2, 3-day moving avg 

Upper Respiratory Infection (URI): 

RR 1.010 (0.998, 1.024) lag 0-2, 3-day moving avg 

Asthma: 

All: 1.001 (0.984, 1.017) lag 0-2, 3-day moving avg 

Pneumonia: 

RR 1.003 (0.984, 1.023) lag 0-2, 3-day moving avg 

COPD: 

RR 1.016 (0.985, 1.049) lag 0-2, 3-day moving avg 

Schwartz (1995) 

New Haven, CT 

Tacoma, WA 

U.S. 

Period of Study: 

1988-1990 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory admissions (460-

519) 

Age groups analyzed: ≥65 

Study design: Time-series 

N: 13,470 

Statistical analyses: Poisson 

regression, log linear 

regression using GLM and 

GAM 

Covariates: dewpoint, temp, 

long-term trends, days of wk 

Statistical package: S- Plus 

Lag: 0-1 

24-h avg 

New Haven 

Mean 78 μg/m3

(29.8 ppb) 

10th: 23 

25th: 35 

50th: 78 

75th: 100 

90th: 159 

Tacoma 

Mean: 44 μg/m3

(16.8 ppb) 

10th: 15 

25th: 26 

50th: 40 

75th: 56 

90th: 74 

Copollutants: 

PM10 O3

In New Haven, risk associated with SO2 was not affected by inclusion of 

PM2.5 in the model and the effect of PM2.5 was not strongly affected by 

inclusion of SO2. This suggests that in New Haven, SO2 and PM2.5 acted 

independently. 

In Tacoma, 2-pollutant model analysis showed risk associated with SO2 was 

attenuated by PM2.5. This suggested risks associated with SO2 and PM2.5

were not independent. Possibly, SO2 acts as a surrogate for PM2.5 in this city. 

Increment: 50 μg/m3 or 18.8 ppb 

New Haven, CT 

RR = 1.03 (CI 1.02, 1.05), lag 0-1. p < 0.001 

2-pollutant model with PM10: 

RR = 1.04 (CI 1.02, 1.06) p < 0.001 

Tacoma, WA 

RR = 1.06 (CI 1.01, 1.12), lag 0-1. p > 0.02 

2-pollutant model with PM10: 

RR = 0.99 (CI 0.93, 1.06) p > 0.5 

Schwartz et al. (1996) 

Cleveland, OH 

Period of Study: 

1988-1990 

Hospital Admissions 

Outcome(s) (ICD9): All 

respiratory disease 

Age groups analyzed: ≥ 65 

Study design: Time-series 

Statistical analyses: Poisson 

regression 

Covariates: Season, 

temperature, day of wk 

Statistical package: 

Lag: 0-1 

24-h avg: 35 ppb 

10th: 13 

25th: 20 

50th: 31 

75th: 45 

90th: 61 

Copollutants: 

PM2.5 O3

Significant associations were seen for PM2.5 and O3, with somewhat weaker 

evidence for SO2. 

Increment: 100 μg/m3

RR 1.03 (0.99, 1.06) lag 0-1 

 F-25 
Study Methods Pollutant Data Findings 

Sheppard et al. (1999) 

Reanalysis (2003) 

Seattle, WA, U.S. 

Period of Study: 

Jan 1987-Dec 1994 

Hospital Admissions 

Outcome(s) (ICD9): Asthma 

(493) 

Age groups analyzed: < 65 

Study design: Time-series 

N: 7,837 

# of Hospitals: 23 

Statistical analyses: Poisson 

regression with adjustment for 

auto-correlation. 

Covariates: 

Statistical package: S-Plus 

Lag: 0,1,2,3 

24-h avg: 8 ppb 

IQR: 5 ppb 

10th: 3.0 

25th: 5.0 

50th: 8.0 

75th: 10.0 

90th: 13.0 

Copollutants: 

PM10 (r = 0.31) 

PM2.5 (r = 0.22) 

O3 (r = 0.07) 

CO (r = 0.24) 

Sources of SO2 adjacent or near to monitoring site. Low concentrations. No 

association with SO2 for asthma but positive association for appendicitis. 

Increment: 5 ppb (IQR) 

GAM with stricter criteria: 

1.0% (−2.0, 3.0) lag 0 

GLM with natural spline 

smoothing: 

0.0% (−3.0, 4.0) lag 0 

Sinclair and Tolsma (2004) 

Atlanta, GA 

Period of Study: 

8/1/1998-8/31/2000 

ED Visits 

Outcome(s): asthma, upper, 

and lower respiratory 

infections. 

Study design: Time-series 

investigation 

Statistical Analysis: Single 

pollutant Poisson general 

linear modeling 

Statistical package: SAS v. 

8.02 

Age Groups Analyzed: All 

# Hospitals: 10 

Lag(s): 0-8 days 

1-hour Max Mean: 

19.28 ppb 

SD:16.28 

Copollutants: 

PM2.5 PM10 NO2 

CO O3

No significant findings for child or adult asthma. 

Significant negative associations with upper respiratory infections for 6-8 day 

lag (RR = 0.98). 

Significant positive association with lower respiratory infections for 0-2 day 

lag (RR = 1.067). 

Not provided. 

Tolbert et al. (2007) 

Atlanta, GA 

Period of Study: 

Jan 1993-Dec 2004 

ED Visits 

Outcome(s) (ICD9): 

Cardiovascular (410-414, 

427, 428, 433-437, 440, 443-

445, 451-453); Respiratory 

(493, 786.07, 786.09, 491, 

492, 496, 460-465, 477, 480-

486, 466.1, 466.11, 466.19) 

Study design: Time-series 

Statistical Analysis: Poisson 

Generalized Linear Model 

(GLM). 

Statistical package: SAS 

Age groups analyzed: All 

ages 

Covariates: Adjustment for 

day-of-wk, hospital entry, 

holidays, time, temperature, 

dew point temperature 

# Hospitals: 41 

N: 238,360 (Cardiovascular) 

1,072,429 (Respiratory) 

Lag(s): 3-day moving avg 

1-h max (ppb): 

14.9 

Range: 1.0, 149.0 

10th: 2.0 

25th: 4.0 

75th: 20.0 

90th: 35.0 

Copollutants: PM10

PM2.5 O3 NO2 CO 

Sulfate 

Total Carbon 

Organic Carbon 

EC 

Water-Soluble 

Metals 

Oxygenated 

Hydrocarbons 

In single pollutant models, O3, PM10, CO, and NO2 significantly associated 

with ED visits for respiratory outcomes. 

Relative Risk (95% CI) 

(per 16.0 ppb SO2) 

1.003 (0.997, 1.009) 

 F-26 
Study Methods Pollutant Data Findings 

Wilson et al. (2005) 

Multicity, U.S. 

(Portland, ME and 

Manchester, NH) 

Period of Study: 

Jan 1996-Dec 2000 

(Manchester) 

Jan 1998-Dec 2000 

(Portland) 

ED Visits 

Outcome(s) (ICD 9 codes): All 

respiratory 

(460-519); Asthma (493) 

Age groups analyzed: 

0-14 yrs; 15-64 yrs; ≥65 yrs 

Study design: Time-series 

Statistical analyses: Multiple 

regression analysis standard 

GAM with more stringent 

criteria parameters 

Covariates: 

Time-trend, season, influenza, 

temperature, humidity, 

precipitation 

Statistical package: S-Plus 

Lag: 0-2 

SO2 1-h max: 

Mean, (SD) (ppb) 

Portland 

All yr: 11.1 (9.1) 

Winter: 17.1 (12.0)

Spring: 10.0 (7.1) 

Summer: 9.1 (8.0)

Fall: 9.7 (7.1) 

Manchester 

All yr: 16.5 (14.7) 

Winter: 25.7 (15.8)

Spring: 14.8 (12.0)

Summer: 10.6 

(15.1) 

Fall: 14.6 (11.1) 

Copollutants: 

O3 PM2.5

Elevated levels of SO2 were positively associated with elevated respiratory 

and asthmatic ER visits. The significance of these relationships is not 

sensitive to analytic or smoothing techniques. 

Increment: 6.3 ppb (IQR) for Portland; IQR for Manchester 

Portland: 

All respiratory: All ages RR 1.05 (1.02, 1.07) lag 0; 

0-14 yrs RR 0.98 (0.93, 1.02) lag 0; 15-64 yrs RR 1.06 (1.03, 1.09) lag 0; 

>65 yrs RR 1.10 (1.05, 1.15) lag 0 

Asthma: All ages RR 1.06 (1.01, 1.12) lag 2; 

0-14 yrs RR 1.03 (0.93, 1.15) lag 2; 15-64 yrs 1.07 (1.01, 1.15) lag 2; 

>65 yrs RR 1.07 (0.90, 1.26) lag 2 

Manchester: 

All respiratory: All ages RR 1.01 (0.99, 1.02) lag 0; 

0-14 yrs RR 1.00 (0.96, 1.04) lag 0; 15-64 yrs RR 1.00 (0.98, 1.03) lag 0; 

>65 yrs RR 1.04 (0.97, 1.11) lag 0 

Asthma: All ages RR 1.03 (0.98, 1.09) lag 2; 

0-14 yrs RR 1.11 (0.98, 1.25) lag 2; 15-64 yrs 1.02 (0.96, 1.08) lag 2; 

>65 yrs RR 1.06 (0.83, 1.36) lag 2 

CANADA 

Bates et al. (1990) 

Vancouver Region, BC, 

Canada 

Period of Study: 

7/1/1984-10/31/1986 

ED Visits 

Outcome(s) (ICD 9): Asthma 

(493); 

Pneumonia (480-486); 

Chronic bronchitis 

(491,492,496); 

Other respiratory (466) 

Age groups analyzed: 

All; 15-60 

Study design: 

# of Hospitals: 9 

Statistical analyses: Pearson 

correlation coefficients were 

calculated between asthma 

visits and environmental 

variables 

Season: 

Warm (May-Oct); 

Cool (Nov-Apr) 

Covariates: NR 

Lag: 0, 1, 2 

May-Oct 

SO2 1-h max: 

Range: 0.0137, 

0.0151 ppm 

Nov-Apr 

Range: 0.012, 

0.0164 ppm 

Number of 

stations: 11 

Copollutants: 

May-Oct: 

O3 (r = 0.23) 

NO2 (r = 0.67) 

COH (r = 0.34) 

SO4 (r = 0.46) 

Nov-Apr: 

O3 (r = 0.47) 

NO2 (r = 0.61) 

COH (r = 0.64) 

SO4 (r = 0.54) 

SO2 effects depend on the season. In the summer a rise in ambient SO2

levels was seen to coincide with a rise in respiratory related hospital 

admissions. 

Correlation Coefficients: 

Warm Season (May-Oct) 

Asthma (15-60 yrs): r = 0.118 lag 0 p < 0.01; r = 0.139 lag 1 

Respiratory (15-60 yrs): r = 0.134 lag 0 p < 0.001; r = 0.164 lag 1 p < 0.001 

Cool Season (Nov-Apr) 

Respiratory (1-14 yrs): r = 0.205 lag 0 p < 0.001; r = 0.234 lag 1 p < 0.001; 

r = 0.234 lag 2 p < 0.001 

(15-60 yrs): r = 0.180 lag 0 p < 0.001; r = 0.214 lag 1 p < 0.001; 

r = 0.215 lag 2 p < 0.001 

(≥ 61 yrs 0: r = 0.257 lag 0 p < 0.001; r = 0.308 lag 1 p < 0.001; 

r = 0.307 lag 2 p < 0.001 

Asthma (≥ 61 yrs): r = 0.125 lag 0 p < 0.001; r = 0.149 lag 1 p < 0.001; 

r = 0.148 lag 2 p < 0.001 

Total ER admissions (≥ 61 yrs): r = 0.13 lag 1 p < 0.01; 

r = 0.13 lag 2 p < 0.01 

Burnett* et al. (1997a) 

16 cities, Canada 

Period of Study: 

 4/1981-12/1991 

Days: 3,927 

Hospital Admissions 

Outcome(s) (ICD9): All 

respiratory admissions (466, 

480-6, 490-4, 496) 

Study design: Time-series. 

N: 720,519 

# of Hospitals: 134. 

Statistical analyses: random 

effects relative risk regression 

model 

Covariates: Long-term trend, 

season, day of wk, hospital 

Statistical package: NR 

Lag: 0, 1, 2 day 

1-h max SO2 (ppb)

Mean: 14.4. SD: 

22.2 

25th: 3 

50th: 10 

75th: 19 

95th: 45 

99th: 97 

O3 r = 0.04 

Copollutants: 

CO, NO2,COH 

Control of SO2 reduced but did not eliminate the O3 association with 

respiratory hospital admissions. 

Increment: 10 ppb 

Single-pollutant 

SO2 and respiratory admissions, p = 0.134 

Multipollutant model (adjusted for CO, O3, NO2, COH, dew point): 

RR 1.0055 (0.9982, 1.0128) lag 0 

 F-27 
Study Methods Pollutant Data Findings 

Burnett* et al. (1997b) 

Toronto, Canada 

Period of Study: 

Summer only, 1992-1994 

Hospital Admissions 

Outcomes (ICD 9 codes): 

Respiratory tracheobronchitis 

(480-6), COPD (491-4, 496) 

Study design: Time-series 

Statistical analyses: Poisson 

regression, GEE, GAM 

Covariates: Temperature, dew 

point temperature, Long-term 

trend, season, influenza, day 

of wk 

Season: Summers only 

Lag: 0,1,2,3,4 days 

Avg SO2: 7.9 ppb 

CV: 64 

Range: 0, 26 

5th: 1 

25th: 4 

50th: 7 

75th: 11 

95th: 18 

Number of 

stations: 6-11 

Copollutants: 

CO (r = 0.37) 

H+ (r = 0.45) 

SO4 (r = 0.42) 

TP (r = 0.55) 

FP (r = 0.49) 

CP (r = 0.44) 

COH (r = 0.50) 

O3 (r = 0.18) 

NO2 (r = 0.46) 

Risks of hospitalization for respiratory disease were summed for O3, NO2, 

and SO2 at 11% increase in admissions. The proportion associated with the 

single-pollutant SO2 was 3.6%. CoH was the strongest predictor of 

hospitalization indicating particle associated pollutants are responsible for 

effects and outcomes measured. 

Increment: 4.00 ppb (IQR) 

Respiratory-percent increase 4.0% (t = 4.14) lag 0 

Copollutant and multipollutant models RR 

(t-statistic): 

SO2, COH: 1.012 (1.10) 

SO2, H+: 1.022 (1.96) 

SO2, SO4: 1.021 (1.93) 

SO2, TP: 1.021 (1.72) 

SO2, FP: 1.022 (1.92) 

SO2, CP: 1.023 (2.03) 

SO2, O3, NO2: 1.019 (1.64) 

Burnett* et al. (1999) 

Toronto, Canada 

Period of Study: 

Jan 1980-Dec 1994 

Hospital Admissions 

Outcome(s) (ICD9): Asthma 

(493); obstructive lung 

disease (490-2, 496); 

Respiratory infection (464, 

466, 480-7, 494) 

Study design: Time-series 

N: ≈ 60,000 (asthma) 

Statistical analyses: Poisson 

regression model with 

stepwise analysis 

Covariates: Long-term trends, 

season, day of wk, daily max 

temperature, daily min 

temperature, daily avg dew 

point temperature, daily avg 

relative humidity 

Statistical package: S-Plus, 

SAS 

Lag: 0,1,2 days, cumulative 

24-h Avg: 5.35 ppb

CV = 110; 

5th: 0 

25th: 1 

50th: 4 

75th: 8 

95th: 17 

100th: 57 

Number of 

stations: 4 

Copollutants: 

PM2.5 (r = 0.46) 

PM10−2.5 (r = 0.28)

PM10 (r = 0.44) 

CO (r = 0.37) 

NO2 (r = 0.54) 

O3 (r = 0.02) 

The percent hospital admissions associated with SO2 increased for: asthma, 

COPD, and respiratory infection. However, in multipollutant models 

significant increases were only seen in asthma and respiratory infection. SO2

effects could be largely explained by other variables in the pollution mix as 

demonstrated by the Multipollutant model. The greatest contribution of SO2 is 

to respiratory infection. However, overall SO2 is a small factor in total 

hospitalization response. 

Increment: 5.35 ppb (Mean) 

Single-pollutant model percent increase (t statistic) 

Asthma: 1.01% (1.76) lag 0-2; OLD: 0.03% (0.05) lag 0-1 

Respiratory infection: 2.40% (5.04) lag 0-2 

Multipollutant model percent increase (SE) 

Asthma: SO2 + CO + O3: 0.89% (SE < 2); 

SO2 + CO + O3 + PM2.5: 0.69% (SE < 2); 

SO2 + CO + O3 + PM10−2.5: 0.16% (SE < 2); 

SO2 + CO + O3 + PM2.5: 0.76% (SE < 2) 

Respiratory infection: SO2 + NO2 + O3: 1.85%; 

SO2 + NO2 + O3 + PM2.5: 0.67 (SE < 2); 

SO2 + NO2 + O3 + PM10−2.5: 1.71 (SE ≥ 3); 

SO2 + NO2 + O3 + PM2.5: 1.00 (SE > 2) 

Burnett* et al. (2001) 

Toronto, Canada 

Period of Study: 

1980-1994 

Hospital Admissions 

Outcome(s) (ICD9): Croup 

(464.4), pneumonia (480-

486), asthma (493), acute 

bronchitis/bronchiolitis (466) 

Age groups analyzed: 

 < 2 yrs 

Study design: Time-series 

Statistical analyses: Poisson 

regression with GAM 

Covariates: Temporal trend, 

day of wk, temperature, 

relative humidity 

Statistical package: 

S-Plus 

Lag: 0-5 days 

1-h max SO2 (ppb)

Mean: 11.8 

CV: 93 

5th: 0 

25th: 5 

50th: 10 

75th: 15 

95th: 32 

99th: 55 

100th: 110 

Number of 

stations: 4 

Copollutants: 

O3 (r = 0.39) SO2 

CO PM2.5 PM10−2.5

SO2 had the smallest effect on respiratory admissions of all pollutants 

considered. 

Increment: NR 

All respiratory admissions: 

Single-pollutant: 

Percent increase: 3.1% (t = 1.900) 

lag 3 

Multipollutant (adjusted for O3): 

Percent increase: 1.21% (t = 0.67) 

lag 3 

 F-28 
Study Methods Pollutant Data Findings 

Cakmak et al. (2007) 

Canada 

(Calgary, Edmonton, Halifax, 

London, Ottawa, Saint John, 

Toronto, Vancouver, Windsor, 

Winnipeg) 

Period of Study: 

4/1/1993-3/31/2000 

Hospital Admissions 

Outcome(s) (ICD9): 

Respiratory (466, 480-486, 

490-494, 496) 

Study design: Time-series 

Statistical Analysis: Poisson 

Statistical package: S-Plus 

Age groups analyzed: All 

ages 

Covariates: Day-of-wk, mean 

daily temperature, max daily 

temperature, min daily 

temperature, change in 

barometric pressure, mean 

relative humidity 

N: 215,544 

Lag(s): 2.6 days 

24-h avg: 4.6 ppb 

Range: 2.8 ppb to 

10.2 ppb 

Copollutants: 

O3 NO2 CO 

SO2 associated with increased hospital admissions. 

% increase 

(per 4.6 ppb SO2) 

Overall 

Single-pollutant model: 1.1% (0.5, 1.8) 

Multi-pollutant model: 0.5% (0.1, 0.9) 

By Gender: Male: 0.4% (−0.2, 1.1); Female: 0.9% (−0.4, 2.1) 

By Education: <Grade 9: 0.8% (0.1, 1.5); Grades 9-13: 0.9% (−0.1, 1.9): 

Some university/trade school: 0.8% (−0.1, 1.7); 

University diploma: 0.3 % (−0.9, 1.5) 

By Income: < 21,309: 0.7% (−0.1, 1.5); 21,309-28,161: 0.5% (−0.4, 1.4); 

28,161-35,905: 0.0% (−1.0, 1.0); >35,905: 0.7% (−0.4, 1.8) 

Fung et al. (2006) 

Vancouver, BC, Canada 

Period of Study: 

6/1/95-3/31/99 

Hospital Admissions 

Outcome(s) (ICD9): All 

respiratory hospitalizations 

(460-519) 

Age groups analyzed: 65+ 

Study design: (1) Time-series 

(2) Case-crossover, (3) DMmodels (Dewanji and 

Moolgavkar, 2000, 2002) 

N: 40,974 

Statistical analyses: (1) 

Poisson, (2) conditional 

logistic regression, (3) DM 

method–analyze recurrent 

data in which the occurrence 

of events at the individual 

level over time is available 

Covariates: Day of wk 

Statistical package: S-Plus 

and R 

Lag: Current day, 3 and 

5 day lag 

SO2 24-h avg: 

Mean: 3.46 ppb 

SD: 1.82 

IQR: 2.50 ppb 

Range: 0.00, 

12.50 

Copollutants: 

CO (r = 0.61) 

COH (r = 0.65) 

NO2 (r = 0.57) 

PM10 (r = 0.61) 

PM2.5 (r = 0.42) 

PM10−2.5 (r = 0.57)

O3 (r = −0.35) 

No significant association was found between hospital admissions and 

current day SO2 levels (lag 0). Significant associations were found with SO2

using a 3, 5, and 7 day moving avg, with the strongest association observed 

with a 7 day lag. The DM method produced slightly higher relative risks 

compared to the Time-series and case crossover results. 

Increment: 2.5 ppb (IQR) 

SO2 Time-series: RR 1.013 (0.997, 1.028) lag 0; 

RR 1.030 (1.010, 1.051) lag 0-3; RR 1.032 (1.008, 1.056) lag 0-5; 

RR 1.031 (1.003, 1.060) lag 0-7 

SO2 Case-crossover: RR 1.010 (0.992, 1.027) lag 0; 

RR 1.028 (1.005, 1.050) lag 0-3; RR 1.030 (1.004, 1.057) lag 0-5; 

RR 1.028 (0.998, 1.058) lag 0-7 

SO2 DM model: RR 1.013 (0.998, 1.027) lag 0; 

RR 1.034 (1.015, 1.053) lag 0-3; RR 1.039 (1.016, 1.061) lag 0-5; 

RR 1.044 (1.018, 1.070) lag 0-7 

DM method produced slightly higher RR estimates on O3, SO2 and PM2.5

compared to time-series and case-crossover, and slightly lower RR estimates 

on COH, NO2, and PM10, though the results were not significantly different 

from one another. 

Kesten et al. (1995) 

Toronto, ON, Canada 

Period of Study: 

7/1/1991-6/30/1992 

ED Visits 

Outcome(s) (ICD 9): Asthma 

(493) 

Age groups analyzed: all ages

Study design: Time-series 

N: 854 

# of Hospitals: 1 

Statistical analyses: Auto 

regression 

Statistical package: SAS 

Lag: 1 or 7 

SO2 24-h avg 

No data was 

provided for 

concentration or 

for correlation with 

other pollutants. 

Copollutants: 

NO2 O3 

API (TRS, CO, 

TSP) 

Fit of an auto-regression model with covariates linked to same day gave no 

evidence of association between asthma and SO2. 

Despite multiple attempts to correlate individual or combinations of pollutants 

with air quality indices, no association was found between ER visits for 

asthma and ambient daily, weekly, or monthly levels of SO2, NO2, or O3. 

No relative risks were provided. 

 F-29 
Study Methods Pollutant Data Findings 

Lin et al. (2003) 

Toronto, ON, Canada 

Period of Study: 

1981-1993 

Hospital Admissions 

Outcome(s) (ICD9): Asthma 

(493) 

Age groups analyzed: 

6-12 

Study design: 

Bi-directional case-crossover 

N: 7,319 

Statistical analyses: 

Conditional logistic regression

Covariates: Daily max and 

min temperatures and avg 

relative humidity 

Lag: Cumulative lag of 1-7 

days. 

SO2 24-h avg: 

5.36 ppb SD: 5.90 

Range: 0, 57.00 

25th: 1.00 

50th: 4.00 

75th: 8.00 

Number of 

stations: 4 

Copollutants: 

CO (r = 0.37) 

NO2 (r = 0.54) 

PM10 (r = 0.44) 

O3 (r = −0.01) 

PM2.5 (r = 0.46) 

PM10−2.5 (r = 0.28) 

SO2 is positively associated with asthma hospitalizations, although the 

relationship varies in boys and girls. 

Increment: 7 ppb (IQR) 

Boys 6-12 yrs; Girls 6-12 yrs: 

Lag 0: OR 1.00 (0.95, 1.05); 1.04 (0.97, 1.11); Lag 0-1: OR 0.99 (0.93, 1.06); 

1.04 (0.95, 1.13 

Lag 0-2: OR 0.98 (0.90, 1.06); 1.05 (0.95, 1.16) 

Lag 0-3: OR 0.96 (0.87, 1.05); 1.09 (0.98, 1.22) 

Lag 0-4: OR 0.95 (0.86, 1.05); 1.13 (1.00, 1.28) 

Lag 0-5: OR 0.93 (0.83, 1.03); 1.17 (1.02, 1.34) 

Lag 0-6: OR 0.93 (0.83, 1.04); 1.20 (1.04, 1.39) 

Multipollutant model with PM10−2.5 and PM2.5 

Boys 6-12 yrs; Girls 6-12 yrs: 

Lag 0: OR 0.98 (0.93, 1.04); 1.06 (0.98, 1.14) 

Lag 0-1: OR 0.99 (0.91, 1.06); 1.03 (0.93, 1.14) 

Lag 0-2: OR 0.96 (0.88, 1.05); 1.04 (0.92, 1.17) 

Lag 0-3: OR 0.95 (0.85, 1.05); 1.08 (0.95, 1.23) 

Lag 0-4: OR 0.94 (0.84, 1.06); 1.12 (0.97, 1.29) 

Lag 0-5: OR 0.91 (0.80, 1.04); 1.18 (1.00, 1.38) 

Lag 0-6: OR 0.91 (0.80, 1.04); 1.28 (1.08, 1.51) 

Lin* et al. (2004c) 

Vancouver, BC, Canada 

Period of Study: 

1987-1998 

Hospital Admissions 

Outcome(s) (ICD9): 

Asthma (493) 

Age groups analyzed: 6-12 

Study design: Time-series 

N: 3,754 (2,331 male, 1,423 

female) 

Statistical analyses: Semiparametric Poisson 

regression with GAM (with 

default and more stringent 

criteria) 

Covariates: Trend, day of wk, 

Statistical package: 

S-Plus 

Lag: Cumulative 

1-7 day 

24-h avg SO2

(ppb) 

Mean: 4.77 

SD: 2.75 

Min: 0 

25th: 2.75 

50th: 4.25 

75th: 6.00 

Max: 24.00 

Number of 

stations: 30 

Copollutants: 

CO (r = 0.67) 

NO2 (r = 0.67) 

O3 (r = −0.10) 

 

Results presented are default GAM, but authors state that use of natural 

cubic splines with a more stringent convergence rate produced similar results

Increment: 3.3 ppb (IQR) 

Boys 6-12 yrs by SES status: Low; High 

Lag 0 RR 1.02(0.94, 1.10); 1.03 (0.95, 1.12) 

Lag 0-1 RR 1.03 (0.94, 1.13); 1.06 (0.96, 1.17) 

Lag 0-2 RR 1.03 (0.93, 1.15); 1.06 (0.95, 1.18) 

Lag 0-3 RR 1.01 (0.90, 1.13); 1.04 (0.92, 1.17) 

Lag 0-4 RR 0.98 (0.88, 1.10); 1.02 (0.90, 1.14) 

Lag 0-5 RR 0.97 (0.86, 1.10); 1.02 (0.89, 1.16) 

Lag 0-6 RR 0.98 (0.86, 1.12); 1.05 (0.91, 1.21) 

Girls 6-12 yrs by SES status: Low; High 

Lag 0 RR 1.05 (0.95, 1.16); 1.07 (0.96, 1.19) 

Lag 0-1 RR 1.11 (0.99, 1.25); 1.07 (0.94, 1.21) 

Lag 0-2 RR 1.11 (0.97, 1.26); 1.07 (0.93, 1.23) 

Lag 0-3 RR 1.18 (1.02, 1.36); 1.02 (0.87, 1.19) 

Lag 0-4 RR 1.18 (1.02, 1.35); 0.99 (0.85, 1.15) 

Lag 0-5 RR 1.19 (1.01, 1.40); 0.95 (0.80, 1.13) 

Lag 0-6 RR 1.15 (0.97, 1.36); 0.98 (0.81, 1.17) 

Multipollutant model (adjusted for NO2) 

Girls, Low SES: 1.17 (1.00, 1.37), 4-day avg; 1.19 (1.00, 1.42), 6-day avg 

Lin et al. (2005) 

Toronto, ON 

Period of Study: 

1998-2001 

Hospital Admissions 

Outcome(s) (ICD9): 

Respiratory infections 

(464,466, 

480-487) 

Age groups analyzed: 

0-14 

Study design: Case-crossover 

N: 6,782 

Statistical analyses: 

Conditional logistic regression

Covariates: 

Statistical package: 

SAS 8.2 

Lag: 0-6 days 

24-h avg: 

Mean: 4.73 ppb 

SD: 2.58 ppb 

Range: 1.00, 

19.67 

25th: 3.00 

50th: 4.00 

75th: 6.00 

Number of 

monitors: 5 

Copollutants: 

PM2.5 (r = 0.47) 

PM10−2.5 (r = 0.29)

PM10 (r = 0.48) 

CO (r = 0.12) 

NO2 (r = 0.61 ) 

Asthma hospitalization for boys was associated with SO2 before the 

adjustment for fine and coarse PM. Asthma hospitalization for girls was not 

associated with SO2 for any lag. 

Increment: 3 ppb (IQR) 

Unadjusted Model: 

Boys only: OR 1.06 (0.97, 1.16) lag 0-3; OR 1.02 (0.92, 1.13) lag 0-5 

Girls only: OR 1.05 (0.94, 1.16) lag 0-3; OR 1.07 (0.95, 1.21) lag 0-5 

Boys and Girls: OR 1.06 (0.99, 1.13) lag 0-3; OR 1.04 (0.96, 1.13) lag 0-5 

Adjusted 

Boys only: OR 1.11 (1.01, 1.21) lag 0-3; OR 1.08 (0.97, 1.21) lag 0-5 

Girls only: OR 1.07 (0.96, 1.19) lag 0-3; OR 1.12 (0.98, 1.28) lag 0-5 

Boys and Girls: OR 1.10 (1.02, 1.18) lag 0-3; OR 1.10 (1.01, 1.20) lag 0-5 

Multipollutant model with PM2.5 and PM2.5 

Boys only: OR 1.02 (0.90, 1.15) lag 0-3; OR 0.99 (0.85, 1.16) lag 0-6 

Girls only: OR 1.09 (0.0.94, 1.26) lag 4; OR 1.07 (0.90, 1.28) lag 6 

Boys and Girls: OR 1.05 (0.95, 1.15) lag 4; OR 1.03 (0.91, 1.16) lag 6 

 F-30 
Study Methods Pollutant Data Findings 

Luginaah et al. (2005) 

Windsor, ON, Canada 

Period of Study: 

4/1/95-12/31/00 

Hospital Admissions 

Outcome(s) (ICD9): 

Respiratory admissions 

(460-519) 

Age groups analyzed: 0-14, 

15-64, 65+, all ages 

Study design: (1) Time-series 

and (2) case-crossover 

N: 4,214. # of Hospitals: 4 

Statistical analyses: 

(1)Poisson regression, GAM 

with natural splines (stricter 

criteria), (2) conditional 

logistic regression with Cox 

proportional hazards model 

Covariates: Temperature, 

humidity, change in 

barometric pressure, day of 

wk 

Statistical package: S-Plus 

Lag: 1,2,3 days 

SO2 avg 24-h 

Max: 27.5 ppb, 

SD: 16.5; 

Range: 0, 129 

Number of 

stations: 4 

Copollutants: 

NO2 (r = 0.22) 

CO (r = 0.16) 

PM10 (r = 0.22) 

COH (r = 0.14) 

O3 (r = −0.02) 

TRS (r = 0.13) 

The effect of SO2 on respiratory hospitalization varies considerably, 

especially at low levels of exposure. 

Increment: 19.25 ppb (IQR) 

Time-series, females; males: 

All ages: 1.041 (0.987, 1.098); 0.953 (0.900, 1.009) lag 1 

0-14 yrs: 1.111 (1.011, 1.221); 0.952 (0.874, 1.037) lag 1 

15-65 yrs: 1.031 (0.930, 1.144); 0.971 (0.845, 1.15) lag 1 

65+ yrs: 1.030 (0.951, 1.115); 0.9409 (0.860, 1.029) lag 1 

Case-crossover, females; males 

All ages: 1.047 (0.978, 1.122); 0.939 (0.874, 1.009) lag 1 

0-14 yrs: 1.119 (0.995, 1.259); 0.923 (0.831, 1.025) lag 1 

15-65 yrs: 1.002 (0.879, 1.141); 0.944 (0.798, 1.116) lag 1 

65+ yrs: 1.020 (0.924, 1.126); 0.968 (0.867, 1.082) lag 1 

Stieb et al. (1996) 

St. John, New Brunswick, 

Canada 

Period of Study: 

1984-1992 

(May-Sep only) 

ED Visits. Outcome(s): 

Asthma 

ICD9 codes: NR 

Age groups analyzed: 0-15, 

>15 

Study design: Time-series 

N: 1,987 

# of Hospitals: 2 

Statistical analyses: SAS 

NLIN (Equivalent to Poisson 

GEE) 

Covariates: Day of wk, longterm trends 

Season: Summers only (MaySep) 

Dose-response investigated?: 

Yes 

Statistical package: SAS 

Lag: 0-3 days 

1-h max SO2 (ppb)

Mean: 38.1 

Range: 0, 390 

95th 110 

Copollutants: 

O3 (r = 0.04) 

NO2 (r = −0.03) 

SO4

2- (r = 0.23) 

TSP (r = 0.16) 

SO2 did not affect the rate of asthma ED visits when O3 was included in the 

model. 

Increment: NR 

SO2 + O3: β = −0.0030 (0.0027) lag 0 

 F-31 
Study Methods Pollutant Data Findings 

Stieb* et al. (2000) 

Saint John, New Brunswick, 

Canada 

Period of Study: 

Retrospective: 

7/92-6/94 

Prospective: 

7/94-3/96 

ED Visits 

Outcome(s): Asthma; COPD; 

Respiratory infection 

(bronchitis, bronchiolitis, 

croup, pneumonia); 

All respiratory 

ICD9 codes: NR 

Age groups analyzed: All 

Study design: Time-series 

N: 19,821 

Statistical analyses: Poisson 

regression, GAM 

Covariates: Day of wk, 

selected weather variables in 

each model 

Season: All yr, summer only 

Dose-response investigated: 

Yes 

Statistical package: S-Plus 

Lag: all yr = 0; summer 

only = 0-3 

24-h avg: 

Annual Mean: 6.7 

(5.6) ppb 

95th: 18.0 

Max: 60.0 

Warm season: 

Mean: 7.6 (5.2) 

ppb 

95th: 18.0 

Max: 29.0 

1-h max: 

Annual Mean: 23.8 

(21.0) ppb 

95th: 62.0 

Max: 161.0 

Warm season: 

Mean: 25.4 (17.8) 

ppb 

95th: 62.0 

Max: 137.0 

Copollutants: 

CO (r = 0.31) 

O3 (r = 0.10) 

NO2 (r = 0.41) 

TRS (r = 0.08) 

PM10 (r = 0.36) 

PM2.5 (r = 0.31) 

H+ (r = 0.24) 

SO4

2− (r = 0.26) 

COH (r = 0.31) 

H2S (r = −0.01) 

Assessed 

multipollutant 

models 

Non-linear effect of SO2 on summertime respiratory visits observed and log 

transformation strengthened the association. 

Increment: 23.8 ppb (mean) 

1-h max: 

Respiratory visits: 

All yr: 3.9% lag 5 

May to Sept: 3.9% lag 0-3 

Multipollutant model (SO2, O3, NO2) 

All yr: 3.7% (1.5, 6.0) lag 5 

Multipollutant model (ln (NO2), O3, SO2 COH) 

May to Sept: 3.9% (1.1, 6.7) lag 0-3 

Villeneuve et al., (2006b) 

Toronto, ON, Canada 

Period of Study: 

1995-2000 

Days: 2,190 

GP Visits 

Outcome(s) (ICD9): Allergic 

Rhinitis (177) 

Age groups analyzed: ≥65 

Study design: Time-series 

N: 52,691 

Statistical analyses: GLM, 

using natural splines (more 

stringent criteria than default) 

Covariates: Day of wk, 

holiday, temperature, relative 

humidity, 

aero-allergens 

Season: All Yr; Warm, MayOct; Cool, Nov-Apr 

Statistical package: S-Plus 

Lag: 0-6 

24-h avg: 4.7 ppb 

SD: 2.8 

IQR: 3.2 ppb 

Range: 0, 24.8 

Number of 

stations: 9 

Copollutants: NO2

O3 

CO 

PM10 

PM10-2.5

PM2.5

There were positive associations between allergic rhinitis and SO2 for 

exposures occurring on the same day as physician visits, but only during the 

winter time. 

Increment: 10.3 ppb (IQR) 

All results estimated from Stick Graph: 

All Yr: 

Mean increase: 1.7% (−0.4, 2.8) lag 0 

Warm: 

Mean increase: 0.3% (−1.9, 2.5) lag 0 

Cool: 

Mean increase: 1.9% (−0.2, 4.1) lag 0 

 F-32 
Study Methods Pollutant Data Findings 

Yang et al. (2003b) 

Vancouver, Canada 

Period of Study: 

1986-1998 

Days: 4748 

Hospital Admissions 

Outcome(s) (ICD9): All 

respiratory admissions (460-

519) 

Study design: 

Case-crossover 

Age groups analyzed: < 3, ≥

65 

Statistical analyses: 

conditional logistic regression 

Lag: 0-5 days 

24-h avg SO2

(ppb): 

Mean: 4.84 

SD: 2.84 

5th: 1.50 

25th: 2.75 

50th: 4.25 

75th: 6.25 

100th: 24.00 

IQR: 3.50 

Number of 

stations: 30 

Copollutants: 

CO NO2 

O3 (r = −0.37) 

COH 

SO2 showed the weakest effect among children and the second weakest 

effect among older adults when compared to all other pollutants considered 

in the study. 

Increment: 3.50 ppb (IQR) 

All respiratory admissions < 3 yrs: 

SO2 alone: OR 1.01 (0.98, 1.05) lag 2 

SO2 + O3: OR 1.01 (0.97, 1.04) lag 2 

SO2 + O3 + CO + COH + NO2: OR 0.98 (0.94, 1.03) lag 2 

All respiratory admissions ≥ 65 yrs: 

SO2 alone: OR 1.02 (1.00, 1.04) lag 0 

SO2 + O3: OR 1.02 (1.00, 1.04) lag 0 

SO2 + O3 + CO + COH + NO2: OR 1.01 (0.98, 1.03) lag 0 

Yang et al. (2005) 

Vancouver, BC, Canada 

Period of Study: 

1994-1998 

Days: 1826 

Hospital Admissions 

Outcome(s) (ICD9): COPD 

excluding asthma (490-2, 

494, 496) 

Age groups analyzed: 65+ 

Study design: Time-series 

N: 6,027 

Statistical analyses: Poisson 

regression with GAM (with 

more stringent criteria) 

Covariates: Temperature, 

relative humidity, day of wk, 

temporal trends, season 

Statistical package: 

S-Plus 

Lag: 0-6 days, moving 

averages 

24-h avg: 3.79 ppb

SD: 2.12; 

IQR: 2.75 ppb; 

Range: 0.75, 

22.67 

Winter: 4.10 (2.87) 

Spring: 3.40 (1.58)

Summer: 4.10 

(1.79) 

Fall: 3.56 (1.92) 

Number of 

stations: 5 

Copollutants: 

PM10 (r = 0.62) 

NO2 (r = 0.61) 

CO (r = 0.67) 

O3 (r = −0.34) 

This study produced a marginally significant association between COPD 

hospitalization and 6-day SO2 exposure. Most previous studies have not 

detected a significant effect of SO2 on respiratory ED visits or 

hospitalizations. 

Increment: 2.75 ppb (IQR) 

COPD 

>65 yrs, yr round: RR 1.00 (0.97, 1.04) lag 0; RR 1.02 (0.98, 1.06) lag 0-1; 

RR 1.04 (0.99, 1.08) lag 0-2; RR 1.04 (0.99, 1.09) lag 0-3; 

RR 1.05 (0.99, 1.11) lag 0-4; RR 1.06 (1.00, 1.13) lag 0-5; 

RR 1.06 (0.99, 1.13) lag 0-6 

2-pollutant model: NO2: RR 0.99 (0.91, 1.08) lag 0; 

CO: RR 0.97 (0.87, 1.07) lag 0-6; O3: RR 1.07 (1.00, 1.14) lag 0-6; 

PM10: 0.97 (0.88, 1.06) lag 0-6 

Multipollutant models: SO2, CO, NO2, O3, PM10: RR 0.94 (0.85, 1.05); 

SO2, CO, NO2, O3: RR 0.96 (0.86, 1.06) 

EUROPE 

Anderson et al. (1997) 

Multicity, Europe 

(Amsterdam, Barcelona, 

London, Milan, Paris, 

Rotterdam) 

Period of Study: 

1977-1989 for Amsterdam 

and Rotterdam 

1986-1992 for Barcelona 

1987-1991 for London 

1980-1989 for Milan 

1987-1992 for Paris 

Hospital Admissions 

Outcome(s) (ICD 9): COPD–

unspecified bronchitis (490), 

chronic bronchitis (491), 

emphysema (492), chronic 

airway obstruction (496) 

Study design: Time-series 

Statistical analyses: APHEA 

protocol, Poisson regression, 

meta-analysis 

Covariates: Trend, season, 

day of wk, holiday, influenza, 

temperature, humidity 

Season: 

Cool, Oct-Mar; 

Warm, Apr-Sep 

Lag: 0,1,2 days and 

0-3 cumulative 

24-h all yr avg 

(μg/m3): 

Amsterdam: 21 

Barcelona: 40 

London: 31 

Milan: 53 

Paris: 23 

Rotterdam: 32 

1-h max 

Amsterdam: 50 

Barcelona: 60 

London: NR 

Milan: NR 

Paris: 47 

Rotterdam: 82 

Copollutants: 

NO2 BS TSP O3

The effect of SO2 varied considerably across the cities; however, the summer 

estimate was significantly associated with COPD for the 1-h measure and 

borderline significant for the daily mean. Both 24-h and 1-h SO2

concentrations were significantly associated with COPD ER admissions in 

the warm season. Only cumulative lags of SO2 showed borderline 

significance. 

Increment: 50 μg/m3 

COPD-Warm season: 

24 h avg 1.05 (1.01, 1.10) 1-h 1.02 (1.00, 1.04) 

COPD-Cool season: 

24 h avg 1.02 (0.98, 1.05) 1-h 1.01 (0.99, 1.03) 

COPD-All yr: 

24-h avg 1.022 (0.981, 1.055) lag 1 

24-h avg 1.021 (0.998, 1.045) lag 0-3, cumulative 

1-h max 1.011 (0.994, 1.029) lag 1 

1-h max 1.015 (1.003, 1.027) lag 0-3, cumulative 

 F-33 
Study Methods Pollutant Data Findings 

Anderson et al. (1998) 

London, England 

Period of Study: 

Apr 1987-Feb 1992 

Days: 1,782 

Hospital Admissions 

Outcome(s) (ICD 9): 

Asthma (493) 

Age groups analyzed: 

< 15, 15-64, 65+ 

Study design: Time-series 

Statistical analyses: APHEA 

protocol, Poisson regression 

Covariates: Time trends, 

seasonal cycles, day of wk, 

public holidays, influenza 

epidemics, temperature, 

humidity 

Season: 

Cool (Oct- Mar); 

Warm (Apr-Sep) 

Statistical package: NR 

Lag: 0, 1, 2 days 

24-h avg SO2

(μg/m3) 

Mean: 32.0 

SD: 11.7 

Range: 9, 100 

5th: 16 

10th: 18 

25th: 24 

50th: 31 

75th: 38 

90th: 46 

95th: 52 

# of monitors: 2 

Copollutants: 

O3 NO2 BS 

The strongest association between SO2 and asthma admissions was for 

those ≥65 yrs in the cool season. A weaker association was observed for 

children in the warm season and all yr. The adult population showed no 

association. 

In 2-pollutant models O3 was overall the strongest pollutant associated with 

hospital admission with weaker associations with NO2 and BS. The most 

consistent yr-round association for All ages was found with BS. When looking 

at all ages combined, SO2 association remained significant in all 2-pollutant 

models except with NO2, both for all yr and the summer (warm) season. 

Increment: 10 ppb in 24-h avg SO2

0-14 yrs 

Whole yr: 1.64% (0.29, 3.01) lag 1; 2.04% (0.29, 3.83) lag 0-3; 

+ O3 1.77% (0.22, 3.36) lag 1; + NO2 1.23% (−0.22, 2.69) lag 1; 

+ BS 1.66% (0.23, 3.12) lag 1 

Warm season: 3.33% (1.09, 5.63) lag 1; 3.40% (0.41, 6.48) lag 0-3; 

+ O3 3.35% (0.89, 5.87) lag 1; + NO2 2.92% (0.58, 5.32) lag 1; 

+ BS 3.66% (1.35, 6.02) lag 1 

Cool season: 0.56% (−1.16, 2.32) lag 1; 1.24% (−0.95, 3.49) lag 0-2 

15-64 yrs 

Whole yr: −0.69% (−2.28, 0.94) lag 2; −0.71% (−2.69, 1.30) lag 0-2 

Warm season: −1.39% (−3.97, 1.27) lag 0; −2.2% (−5.46, 11.8) lag 0-2 

Cool season: −0.24% (−2.28, 1.84) lag 0; 0.20% (−2.28, 2.74) lag 0-2 

≥ 65 yrs 

Whole yr: 2.82% (−0.82, 5.96) lag 2; 3.06% (−0.72, 6.98) lag 0-3 

Warm season: −2.62% (−7.31, 2.31) lag 2; −4.27% (−9.89, 1.71) lag 0-3 

Cool season: 5.85% (1.81, 10.05) lag 2; 7.28% (2.19, 12.62) lag 0-3; 

+ O3 7.84% (2.48, 13.48) lag 1; + NO2 4.19% (−0.53, 9.13) lag 1; 

+ BS 5.29% (0.42, 10.40) lag 1 

All Ages 

Whole yr: 1.64% (0.54, 2.75) lag 1; 2.75% (1.22, 4.30) lag 0-3; 

+ O3 1.48% (0.24, 2.73) lag 1; + SO2 1.14% (−0.04, 2.33) lag 1; 

+ BS 1.54%(0.36, 2.73) lag 1 

Warm season: 2.02% (0.22, 3.85) lag 1; 2.60% (0.02, 5.25) lag 0-3; 

+ O3 1.91% (0.05, 3.81) lag 1; + NO2 1.64% (−0.23, 3.56) lag 1 

+ BS 2.18% (0.32, 4.07) lag 1 

Cool season: 1.41% (0.0, 2.83) lag 1; 2.83% (0.89, 4.81) lag 0-3; 

+ O3 −0.09% (−1.61, 1.82) lag 1; + NO2 0.83% (−0.67, 2.34) lag 1; 

+ BS 1.11% (−0.41, 2.66) lag 1 

Anderson* et al. (2001) 

West Midlands conurbation, 

United Kingdom 

Period of Study: 

10/1994-12/1996 

Hospital Admissions 

Outcome(s) (ICD9): All 

respiratory (460-519), Asthma 

(493), COPD (490-496, 

excluding 493) 

Age groups analyzed: 0-14, 

15-64, 65+ 

Study design: Time-series 

Statistical analyses: followed 

APHEA 2 protocol, GAM 

Covariates: Season, 

temperature, humidity, 

epidemics, day of wk, 

holidays 

Statistical package: 

S-Plus 4.5 Pro 

Lag: 0,1,2,3, 0-1, 0-2, 0-3 

24-h avg: 7.2 ppb, 

4.7 (SD) 

Min: 1.9 ppb 

Max: 59.8 ppb 

10th: 3.3 ppb 

90th: 12.3 ppb 

# of monitors: 5 

Copollutants: 

PM10 (r = 0.55) 

PM10−2.5 (r = 0.31)

PM2.5 (r = 0.52) 

BS (r = 0.50) 

SO4

2- (r = 0.19) 

NO2 (r = 0.52) 

O3 (r = -0.22) 

CO (r = -0.29) 

When admissions were analyzed by subgroups, respiratory and asthma 

admissions were positively correlated with SO2. SO2 significantly associated 

with asthma and respiratory admissions for the 0 to 14-yr-age group; 

however, little evidence of a seasonal interaction was observed. 

Increment: 9 ppb (90th-10th) 

All respiratory: 

All ages: 1.3% (−0.7, 3.4) lag 0-1 

0-14 yrs: 4.6% (1.40, 7.8) lag 0-1 

15-64 yrs: −0.9% (−4.8, 3.3) lag 0-1 

≥ 65 yrs: −2.0% (−4.9, 1.1) lag 0-1 

COPD with asthma: 

0-14 yrs: 10.9% (4.50, 17.8) lag 0-1 

15-64 yrs: 2.4% (−5.5, 10.9) lag 0-1 

≥ 65 yrs: −4.2% (−8.9, 0.8) lag 0-1 

 F-34 
Study Methods Pollutant Data Findings 

Atkinson et al. (1999b) 

London, England 

Period of Study: 

1992-1994 

Days: 1,096 

Hospital Admissions 

Outcome(s) (ICD9): All 

respiratory (460-519); Asthma 

(493); Asthma and COPD 

(490-496); LRD (466,480-

486) 

Age groups analyzed: all 

ages, 0-14 yr, 15-64 yr and 

≥65 yr 

Study design: Time-series 

N: 165,032 (respiratory 

admissions), 189,032 

(cardiovascular admissions) 

Statistical analyses: Poisson 

regression following APHEA 

protocol 

Covariates: Long-term 

seasonal patterns, day of wk, 

temperature, humidity, 

influenza. 

Statistical package: SAS 

Investigated Dose/Response: 

Yes 

Lag: 0,1,2,3 days 

SO2− 24-h (μg/m3)

Mean: 21.2 (7.8) 

μg/m3

Min: 7.4 

10th: 13 

50th: 19.8 

90th: 31 

Max: 82.2 

# of monitors: 5 

Copollutants: O3, 

CO, PM10, BS, 

NO2

Correlation 

coefficients ranged 

between 

r = 0.5 and 0.6 

Asthma was closely linked with PM, CO, NO2, and traffic pollution. When SO2

and PM10 were included in the same model, the magnitude of the individual 

associations was reduced, as were their statistical significance. This 

reduction occurred in children, adults and the elderly. The other pollutants all 

had the effect of reducing the magnitude of the individual SO2 and PM2.5

associations, although their statistical significance was unaffected. This 

indicates that both SO2 and PM2.5 were indicators of the same pollutant 

mixture. 

Increment: 18 μg/m3

All respiratory: All ages 2.01% (0.29, 3.76) lag 1; 

0-14 yrs 5.14% (2.59, 7.76) lag 1; 15-64 yrs 1.90% (−0.79, 4.66) lag 3; 

≥ 65 yrs 2.25 (−0.09, 4.65) lag 3 

Asthma: All ages: 3.38 (0.42, 6.43) lag 1; 0-14 yrs: 6.74% (2.92, 10.69) lag 1; 

15-64 yrs: 4.58% (−0.18, 9.57) lag 3; ≥ 65 yrs: 6.31% (−1.59, 14.83) lag 2 

COPD and Asthma: ≥ 65 yrs: 1.53% (−1.83, 5.00) lag 3 

Lower Respiratory: ≥ 65 yrs: 5.16% (1.19, 9.28) lag 3 

Atkinson et al. (1999a) 

London, United Kingdom 

Period of Study: 

1/92-12/94 

ED Visits 

Outcome(s) (ICD 9): 

Respiratory ailments 

(490-496), including asthma, 

wheezing, inhaler request, 

chest infection, COPD, 

difficulty in breathing, cough, 

croup, pleurisy, noisy 

breathing 

Age groups analyzed: 

0-14; 15-64; ≥ 65; 

All ages 

Study design: Time-series 

N: 98,685 

# of Hospitals: 12 

Statistical analyses: Poisson 

regression, APHEA protocol 

Covariates: Long-term trend, 

season, day of wk, influenza, 

temperature, humidity 

Statistical package: SAS 

Lag: 0,1,0-2 and 

0-3 days 

24-h avg: 21.2 

μg/m3, SD: 7.8 

10th: 13.0 

50th: 19.8 

90th: 31.0 

Range: 7.4, 82.2 

# of Stations: 5 

Copollutants: 

SO2 O3 (8 h) 

CO (24 h avg), 

PM10 (24 h avg) 

BS 

SO2 was closely related to PM10, but 2-pollutant models showed that the 

effect of SO2 was decreased by NO2 and PM10 inclusion. Inclusion of other 

pollutants did not significantly decrease the influence of SO2 on ER 

admissions in 

2-pollutant models. 

Increment: 18 μg/m3 in 24-h 

Single-pollutant model 

Asthma only: 

 0-14 yrs 9.92% (4.75, 15.34) lag 1 

 15-64 yrs 4.19% (−0.53, 9.13) lag 1 

 All ages 4.95% (1.53, 8.48) lag 1 

All respiratory: 

 0-14 yrs 6.01% (2.98, 9.12) lag 2 

 15-64 yrs 2.72% (−0.18, 5.70) lag 3 

 65+ yrs −1.82% (−5.72, 2.25) lag 3 

 All Ages 2.81% (0.72, 4.93) lag 1 

Copollutant models for asthma among children: 

 SO2 + NO2: 5.42% (0.18, 10.93) 

 SO2 + O3: 8.39% (3.82, 13.17) 

 SO2 + CO: 8.05% (3.45, 12.86) 

 SO2 + PM10: 5.63 (0.53, 10.98) 

 SO2 + BS: 8.03 (3.32, 12.96) 

 F-35 
Study Methods Pollutant Data Findings 

Atkinson et al. (2001) 

Multicity, Europe (Barcelona, 

Birmingham, London, Milan, 

Netherlands, Paris, Rome, 

Stockholm) 

Period of Study: 

1997-1998 

Hospital Admissions 

Outcome(s) (ICD 9): Asthma 

(493), COPD (490-496), All 

respiratory (460-519) 

Study design: Time-series 

Statistical analyses: APHEA 

protocol, Poisson regression, 

meta-analysis 

Covariates: Season, 

temperature, humidity, 

holiday, influenza 

Statistical package: NR 

Lag: NR 

1-h max of SO2

(μg/m3) 

Barcelona: NR 

Birmingham: 24.3

London: 23.6 

Milan: 29.1 

Netherlands: 8.5 

Paris: 17.7 

Rome: 9.8 

Stockholm: 3.8 

Copollutants: 

NO2, O3, CO, BS, 

PM10

Correlation 

coefficients with 

PM10: 

Barcelona: 0.32 

Birmingham: 0.77

London: 0.72 

Milan: 0.64 

Netherlands: 0.67

Paris: 0.63 

Rome: 0.15 

Stockholm: 0.36 

The inclusion of SO2 in the models only modified PM10 associations in the 0- 

to 14-yr age group. 

Increment: 10 μg/m3 for PM10; change in SO2 not described. 

Asthma, 0 to 14 yrs: 

For PM10: 1.2 (0.2, 2.3) 

For PM10 + SO2: 0.8 (−3.7, 5.6) 

Asthma, 15 to 64 yrs: 

For PM10: 1.1 (0.3, 1.8) 

For PM10 + SO2: 1.6 (0.6, 2.6) 

COPD + Asthma, ≥ 65 yrs: 

For PM10: 1.0 (0.4, 1.5) 

For PM10 + SO2: 1.3 (0.7, 1.8) 

All respiratory, ≥ 65 yrs of age: 

For PM10: 0.9 (0.6, 1.3) 

For PM10 + SO2: 1.1 (0.7, 1.4) 

Boutin-Forzano et al. (2004) 

Marseille, France 

Period of Study: 

4/97-3/98 

ED Visits 

Outcome(s): Asthma 

ICD 9 Code(s): NR 

Age groups analyzed: 3-49 

Study design: Case-crossover

N: 549 

Statistical analyses: Logistic 

regression 

Covariates: Minimal daily 

temperature, max daily 

temperature, min daily relative 

humidity, max daily relative 

humidity, day of wk 

Statistical package: NR 

Lag: 0-4 days 

Mean: SO2: 

22.5 μg/m3

Range: 0.0, 94.0 

Copollutants: 

NO2 (r = 0.56) 

O3 (r = −0.25) 

No association was observed between ER visits for asthma and SO2 levels. 

Only single-pollutant models were utilized. 

Increment: 10 μg/m3

Increased ER visits: 

OR 1.0023 (0.9946, 1.0101) lag 0 

OR 0.9995 (0.9923, 1.0067) lag 1 

OR 0.9996 (0.9923, 1.0069) lag 2 

OR 0.9970 (0.9896, 1.0045) lag 3 

OR 0.9964 (0.9889, 1.0040) lag 4 

Buchdahl et al. (1996) 

London, United Kingdom 

Period of Study: 

3/1/92-2/28/93 

ED visits. 

Outcomes: Daily acute 

wheezy episodes 

Age groups analyzed: ≤ 16 

Study design: Case-control 

N: 1,025 cases, 4,285 

controls 

# of Hospitals: 1 

Statistical analyses: Poisson 

regression 

Covariates: Season, 

temperature, wind speed 

Season: Spring (Apr-Jun), 

Summer (Jul-Sep), Autumn 

(Oct-Dec), Winter (Jan-Mar) 

Statistical package: Stata. 

Lag: 0-7 days 

SO2 1-h yr round 

Mean: 22 μg/m3, 

SD: 14 

Spring: 20 (14) 

Summer: 18 (22) 

Fall: 24 (14) 

Winter: 25 (14) 

Copollutants: 

NO2 (r = 0.62) 

O3 (r = −0.28) 

Variations in SO2 could not explain the U-shaped relationship between O3

and incidence of asthma. 

Increment: 14 μg/m3 (Std. Dev.) 

No adjustments to model: 

RR 1.16 (1.10, 1.23) lag not specified 

Adjusted for temperature and season: 

RR 1.12 (1.06, 1.19) lag not specified 

Adjusted for temperature, season and wind speed: 

RR 1.08 (1.00, 1.16) lag not specified 

 F-36 
Study Methods Pollutant Data Findings 

Castellsague et al. (1995) 

Barcelona, Spain 

Period of Study: 

1986-1989 

ED visits 

Outcome(s): Asthma 

Age groups analyzed: 15-64 

Study design: Time-series 

# of Hospitals: 4 

Statistical analyses: Poisson 

regression 

Covariates: Long time trend, 

day of wk, temperature, 

relative humidity, dew point 

temperature 

Seasons : Winter : Jan-Mar; 

Summer : Jul-Sep 

Dose-response investigated: 

Yes 

Lag: 0, 1-5 days and 

cumulative; Summer: lag 2 

days, Winter: lag 1 day 

Mean SO2 (μg/m3) 

Summer: 40.8 

25th: 25 

50th: 36 

75th: 54 

95th: 82 

Winter: 52.0 

25th: 36 

50th: 49 

75th: 67 

95th: 94 

# of Stations: 15 

manual, 3 

automatic 

Copollutants: 

NO2 O3

Interaction between pollutants and asthma emergency room visits was 

influenced by soy-bean dust in the air. The daily mean of asthma visits and 

level of SO2 were higher in the winter than in the summer. A positive but not 

statistically significant increase in relative risk was found for SO2 in the 

summer. SO2 levels were higher in the winter, but the RR was lower 

compared to the RR in the summer. SO2 was not significantly associated with 

asthma related ER visits. 

Increment: 25 μg/m3 

Seasonal differences: 

Summer: RR 1.052 (0.980, 1.129) lag 2 

Winter: RR 1.020 (0.960, 1.084) lag 1 

Dab et al. (1996) 

Paris, France 

Period of Study: 

1/1/87-9/30/92 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory 

(460-519), Asthma (493), 

COPD 

(490-496) 

Age groups analyzed: All 

ages 

Study design: Time-series 

Number of hospitals: 27 

Statistical analyses: Poisson 

regression, followed APHEA 

protocol 

Covariates: Temperature, 

relative humidity, influenza, 

long-term trend, season, 

holiday, medical worker strike 

Lag: 0,1,2 days, 0-3 

cumulative 

All Yr: 

24-h avg: 29.7 

μg/m3

Median:23.0 

5th: 7.0 

99th: 125.0 

1-h max: 59.9 

Median: 46.7 

5th: 14.0 

99th: 232.7 

Warm season 

24-h avg: 20.1 

Median:18.3 

5th: 6.0 

99th: 49.3 

1-h max:42.7 

Median:37.0 

5th: 13.0 

99th: 133.7 

Cold season 

24-h avg: 40.1 

μg/m3

Median:31.3 

5th: 8.7 

99th: 149.0 

1-h max:78.3 

Median:60.7 

5th: 17.0 

99th: 268.3 

Copollutants: 

NO2 O3 PM13 BS 

1-h max SO2 levels yielded lower relative risk when compared to 24-h avg 

levels. COPD effects were only significantly associated with SO2 with no lag. 

The strongest association was observed with PM13; 4.5% increase in 

respiratory admission per 100 μg/m3 increment. SO2 was a close second. 

Neither analysis by age or by season showed a significant sensitivity for 

hospital admissions. The strongest association for asthma admission for all 

pollutants was with SO2 24-h avg of 7% (0.14, 14.10), but 1-hr max level was 

not significant. The strongest association for admission with COPD diagnosis 

was also for 24-h avg of SO2 (9.9% [2.3, 18]). Increment: 100 μg/m3 

All respiratory (1987-1990): 

24-h avg RR 1.042 (1.005, 1.080) lag 0-2 

1-h max RR 1.018 (0.988, 1.048) lag 0-2 

Asthma (1987-1992): 

24-h avg RR 1.070 (1.004, 1.141) lag 2 

1-h max RR 1.047 (0.998, 1.098) lag 2 

COPD: 

24-h avg RR 1.099 (1.023, 1.180) lag 0 

1-h max RR 1.051 (1.025, 1.077) lag 0 

 F-37 
Study Methods Pollutant Data Findings 

de Diego Damiá et al. (1999) 

Valencia, Spain 

Period of Study: 

3/1994-3/1995 

ED visits 

Outcome(s) (ICD 9): Asthma 

(493) 

Age groups analyzed: > 12 

N: 515 

# of Hospitals: 1 

Statistical analyses: Stepwise 

regression and ANOVA; 

Linear regression 

Covariates: Season and 

temperature 

Statistical package: SPS 

24-h avg SO2

(μg/m3) 

Winter 

 Mean: 56. 

Range: 30, 86 

Spring 

 Mean: 47. 

Summer 

 Mean: 40. 

Autumn 

 Mean: 50. 

# of monitors: 1 

Copollutants: 

BS (r = 0.54) 

The SO2 concentration was averaged for each season and quartiles of 

concentration determined. Asthma visits that occurred in each season were 

examined. There were no significant associations with asthma ER visits with 

any season or with any quartile of SO2 exposure. 

Mean number of asthma-related ED visits based on quartile of SO2

All yr: < 41 μg/m3: 8.6 

41-50 μg/m3: 9.1 

51-56 μg/m3: 11.6 

>56 μg/m3: 11.9 

Fusco* et al. (2001) 

Rome, Italy 

Period of Study: 

1/1995-10/1997 

Hospital Admissions 

Outcomes 

(ICD 9 codes): All Respiratory 

(460-519, excluding 

470-478); Acute respiratory 

infections including 

pneumonia (460-466, 480-

486), COPD 

(490-492, 494-496), asthma 

(493) 

Age groups analyzed: All 

ages, 0-14 

Study design: Time-series 

Statistical analyses: Poisson 

regression with GAM 

Covariates: Influenza 

epidemics, day of study, 

temperature, humidity, day of 

wk, holidays 

Statistical package: S-Plus 4 

Lag: 0, 1, 2, 3, 4 

24-h avg: 9.1 

(5.8) μg/m3 

25th: 5.1 

50th: 7.9 

75th: 12.0 

# of monitors: 5 

Copollutants: 

O3 (r = −0.35) 

CO (r = 0.56) 

NO2 (r = 0.33) 

Particles; 

r = 0.25 

SO2 did not have an effect on respiratory hospitalizations. 

Increment: 6.9 μg/m3 (IQR) 

Respiratory conditions: 

All ages: 0.4% (−1.3, 2.2) lag 0. 0.8% (−0.9, 2.4) lag 1. 0.3% (−1.3, 1.8) lag 2

0-14 yrs: −0.7% (−4.0, 2.7) lag 0; −2.0 (−5.2, 1.3) lag 1; −0.8 (−3.8, 2.3) lag 2

Acute respiratory infections: All ages: 0.4% (−2.1, 3.0) lag 0; 

1.4% (−1.0, 3.9) lag 1; 1.2% (−1.0, 3.5) lag 2; 

0-14 yrs:−0.1% (−3.9, 3.8) lag 0; −2.7% (−6.3, 1.0) lag 1; 

−1.2% (−4.5, 2.2) lag 2 

Asthma: All ages: −1.5% (−6.6, 3.9) lag 0; −1.5% (−6.5, 3.7) lag 1; 

2.5% (−2.2, 7.4) lag 2; 0-14 yrs: −2.6 (−10.4, 6.0) lag 0; 

4.3% (−3.5, 12.7) lag 1; 5.5% (−1.8, 13.2) lag 2 

COPD: All ages: 1.0% (−1.9, 4.0) lag 0; −1.1% (−3.9, 1.8) lag 1; 

−0.5% (−3.1, 2.1) lag 2 

Galan et al. (2003) 

Madrid, Spain 

Period of Study: 

1995-1998 

ED Visits 

Outcome(s) (ICD9): Asthma 

(493) 

Age groups analyzed: All 

Study design:Time-series 

N: 4,827 

Statistical analyses: Poisson 

regression, (1) classic APHEA 

protocol and (2) GAM with 

stringent criteria 

Covariates: Trend, yr, season, 

day of wk, holidays, 

temperature, humidity, 

influenza, acute respiratory 

infections, pollen 

Statistical package: NR 

Lag: 0-4 days 

24-h Mean: 

23.6 μg/m3 

SD: 15.4 

10th: 9.2 

25th: 12.3 

50th: 18.7 

75th: 31.3 

90th: 43.9 

Range: 5, 121.2 

# of Stations: 15 

Copollutants: 

PM10 (r = 0.581) 

NO2 (r = 0.610) 

O3 (r = −0.547) 

SO2 registered a predominately winter based pattern, and was positively 

correlated with PM2.5, NO2. The lag that described the strongest association 

was 3 days. 

Multipollutant models were fitted for cold season pollutants. SO2 was the 

most affected when PM2.5 was included in the model. 

Parametric estimates using APHEA protocol produced similar results as 

GAM. 

The SO2 association may be due to the concealing effects of other pollutants. 

PM2.5 accounted for most of the observed effects. 

Increment : 10 μg/m3 

Asthma: RR lag 0 1.018 (0.984, 1.054); RR lag 1 1.005 (0.972, 1.039); 

RR lag 2 1.002 (0.970, 1.036); RR lag 3 1.029 (0.997, 1.062); 

RR lag 4 1.025 (0.994, 1.058) 

Multipollutant model: SO2/PM10 0.966 (0.925, 1.009) 

 F-38 
Study Methods Pollutant Data Findings 

Garty et al. (1998) 

Tel Aviv, Israel 

Period of Study: 

 1/1/1993-12/31/1993 

ED Visits 

Outcome(s): Asthma 

ICD 9 Code(s): NR 

Age groups analyzed: 1-18 

Study design: Descriptive 

study with correlations 

N: 1,076 

Statistical analyses: Pearson 

correlation and partial 

correlation coefficients 

Covariates: Max and min 

ambient temperatures, 

relative humidity and 

barometric pressure 

Statistical package: Statistix 

24-h mean of SO2

(estimated from 

histogram): 27 

μg/m3 

Range: 11, 64 

Copollutants: 

NOX SO2 O3

Asthma morbidity was higher in the autumn and winter than the rest of the yr. 

The number of ER visits is Sep was exceptionally high. 

The percent of total variance showed positive correlation between asthma 

ER visits in children and high levels of NOX, SO2, and increased barometric 

pressure. NOX enhances the effects of SO2, whereas O3 had a reverse 

relation to SO2. 

Air borne pollen was not a significant contributor to ER visits. 

Correlation between SO2 and ER visits for asthma: 

All yr: Daily data r = 0.24; Running mean for 7 days r = 0.53 

Excluding Sep: Daily data r = 0.31; Running mean for 7 days r = 0.64 

Hagen et al. (2000) 

Drammen, Norway 

Period of Study: 

1994-1997 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory admissions (460-

519) 

Age groups analyzed: All 

ages 

Study design: Time-series 

Number of hospitals: 1 

Statistical analyses: Poisson 

regression with GAM 

(adhered to HEI phase 1.B 

report) 

Covariates: Time trends, day 

of wk, holiday, influenza, 

temperature, humidity 

Lag: 0,1,2,3 days 

SO2 24-h avg 

(μg/m3): 3.64, 

SD: 2.41 

25th: 2.16 

50th: 2.92 

75th: 4.38 

# of Stations: 2 

Copollutants: 

PM10 (r = 0.42) 

NO2 (r = 0.58) 

benzene (r = 0.29)

NO (r = 0.47) 

O3 (r = −0.24) 

Formaldehyde 

(r = 0.54) 

Toluene (r = 0.48) 

SO2 was significantly associated with respiratory hospital admissions. This 

relationship was robust to the inclusion of PM2.5, but attenuated when both 

PM2.5 and benzene were included in the model. 

Increment: SO2: 2.22 μg/m3 (IQR) 

Single-pollutant model 

Respiratory disease only: 

1.056 (1.013, 1.101) 

All disease: 0.990 (0.974, 1.007) 

2-pollutant model with PM10 

1.051 (1.005, 1.099) 

3-pollutant model with PM10 + Benzene 

1.040 (0.993, 1.089) 

Hajat et al. (1999) 

London, United Kingdom 

Period of Study: 

1992-1994 

GP visits 

Outcome(s) (ICD9): Asthma 

(493); Lower respiratory 

disease (464, 466, 476, 480-

3, 490-2, 485-7, 4994-6, 500, 

503-5, 510-5) 

Age groups analyzed: 0-14; 

15-64; 65+; all ages 

Study design: Time-series 

analysis 

Statistical Analysis: Poisson 

regression, APHEA protocol 

Covariates: Long-term trends, 

seasonality, day of wk, 

temperature, humidity 

Season: 

Warm, Apr-Sep; Cool, OctMar; All-yr 

Dose-response investigated? 

Yes 

Statistical package: SAS 

Lag: 0-3 days, cumulative 

All yr 

24-h avg: 21.2 

μg/m3, SD: 7.8 

10th: 13.0 

90th: 31.0 

Warm: 

24-h avg: 20.5 

μg/m3, SD: 6.5 

10th: 13.4 

90th: 28.4 

Cool: 

24-h avg: 22.0 

μg/m3, SD: 9.0 

10th: 12.8 

90th: 33.3 

Copollutants: 

NO2 (r = 0.61) 

BS (r = 0.57) 

CO (r = 0.51) 

PM10 (r = 0.63) 

O3 (r = −0.11) 

This study showed weak, but consistent associations between SO2 and 

consultations for asthma and other LRD, especially in children. Bubble plot 

suggests a concentration-response relationship. 

Increment: 18 μg/m3 

(90th-10th percentile) 

Asthma: All ages: 3.6% (0.3, 6.9) lag 2; 4.4% (0.9, 7.9) lag 0-2; 

0-14 yrs 4.9% (0.1, 9.8) lag 1; 4.4% (−0.7,9.7) lag 0-2 

Warm: 9.0% (2.2, 16.2) lag 1; Cool: 2.0% (4.5, 8.9) lag 1 

15-64 yrs 3.6% (−0.6, 8.0) lag 2; 3.5% (−1.0, 8.2) lag 0-3 

Warm: 2.5% (−3.3, 8.7) lag 2; Cool : 4.5% (−1.4, 10.7) lag 2 

65+ yrs 4.5% (−3.5, 13.1) lag 1; 4.8% (−2.9, 13.2) lag 0-1 

Warm: 7.5% (−4.0, 20.3) lag 1; Cool: 2.0% (−8.6, 13.9) lag 1 

Lower respiratory disease: 

All ages 1.8% (0.2, 3.4) lag 2; 2.2% (0.4, 4.1) lag 0-2; 

0-14 yrs 4.5% (1.4, 7.8) lag 2; 5.7% (1.7, 9.7) lag 0-3; 

Warm: 2.4% (−2.6, 7.7) lag 2; Cool: 5.8% (1.6, 10.2) lag 2 

15-64 yrs 1.5% (−0.7, 3.7) lag 1; 1.6% (−0.9, 4.1) lag 0-3 

Warm: −0.5% (−3.8, 2.9) lag 1; Cool: 2.5% (−0.5, 5.5) lag 1 

65 + −2.2% (−4.9, 0.6) lag 0; −1.4% (−4.4, 1.7) lag 0-1 

Warm: −3.1% (−6.9, 0.9) lag 0; Cool: −1.6 % (−5.3, 2.3) lag 0 

2-pollutant model – Asthma: SO2 alone 4.9% (0.1, 9.8); 

SO2/O3 5.9% (1.1, 10.9); SO2/NO2 2.7% (−2.7, 8.4); 

SO2/PM2.5 3.4% (−3.0, 10.2) 

2-pollutant model-Lower respiratory disease: 

SO2 alone 4.5% (1.4, 7.8); SO2/O3 4.8% (1.6, 8.1); 

SO2/NO2 3.1% (−0.6, 6.9); SO2/PM2.5 3.8% (0.4, 7.2) 

 F-39 
Study Methods Pollutant Data Findings 

Hajat* et al. (2001) 

London, United Kingdom 

Period of Study: 

1992-1994 

GP visits 

Outcome(s) (ICD9): Allergic 

Rhinitis (477) 

Age groups analyzed: 

0-14; 15-64; 65+; all ages 

Study design:Time-series 

analysis 

N: 4,214 

Statistical Analysis: Poisson 

regression, GAM 

Covariates: Long-term trends, 

seasonality, day of wk, 

temperature, humidity, 

variation in practice 

population, counts for lagged 

allergic pollen measures, daily 

number of consultations for 

influenza 

Dose-response investigated? 

Yes 

Statistical package: 

S-Plus 

Lag: 0-6 days, cumulative 

24-h avg: 21.2 

μg/m3, 

SD: 7.8 

10th: 13.0 

90th: 31.0 

Copollutants: 

NO2 (r = 0.61) 

BS (r = 0.57) 

CO (r = 0.51) 

PM10 (r = 0.63) 

O3 (r = −0.11) 

The number of allergic rhinitis admissions peaked in Apr and June. After 2-

pollutant model analysis, SO2 still remained highly significant in the presence 

of other pollutants. For both children and adults exposure-response 

associations showed that risk levels off at higher SO2 levels. 

Increment: 18 μg/m3 

(90th-10th percentile) 

Single-pollutant model 

< 1 to 14 yrs: 

24.5% (14.6, 35.2) lag 4 

24.9% (11.9, 39.4) lag 0-4 

15 to 64 yrs: 

14.3% (6.2, 23.0) lag 3 

15.5% (9.1, 22.3) lag 0-5 

>64 yrs-too small for analysis 

2-pollutant models 

< 1 to 14 yrs: 

SO2 & O3: 22.1% (12.0, 33.1) 

SO2 & NO2: 28.5% (15.5, 42.9) 

SO2 & PM10: 27.2% (15.3, 40.2 

15 to 64 yrs: 

SO2 & O3: 8.5% (3.4, 13.9) 

SO2 & NO2: 8.3% (1.7, 15.3) 

SO2 & PM10: 6.7% (0.7, 13.0) 

Hajat* et al. (2002) 

London, United Kingdom 

Period of Study: 

1992-1994 

GP visits 

Outcome(s) (ICD9): Upper 

respiratory disease, excluding 

Rhinitis 

(460-3, 465, 470-5, 478) 

Age groups analyzed: 

0-14; 15-64; 65+; all ages 

Study design: 

Time-series analysis 

Statistical Analysis: Poisson 

regression, GAM 

Covariates: Long-term trends, 

seasonality, day of wk, 

holidays, temperature, 

humidity, variation in practice 

population, counts for lagged 

allergic pollen measures, daily 

number of consultations for 

influenza 

Season: 

Warm, Apr-Sep; 

Cool, Oct-Mar 

Dose-response investigated? 

Yes 

Statistical package: 

S-Plus 

Lag: 0,1,2,3 days 

All yr: 

24-h avg: 21.2 

μg/m3, 

SD: 7.8 

10th: 13.0 

90th: 31.0 

Warm: 

24-h avg: 20.5 

μg/m3, 

SD: 6.5 

10th: 13.4 

90th: 28.4 

Cool: 

24-h avg: 22.0 

μg/m3, 

SD: 9.0 

10th: 12.8 

90th: 33.3 

# of Stations: 3 

Copollutants: 

NO2 (r = 0.61) 

BS (r = 0.57) 

CO (r = 0.51) 

PM10 (r = 0.63) 

O3 (r = −0.11) 

Increased consultations for URD were most strongly associated with SO2 in 

children. For adults and the elderly the strongest associations were for PM10

and NO2. The most consistent lag in adults and the elderly for development 

of URD was 2 days (one day after a pollution event). 

Increment: 18 μg/m3 

(90th-10th percentile) 

Single-pollutant model: 

All yr: 0-14 yr: 3.5% (1.4, 5.8) lag 0; 15-64 yrs: 3.5% (0.5, 6.5) lag 1; 

>65 yrs: 4.6% (0.4, 9.0) lag 2 

Warm: 0-14 yrs: 3.2% (−0.5, 7.0) lag 0; 15-64 yrs: 4.6% (1.5, 7.7) lag 1; 

≥ 65 yrs: 1.6% (−4.8, 8.5) lag 2 

Cool: 0-14 yrs: 5.5% (2.4, 8.7) lag 0; 15-64 yrs: 2.7 (0.0, 5.4) lag 1; 

>65 yrs: 5.7% (0.4, 11.4) lag 2 

2-pollutant models 

0-14 yrs: SO2 & O3: 1.0% (−2.2, 4.2); SO2 & NO2: 4.7% (2.2, 7.4); 

SO2 & PM10: 4.6% (2.1, 7.2) 

For 15-64 yrs: SO2 & O3: 3.7% (0.6, 7.0); SO2 & NO2: 2.6% (−0.0, 5.2); 

SO2 & PM10: 2.4% (−0.1, 5.0) 

For >65 yrs: SO2 & O3: 9.0% (1.7, 16.9); SO2 & NO2: 4.3% (−1.2, 10.2); 

SO2 & PM10: 3.2% (−1.9, 8.7) 

 F-40 
Study Methods Pollutant Data Findings 

Llorca et al. (2005) 

Torrelavega, Spain 

Period of Study: 

1992-1995 

Days: 1,461 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory admissions (460-

519) 

Age groups analyzed: All 

ages 

Study design:Time-series 

Number of hospitals: 1 

Statistical analyses: Poisson 

regression 

Covariates: Short and Longterm trends 

Statistical package: Stata 

Lag: NR 

24-h avg SO2: 

13.3 μg/m3, 

SD: 16.7 

# of Stations: 3 

Copollutants: 

NO2 (r = 0.588) 

NO (r = 0.544) 

TSP (r = −0.40) 

SH2 (r = 0.957) 

Associations between SO2 and admissions observed in the single-pollutant 

model disappear in a 5-pollutant model. Only NO2 was significantly 

associated with admissions. 

No relation was described for sulfur compounds including H2S or SO2. The 

concentration of SO2 changes with temperature changes, which may be 

responsible for cardiac stress. 

SO2 was not significantly associated with cardiac respiratory or cardiorespiratory admissions 

Increment: 100 μg/m3

Single-pollutant model 

All cardio-respiratory admissions: RR 0.98 (0.89, 1.07) 

Respiratory admissions: 1.04 (0.90, 1.19) 

5-pollutant model 

All cardio-respiratory admissions: RR 0.98 (0.80, 1.21) 

Respiratory admissions: 0.89 (0.64, 1.24) 

Oftedal et al. (2003) 

Drammen, Norway 

Period of Study: 

11/1994-12/2000 

Hospital Admissions 

Outcomes (ICD 10): All 

respiratory admissions (J00-

J99) 

Age groups analyzed: All 

ages 

Study design: Time-series 

Statistical analyses: Semiparametric Poisson 

regression, GAM with more 

stringent criteria 

Covariates: Temperature, 

humidity, influenza 

Lag: 2,3 days 

Mean: 2.9 μg/m3, 

SD: 2.1 

IQR: 2.03 μg/m3

Copollutants: PM10

NO2

O3 

Benzene 

Formaldehyde 

Toluene 

The study found positive associations between daily number of hospital 

admissions for acute respiratory diseases and concentrations of SO2; 

associations did not change substantially from the first to the second 

3-yr period. 

Increment: 2.03 μg/m3 (IQR) 

All respiratory disease 

1.042 (1.011, 1.073) 

Ponce de Leon et al. (1996) 

London, England 

Period of Study: 

04/1987-1988; 

1991-02/1992 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory (460-519) 

Age groups analyzed: 0-14, 

15-64, 65+, all ages 

Study design: Time-series 

N: 19,901 

Statistical analyses: APHEA 

protocol, Poisson regression 

GAM 

Covariates: Long-term trend, 

season, influenza, day of wk, 

holiday, temperature, humidity

Season: 

Cool, Oct-Mar; Warm: AprSep 

Dose-response Investigated?: 

Yes 

Statistical package: SAS 

Lag: 0, 1, 2 days, 0-3 

cumulative avg. 

SO2 24-h avg: 

32.2 μg/m3, 

SD: 12.6 

5th: 15 

10th: 18 

25th: 24 

50th: 31 

75th: 39 

90th: 47 

95th: 54 

# of stations: 2 

Copollutants: 

NO2 (r = 0.44) 

BS (r = 0.44) 

O3 (r = −0.067) 

Though significant effects were observed with SO2 in some age groups, they 

were not consistent or similar in magnitude to those of O3. 

Increment: 90th-10th percentile 

(24-h avg: 29 μg/m3). 

All yr: 

All ages 1.0092 (0.9926, 1.0261) lag 1 

0-14 yrs 1.0093 (0.9837, 1.0356) lag1 

15-64 yr 1.0223 (0.9942, 1.0511) lag 1 

≥ 65 yr 1.0221 (0.9970, 1.0478) lag 2 

Warm season: 

All ages 1.0111 (0.9864, 1.0364) lag 1 

0-14 yrs 1.0468 (1.0066, 1.0885) lag 1 

15-64 yr 0.9996 (0.9596, 1.0411) lag 1 

>65 yr 1.0124 (0.9772, 1.0489) lag 2 

Cool season: 

All ages 1.0079 (0.9857, 1.0306) lag 1 

0-14 yrs 0.9848 (0.9515, 1.0192) lag 1 

15-64 yr 1.0389 (1.0010, 1.0783) lag 1 

>65 yr 1.0280 (0.9945, 1.0625) lag 2 

 F-41 
Study Methods Pollutant Data Findings 

Pönkä (1991) 

Helsinki, Finland 

Period of Study: 

1987-1989 

Hospital Admissions 

Outcome(s) (ICD9): Asthma 

(493) 

Age groups analyzed: 0-14; 

15-64; ≥ 65 yrs 

Study design: Time-series 

N: 4,209 

Statistical analyses: 

Correlations and partial 

correlations 

Covariates: Min temperature 

Statistical package: 

Lag: 0-1 

24-h avg: 19.2 

(12.6) μg/m3

Range: 0.2, 94.6 

Number of 

monitors: 4 

Copollutants: 

NO2 (r = 0.4516) 

NO (r = 0.4773) 

O3 (r = -0.1778) 

TSP (r = 0.1919) 

CO 

The frequency of all admissions for asthma was significantly correlated to 

SO2. 

Child asthma admissions were not significantly correlated with SO2, but were 

correlated to O3 and NO. SO2 was also significantly correlated with elderly 

admissions. Increased hospitalization correlated with SO2 was also observed 

for adults. 

Hospital admissions were more strongly correlated with SO2 than other 

pollutants. ER visits were more strongly correlated with a mixture of 

pollutants (TSP, SO2, O3, and temperature). 

Multipollutant model 

co-linear results of SO2, CO, NO2, and NO suggest a mixture of pollutants is 

responsible for asthma admissions. 

Correlations between hospital admissions (HA) for asthma and pollutants and 

temperature by ages. 

0-14 yrs: HA: −0.01391; Emergency HA: 0.0332 

15-64 yrs: HA: 0.1039 p = 0.0006; Emergency HA: 0.1199 p < 0.0001 

≥ 65 yrs: HA: 0.0796 p = 0.0085; Emergency HA: 0.1169 p < 0.0001 

Partial correlations between admissions for asthma and SO2 were 

standardized for temperature. 

HA: 0.0770 p = 0.0172; Emergency HA: 0.1050; p = 0.0011 

Pönkä and Virtanen (1994) 

Helsinki, Finland 

Period of Study: 

1987-1989 

Days: 1096 

Hospital Admissions 

Outcome(s) (ICD 9): Chronic 

bronchitis and emphysema 

(493) 

Age groups analyzed: < 65, ≥

65 

Study design: Time-series 

Statistical analyses: Poisson 

regression 

Covariates: Season, day of 

wk, yr, influenza, humidity, 

temperature 

Season: Summer (Jun-Aug), 

Autumn (Sep-Nov), 

Winter (Dec-Feb), 

Spring (Mar-May) 

Lag: 0-7 days 

24-h avg: 

19 μg/m3

SD: 12.6; 

Range: 0.2, 95 

# of stations: 2 

Copollutants: 

NO2 O3 TSP 

SO2 was significantly associated with increased admissions for chronic 

bronchitis and emphysema for patients < 65 yrs of age with a lag of 0 and 3 

days. 

In the steps leading to regression analysis no association was observed 

between SO2 levels and the ≥65 population. Multipollutant models were only 

used to examine NO2 and SO2. 

SO2 had no significant association with morbidity caused by chronic 

bronchitis and emphysema in the ≥ 65 yr old population. 

Increment: NR 

Chronic bronchitis and emphysema: 

< 65 yrs: RR 1.31 (1.01, 1.70) lag 0; RR 0.96 (0.73, 1.27) lag 1; 

RR 0.78 (0.59, 1.03) lag 2; RR 1.39 (1.05, 1.86) lag 3; 

RR 0.89 (0.68, 1.16) lag 4; RR 1.28 (0.97, 1.70) lag 5; 

RR 0.91 (0.69, 1.20) lag 6; RR 1.09 (0.84, 1.40) lag 7 

65+ yrs: NR 

Pönkä and Virtanen (1996) 

Helsinki, Finland 

Period of Study: 

1987-1989 

Hospital Admissions 

Outcome(s) (ICD9): Asthma 

(493) 

Age groups analyzed: 0-14, 

15-64, 65+ 

Study design: Time-series 

Statistical analyses: 

Covariates: Long-term trend, 

season, epidemics, day of wk, 

holidays, temperature, relative 

humidity 

Statistical package: 

Lag: 0-2 

24-h avg (μg/m3): 

Winter: 26 

Spring: 22 

Summer: 13 

Fall: 15 

Copollutants: 

NO2 O3 TSP 

Significant associations were observed between daily SO2 concentrations 

and daily counts of hospitalizations among 15- to 64-yr-old patients and 

among those over 64 yrs old, but not among children. These effects were 

observed when mean daily SO2 values were lower than the max value 

recommended by WHO (125 μg/m3). 

Parameter estimates (PE) and standard error (SE) for a 1-unit increase: 

Asthma: 

15-64 yrs: PE 0.2176 (0.1081) p = 0.44 lag 2; 

PE 0.3086 (0.1545) p = 0.046 lag 0-3 

Asthma: 

65+ yrs: PE 0.2412 (0.0956) p = 0.012 lag 2 

 F-42 
Study Methods Pollutant Data Findings 

Prescott et al. (1998) 

Edinburgh, United Kingdom 

Period of Study: 

10/92-6/95 

Hospital Admissions 

Outcome(s) (ICD 9): 

Pneumonia (480-7), COPD + 

Asthma (490-496) 

Age groups analyzed: 

< 65, 65+ 

Study design: Time-series 

Statistical analyses: Poisson 

log linear regression 

Covariates: Trend, seasonal 

and weekly variation, 

temperature, wind speed, day 

of wk 

Lag: 0,1, or 3 day rolling avg 

SO2: 14.5 (9.0) 

ppb 

Min: 0 ppb 

Max: 153 ppb 

# of Stations: 1 

Copollutants: 

CO PM10 NO2 

O3 BS 

No effect of SO2 on hospitalizations observed in either age category. 

Increment: 10 ppb 

Respiratory admissions: 

>65 yrs −2.5 (−11.0, 6.9) lag 0-2; < 65 yrs 0.0 (−8.3, 9.1) lag 0-2 

Rossi et al. (1993) 

Oulu, Finland 

Period of Study: 10/1/1985-

9/30/1986 

ED Visits 

Outcome(s) (ICD 9): Asthma 

(493) 

Age groups analyzed: 15-85 

Study design: Time-series 

N: 232 

Statistical analyses: 

Pearson’s and partial 

correlation coefficients and 

multiple regression with 

stepwise discriminate analysis

Covariates: Temperature, 

humidity 

Statistical package: BMDP 

software 

Lag: 0,1,2,3 

24-h avg: 

10.0 μg/m3 

Range: 0, 56 

1-h max: 

31.0 μg/m3 

Range: 1, 24 

# of monitoring 

stations: 4 

Copollutants: 

NO2 (r = 0.48) 

TSP (r = 0.31) 

H2S 

Same day ER visits were correlated to daily SO2 levels, but the significance 

was lost with longer lag periods. 

When asthma visits were analyzed, SO2 was positively and significantly 

correlated with asthma visits in the same wk and the wk after. 

After regression analyses, SO2 became insignificant. 

Pearson correlation coefficients 

ED asthma visits and same day SO2: 

r = 0.13 p < 0.01 lag 0 

Weekly ED asthma visits and same wk SO2: r = 0.28 p < 0.05 

Weekly ED asthma visits and next wk SO2: 0.30 p < 0.05 

Multipollutant (NO2; TSP; H2S) 

Regression coefficient: 

All yr: β = 0.037, p = 0.535 

Winter: β = −0.024, p = 0.710 

Summer: β = −0.003, p = 0.991 

 F-43 
Study Methods Pollutant Data Findings 

Schouten et al. (1996) 

Multicity, The Netherlands 

(Amsterdam, Rotterdam) 

Period of Study: 

04/01/77-09/30/89 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory (460-519), COPD 

(490-2, 494, 496), Asthma 

(493) 

Age groups analyzed: 

15-64, 65+, all ages 

Study design: Time-series 

Statistical analyses: APHEA 

protocol, Poisson regression 

Covariates: Long-term trend, 

season, influenza, day of wk, 

holiday, temperature, humidity

Season: 

Cool, Nov-Apr; 

Warm: May-Oct 

Lag: 0,1,2 days; and 

cumulative 0-1 and 0-3 day 

lags 

24-h avg SO2 

Amsterdam 

Mean/Med: 

28/21 μg/m3

Rotterdam 

Mean: 40/32 

μg/m3

Daily 1-h max 

Amsterdam 

Mean/Med: 

65/50 μg/m3

Rotterdam 

Mean/Med: 

99/82 μg/m3

# of stations: 1 per 

city 

Copollutants: 

NO2 BS O3

The relationship between short-term air pollution and hospital admissions 

was not always consistent at low levels of exposure. One statistically 

significant association between hospital admissions and asthma (all ages) 

occurred in Amsterdam after a cumulative lag of 

1-3 days in the summer. Higher SO2 levels were reported for the winter; 

therefore, this association was not a concentration response. 

In Rotterdam neither 1 day nor cumulative lags in the summer or winter 

increased asthma admissions to statistical significance. Rotterdam had much 

higher mean SO2 concentrations. There were no significant associations to 

hospital admissions when higher pollution levels were prevalent. 

The analysis of all respiratory hospital admissions for all ages in the entire 

country (Netherlands) produced a statistically significant association for both 

1-h and 24-h periods (100 μg/m3). 

Increment: 100 μg/m3 increment. 

All respiratory, Amsterdam 

24-h avg: 15-64 yrs: RR 0.944 (0.864, 1.032) lag 2; 

RR 0.915 (0.809, 1.035) lag 0-3; >65 yrs: RR 1.046 (0.965, 1.134) lag 2; 

RR 1.008 (0.899, 1.131) lag 0-3 

1-h max: 15-64 yrs: RR 0.989 (0.952, 1.028) lag 2; 

RR 0.977 (0.927, 1.030) lag 0-3; >65 yrs: RR 1.022 (0.985, 1.060) lag 2; 

RR 1.010 (0.955, 1.068) lag 0-3; RR 0.941 (0.863, 1.026) lag 0-3 

COPD, Amsterdam 

24-h avg–all ages: RR 0.907 (0.814, 1.011) lag 0; 

RR 0.948 (0.838, 1.072) lag 0-1 

1-h max–all ages: RR 0.978 (0.933, 1.026) lag 0; 

RR 0.995 (0.940, 1.053) lag 0-1 

Asthma, Amsterdam 

24-h avg–all ages: RR 0.802 (0.696, 0.924) lag 1; 

RR 0.792 (0.654, 0.958) lag 0-3 

1-h max–all ages: RR 0.995 (0.942, 1.051) lag 0 

All respiratory, Rotterdam 

24-h avg: 15-64 yrs: RR 0.941 (0.855, 1.036) lag 1; 

RR 0.895 (0.787, 1.019) lag 0-2; 

>65 yrs (1977-1981): RR 1.027 (0.904, 1.165) lag 2; 

RR 1.011 (0.834, 1.227) lag 0-3; 

>65 yrs (1982-1984): RR 1.087 (0.890, 1.328) lag 0; 

RR 1.258 (0.926, 1.710) lag 0-3; 

>65 yrs (1985-1989): RR 1.045 (0.908, 1.204) lag 0; 

RR 0.968 (0.787, 1.190) lag 0-3 

1-h max: 15-64 yrs: RR 0.989(0.953, 1025) lag 1; 

RR 0.965 (0.915, 1.018) lag 0-2; 

>65 yrs (1977-1981): RR 0.892 (0.842, 0.945) lag 0; 

RR 0.987 (0.907, 1.074) lag 0-3; 

>65 yrs (1982-1984): RR 1.005 (0.933, 1.081) lag 0; 

RR 1.062 (0.938, 1.202) lag 0-3; 

>65 yrs (1985-1989): RR 1.010 (0.955, 1.068) lag 0; 

RR 1.064 (0.992, 1.141) lag 0-1 

COPD, Rotterdam 

24-h avg–all ages: RR 0.963 (0.874, 1.059) lag 2; 

RR 1.019 (0.887, 1.172) lag 0-3 

1-h max–all ages: RR 0.991 (0.955, 1.029) lag 2; 

RR 1.013 (0.953, 1.076) lag 0-3 

 F-44 
Study Methods Pollutant Data Findings 

Spix et al. (1998) 

Multicity (Amsterdam, 

London, Milan, Paris, 

Rotterdam), Europe 

Period of Study: 

1977 and 1991 

Hospital Admissions 

Outcome(s) (ICD9): All 

respiratory (460-519); 

Asthma (493) 

Age groups analyzed: 

15-64, 65+ 

Study design: Time-series 

Statistical analyses: Poisson 

regression following APHEA 

protocol. Pooled metaanalysis adjusted for 

heterogeneity 

Covariates: trend, seasonality, 

day of wk, holiday, 

temperature, humidity, 

unusual events (strikes, etc.) 

Lag: 1 to 3 days 

SO2 daily avg 

(μg/m3) 

London: 29 

Amsterdam: 21 

Rotterdam: 25 

Paris: 23 

Milan: 66 

Copollutants: 

NO2 O3 BS TSP 

Daily counts of adult respiratory admissions were not consistently associated 

with daily mean levels of SO2. Heterogeneity between cities was likely due to 

the number of stations or temperature. Only hospital admissions for ≥ 65 yr 

olds were significantly associated with SO2 in the warm season. 

Increment: 50 μg/m3

All cities, yr round 

15-64 yrs: RR 1.009 (0.992, 1.025) 

Warm RR 1.01 (0.98, 1.04) 

Cold RR 1.01 (0.97, 1.07) 

≥ 65 yrs RR 1.02 (1.005, 1.046) 

Warm RR 1.06 (1.01, 1.11) 

Cold RR 1.02 (0.99, 1.04) 

APHEA protocol pooled result from ≥65 yrs old from Europe 

All respiratory 

RR 1.02 (1.00, 1.05) 

Sunyer et al. (1997) 

Multicity, Europe (Barcelona, 

Helsinki, Paris, London) 

Period of Study: 

1986-1992 

Hospital admissions/ED Visits

Outcome(s) (ICD 9): Asthma 

(493) 

Age groups analyzed: < 15, 

15-64 

Study design: Time-series 

Statistical analyses: APHEA 

protocol, Poisson regression, 

GEE; meta-analysis 

Covariates: Humidity, 

temperature, influenza, 

soybean, Long-term trend, 

season, day of wk 

Season: 

Cool, Oct-Mar; 

Warm: Apr-Sep 

Lag: 0,1,2,3 and cumulative 

1-3 

24-h median 

(range) (μg/m3) 

Barcelona: 41 

(2, 160) 

Helsinki: 16 

(3, 95) 

London: 31 

(9, 100) 

Paris: 23 

(1, 219) 

# of stations: 

Barcelona: 3 

London: 4 

Paris: 4 

Helsinki: 8 

Copollutants: 

NO2 

BS 

O3

SO2 alone or as part of a mixture was a factor that exacerbated asthma 

admissions. 

In 2-pollutant models with SO2 and BS, the association of BS with SO2 was 

attenuated for < 15 yr olds, compared to single-pollutant model associations. 

In addition, the association of NO2 was also attenuated by the inclusion of 

SO2. 

Increment: 50 μg/m3 of 24-h avg for all cities combined. 

Asthma: 

15-64 yrs: 

0.997 (0.961, 1.034) lag 2 

1.003 (0.959, 1.050) lag 0-3, cum 

< 15 yrs: 

1.075 (1.026, 1.126) lag 1 

1.061 (0.996, 1.131) lag 2-3, cum 

2-pollutant models 

SO2/BS: 

< 15 yrs 1.092 (1.031, 1.156) lag 0-1 

SO2/NO2 

< 15 yrs 1.075 (1.019, 1.135) 

Sunyer* et al. (2003) 

Multicity study (Birmingham 

(B), London (L), Milan (M), 

Netherlands (N), Paris (P), 

Rome (R) and Stockholm (S), 

Europe 

Period of Study: 

1992 and 1997 

Hospital admissions/ED Visits

Outcome(s) (ICD 9): Asthma 

(493); COPD and Asthma 

(490-496); all respiratory 

(460-519) 

Age groups analyzed: All, 0-

14 yrs; 16-64 yrs; ≥ 65 yrs 

Study design:Time-series 

Poisson regression with GAM 

following APHEA 2 protocol 

Covariates: temperature, 

humidity, Long-term trend, 

season 

Lag: 0, 1 

SO2 24-h avg and 

SD (μg/m3) 

B 24.3 (12.7) 

L 23.6 (23.7) 

M 32.5 (37.5) 

N 8.5 (7.7) 

P 17.7 (12.5) 

R 9.8 (9.9) 

S 6.8 (6.2) 

Copollutants: 

PM10 (r = 0.64) 

CO (r = 0.53) 

The magnitude of association with asthma across the seven cities was 

comparable to earlier studies of London, Helsinki and Paris. 

Exposure factors may be important. Children may spend greater time 

outdoors compared with adults. Pneumonia requires chronic exposure to 

produce inflammatory response and infection, whereas asthma is an acute 

response. 

Increment: 10 μg/m3

Asthma : 0-14 yrs: 1.3% (0.4, 2.2) ; 15-64 yrs : 0.0% (−0.9, 1.00) 

COPD and Asthma: ≥ 65 yrs : 0.6% (0.0, 1.2) 

All Respiratory: ≥ 65 yrs 0.5% (0.1, 0.9) 

Asthma: 0-14 yrs: SO2 + PM10: −3.7% (p > 0.1) ; SO2 + CO: −0.7% (p > 0.1) 

 F-45 
Study Methods Pollutant Data Findings 

Sunyer et al. (1991) 

Barcelona, Spain 

Period of Study: 

1985-1986 

ED Visits 

Outcome(s) COPD 

(ICD 9): 490-496 

Age groups analyzed: > 14 

Study design: 

Time-series 

# of Hospitals: 4 

Statistical analyses: 

multivariate linear regression 

Covariates: Meteorology, 

season, day of wk 

Statistical package: 

Lag: 0 to 2 days 

24-h avg (SD): 

56.5 (22.5) μg/m3

98th: 114.3 

Range: 17, 160 

1-h max (SD): 

141.9 (98.8) μg/m3

98th: 461.3 

Range: 14,720 

Number of 

monitors: 17 

Copollutants: 

BS, CO, NO2, O3

An incremental change of 25 μg/m3 in SO2 was correlated with an adjusted 

increase of 0.5 daily visits due to COPD. 

SO2 and ER visits were more strongly correlated in warm weather. 

Even at 24-h avg levels less than 100 μg/m3, effects of SO2 were statistically 

significant for COPD admissions. 

Change in 24-h avg SO2 daily ER μg/m3 admissions P-value 

150 0.55 < 0.01 

100 0.7 < 0.01 

72 0.7 0.04 

52 0.41 > 0.05 

39 −1.27 > 0.05 

0.5 excess daily admissions per 25 μg/m3 increment of SO2. 

Sunyer et al. (1993) 

Barcelona, Spain 

Period of Study: 

1985-1989 

ED Visits 

Outcome(s) (ICD 9): COPD 

(490-492; 

494-496) 

Study design: 

Time-series 

Statistical analyses: 

Autoregressive linear 

regression 

Statistical package: 

Lag: 1,2 

SO2, 24-h 

Winter Tertiles 

(μg/m3) 

< 40.4 

40.4, 61 

>61 

Winter Tertiles 

(μg/m3) 

< 28.1 

28.1, 46.1 

>46.1 

Copollutants: BS 

SO2 concentrations were associated with the number of COPD ER 

admissions in the winter and summer. An increase of 25 μg/m3 in SO2

produced an adjusted change of ~6% and 9%, respectively, in the number of 

COPD emergencies in the winter and summer. Controlling for particulate 

matter resulted in a loss of significance. Co linearity of BS with SO2 was 

observed. 

Effects were expressed as adjusted changes in daily COPD ER admissions 

based on an increment of 25 μg/m3. 

Winter: 6% 

Summer: 9% 

Mean ER admissions for COPD (winter) were 15.8 (range 3, 34) and 8.3 

(range 1, 24) in the summer. 

Tenias et al. (1998) 

Valencia, Spain 

Period of Study: 

1993-1995 

Seasons: 

Cold: Nov-Apr 

Warm: May-Oct 

ED Visits 

Outcome(s): Asthma 

ICD 9 Code(s): NR 

Age groups analyzed: > 14 

Study design: Time-series 

N: 734 

Statistical analyses: Poisson 

regression, APHEA protocol 

Covariates: seasonality, 

temperature, humidity, longterm trend, day of wk, 

holidays, influenza 

Season: Cold: Nov-Apr; 

Warm: May-Oct 

Dose-response investigated: 

Yes 

Statistical package: NR 

Lag: 0-3 days 

24 h avg: 26.6 

μg/m3

25th: 17.9 

50th: 26.2 

75th: 34.3 

95th: 42.6 

Cold: 31.7 

Warm: 21.7 

1-h max: 

56.3 μg/m3

25th: 36.3 

50th: 52.2 

75th: 72.2 

95th: 95.2 

Cold: 64.6 

Warm: 48.2 

# of Stations: 2 

Copollutants: 

24 h avg: 

O3 (r = −0.431) 

NO2 (24 h av) 

(r = 0.265) 

NO2 (1-h) 

(r = 0.199) 

1-h: 

O3 (r = −0.304) 

NO2 (24 h avg) 

(r = 0.261) 

NO2 (1-h) 

(r = 0.201) 

SO2 showed the strongest correlation to asthma admissions during the warm 

mos. 

Multipollutant models showed that O3 and BS had a small effect on the 

association between SO2 and asthma ER visits while NO2 greatly depressed 

these effects. It is likely that NO2 was the dominant pollutant for respiratory 

outcomes. SO2 was the “most vulnerable pollutant” to the presence of other 

pollutants. 

Increment: 10 μg/m3

SO2 24-h avg: 

All yr 1.050 (0.973, 1.133) lag 0 

Cold 1.032 (0.937, 1.138) lag 0 

Warm 1.070 (0.936, 1.224) lag 0 

SO2 1-h max: 

All yr 1.027 (0.998, 1.057) lag 0 

Cold 1.018 (0.980, 1.057) lag 0 

Warm 1.038 (0.990, 1.090) lag 0 

 F-46 
Study Methods Pollutant Data Findings 

Tenias et al. (2002) 

Valencia, Spain 

Period of Study: 

1994-1995 

ED Visits 

Outcome(s): COPD 

ICD 9 Code(s): NR 

Age groups analyzed: > 14 

Study design: Time-series 

N: 1,298 

# of Hospitals: 1 

Statistical analyses: Poisson 

regression, APHEA protocol; 

basal models and GAM 

Covariates: Seasonality, 

annual cycles, temperature, 

humidity, day of wk, feast 

days 

Season: 

Cold, Nov-Apr; 

Warm, May-Oct 

Dose-response investigated: 

Yes 

Statistical package: NR 

Lag: 0-3 days 

24 h avg: 26.6 

μg/m3

5th: 8.2 

50th: 26.2 

75th: 34.3 

95th: 42.6 

Cold: 31.7 

Warm: 21.7 

1-h max: 56.3 

μg/m3

5th: 16.8 

50th: 52.2 

75th: 72.2 

95th: 95.2 

Cold: 64.6 

Warm: 48.2 

Copollutants: 

BS (r = 0.687) 

NO2 (r = 0.194) 

CO (r = 0.734) 

O3 (r = −0.431) 

SO2 did not show any significant association with COPD ER visits for all 

seasons analyzed. 

SO2 did not affect O3 or CO association to ER admission for COPD when 

assessed together in the Multipollutant model. 

Possibility of a linear relationship between pollution and risk of emergency 

cases could not be ruled out. 

Increment: 10 μg/m3. 

24-h avg SO2:

All yr RR 0.971 (0.914, 1.031) lag 0 

Cold, 24-h avg: RR 0.970 (0.905, 1.038) lag 0 

Warm, 24-h avg: RR 0.982 (0.885, 1.090) lag 0 

1-h max SO2:

All yr RR 0.981 (0.958, 1.027) lag 3 

Cold, 24-h avg: RR 0.972 (0.945, 1.000) lag 3 

Warm, 24-h avg: RR 1.003 (0.979, 1.056) lag 3 

Thompson et al. (2001) 

Belfast, Northern Ireland 

Period of Study: 

1993-1995 

Hospital admissions/ED Visits

Outcome(s): Asthma 

ICD 9 Code(s): NR 

Age groups analyzed: 

Children 

Study design: Time-series 

N: 1,044 

Statistical analyses: Followed 

APHEA protocol, Poisson 

regression analysis 

Covariates: Season, longterm trend, temperature, day 

of wk, holiday 

Season: 

Warm (May-Oct); Cold (NovApr) 

Statistical package: Stata 

Lag: 0-3 

Warm Season 

SO2 (ppb): 

Mean: 12.60; 

SD: 10.60; 

IQR: 6.0, 16.0 

Cold Season 

SO2 (ppb): 

Mean: 20.40; 

SD: 17.90; 

IQR: 11.0, 24.0 

Copollutants: 

PM10 (r = 0.66) 

NO2 (r = 0.82) 

NOX (r = 0.83) 

NO (r = 0.76) 

O3 (r = −0.58) 

CO (r = 0.64) 

Benzene 

(r = 0.80) 

This study found weak, positive associations for SO2 and adverse respiratory 

outcomes in asthmatic children. 

SO2 Increment: Per doubling (ppb) 

Lag 0 RR 1.07 (1.03, 1.11) 

Lag 0-1 RR 1.09 (1.04, 1.15) 

Lag 0-2 RR 1.08 (1.02, 1.15) 

Lag 0-3 RR 1.08 (1.01, 1.15) 

Warm only Lag 0-1 RR 1.11 (1.04, 1.19) 

Cold only Lag 0-1 RR 1.07 (1.00, 1.15) 

Adjusted for Benzene Lag 0-1 RR 0.99 (0.90, 1.09) 

Tobías et al. (1999) 

Barcelona, Spain 

Period of Study: 

 1986-1989 

ED Visits 

Outcome(s): Asthma 

ICD9: NR 

Age groups analyzed: > 14 

Study design: Time-series 

Statistical analyses: Poisson 

regression, followed APHEA 

protocol 

Covariates: Temperature, 

humidity, long-term trend, 

season, day of wk 

Statistical package: NR 

Lag: NR 

24-h avg SO2

μg/m3

Non-epidemic 

Days: 85.8 (62.4) 

Epidemic Days: 

116.3 (79.3) 

Copollutants: 

BS NO2 O3

The study failed to find a significant association between SO2 and asthma ED 

visits. 

β x 104 (SE x 104 ) using Std Poisson 

Without modeling asthma epidemics: 3.99 (4.14) 

Modeling epidemics with 1 dummy variable: 1.64 (2.76) 

Modeling epidemics with 6 dummy variables: 1.53 (2.75) 

Modeling each epidemic with dummy variable: 2.20 (2.65) 

β x 104 (SE x 104) using Autoregressive Poisson 

Without modeling asthma epidemics: 6.99 (14.37) 

Modeling epidemics with 1 dummy variable: 1.68 (2.77) 

Modeling epidemics with 6 dummy variables: 1.72 (2.75) 

Modeling each epidemic with dummy variable: 2.85 (2.89) 

 F-47 
Study Methods Pollutant Data Findings 

Vigotti et al. (1996) 

Milan, Italy 

Period of Study: 

1980-1989 

 

Hospital Admissions 

Outcomes 

(ICD 9 codes): Respiratory 

disease (460-519). 

Age groups analyzed: 

15-64 yrs and >64 yrs 

Study design: Time-series 

N: >73,000 

Statistical analyses: APHEA 

protocol 

Covariates: 

Season: Cold season (Oct. to 

Mar) and Warm season 

(Apr to Sep) 

Lag: 0, cumulative 4 day 

(0-3) 

24-h avg: 

117.7 μg/m3

Range: 3.0, 827.8

5th: 15.0 

25th: 34.0 

50th: 65.5 

75th: 162.5 

95th: 376.3 

Winter:248.6 

Range: 30.6, 

827.8 

5th: 78.8 

25th: 138.5 

50th: 216.0 

75th: 327.8 

95th: 527.0 

Summer:30.5 

Range: 3.0, 113.8

5th: 9.1 

25th: 18.5 

50th: 27.8 

75th: 39.2 

95th: 62.7 

# of monitors: 4; 

r = 0.89, 0.91 

Copollutants: 

TSP (r = 0.63) 

The effect of single day or cumulative day exposure to SO2 was more 

pronounced during the cool mos. Interaction between seasons was not 

significant. SO2 did not interact with TSP. No differences were noted between 

age groups. 

There were increased, but not significant (borderline), risks for increased 

hospital admissions based on an increment change in SO2 of 125 μg/m3 in 

the winter. 

Increment: 100 μg/m3

All respiratory 

15-64 yrs: 

All yr round: RR 1.05 (1.00, 1.10) lag 0 

Warm: RR 1.04 (0.98, 1.11) lag 0 

Cool: RR 1.06 (1.00, 1.13) lag 0 

>64 yrs: 

All yr: RR 1.04 (1.00, 1.09) lag 0 

Warm: RR 1.02 (0.96, 1.08) lag 0 

Cool: RR 1.05 (1.00, 1.11) lag 0 

Walters et al. (1994) 

Birmingham, United Kingdom 

Period of Study: 

1988-1990 

Hospital Admissions 

Outcome(s) (ICD9): 

Asthma (493) and acute 

respiratory conditions 

(466, 480-486, 490-496) 

Study design:Time-series 

Statistical analyses: 

Least squares regression 

Covariates: Temperature, 

pressure, humidity 

Lag: 3 day moving avg. 

SO2 24-h avg 

(μg/m3) 

All yr: 39.06 

Max: 126.3 

Spring: 42.9 

Summer: 37.8 

Autumn: 40.9 

Winter: 34.2 

Copollutants: BS 

In 2-pollutant models BS remained significant but SO2 was no longer 

associated significantly with admission. 

A 100 μg/m3 increment in SO2 might result in four (0-7) more asthma 

admissions and 15.5 (6-25) move respiratory admissions/day. Spring and 

autumn did not show associations with admissions for asthma or respiratory. 

Increment of 100 μg/m3

Asthma: 

Summer: 1.4% (−10, 39) lag 0 

Winter: 2.7% (−0.8, 6.1) lag 0 

All respiratory: 

Summer: 5.9% (1.1, 10.6) lag 0; (p < 0.02) 

Winter: 18% (8.8, 26.8) lag 0; (p < 0.0002) 

 F-48 
Study Methods Pollutant Data Findings 

AUSTRALIA/NEW ZEALAND 

Barnett et al. (2005) 

Multicity, Australia/New 

Zealand; (Auckland, 

Brisbane, Canberra, 

Christchurch, Melbourne, 

Perth, Sydney) 

Period of Study: 

Jan 1998-Dec 2001 

Hospital Admissions 

Outcomes (ICD 9/ICD 10): 

All respiratory 

(460-519/J00-J99 excluding 

J95.4-J95.9, RO9.1, RO9.8), 

asthma (493/J45, J46, J44.8), 

COPD (490-492, 494-

496/J40-J44, J47, J67), 

pneumonia with bronchitis 

(466, 480-486/J12-17, J18.0 

j18.1 J18.8 J18.9 J20 J21) 

Age groups analyzed: <1, 1-4, 

5-14 

Study design: Case-crossover

Statistical analyses: 

Conditional logistic 

regression, random effects 

meta-analysis 

Covariates: Temperature, 

current-previous day 

temperature, relative humidity, 

pressure, extremes of hot and 

cold, day of wk, holiday, day 

after holiday 

Season: Cool, May-Oct; 

Warm, Nov-Apr 

Statistical package: SAS 

Lag: 0-1 days 

24-h avg (ppb) 

(range): 

Auckland: 4.3 

(0, 24.3) 

Brisbane: 1.8 

(0, 8.2) 

Canberra: NA 

Christchurch: 2.8 

(0, 11.9) 

Melbourne: NA 

Perth: NA 

Sydney: 0.9 

(0, 3.9) 

Daily 1-h max 

(range): 

Auckland: NA 

Brisbane: 7.6 

(0, 46.5) 

Canberra: NA 

Christchurch: 10.1 

(0.1, 42.1) 

Melbourne: NA 

Perth: NA 

Sydney: 3.7 

(0.1, 20.2) 

Increased hospital admissions were significantly associated with SO2 for 

acute bronchitis, pneumonia, and respiratory diseases. In multipollutant 

models the impacts of particulate matter and NO2 were isolated. 

There were seasonal impacts on pneumonia and acute bronchitis admissions 

in the 1- to 4-yr-old age group for SO2. 

Increment: 5.4 ppb 

(1-h max IQR) 

Pneumonia and acute bronchitis: 

0 yrs: No analysis 

1-4 yrs: 6.9% (2.3, 11.7) lag 0-1 

Respiratory: 

0 yrs: 3.2% (0.3, 6.3) lag 0-1 

1-4 yrs: 2.7% (0.6, 4.8) lag 0-1 

5-14 yrs: No analysis 

Asthma: 

0 yrs: No analysis (poor diagnosis) 

1-4 yrs: No analysis 

5-14 yrs: No analysis 

Lam (2007) 

Australia (New South Wales; 

Sydney) 

Period of Study: 

2001-2002 

ED Visits 

Outcome(s): Fever, 

gastroenteritis, asthma/other 

respiratory problems 

Study design: Time-series 

Statistical Analysis: Auto 

Regression Integrated Moving 

Average (ARIMA) statistical 

modeling 

Statistical package: SPSS 

Age groups analyzed: < 6 

Covariates: NR 

Lag(s): NR 

24-h avg (ppm): 

0.35 (0.19) 

Range: 0.10, 0.90 

1-h max (ppm): 

0.38 (0.20) 

Range: 0.10, 1.80 

Copollutants: 

PM10

PM2.5

NO2

O3

Bivariate correlations resulted in ARIMA models for fever and NO2 max, 

gastroenteritis and O3 avg and NO2 max; and respiratory problems and O3

max. Neither NO2 nor O3 was significantly associated with any of the 

childhood illnesses analyzed. 

SO2 was not significantly correlated with fever, gastroenteritis, or respiratory 

problems; therefore, SO2 was not included in the ARIMA models. 

 F-49 
Study Methods Pollutant Data Findings 

Petroeschevsky et al. (2001) 

Brisbane, Australia 

Period of Study: 

1987-1994 

Days: 2922 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory (460-519); Asthma 

(493) 

Age groups analyzed: 0-4, 

5-14, 15-64, 65+, all ages 

Study design: Time-series 

N: 33,710 (respiratory), 

13,246 (asthma) 

Statistical analyses: APHEA 

protocol, Poisson regression, 

GEE 

Covariates: Temperature, 

humidity, season, infectious 

disease, day of wk, holiday 

Season: Summer, Autumn, 

Winter, Spring, All yr 

Dose-response investigated? 

Yes 

Statistical package: SAS 

Lag: Single: 1,2,3 day 

Cumulative: 0-2, 0-4 

Mean: 24-h avg: 

Overall: 4.1 ppb 

Summer: 3.9 ppb 

Autumn: 4.2 ppb 

Winter: 4.8 ppb 

Spring: 3.7 ppb 

Mean: 1-h max 

Overall: 9.2 ppb 

Summer: 7.8 ppb 

Autumn: 9.3 ppb 

Winter: 11.3 ppb 

Spring: 8.4 ppb 

# of stations: 3 

Copollutants: 

BSP 

O3 

NO2

SO2 was highly correlated with max daily ER admissions for respiratory 

conditions. The highest association was observed in the winter followed by 

autumn, spring, and summer. For asthma, the highest association was 

observed in the winter and autumn. 

No statistically significant contributions for respiratory admissions were 

reported for the age group 5-14 yr olds for any pollutant. 

Increment: 0 ppb 

Respiratory: 

0-4 yrs 24-h avg 1.224 (1.087, 1.377) lag 0-4 

5-14 yrs 1-h max 1.049 (0.986, 1.116) lag 0-4 

15-64 yrs 24-h avg 1.033 (0.895, 1.118) lag 1 

65+ yrs 24-h avg 1.121 (1.019, 1.234) lag 0 

All ages 24-h avg 1.080 (1.030, 1.131) lag 1 

Asthma: 

0-14 yrs 24-h avg 1.080 (0.971, 1.201) lag 0 

15-64 yrs 1-h max 0.941 (0.900, 0.984) lag 0 

All ages 24-h avg 0.941 (0.876, 1.011) lag 2 

LATIN AMERICA 

Braga* et al. (1999) 

São Paulo, Brazil 

Period of Study: 

10/1992-10/1993 

 

Hospital Admissions 

Outcome(s) (ICD9): All 

respiratory (466, 480-486, 

491-492, 496) 

Age groups analyzed: 

<13 yrs 

Study design:Time-series 

N: 68,918 

# of Hospitals: 

112 Statistical analyses: 

Multiple linear regression 

models (least squares). Also 

used Poisson regression 

techniques. GLM and GAM 

using LOESS for smoothing. 

Covariates: Season, 

temperature, humidity, day of 

wk, 

Statistical package: SPSS, SPlus 

Lag: 1,2,3,4,5,6,7 moving 

avgs 

24-h avg 22.40 

(9.90) μg/m3

Min: 6.4 

Max: 69.6 

# of monitors: 13 

Copollutants: 

PM10 (r = 0.73) 

CO (r = 0.62) 

NO2 (r = 0.53) 

O3 

SO2 did not show a correlation with respiratory hospital admissions with any 

lag structure. 

Increment: 22.4 μg/m3

0.12 (−0.04, 0.28) lag 0 

0.18 (−0.00, 0.37) lag 0-1 

0.19 (−0.01, 0.39) lag 0-2 

0.18 (−0.04, 0.40) lag 0-3 

0.18 (−0.05, 0.42) lag 0-4 

0.12 (−0.13, 0.36) lag 0-5 

0.08 (−0.18, 0.35) lag 0-6 

 F-50 
Study Methods Pollutant Data Findings 

Braga* et al. (2001) 

São Paulo, Brazil 

Period of Study: 

1/93-11/97 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory admissions (460-

519) 

Age groups analyzed: 0-19, ≤

2, 3-5, 6-13, 14-19 

Study design: Time-series 

Statistical analyses: Poisson 

regression with GAM 

Covariates: Long-term trend, 

season, temperature, relative 

humidity, day of wk, holiday 

Statistical package: S-Plus 

4.5 

Lag: 0-6 moving avg 

SO2 Mean: 

21.4 μg/m3; 

SD: 11.2 

IQR: 14.4 μg/m3

Range: 1.6, 76.1 

# of stations: 5-6 

Copollutants: 

PM10 (r = 0.61) 

NO2 (r = 0.54) 

CO (r = 0.47) 

O3 (r = 0.17) 

Children < 2 yrs were most susceptible to the effect of each pollutant. 

Pneumonia and bronchopneumonia were the main cause of hospital 

admissions (71%) in the < 2-yr-old group. Bronchitis/asthma were more 

important for the intermediate age groups. However, in all age groups the 

largest increase in admissions was caused by chronic disease in tonsils and 

adenoids. 

Multipollutant models rendered all pollutants except PM10 and SO2 from 

significance. The effect of PM10 stayed relatively unchanged while SO2 was 

reduced; however, it remained significant. 

Increment: μg/m3 (IQR) 

All respiratory admissions: < 2 yrs: 5.9% (4.5, 7.4); 

3-5 yrs: 1.6% (−1.3, 4.4); 6-13 yrs: 0.6% (−2.2, 3.5); 

14-19 yrs: 1.3% (−3.2, 5.8); All ages 4.5% (3.3, 5.8) 

Farhat* et al. (2005) 

São Paulo, Brazil 

Period of Study: 

Aug 1996-Aug 1997 

Hospital Admissions/ED Visits

Outcome(s) (ICD9): Lower 

Respiratory Disease (466, 

480-5) 

Age groups analyzed: < 13 

Study design: Time-series 

N: 4,534 

# of Hospitals: 1 

Statistical analyses: 

1) Poisson regression and 2) 

GAM – no mention of more 

stringent criteria 

Covariates: Long-term trends, 

seasonality, temperature, 

humidity 

Statistical package: 

S-Plus 

Lag: 0-7 days, 2,3,4 day 

moving avg 

24-h avg: 

Mean: 23.7 μg/m3

SD: 10.0 

Range: 3.4, 75.2 

IQR: 12.5 

# of Stations: 6 

Copollutants: 

PM10 (r = 0.69) 

NO2 (r = 0.66) 

CO (r = 0.49) 

O3 (r = 0.28) 

This study reports a significant effect of air pollution on respiratory morbidity, 

though several pollutants were associated with increased respiratory events, 

making it difficult to isolate a single agent as the main atmospheric 

contaminant. 

Increment: 12.5 μg/m3 (IQR) 

Single-pollutant models (estimated from graphs): 

Pneumonia: ~21% (4.8, 37) 

Asthma: ~12% (−10, 38) 

Pneumonia multipollutant models: 

Adjusted for: PM10 13.3 (−5.7, 32.3) 6-day avg; 

NO2 16.5 (−1.6, 34.6) 6-day avg; CO 18.4 (0.5, 36.2) 6-day avg; 

O3 18.4 (0.5, 36.2) 6-day avg 

Multipollutant model: 13.3 (−5.9, 32.6) 6-day avg 

Asthma multipollutant models: 

Adjusted for: PM10 3.8 (−23.3, 31.0) 2-day avg; 

NO2 −1.2 (−27.4, 25.0) 2-day avg; CO 6.2 (−18.8, 31.2) 2-day avg; 

O3 9.4 (−14.6, 33.5) 2-day avg 

Multipollutant model: −0.5 (−27.7, 26.6) 2-day avg 

Gouveia and Fletcher (2000) 

São Paulo, Brazil 

Period of Study: 

11/92-9/94 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory; Pneumonia (480-

486); asthma or bronchitis 

(466, 490, 491, 493) 

Age groups analyzed: 

< 1; < 5 yrs 

Study design: Time-series 

Statistical analyses: Poisson 

regression Covariates: Longterm trend, season, 

temperature, relative humidity, 

day of wk, holiday, strikes in 

public transport or health 

services 

Season: 

Cool (May-Oct), 

Warm (Nov-Apr) 

Statistical package: SAS 

Lag: 0, 1, 2 days 

24-h avg: 

Mean: 18.3 μg/m3

SD: 9.0 

Range: 3.2, 61.1 

5th: 7.6 

25th: 11.9 

50th: 16.6 

75th: 22.2 

95th: 35.8 

# of stations: 4 

Copollutants: 

PM10 (r = 0.72) 

NO2 (r = 0.37) 

CO (r = 0.65) 

O3 (r = 0.08) 

Current ambient air pollution concentrations have short-term adverse effects 

on children’s respiratory morbidity assessed through admissions to hospitals. 

Increment: 27.1 μg/m3 

(90th – 10th) 

All Respiratory: 

< 5 yrs RR 1.038 (0.983, 1.096) lag 1 

< 5 yrs Cool RR 1.06 (0.99, 1.11) (estimated from graph) 

< 5 yrs Warm RR 0.98 (0.89, 1.07) (estimated from graph) 

Pneumonia: 

< 5 yrs RR 1.024 (0.961, 1.091) lag 1 

< 1 yr RR 1.071 (0.998, 1.149) lag 0 

Asthma: 

< 5 yrs RR 1.106 (0.981, 1.247) lag 2 

 F-51 
Study Methods Pollutant Data Findings 

Ilabaca et al. (1999) 

Santiago, Chile 

Period of Study: 

2/1/95–8/31/96 

Days: 578 

ED Visits 

Outcome(s) (ICD9): Upper 

respiratory illness (460-465, 

487); 

Lower respiratory illness (466, 

480-486, 490-494, 496, 

519.1, 033.9); Pneumonia 

(480-486) 

Age groups analyzed: < 15 

Study design: Time-series 

# of Hospitals: 1 

Statistical analyses: Poisson 

regression 

Covariates: Long-term trend, 

season, day of wk, 

temperature, humidity, 

influenza epidemic 

Season: 

Warm (Sep-Apr), Cool (MayAug) 

Lag: 0-3 days 

24-h avg SO2

(μg/m3) 

Warm: 

Mean: 14.9 

Median: 13.2 

SD: 8.8 

Range: 1.9, 60.2 

5th: 5.6 

95th: 32.0 

Cool: 

Mean: 31.8 

Median: 28.2 

SD: 18.4 

Range: 5.6, 92.1 

5th: 9.4 

95th: 75.2 

# of stations: 4 

Copollutants: 

Warm: 

NO2 (r = 0.6556) 

O3 (r = 0.1835) 

PM10 (r = 0.6687) 

PM2.5 (r = 0.5764)

Cool: 

NO2 (r = 0.7440) 

O3 (r = 0.1252) 

PM10 (r = 0.7337) 

PM2.5 (r = 0.6874) 

SO2 was related to the number of respiratory ED visits, but because of the 

high correlation between contaminants, it is difficult to establish independent 

health effects. These results support the fact that exposure to air pollution 

mixtures may decrease immune functions and increase the risk for 

respiratory infections among children. 

Increment: IQR 

All respiratory: 

Cool: Lag 2 IQR: RR 1.0289 (1.0151, 1.0428); 

Lag 3 IQR: RR 1.0374 (1.0236, 1.0513); 

Lag avg 7 IQR: RR 1.0230 (1.0086, 1.0377) 

Warm: Lag 2 IQR: RR 1.0029 (0.9860, 1.0200) 

Lag 3 IQR: RR 1.0108 (0.9937, 1.0282) 

Lag avg 7 IQR: RR 1.0108 (0.9756, 1.0473) 

Upper respiratory: 

Cool: Lag 2 IQR: RR 1.0584 (1.0394, 1.0778) 

Lag 3 IQR: RR 1.0513 (1.0324, 1.0706) 

Lag avg 7 IQR: RR 1.0316 (1.0120, 1.0515) 

Warm: Lag 2 IQR: RR 1.0061 (0.9850, 1.0277) 

Lag 3 IQR: RR 1.0130 (0.9916, 1.0349) 

Lag avg 7 IQR: RR 0.9815 (0.9390, 1.0260) 

Pneumonia: 

Cool: Lag 2 IQR: RR 1.0164 (0.9757, 1.0587) 

Lag 3 IQR: RR 1.0342 (0.9938, 1.0762) 

Lag avg 7 IQR: RR 1.0291 (0.9850, 1.0751) 

Warm: Lag 2 IQR: RR 1.1010 (1.0404, 1.1653) 

Lag 3 IQR: RR 1.0248 (0.9669, 1.0862) 

Lag avg 7 IQR: RR 1.2151 (1.0771, 1.3709) 

Lin et al. (1999) 

São Paulo, Brazil 

Period of Study: 

May 1991-Apr 1993 

Days: 621 

ED Visits 

Outcome(s): Respiratory 

disease, Upper respiratory 

illness, Lower respiratory 

illness, Wheezing 

ICD 9Code(s): NR 

Age groups analyzed: < 13 

Study design: Time-series 

# of Hospitals: 1 

Statistical analyses: Gaussian 

and Poisson regression 

Covariates: Long-term trend, 

seasonality, day of wk, 

temperature, humidity 

Statistical package: NR 

Lag: 5-day lagged moving 

avgs 

SO2 μg/m3: 

Mean: 20 

SD: 8 

Range: 4, 60 

Number of 

stations: 3 

Copollutants: 

NO2 (r = 0.38) 

CO (r = 0.56) 

PM10 (r = 0.73) 

O3 (r = 0.21) 

The results of this study demonstrate a significant association between the 

increase in emergency visits for all respiratory illness, especially URI, and 

SO2 levels. 

Increment: 10 μg/m3

All respiratory illness: 

SO2 alone RR 1.079 (1.052, 1.107) 5-day moving avg 

SO2 + PM10 + O3 + NO2 + CO RR 0.938 (0.900, 0.977) 

Lower respiratory illness: 

SO2 alone RR 1.052 (0.984, 1.125) 5-day moving avg 

SO2 + PM10 + O3 + NO2 + CO RR 0.872 (0.783, 0.971) 

Upper respiratory illness: 

SO2 alone RR 1.075 (1.044, 1.107) 5-day moving avg 

SO2 + PM10 + O3 + NO2 + CO RR 0.951 (0.906, 0.999) 

Wheezing: 

SO2 alone RR 1.034 (0.975, 1.096) 5-day moving avg 

SO2 + PM10 + O3 + NO2 + CO RR 0.908 (0.824, 1.002) 

 F-52 
Study Methods Pollutant Data Findings 

Martins* et al. (2002) 

São Paulo, Brazil 

Period of Study: 

5/96-9/98 

ED Visits 

Outcome(s) (ICD10): Chronic 

Lower Respiratory Disease 

(CLRD) (J40-J47); includes 

chronic bronchitis, 

emphysema, other COPDs, 

asthma, bronchiectasia 

Age groups analyzed: >64 

Study design: Time-series 

N: 712 

# of Hospitals: 1 

Catchment area: 13,163 total 

ER visits 

Statistical analyses: Poisson 

regression and GAM – no 

mention of more stringent 

criteria 

Covariates: Weekdays, time, 

min temperature, relative 

humidity, daily number of nonrespiratory emergency room 

visits made by elderly 

Statistical package: S-Plus 

Lag: 2-7 days and 3 day 

moving avgs 

SO2 24-h avg 

(μg/m3): 18.7, 

SD: 10.6 

Range: 2.0, 75.2 

IQR: 15.1 μg/m3

# of stations: 13 

Copollutants: 

O3 (r = 0.28) 

NO2 (r = 0.67) 

PM10 (r = 0.72) 

CO (r = 0.51) 

The results of the study show a significant association between SO2 and 

CLRD among the elderly. 

Increment: IQR of μg/m3 

Percent increase: 17.5 

(5.0, 23.0) lag 3-day moving avg (estimated from graph) 

Single-pollutant model 

β = 0.0140 (0.0056) 

Multipollutant model (with O3) 

β = 0.0104 (0.0059) 

ASIA 

Agarwal et al. (2006) 

Safdarjung area of south Deli 

Period of Study: 

2000-2003 

Hospital Admissions 

Outcome(s) (ICD9): COPD, 

asthma and emphysema 

Study design: time-series 

Statistical Analysis: 

Performed Kruskal-Wallis one 

way analysis of variance by 

rank, chi-square analysis. 

Statistical package: SPSS 

Age groups analyzed: all 

Covariates: Temperature-min 

and maximum, relative 

humidity at 0830 and 1730 h 

and wind speed 

N: NR 

# Hospitals: 1 

Lag: none 

Mean, SD 

Quarter 1: 

16.7, 5.5 

Quarter 2: 

13.6, 2.6 

Quarter 3: 

12.8, 3.1 

Quarter 4: 

14.3, 2.8 

Copollutants: 

NO2 SPM RSPM 

SO2 was found to be in “low” category the entire time, so no analysis could 

be performed 

 F-53 
Study Methods Pollutant Data Findings 

Chew et al. (1999) 

Singapore 

Period of Study: 

1990-1994 

Hospital Admissions/ED Visits

Outcome(s) (ICD 9): Asthma 

(493) 

Age groups analyzed: 3-12, 

13-21 

Study design: Time-series 

N: 23,000 

# of Hospitals: 2 

Statistical analyses: Linear 

regression, GLM 

Covariates: variables that 

were significantly associated 

with ER visits were retained in 

the model 

Statistical package: 

SAS/STAT, SAS/ETS 6.08 

Lag: 1, 2 days avgs 

24-h avg: 38.1 

μg/m3, 

SD: 21.8 

Range: 3.0, 141.0 

# of Stations: 15 

Copollutants: 

NO2 O3 TSP 

SO2 was positively correlated to daily ER visits and hospitalization for asthma 

in children (3-12 yrs), but not adolescents. The association of ER visits with 

SO2 persisted after standardization for meteorological and temporal 

variables. An adjusted increase in 2.9 ER visits for every 20 μg/m3 increase 

in ambient SO2 levels with a lag of 1 was observed. 

The increased number of ER visits/day for each quartile are listed below: 

Q1: < 9; Q2: 10-12; Q3: 13-16; Q4: > 16 

Categorical analysis (via ANOVA) 

p-value and Pearson correlation coefficient (r) using continuous data 

comparing daily air pollutant levels and daily number of ER visits 

Age Group: 3-12 13-21 

Lag 0 r = 0.04 r = 0.05; p < 0.001 p = 0.086 

Lag 1 r = 0.10 r = 0.06; p < 0.001 p = 0.016 

Lag 2 r = 0.08 r = 0.07; p < 0.001 p = 0.019 

Hwang and Chan (2002) 

Taiwan 

Period of Study: 

1998 

ED Visits 

Outcome(s) (ICD 9): Lower 

Respiratory Disease (LRD) 

(466, 480-6) including acute 

bronchitis, acute bronchiolits, 

pneumonia 

Age groups analyzed: 

0-14, 15-64, ≥ 65, all ages 

Study design: Time-series 

Catchment area: Clinic 

records from 50 communities 

Statistical analyses: Linear 

regression, GLM 

Covariates: temperature, dew 

point temperature, season, 

day of wk, holiday 

Lag: 0,1,2 days and avgs 

24-h avg: 5.4 ppb, 

SD: 3.0 

Range: 1.5, 16.9 

Copollutants: 

NO2 PM10 O3 CO 

No correlations for 

individualpollutants. 

Colinearity of pollutants prevented use of multipollutant models 

Increment: 10% change in SO2 (natural avg) which is equivalent to 2.4 ppb. 

NOTE: The percent change is for the rate of clinic use NOT for relative risk 

for adverse effect. 

Increased clinic visits for lower respiratory disease (LRD) by age group 

0-14 yrs: 

Lag 0 0.5% (0.3, 0.6) 

15-64 yrs: 

Lag 0 0.7% (0.5, 0.8) 

≥65 yrs: 

Lag 0 0.8% (0.6, 1.1) 

All ages: 

Lag 0 0.5% (0.4, 0.7) 

Ko et al. (2007b) 

Hong Kong 

Period of Study: 

2000-2005 

Hospital Admissions 

Outcome(s) (ICD9): Asthma 

Study design: Retrospective 

ecological study 

Statistical Analysis: 

Generalized additive models 

with Poisson distribution. 

Age groups analyzed: All 

Covariates: 

N: 69,716 

# Hospitals: 15 

Lag: 0-5 days 

Mean, SD (μg/m3) 

Whole yr: 

18.8, 13.1 

< 20 °C: 18.0, 10.0

≥20 °C: 19.1, 14.1 

Copollutants: 

NO2 (r =0.573) 

PM10 (r =0.430) 

PM2.5 (r =0.482) 

O3 (r =0.123) 

SO2 had a non-significant effect on respiratory admissions. 

Relative Risk (95% CI) 

Lag 0: 1.004 (0.998, 1.011) 

Lag 1: 1.000 (0.994, 1.007) 

Lag 2: 0.999 (0.993, 1.006) 

Lag 3: 1.002 (0.998, 1.008) 

Lag 4: 1.004 (0.997, 1.010) 

Lag 5: 0.997 (0.990, 1.003) 

Lag 0,1: 1.003 (0.996, 1.011) 

Lag 0,2: 1.003 (0.994, 1.011) 

Lag 0,3: 1.004 (0.994, 1.014) 

Lag 0-4: 1.007 (0.996, 1.017) 

Lag 0-5: 1.004 (0.993, 1.016) 

 F-54 
Study Methods Pollutant Data Findings 

Ko et al. (2007a) 

Hong Kong 

Period of Study: 

2000-2004 

Hospital Admissions 

Outcome(s) (ICD9): COPD 

Study design: Retrospective 

ecological study 

Statistical Analysis: Poisson 

distribution 

Age groups analyzed: All 

ages 

Covariates: Autocorrelation 

and overdispersion were 

corrected 

N: 119,225 

# Hospitals: 15 

Lag: 0-5 days 

15.0 μg/m3

SD: 11.6 

Copollutants: 

NO2 PM10 O3 PM2.5

Positive association with hospital admission for acute exacerbations of 

COPD. 

Relative Risk (95% CI) 

Lag 0: 1.007 (1.001, 1.014) 

Lag 1: 0.991 (0.981, 1.001) 

Lag 2: 0.992 (0.985, 1.000) 

Lag 3: 1.006 (0.999, 1.013) 

Lag 4: 1.004 (0.998, 1.011) 

Lag 5: 1.004 (0.997, 1.010) 

Lag 0-1: 0.998 (0.991, 1.006) 

Lag 0-2: 0.993 (0.985, 1.001) 

Lag 0-3: 0.998 (0.989, 1.007) 

Lag 0-4: 1.001 (0.991, 1.010) 

Lag 0-5: 1.004 (0.994, 1.014) 

Lee* et al. (2002) 

Seoul, Korea 

Period of Study: 

12/1/97-12/31/99 

Days: 822 

Hospital Admissions 

Outcomes (ICD 10): Asthma 

(J45–J46) 

Age groups analyzed: < 15 

Study design: Time-series 

N: 6,436 

Statistical analyses: Poisson 

regression, log link with GAM 

Covariates: Time, day of wk, 

temperature, humidity 

Season: Spring (Mar-May), 

Summer (Jun-Aug), 

Fall (Sep-Nov), 

Winter (Dec-Feb) 

Lag: 0-2 days cumulative 

24-h avg SO2

(ppb) 

Mean: 7.7 

SD: 3.3 

5th: 3.7 

25th: 5.1 

50th: 7.0 

75th: 9.5 

95th: 14.3 

# of stations: 27 

Copollutants: 

NO2 (r = 0.723) 

O3 (r = −0.301) 

CO (r = 0.812) 

PM10 (r = 0.585) 

This study reinforces the possible role of SO2 on asthma attacks, although it 

should be interpreted with caution because the effect estimates are close to 

the null and because results in the multipollutant models are inconsistent. 

Increment: 14.6 ppb (IQR) 

Asthma: 

SO2 RR 1.11 (1.06, 1.17) lag 0-2 

SO2 + PM10 RR 1.08 (1.02, 1.14) lag 0-2 

SO2 + NO2 RR 0.95 (0.88, 1.03) lag 0-2 

SO2 + O3 RR 1.12 (1.06, 1.17) lag 0-2 

SO2 + CO RR 0.99 (0.92, 1.07) lag 0-2 

SO2 + O3 + CO + PM10 + NO2 RR 0.949 (0.868, 1.033) 

Lee et al. (2006) 

Hong Kong, China 

Period of Study: 

1997-2002 

Days: 2,191 

Hospital Admissions 

Outcome(s) (ICD 9): Asthma 

(493) 

Age groups analyzed: ≤18 

Study design: Time-series 

N: 26,663 

Statistical analyses: Semiparametric Poisson 

regression with GAM (similar 

to APHEA 2) 

Covariates: Long-term trend, 

temperature, relative humidity, 

influenza, day of wk, holiday 

Statistical package: SAS 8.02 

Lag: 0-5 days 

SO2 24-h avg: 

17.7 μg/m3, 

SD: 10.7 

IQR: 11.1 μg/m3 

25th: 10.6 

50th: 15.2 

75th: 21.7 

# of stations: 

9-10 

Copollutants: 

PM10 (r = 0.37) 

PM2.5 (r = 0.47) 

NO2 (r = 0.49) 

O3 (r = −0.17) 

Absence of an association of SO2 with asthma admissions was attributed to 

low ambient SO2 levels during the study period due to restrictions on sulfur 

content in fuel. 

Increment: 11.1 μg/m3 (IQR) 

Asthma: 

Single-pollutant model 

Lag 0 −1.57% (−2.87, −0.26) 

Lag 1 −1.77% (−3.06, −0.46) 

Lag 2 −1.15% (−2.42, 0.14) 

Lag 3 0.82% (−0.45, 2.11) 

Lag 4 1.40% (0.13, 2.69) 

Lag 5 1.46% (0.19, 2.74) 

Multipollutant model–including PM, NO2, and O3: 

0.81% (−0.75, 2.4) lag 5 

Other lags NR 

 F-55 
Study Methods Pollutant Data Findings 

Lee et al. (2007) 

Kaohsiung, Taiwan 

Period of Study: 

1996-2003 

Hospital Admissions 

Outcome(s) (ICD9): COPD 

(490-492, 494, and 496) 

identified by records from the 

National Health Insurance 

(NHI) program 

Study design: Case-crossover

Statistical Analysis: 

Conditional logistic regression

Statistical package: SAS 

Age groups analyzed: All 

ages 

Covariates: Adjustment for 

temperature and humidity 

N: 25,108 

# Hospitals: 63 

Lag: Cumulative lag up to 2 

days 

24-h avg (ppb): 

9.49 

Range: 0.92, 

31.33 

Copollutants: 

PM10 NO2 CO O3

All pollutants, except SO2, were significantly associated with COPD hospital 

admissions on warm days, while on cold days all pollutants were found to be 

significantly associated. In two pollutant models, CO and O3 were 

significantly associated with each of the other pollutants on warm days, and 

on cool days, only NO2 was significantly associated with all pollutants. 

Odds Ratio (95% CI) 

Single-pollutant model (per 5.79 ppb SO2): 

≥ 25 °C: 1.024 (0.973, 1.077); < 25 °C: 1.190 (1.093, 1.295) 

Co-pollutant model (per 5.79 ppb SO2): 

≥ 25 °C: 

SO2 + PM10: 1.002 (0.951, 1.054) 

SO2 + NO2: 0.979 (0.926, 1.034) 

SO2 + CO: 0.929 (0.876, 0.985) 

SO2 + O3: 1.057 (1.004, 1.113) 

< 25 °C: 

SO2 + PM10: 1.043 (0.952, 1.143) 

SO2 + NO2: 0.767 (0.689, 0.855) 

SO2 + CO: 1.004 (0.915, 1.103) 

SO2 + O3: 1.198 (1.100, 1.304) 

Tanaka et al. (1998) 

Kushiro, Japan 

Period of Study: 

1992-1993 

ED Visits 

Outcome(s): Asthma 

Age groups analyzed: 

15-79 

Study design: Time-series 

N: 102 

# of Hospitals: 1 

Statistical analyses: Poisson 

regression 

Covariates: temperature, 

vapor pressure, barometric 

pressure, relative humidity, 

wind velocity, wind direction at 

maximal velocity 

Statistical package: NR 

SO2 24-h avg 

3.2 (2.4) ppb in fog

3.7 (1.9) ppb in fog 

free days 

Max SO2 24-h avg 

< 11 ppb 

Copollutants: 

NO NO2 

SPM (TSP) O3

The results reveal that ED visits by atopic subjects increased on low SO2

days. This observation is inconsistent with most air pollution epidemiology, as 

high levels of air pollutants have conventionally been linked with asthma 

exacerbation. 

Increment: 5 ppb 

Nonatopic: 

OR 1.18 (0.96, 1.46) 

Atopic: 

OR 0.78 (0.66, 0.93) 

Tsai et al. (2006) 

Kaohsiung, Taiwan 

Period of Study: 

1996-2003 

Days: 2922 

Hospital Admissions 

Outcome(s) (ICD 9): Asthma 

(493) 

Study design: 

Case-crossover 

N: 17,682 

Statistical analyses: 

conditional logistic regression 

Covariates: Temperature, 

humidity 

Season: 

Warm (≥ 25 °C); 

Cool (< 25 °C) 

Statistical package: SAS 

Lag: 0-2 days Cumulative 

SO2 24-h avg: 

9.49 ppb 

Range: 0.92, 

31.33 

25th: 6.37 

50th: 8.94 

75th: 12.16 

# of stations: 6 

Copollutants: 

PM10 NO2 O3 CO 

Positive associations were observed between air pollutants and hospital 

admissions for stroke. In single-pollutant models SO2 was not associated with 

either PIH or IS. The season did not affect these associations. SO2 was also 

not significant in 

2-pollutant models. 

Increment: 5.79 ppb (IQR) 

Seasonality 

Single-pollutant model: >25 °C 1.018 (0.956, 1.083) lag 0-2; 

< 25 °C 1.187 (1.073, 1.314) lag 0-2 

Dual-pollutant model: 

Adjusted for PM10: >25 °C 0.993 (0.932, 1.058) lag 0-2; 

< 25 °C 1.027 (0.921, 1.146) lag 0-2 

Adjusted for CO: >25 °C 0.910 (0.847, 0.978) lag 0-2; 

< 25 °C 1.036 (1.027, 1.046) lag 0-2 

Adjusted for NO2: >25 °C 0.967 (0.903, 1.035) lag 0-2; 

< 25 °C 0.735 (0.646, 0.835) lag 0-2 

Adjusted for O3: >25 °C 1.055 (0.990, 1.123) lag 0-2; 

< 25 °C 1.195 (1.080, 1.323) lag 0-2 

 F-56 
Study Methods Pollutant Data Findings 

Wong et al. (1999) 

Hong Kong, China 

Period of Study: 

1994-1995 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory admissions (460-

6, 471-8, 480-7, 490-6); 

Asthma (493), COPD (490-

496), Pneumonia (480-7) 

Age groups analyzed: 

0-4, 5-64, ≥ 65, all ages 

# of Hospitals: 12 

Study design:Time-series 

Statistical analyses: Poisson 

regression (followed APHEA 

protocol) 

Covariates: Trend, season, 

day of wk, holiday, 

temperature, humidity 

Statistical package: SAS 8.02 

Lag: days 0-3 cumulative 

Median 24-h avg 

SO2: 

17.05 μg/m3

Range: 2.74, 

68.49 

25th: 12.45 

75th: 25.01 

# of stations: 7 

Copollutants: 

O3 SO2 PM10 

Adverse respiratory effects of SO2 were noted at low concentrations. Results 

for respiratory outcomes were attributed to the elderly population. This was 

also true for the other pollutants. Therefore, it is difficult to be certain that the 

effects were due mainly to SO2. 

Pair-wise comparisons in multipollutant models showed significant 

interactions of PM2.5, NO2, and O3. 

Increment = 10 μg/m3 

Overall increase in admissions: 1.013 (1.004, 1.021) lag 0 

Respiratory relative risks (RR): 0-4 yrs: 1.005 (0.991, 1.018) lag 0; 

5-64 yrs: 1.008 (0.996, 1.021) lag 0; >65 yrs: 1.023 (1.012, 1.036) lag 0 

Asthma: 1.017 (0.998, 1.036) lag 0 

COPD: 1.023 (1.011, 1.035) lag 0 

Pneumonia: 0.990 (0.977, 1.004) lag 4 

Wong et al. (2001b) 

Hong Kong, China 

Period of Study: 

1993-1994 

Hospital Admissions 

Outcome(s) (ICD 9): Asthma 

(493) 

Age groups analyzed: ≤ 15 

N: 1,217 

# of Hospitals: 1 

Study design: Time-series 

Statistical analyses: Poisson 

regression (followed APHEA 

protocol) 

Covariates: Season, 

temperature, humidity 

Season: 

Summer (Jun-Aug), Autumn 

(Sep-Nov), 

Winter (Dec-Feb), 

Spring (Mar-May) 

Lag: 0, 1, 2, 3, 4, 5 days; and 

cumulative 0-2 and 0-3 days. 

24-h avg SO2 

Mean: 12.2 μg/m3

SD: 12.9 

Range: 0, 98 

μg/m3 

Autumn: 10.6 (9.6)

Winter: 10.0 (7.5) 

Spring: 9.6 (8.8) 

Summer: 18.5 

(19.5) 

# of stations: 9 

Copollutants: 

PM10 NO2

SO2 levels were found to be the highest during the summer. There were 

consistent and statistically significant associations between asthma 

admission and increased daily levels of SO2. No associations were noted in 

the spring or winter. No significant associations were found between hospital 

admissions and day of the wk, humidity, temperature or atmospheric 

pressure. 

Total admissions were limited to one hospital. 

Increment: 10 μg/m3 

Asthma: 

All yr: RR 1.06 p = 0.004 

Autumn: NR 

Winter: NR 

Spring: NR 

Summer: NR 

 F-57 
Study Methods Pollutant Data Findings 

Wong* et al. (2002a) 

London England and Hong 

Kong 

Period of Study: 

London: 

1992-1994 

Hong Kong: 

1995-1997 

Days: 1,096 

Hospital Admissions 

Outcome(s) (ICD 9): All 

respiratory admissions (460-

519); asthma (493) 

Age groups analyzed: 

 15-64, 65+, all ages 

Study design: Time-series 

Statistical analyses: APHEA 

protocol, Poisson regression 

with GAM 

Covariates: Long-term trend, 

season, influenza, day of wk, 

holiday, temperature, 

humidity, thunderstorms 

Season: 

Cool, Oct-Mar; 

Warm: Apr-Sep 

Dose-Response 

Investigated?: Yes 

Statistical package: S-Plus 

Lag: 0, 1, 2, 3, 4 days, 0-1 

cum. avg. 

24-h avg SO2

μg/m3 

Hong Kong: 

Mean: 17.7 

Warm: 18.3 

Cool: 17.2 

SD: 12.3 

Range: 1.1, 90.0 

10th: 6.2 

50th: 14.5 

90th: 32.8 

London: 

Mean: 23.7 

Warm: 22.2 

Cool: 25.3 

SD: 12.3 

Range: 6.2, 113.6

10th: 13.2 

50th: 20.6 

90th: 38.1 

Copollutants: 

Hong Kong : 

PM10 (r = 0.30) 

NO2 (r = 0.37) 

O3 (r = −0.18) 

London: 

PM10 (r = 0.64) 

NO2 (r = 0.71) 

O3 (r = −0.25) 

Similar non-statistically significant associations between asthma hospital 

admissions and SO2 were found in both cities. The association between 

respiratory hospital admissions and SO2 showed significance in the cold 

season in Hong Kong and on an all yr basis. Respiratory hospital admissions 

were not significantly associated with SO2 in Britain. 

In the 2-pollutant model the association between respiratory hospital 

admission and SO2 in London was insignificant, and remained insignificant 

after adjusted for the second pollutants. 

In Hong Kong, the positive association of SO2 was most affected by NO2, 

losing statistical significance. The positive association remained robust when 

adjusted for O3, and a slight decrease in association after adjusted for PM2.5. 

Increment: 10 μg/m3

Asthma, 15-64 yrs 

Hong Kong: ER −0.1 (−2.4, 2.2) lag 0-1; ER −1.5 (−3.4, 0.5) lag; 

Warm: ER 1.5 (−1.5, 4.6) lag 0-1; Cool: ER −2.0 (−5.4, 1.4) lag 0-1 

London: ER 0.7 (−1.0, 2.5) lag 0-1; ER 2.1 (0.7, 3.6) lag 3 

Warm: ER −1.4 (−4.7, 1.9) lag 0-1; Cool: ER 1.6 (−0.5, 3.8) lag 0-1 

Respiratory 65+ yrs 

Hong Kong: ER 1.8 (0.9, 2.6) lag 0-1; ER 1.7 (1.0, 2.4) lag 0 

Warm: ER 1.1 (0.0, 2.2) lag 0-1; Cool: ER 2.7 (1.4, 4.0) lag 0-1 

+O3 ER 1.9 (1.1, 2.8) lag 0-1; +PM2.5 ER 1.2 (0.3, 2.2) lag 0-1; 

+NO2 ER 0.3 (−0.7, 1.4) lag 0-1 

London: ER 0.2 (−0.6, 1.1) lag 0-1; ER 1.2 (0.5, 2.0) lag 3; 

Warm: ER 1.3 (−0.5, 3.1) lag 0-1; Cool: ER −0.3 (−1.3, 0.8) lag 0-1 

+O3 ER 0.5 (−0.4, 1.5) lag 0-1; +PM2.5 ER 1.2 (0.3, 2.2) lag 0-1 

+NO2 ER 0.5 (−0.7, 1.7)lag 0-1 

Yang and Chen (2007) 

Taipei, Taiwan 

Period of Study: 

1996-2003 

Hospital Admissions 

Outcome(s) (ICD9): COPD 

(490-492, 494, and 496) 

identified by records from the 

National Health Insurance 

(NHI) program 

Study design: Case-crossover

Statistical Analysis: 

Conditional logistic regression

Statistical package: SAS 

Age groups analyzed: All 

ages 

Covariates: Adjustments for 

weather variables, day of the 

wk, seasonality, and long-term 

time trends 

N: 46,491 

# Hospitals: 47 

Lag: Cumulative lag up to 2 

days 

24-h avg (ppb): 

4.33 

Range: 0.15, 

17.82 

25th: 2.67 

50th: 3.90 

75th: 5.46 

Copollutants: 

PM10 NO2 CO O3

In single-pollutant models, all pollutants, except SO2, significantly associated 

with COPD hospital admissions on warm days (≥20 °C). On cold days (< 20 

°C), only SO2 was significantly associated with COPD hospital admissions. In 

multi-pollutant models, NO2 and O3 were significantly associated with each 

pollutant on warm days. 

Odds Ratio (95% CI), 

Single-pollutant model (per 2.79 ppb SO2): ≥ 20 °C: 1.006 (0.970, 1.043); 

< 20 °C: 1.071 (1.015, 1.129) 

Odds Ratio (95% CI), 

Co-pollutant model (per 2.79 ppb): 

≥ 20 °C: 

SO2 + PM10: 0.909 (0.872, 0.949) 

SO2 + NO2: 0.835(0.798, 0.873) 

SO2 + CO: 0.920(0.884, 0.958) 

SO2 + O3: 0.978(0.943, 1.015) 

< 20 °C: 

SO2 + PM10: 1.067 (0.997, 1.141) 

SO2 + NO2:1.147 (1.072, 1.227) 

SO2 + CO: 1.140 (1.066, 1.219) 

SO2 + O3: 1.064 (1.009, 1.123) 

*Default GAM 

APHEA: Air Pollution and Health: a European Approach 

 F-58 
Table F-3. Short-term exposure to SO2 and cardiovascular morbidity in field/panel studies. 

Study Methods Pollutant Data Findings 

UNITED STATES 

Dockery et al. 

(2005) 

Boston, MA 

Period of Study: 

Jul 1995-Jul 2002 

Cohort study of 203 cardiac patients with 

implanted cardioverter defibrillators. 

Patients were followed for an avg of 3.1 yrs 

from 1995-2002 to assess the role of air 

pollution on the incidence of ventricular 

arrhythmias. The association of arrhythmic 

episode-days and air pollutions analyzed 

with logistic regression using GEE with 

random effects. Model adjusted for patient, 

season, min temperature, mean humidity, 

day of the wk, and previous arrhythmia 

within 3 days. Only effects of 2-day running 

mean of air pollution concentration reported.

48-h avg SO2; 

Median: 4.9 ppb 

25th%: 3.3 ppb 

75%: 7.4 ppb 

95%: 12.8 ppb 

Copollutants: 

PM2.5 BC SO4

2- 

PN NO2 CO O3

No statistically significant association between any of the air pollutant and 

ventricular arrhythmias when all events were considered. However, 

ventricular arrhythmias within 3 days of a prior event were statistically 

significant with SO2, PM2.5, BC, NO2, CO, and marginally with SO4

2- , but 

not with O3 or PN. CO, NO2, BC, and PM2.5 correlated, thus it was 

impossible to differentiate the independent effects. Since the increased 

risk of ventricular tachyarrhythmia was associated with air pollution 

observed among patients with a recent tachyarrhythmia, it was suggested 

that air pollution acts in combination with cardiac electrical instability to 

increase risk of arrhythmia. 

For IQR (4.0 ppb) increase in 48-h mean SO2 

All events: OR = 1.04 (0.94, 1.14), p = 0.28 

Prior arrhythmia event: < 3 Days: 1.30 (95% CI: 1.06, 1.61), p = 0.013 

Prior arrhythmia event: >3 Days: 0.98 (0.87, 1.11) p = 0.78 

Gold et al. (2000) 

Boston, MA 

Period of Study: 

June-Sep 1997 

Panel study on 21 active Boston residents 

aged 53-87 yrs to investigate the 

association between short-term changes in 

ambient air pollution and short-term 

changes in cardiovascular function. 

Participants observed up to 12 times from 

June to Sep 1997 (163 observations made 

in total). Protocol involved 25 mins per wk of 

continuous ECG monitoring, that included 

5 mins of rest, 5 mins of standing, 5 mins of 

exercise outdoors, 5 mins of recovery, and 

20 cycles of slow breathing. Fixed effects 

models adjusted for time-varying covariates 

and individuals traits. 

24-h avg 3.2 ppb

Range: 0, 12.6 

ppb 

IQR: 3.0 ppb 

Copollutants: 

PM2.5 PM10-2.5 

O3 NO2 CO 

In single-pollutant models, 24-h mean SO2 associated with reduced heart 

rate in the first rest period but not overall. Associations weaker for shorter 

averaging periods. Association between SO2 and heart rate not significant 

with the multipollutant model (SO2 and PM2.5). SO2 not associated with 

r-MSSD. 

Heart rate, first rest period, mean 66.3 bpm 

Single-pollutant model: Estimated effect (SE) −1.0 (0.5); 

% mean 1.5, p = 0.03 

Heart rate, first rest period, mean 66.3 bpm 

Multipollutant model (PM2.5 and SO2): 

SO2 estimated effect (SE) −0.8 (0.5); % mean 1.2, p = 0.09 

PM2.5 estimated effect (SE) −1.6 (0.7); % mean 2.5, p = 0.03 

Overall heart rate, mean 74.9 bpm 

Single-pollutant model: Estimated effect (SE) −0.5 (0.5), p = 0.30 

Multipollutant model: SO2 estimated effect (SE) −0.2 (0.5), p = 0.6 PM2.5;

Estimated effect (SE) −1.9 (0.7), p = 0.01% mean 2.6 

Liao et al. (2004) 

Three locations in 

U.S.: Forsyth 

County, NC; 

Jackson, MS; 

Minneapolis, MN 

Period of Study: 

1996-1998 

Cross-sectional study of 6,784 cohort 

members of the Atherosclerosis Risk in 

Communities Study. Participants were 45-64 

yrs of age; baseline clinical examinations 

conducted from 1987-1989. HRV data 

collected from 1996-1998. Air pollutants 

obtained form EPA AIRS for this same 

period. Resting, supine, 5-min beat-to-beat 

RR interval data were collected over a 4-h 

period. Multivariable linear regression 

models used to assess associations 

between pollutants measured 1-3 days prior 

to HRV measurements. Models controlled 

for age, ethnicity-center, sex, education, 

current smoking, BMI, heart rate, use of 

cardiovascular medication, hypertension, 

prevalent coronary heart disease, and 

diabetes. 

Mean (SD) SO2

measured 1 day 

prior to HRV 

measurement 

was 4 (4) ppb 

Copollutants: 

PM10 O3 

CO NO2

Significant interaction between SO2 and prevalence of coronary heart 

disease for low-frequency power analyses. SO2 inversely associated with 

SD of normal R-R intervals and low-frequency power and positively 

associated with heart rate. SO2 association with low-frequency power 

stronger among those with history of coronary heart disease. Effect size 

of PM10 larger than for gaseous pollutants. 

Log-transformed low-frequency power effect estimate and SE per 1 SD 

increment (4 ppb) SO2 lag 1 day: 

Log transformed high-frequency power −0.024 (SE 0.016) 

Standard deviation of normal R-R intervals −0.532 (SE 0.270), p < 0.05 

Heart rate: 0.295 (SE 0.130), p < 0.05 

Prevalent CHD: −0.122 (SE 0.056), p < 0.01 

 No prevalent CHD −0.012 (SE 0.016) 

 F-59 
Study Methods Pollutant Data Findings 

Liao et al. (2005) 

United States 

Period of Study: 

1996-1998 

Cross-sectional survey 10,208 participants 

(avg age 54 yrs) from Atherosclerosis Risk 

in Communities (ARIC) study cohort to 

assess the association between criteria air 

pollutants and hemostatic and inflammatory 

markers. 57% of participants were female 

and 66% male. Used hemostatis/ inflammation variables collected during the baseline 

examination and air pollution data 1-3 days 

prior to the event. Used multiple linear 

regression models that controlled for age, 

sex, ethnicity-center, education, smoking, 

drinking status, BMI, history of chronic 

respiratory disease, humidity, seasons, 

cloud cover, and temperature. Also history 

of CVD and diabetes if not effect modifier in 

a particular model. 

SO2 mean (SD) 

0.0005 (0.004) 

ppm 

Q1-3: 0.005 

(0.003) ppm 

Q4: 0.006 

(0.005) ppm 

Copollutants: 

PM10 CO NO2 O3

Significant curvilinear association between SO2 witfactor VIII-C, WBC, 

and serum albumin. Curvilinear association indicated threshold effect 

Results shown in graph. 

Luttmann-Gibson 

et al. (2006) 

Steubenville, OH 

Period of Study: 

2000 

Conducted a panel study during the summer 

and fall of 2000, which consisted of 32 

subjects 54-90 yrs old living in Steubenville, 

OH. Used linear mixed models, fixed effects 

of pollution, age, gender, race, obesity, 

season, time of day, apparent temperature, 

and a first order autoregressive process for 

within-subject residuals to examine the 

relation between air pollution and logtransformed HRV parameters and heart 

rate. 

24-h avg (ppb): 

4.1 

Copollutants: 

PM2.5 SO4

2−

EC NO2 

O3

Increasing concentrations of PM2.5 and SO4

2- in the previous day were 

both found to be associated with reduced HRV. No association was 

observed between increasing SO2 concentrations in the previous day and 

HRV. 

% Change (95% CI) (per 4.3 ppb SO2) 

Standard Deviation of Normal RR Intervals: (SSDN) 0.7 (−1.0, 2.5) 

Differences Between Adjacent RR Intervals: (r-MSSD) 0.5 (−2.8, 4.0) 

High-Frequency Power (HF) 1.7 (−4.9, 8.7); 

Low-Frequency Power (LF) 4.9 (−1.4, 11.5); 

Heart Rate (HR) 0.3 (−0.2, 0.8) 

Metzger et al. 

(2007) 

Atlanta, GA 

Period of Study: 

1993-2002 

Collected information on 518 patients (6287 

event-days) for ventricular tachyarrhythemic 

events over 10-yr period. Used GEE 

analysis, a case-crossover analysis, and a 

sensitivity analysis stratified on subject 

15.5 ppb (±16.4) 

Copollutants: 

PM10 O3

NO2 CO PM2.5

Little evidence of associations between ambient air quality measurements 

and ventricular tachyarrhythmic events. 

Odds ratio (95% CI) 

All events: 1.002 (0.968-1.037) 

Events resulting in cardiac pacing or defibrillation: 0.988 (0.936-1.042) 

Events resulting in defibrillation: 1.004 (0.911-1.105) 

Primary GEE model: 1.002 (0.968-1.037) 

Controlling for min temperature: 1.010 (0.976-1.046) 

Using an unconstrained distributed Lag: 0.996 (0.952-1.083) 

Warm Season: 1.029 (0.989-1.116) 

Cold Season: 0.986 (0.956-1.023) 

Park et al. (2005b) 

Greater Boston 

area, MA 

Period of Study: 

Nov 2000-Oct 

2003 

Cross-sectional study of effect of ambient 

air pollutants on heart rate variability (HRV) 

in 497 men who were in the Normative 

Aging Study and examined from Nov 2000 

and Oct 2003. HRV measured between 

0600 and 1300 h after resting for 5 mins. 4-

h, 24-h, and 48-h moving avgs of air pollution matched to time of ECG measurement. 

Linear regression models included: age, 

BMI, fasting blood glucose, cigarette 

smoking, use of cardiac medications, room 

temp, season, and the lagged moving avg of 

apparent temp corresponding to the moving 

avg period for the air pollutant. Mean arterial 

blood pressure (MAP) and apparent 

temperature also included. Assessed 

modifying effects of hypertension, IHD, 

diabetes or use of cardiac/antihypertensive 

meds. 

24-h avg SO2

4.9 ppb 

SD: 3.4 

Range: 0.95, 

24.7 ppb 

Copollutants: 

PM2.5 PNC 

BC NO2 

O3 CO 

No significant association between HRV and SO2 for any of the averaging 

periods, but positive relationship. 

4-h moving avg SO2: (per 1 SD, 3.4 ppb SO2) 

Log10 SDNN: 2.3 (−1.7, 6.4) 

Log10 HF:5.6 (−4.9, 17.3) 

Log10 LF: 2.2 (−5.9, 11.1) 

Log10 (LF:HF): −3.2 (−10.1, 4.2) 

 F-60 
Study Methods Pollutant Data Findings 

Peters et al. 

(2000a) 

Eastern Massachusetts, U.S. 

Period of Study: 

1995-1997 

Pilot study to test hypothesis that patients 

with implanted cardioverter defibrillators 

would experience potentially life-threatening 

arrhythmias associated with air pollution 

episodes. Records detected arrhythmias 

and therapeutic interventions downloaded 

from the implanted defibrillator. Mean age of 

patients 62.2 yrs. 100 patients followed for 

over 3 yrs for 63,628 person-days. 33 

patients with any discharges and 6 patients 

with 10 or more events. Data analyzed by 

logistic regression models using fixed 

effects models with individual intercepts for 

each patient. Model controlled for trend, 

season, meteorologic conditions, and day of 

week. Evaluated air pollutants on same day, 

lags 1, 2, and 3 days, and 5-day mean. 

24-h avg SO2: 

7 ppb 

Median: 5 ppb 

Max: 87 ppb 

Copollutants: 

PM10 PM2.5 

BC CO 

O3 NO2

No association between increased defibrillator discharges and SO2. 

33 patients with at least 1 defibrillator discharge 

Odds Ratio (95% CI) 

Lag 0 0.76 (0.48, 1.21); Lag 1 0.91 (0.60, 1.37) 

Lag 2 0.89 (0.59, 1.34); Lag 3 1.09 (0.78, 1.52) 

5-day mean 0.85 (0.50, 1.43) 

6 patients with at least 10 discharges 

Odds Ratio (95% CI) 

Lag 0 0.72 (0.40, 1.31); Lag 1 0.77 (0.44, 1.37) 

Lag 2 1.01 (0.63, 1.61); Lag 3 1.08 (0.72, 1.62) 

5-day mean 0.75 (0.38, 1.47) 

Peters et al. (2001) 

Greater Boston 

area, MA 

Period of Study: 

Jan 1995-May 

1996 

Case cross over Study design used to 

investigate association between air pollution 

and risk of acute myocardial infarctions in 

772 patients (mean age 61.6 yrs) with MI as 

part of the determinants of myocardial 

infarction onset study. For each subject, one 

case period was matched to 3 control 

periods, 24 h apart. Used conditional logistic 

regression models that controlled for 

season, day of wk, temperature, and relative 

humidity. 

24-h avg SO2: 

7 ppb 

SD: 7 ppb 

1-h avg SO2: 

7 ppb 

SD: 10 ppb 

Copollutants: 

PM2.5, PM10, 

PM10-2.5 , BC 

O3, CO, NO2

SO2 not statistically associated with risk of onset of MI. Limitation of study 

is only 1 air pollution monitoring site available. 

OR for 2-h avg SO2 and 24-h avg SO2 estimated jointly: 

2 h per 2 ppb increase SO2

Unadjusted: 1.00 (0.87, 1.14) 

Adjusted: 0.96 (0.83, 1.12) 

24 h per 2 ppb increase 

Unadjusted: 0.92 (0.71, 1.20) 

Adjusted: 0.91 (0.67, 1.23) 

Rich et al. (2005) 

Boston, MA 

Period of Study: 

Jul 1995-Jul 2002 

Case cross-over design used to evaluate 

association between ventricular arrhythmias 

detected by implantable cardioverter 

defibrillators and air pollution. Same study 

population as Dockery et al. (2005): 203 

patients with ICD and residential zip codes 

within 40 km of central particle monitoring 

site. Analyses conducted on 84 subjects 

with confirmed ventricular arrhythmias 

during the follow-up. Case periods defined 

by time of each confirmed arrhythmic event. 

Control periods (3-4 per case) selected by 

matching on weekday and hour of the day 

within the same calendar mo. Used 

conditional logistic regression that controlled 

for temperature, dew point, barometric 

pressure, and a frailty term for each subject. 

ORs presented for IQR increase in mean 

concentration and averaging time. Moving 

avg of concentrations considered: lags 0-2, 

0-6, 0-23, and 0-47 h. 

1-h avg SO2: 

Median: 4.3 ppb 

25th %: 2.6 

75th %: 7.5 

Max: 71.6 

24-h avg SO2: 

Median: 4.8 

25th %: 3.2 

75th %: 7.3 

Max: 31.4 

Copollutants: 

PM2.5 BC 

NO2 CO O3

An IQR increase in the 24-h moving avg SO2 (4.1 ppb) marginally 

associated with a 9% increased risk of ventricular arrhythmia and an 

increased risk with 48-h moving avg. There was no risk associated with 

24-h moving avg after controlling for PM2.5 cases that had a prior 

ventricular arrhythmia within 72-h had greater risk associated with SO2

compared to those without a recent event, suggesting that risk is greater 

among cases with more irritable or unstable myocardium. 

Odds ratios 

Single-pollutant model: 0-2-h lag (per 4.7 ppb) 1.07 (0.97, 1.18); 

0-6-h lag (per 4.5 ppb) 1.09 (0.98, 1.20); 

0-23-h lag (per 4.1 ppb) 1.09 (0.97, 1.22); 

0-47-h lag (per 4.0 ppb) 1.17 (1.02, 1.34) 

2-pollutant model: SO2 and PM2.5 per 4.1 ppb SO2: 1.00 (0.84, 1.20); 

SO2 and O3 per 4.1 ppb SO2: 1.12 (0.99, 1.27) 

Per 4.1 ppb increase SO2 

Prior arrhythmia event < 3 Days: 1.20 (1.01, 1.44) 

Prior arrhythmia event >3 Days: 0.96 (0.83, 1.10) 

Rich et al. (2006b) 

Boston, MA 

Period of Study: 

June 1995-

December 2002 

Case-crossover study consisting of 203 

individuals with implantable cardioverter 

defibrillators (ICDs) implanted between Jun 

1995 and Dec 1999. Used conditional 

logistic regression, which included variables 

for mean pollutant concentration in the hour 

of the arrhythmia, and natural splines for 

mean temperature, dew point, and 

barometric pressure in the 24 h before the 

arrhythmia. The regression analyses were 

run for each pollutant individually to 

examine the association between increasing 

pollutant levels and paroxysmal atrial 

fibrillation episodes (PAF). 

Max 24-h avg 

(ppb): 31.4 

Max 1-h max 

(ppb): 71.6 

Copollutants: 

PM2.5 BC 

NO2 CO O3 

O3 was significantly associated with PAF in the hour preceding the 

arrhythmia, but the effect was not significant when analyzing the 

preceding 24-h. Increasing levels of PM2.5, NO2, and BC resulted in nonsignificant positive associations with PAF. SO2 was not associated with 

PAF. 

Odds Ratio (per 4.9 ppb SO2): 1.02 (0.81, 1.28) lag 0 h 

Odds Ratio (per 4.1 ppb SO2): 0.99 (0.71, 1.39) lag 0-23-h 

 F-61 
Study Methods Pollutant Data Findings 

Rich et al. (2006a) 

St. Louis, Missouri 

Period of Study: 

5/9/2001-

12/31/2002 

Case-crossover design study of 56 patients 

with implantable cardioverter defibrillators. 

Subjects ranged from 20 to 88 yrs (mean 

63). Case period defined by time of 

confirmed ventricular arrhythmia. Control 

periods matched on weekday and hour of 

the day within the same calendar mo. Used 

conditional logistic regression model that 

included mean of the previous 24-h 

temperature, relative humidity, barometric 

pressure, mean pollutant concentration in 

the 24 h before the arrhythmia. Model also 

included a frailty term for each subject. 

599 days 

25th %: 2 ppb 

50th %: 4 ppb 

75th %: 7 ppb 

Daily IQR: 5 ppb 

Case/control IQR: 

5 ppb 

Copollutants: 

PM2.5, EC, OC, 

NO2 , CO, O3

Statistically significant increase in risk of ventricular arrhythmias 

associated with each 5 ppm increase in 24-h moving avg SO2. 

OR for ventricular arrhythmia associated with IQR increase 

6-h moving avg SO2 per 4 ppb: 1.04 (95% CI: 0.96, 1.12) 

12-h moving avg SO2 per 5 ppb: 1.17 (95% CI: 1.04, 1.30) 

24-h moving avg SO2 per 5 ppb: 1.24 (95% CI: 1.07, 1.44) 

48-h moving avg SO2 per 4 ppb: 1.15 (95% CI: 1.00, 1.34) 

Sarnat et al. (2006) 

Steubenville, OH 

Period of Study: 

(June 4-Aug 18, 

Sep 25-Dec 15) 

2000 

Panel study consisting of 32, non-smoking 

older adults approximately 53-90 yrs old. 

Electrocardiograms (ECGs) and 

questionnaires regarding symptoms were 

administered on a weekly basis. Used a 

logistic mixed effects regression to examine 

the association between increasing air 

pollutant concentrations and 

supraventricular ectopy (SVE) and 

ventricular ectopy (VE). 

1-day avg SO2 

24-h avg (ppb): 

10.4 (8.3) 

Range: 1.8, 58.3 

5-day moving avg

24-h avg (ppb): 

10.7 (5.5) 

Range: 2.4, 31.3 

Copollutants: 

PM2.5, EC, O3, 

NO2, SO4

2−, CO 

PM2.5 was significantly associated with SVE, whereas, SO4

2− and O3 were 

marginally associated in models including 5-day moving avg pollutant 

concentrations. However, no pollutants were found to be associated with 

VE in similar models. Overall, subjects that reported previous 

cardiovascular conditions (e.g., myocardial infarction and hypertension) 

were found to be more susceptible to SVE due to increasing air pollutant 

concentrations. 

Odds Ratio (per 5.4 ppb SO2) 

5-day moving avg: 

SVE: 1.04 (0.78, 1.39) 

VE: 1.28 (0.85, 1.92) 

Schwartz et al. 

(2005) 

Boston, MA 

Period of Study: 12 

wks during the 

summer of 1999 

Panel study of 28 subjects (aged 61-89 yrs) 

to examine association between 

summertime air pollution and HRV. Subjects 

examined once a wk up to 12 wks and HRV 

measured for approximately 30 mins. 

Analyses used hierarchical models that 

controlled for baseline medical condition, 

smoking history, day of wk and hour of day, 

indicator variable for whether subjects had 

taken their medication before they came, 

temperature and time trend. 

24-h avg SO2:

25th %: 

0.017 ppm 

50th %: 

0.020 ppm 

75th %: 0.54 ppm 

Copollutants: 

O3 NO2 

CO PM2.5 BC 

No significant association with SO2

Percentage change in HRV associated with IQR (0.523 ppm) increase in 

SO2

1-h avg SO2: SDNN (ms) 0.4 (−1.3 to 2.1); RMSSD (ms) 1.4 (−2.6 to 5.5);

PNN50 (ms) 3.8 (−12.1 to 22.5) 

24-h avg SO2: SDNN (ms) 0.4 (−4.2 to 5.1); 

RMSSD (ms) −0.3 (−1.3 to 0.8); PNN50 (%) −0.2 (20.9 to 17.6); 

LFHFR 2.9 (−4.9 to 11.4) 

Sullivan et al. 

(2005) 

King County, 

Washington 

Period of Study: 

1988-1994 

Case-crossover study of 5,793 confirmed 

cases of acute MI. Data was analyzed using 

simple descriptive analyses and Pearson’s 

correlation coefficient. 

9 ppb 

Range: 0-38 ppb 

Copollutants: 

PM2.5 PM10 CO 

Increases in SO2 were not associated with MI after adjusting for relative 

humidity and temperature 

Odds Ratio (95% CI) (per 10 ppb SO2) 

Averaging time: 1-h: 0.97 (0.94, 1.01); 2 h: 0.98 (0.95, 1.01); 

4 h: 0.99 (0.96, 1.03); 24 h: 1.0 (0.95, 1.06) 

Wheeler et al. 

(2006) 

Atlanta, Georgia 

Period of Study: 

19992000 

30 individuals with myocardial infarction or 

COPD were administered a questionnaire 

and an HRV protocol. Linear mixed-effect 

models were used to analyze the data. 

Mean: 1.9 ppb 

Copollutants: 

PM2.5, EC, O3

CO, NO2

No association with SO2 

CANADA 

Rich et al. (2004) 

Vancouver, British 

Columbia, Canada 

Period of Study: 

Feb-Dec 2000 

Case-crossover analysis used to investigate 

association between air pollution and 

cardiac arrhythmia in 34 patients (aged 15-

85 yrs, mean 62) with implantable 

cardioverter defibrillators. Study included 

only patients who experienced at least 1 

ICD discharge during the study period. 

Control days were 7 days before and 7 days 

after day of ICD discharge. Conditional 

logistic regression analyses were stratified 

by individual. 

24-h avg: 2.6 ppb

SD: 1.3 ppb 

IQR: 1.6 ppb 

Copollutants: 

PM2.5, EC, OC 

SO4

2−, PM10, CO 

NO2, O3

No statistically significant association between SO2 and implantable 

cardioverter defibrillator discharges. However, when an analysis was 

stratified by season, OR for SO2 were higher in the summer compared to 

winter. 

No quantitative results provided. Results shown in graph. 

 F-62 
Study Methods Pollutant Data Findings 

Vedal et al. (2004) 

Vancouver, British 

Columbia, Canada 

Period of Study: 

1997-2000 

Retrospective, longitudinal panel study of 

50 patients, aged 12-77 yrs with implantable 

cardioverter defibrillators. Total of 

40,328 person-days over 4-yr period. GEE 

used to assess associations between short 

term increases in air pollutants and 

implantable cardioverter defibrillator 

discharges. Models controlled for temporal 

trends, meteorology, and serial 

autocorrelation. 

24-h mean (SD) 

SO2: 2.4 (1.2) ppb

Range: 0.3, 8.1 

ppb 

Median: 2.2 ppb 

25th: 1.5 

75th: 3.1 

Copollutants: 

PM10 O3 

NO2 CO 

Concluded that in general no consistent effect of air pollution on cardiac 

arrhythmias in this population. There were no statistically significant 

associations between SO2 and cardiac arrhythmias at any lag day, but 

positive associations at lag 2. When analysis was restricted to only 

patients who had at least 2 arrhythmias per yr over their period of 

observation (N: 16), a positive and significant association was seen with 

SO2 at 2 days lag. When analysis was restricted to patients averaging 3 

or more arrhythmias per yr (N: 13), there was no significant association, 

but a positive association was seen at 2 days lag. 

When stratified by season, SO2 effects were in the in the positive 

direction in the winter, but in the negative direction in the summer. 

Authors noted results may be due to chance because of multiple 

comparisons or SO2 may be surrogate for some other factor. 

Summer analysis: significant negative association with SO2 at lag days 2 

and 3 (data not shown). When stratified to patients with 2 or more 

arrhythmia event-days per yr, significant negative associations observed 

with SO2 at lag of 3 days. Winter analysis: significant positive effect of 

SO2 at 3 days lag (data not shown). If restricted to patients with at least 2 

arrhythmias per yr, a significant positive association was seen at lags 2 

and 3 days. When restricted to patients with 3 or more arrhythmia event 

days per yr, positive associations observed for SO2 at lags of 2 and 3 

days. 

No quantitative results, but % change in arrhythmia event-day rate for 

each SD increase in pollution concentration on log scale provided in 

figures. 

EUROPE 

Berger et al. 

(2000) 

Erfurt, Germany 

Period of Study: 

Oct 2000-Apr 2001 

Prospective panel study of 57 non-smoking 

men, of which 74% are ex-smokers, with 

coronary heart disease aged 52-76 yrs old. 

Subjects underwent 24-h electrocardiogram 

(ECG) recordings and analysis once every 4 

wks. Associations analyzed using Poisson 

and linear regression modeling, for 

supraventricular and ventricular tachycardia, 

respectively, adjusting for trend, weekday, 

and meteorologic data. 

24-h avg (SD) 

(μg/m3): 4.1 (1.8) 

Range: 3.0, 11.7 

Copollutants: 

Ultrafine Particles

Accumulation 

Mode Particles 

PM2.5 PM10

NO2 CO NO 

UFP, ACP, PM2.5, and NO2 associated with increased risk for 

supraventricular tachycardia and ventricular tachycardia at almost all 

lags. The majority of statistically significant associations was observed in 

the previous 24-71-h and with the 5-day moving avg. Associations were 

not observed for increasing concentrations of SO2. 

Relative Risk (per 1.5 μg/m3 SO2) 

Supraventricular Extrasystoles: 

0.92 (0.77, 1.09) lag 0. 0.98 (0.86, 1.12) lag 0-23-h 

1.04 (0.93, 1.16) lag 24-47 h. 1.14 (0.98, 1.34) lag 48-71-h 

0.95 (0.83, 1.09) lag 72-95-h. 1.01 (0.80, 1.27) lag 5-d avg 

Ventricular Extrasystoles: 

−2.1 (−6.1, 2.1) lag 0. −1.8 (−6.1, 2.7) lag 0 – 23-h 

−0.1 (−4.4, 4.4) lag 24 – 47 h. 4.5 (−0.4, 9.5) lag 48-71-h 

−2.2 (−6.4, 2.3) lag 72-95-h. −1.2 (−7.5, 5.5) lag 5-d avg 

Henrotin et al. 

(2007) 

Dijon, France 

Period of Study : 

1994-2004 

Bi-directional case-crossover design to 

examine association between air pollutant 

and ischaemic stroke onset 

(2,078 cases). 

Mean: 6.9 μg/m3

SD: 7.5 

Min: 0 

Max: 65 

Copollutants: 

SOX, O3, CO 

PM10 

SO2 not significantly associated with occurrence of strokes 

Odds Ratio (95% CI) 

Ischaemic stroke: 

D0: 0.978 (0.868, 1.103). D-1: 0.978 (0.863, 1.108) 

D-2: 1.015 (0.902, 1.143). D-3: 1.003 (0.892, 1.127) 

Hemorrhagic stroke: 

D0: 1.099 (0.815, 1.483). D-1: 1.014 (0.747, 1.376) 

D-2: 0.961 (0.712, 1.297). D-3: 0.954 (0.729, 1.248) 

 F-63 
Study Methods Pollutant Data Findings 

Ibald-Mulli et al. 

(2001) 

Augsburg, 

Germany 

Period of Study: 

1984-85, 1987-88 

Retrospective analysis of 2,607 subjects 

(25-64 yrs, subset of the participants of first 

and second MONICA survey who had valid 

electrocardiograms recordings in both 

surveys and blood pressure 

measurements). Used regression models 

for repeated measures that controlled for 

age, current smoking, and cardiovascular 

medication, BMI, total and high density 

lipoprotein cholesterol, temp, RH, and 

barometric pressure. 

24-h avg SO2

(μg/m3) 

1984-1985: 

Mean: 60.2 

SD: 47.4 

Range: 13.0, 

238.2 

follow up 1987-

1988 

Mean: 23.8 

SD: 12.3 

Range: 5.6, 71.1 

Copollutants: 

TSP, CO 

SO2 and TSP associated with increases in systolic blood pressure. In the 

multipollutant model with TSP, the effect of TPS remained significant, but 

the SO2 effect was substantially reduced. No clear association between 

SO2 and CO and diastolic blood pressure was observed. 

Same day concentrations: mean change in systolic blood pressure per 

5th to 95th percentile increase in SO2 (per 80 μg/m3) 

Same day concentrations (per 80 μg/m3): 

Men (N: 1339): 0.96 (0.07, 1.85); Women (N: 1268): 0.96 (−0.46, 1.49); 

Men and women: 0.74 (0.08, 1.40) 

5-day avgs; mean change in systolic blood pressure per 5th to 95th 

percentile increase in SO2 (per 75 μg/m3): Men: 0.97 (0.09, 1.85); 

Women: 1.23 (0.23, 2.22); Men and women: 1.07 (0.41, 1.73) 

2-pollutant model: Men and women: 0.23 (−0.50, 0.96) 

Peters et al. (1999) 

Augsburg, 

Germany 

Period of Study: 

Winter 1984-1985 

Winter 1987-1988 

Retrospective analysis on subsample of 

2,681 subjects (25-64 yrs) of the MONICA 

cohort who had valid electrocardiogram 

readings from both surveys and no acute 

infections. GEE for clusters used to assess 

association between heart rate and air 

pollution. Analyses adjusted for 

temperature, relative humidity, and air 

pressure. 

24-h avg SO2

(μg/m3) 

Winter 1984-85 

Outside episode: 

Mean: 48.1 

SD: 23.1 

Range: 13, 103 

Winter 1984-85 

During episode: 

Mean: 200.3 

SD: 26.6 

Range: 160, 238

Winter: 1987-88 

Mean: 23.6 

SD: 12.2 

Range: 6, 71 

Copollutants: 

CO, TSP 

Increases in SO2 concentrations associated with increases in heart rate 

Mean change in heart rate per 5th to 95th percentile SO2

Same day concentrations (per 80 μg/m3 SO2) 

Men: 1.02 (0.41, 1.63) 

Women: 1.07 (0.41, 1.73) 

Men and women: 1.04 (0.60, 1.49) 

5-day avg (per 75 μg/m3 SO2) 

Men: 1.29 (0.68, 1.90) 

Women: 1.26 (0.57, 1.95) 

Men and women: 1.28 (0.82, 1.74) 

Ruidavets et al. 

(2005) 

Toulouse, France 

Period of Study : 

1995-1997 

Cross-sectional survey of 863 randomly 

chosen adults (35-65 yrs) living in Toulouse 

(MONICA center) to examine the 

relationship between resting heart rate and 

air pollution. Resting heart rate was 

measured twice in a sitting position after a 

five minute rest. Used polytomous logistic 

regression models with quintiles of RHR. 

Final model controlled for sex, physical 

activity, systolic blood pressure, 

cardiovascular drug use, CRP, relative 

humidity, and season mos. 

Mean SO2: 13.3 

(7.5) μg/m3 

Range: 1.3, 

47.7 μg/m3

Copollutants: 

NO2, O3

Marginally significant association between SO2 and RHR in Q5 compared 

with Q1. No associations with SO2 at 1, 2, or 3 days lag. 

OR based on daily levels of SO2. 

OR for resting heart rate = 1.19 (95% CI: 1.02, 1.39) in 5th quintile (>75 

bpm) compared to first quintile (< 60 bpm) for 5 μg/m3 increase in SO2

same day 0 am-12 pm. 

OR for resting heart rate 1.14 (95% CI: 1.01 to 1.30) in 5th quintile (>75 

bpm) compared to first quintile (< 60 bpm) for 5μg/m3 increase in SO2

same day 12 am-12 pm 

Not-significant associations not listed 

LATIN AMERICA 

Holguín et al. 

(2003) 

Mexico City, 

Mexico 

Period of Study: 

Feb 8 to Apr 30, 

2000 

Panel study of 34 nursing home residents 

(60-96 yrs) to assess association between 

heart rate variability and air pollution. Heart 

rate variability measured every alternate day 

for 3 mos. Thirteen of the subjects had 

hypertension. Used GEE models that 

controlled for age and avg heart rate during 

HRV measurement. 

24-h avg SO2

(ppb) 

Mean: 24 

SD: 12 

Range: 6, 85 

Copollutants: 

Indoor PM2.5 

Outdoor PM2.5 

O3, NO2, CO 

SO2 not related to heart rate variability on the same day or lag 1 day 

Change in HRV per 10 ppb 

Beta Coefficient (95% CI) 

HRV-HF −0.003 (−0.035, 0.035) 

HRV-LF −0.004 (−0.004, 0.003) 

HRV-LF/HF 0.012 (−0.060, 0.082) 

 

 F-64 
Table F-4. Short-term exposure to SO2 and emergency department visits and hospital 

admissions for cardiovascular diseases. 

Study Methods Pollutant Data Findings 

UNITED STATES 

Gwynn et al. (2000) 

New York (Buffalo; 

Rochester) 

Period of Study: 

May 1988-Oct 1990 

Hospital Admissions 

Outcome(s) (ICD9): Respiratory (466, 480-486), 

Circulatory (401-405, 410-417), Total (minus 800) 

from Statewide Planning and Research 

Cooperative System (SPARCS) 

Study design: Time-series 

Statistical Analysis: Loess fits of temperature and 

relative humidity 

Age groups analyzed: all ages 

Covariates: adjustments for weather 

Lag(s): 0, 3 

24-h avg (ppb): 12.2 

Range: 1.63, 37.7 

Copollutants: 

H+ SO4

2− PM10 

Filled PM10 

O3 CO NO2 CoH 

β = 0.000245 (0.000917) 

t = 0.27 

Relative Risk (per 7 ppb SO2) 

1.002 

Koken et al. (2003) 

Denver, U.S. 

Period of Study: 

Jul and Aug, 

1993-1997 

N: 310 days 

Outcome(s) (ICD9): 

Acute MI 410.00-410.92; 

Atherosclerosis 14.00-414.05; 

Pulmonary Heart Failure 416.0-416.9; Dysrhythmia 

427.0-427.9; CHF 428.0. 

Discharge data from Agency for Healthcare 

Research and Quality (AHRQ) database. 

Age group analyzed: 65+ yrs 

Study population: 60,000 

Covariates: seasonal adjustment not needed. 

Adjustment for temperature, dew point temperature 

made. 

Study design: Time-series 

Statistical analysis: GLMs to analyze frequency of 

admissions as a function of exposure. GEEs to 

estimate parameters in Poisson regression models, 

adjusting for overdispersion. 

Lag(s): 0-4 day 

SO2 24-h avg (ppb) 

Mean (SD): 5.7 (2.94) 

Min: 0.4 

25th: 3.8 

50th: 5.3 

75th: 7.2 

Max: 18.9 

Copollutants: 

O3 (r = −0.10) 

CO (r = 0.21) 

PM10 (r = 0.36) 

NO2 (r = 0.46) 

Effects were reported as percent change in 

hospitalizations based on an increment of 3.4 ppb. 

Single-pollutant model 

Dysrhythmia 

8.9% (−0.34, 18.93) lag 0, adjusted for gender but 

not temperature 

SO2 was found to be associated with cardiac 

dysrhythmia but not other outcomes. No association 

was observed for PM or NO2 with the outcomes. 

Low et al. (2006) 

New York City, NY 

Period of 

Study:1995-2003 

N: 3,287 days 

Outcome(s) (ICD): Ischemic stroke 433-434; 

Undetermined stroke 436; monitored intake in 11-

hospitals (ER or clinic visits). Excluded stroke 

patients admitted for rehabilitation. 

Study design: Time-series 

Statistical Analysis: Autoregressive integrated 

moving avg (ARIMA) models 

Software package: SAS 

SO2 24-h avg (ppm) 

Mean (SD): 0.01098 

(0.009124) 

Min: 0 

25th: 0.005 

Median: 0.009 

75th: 0.014 

Max: 0.096 

Copollutants: : 

PM10 (0.042) 

NO2 (0.33) 

CO (0.303) 

Pollen (0.085) 

At the highest concentration of SO2 (96 ppb) in New 

York city over the study period the expected increase 

in strokes would be 0.857 visits on the day of the 

event. 

Each 1000 ppb (1 ppm) SO2 would produce an 

additional 8.878 visits (SE 4.471) 

(p = 0.0471) for stroke. 

 F-65 
Study Methods Pollutant Data Findings 

Metzger et al. 

(2004) 

Atlanta, GA 

Period of Study: 

Jan 1993-Aug 31 

2000 

N: 4 yrs 

Outcome(s): IHD 410-414; AMI 410; Dysrhythmias 

427; cardiac arrest 427.5; congestive heart failure 

428; peripheral and cerebrovascular disease 433-

437, 440, 

443-444, 451-453; atherosclerosis 440; stroke 436. 

ED visits from billing records. 

N: 4,407,535 visits, 37 CVD visits/days 

# Hospitals: 31 

Age groups analyzed: adults ≥ 19, elderly 56+ 

Statistical Analysis: Poisson regression, GLM. 

Sensitivity analyses using GEE and GAM (strict 

convergence criteria) 

Covariates: long-term trends, mean and dew point 

temp, relative humidity (cubic splines) 

Statistical Software: SAS 

Season: Warm (Apr 15-Oct 14), Cool (Oct 15-Apr 

14) 

Lag(s): 0-3 days 

SO2 1-h max (ppb) 

Median: 11.0 

10th-90th Range: 2.0 to 39 ppb 

Copollutants: 

PM10 (0.20) 

O3 (0.19) 

NO2 (0.34) 

CO (0.26) 

PM2.5 (0.17) 

PM10-2.5 (0.21) 

Ultrafine (0.24) 

Multipollutant models used. All 

models specified a priori. 

Results presented for RR of an incremental increase 

in SO2 of 20 ppb (a priori lag 3 day moving avg). 

All CVD: 1.007 (0.993, 1.022) 

Dysrhythmia: 1.001 (0.975, 1.028) 

CHF: 0.992 (0.961, 1.025) 

IHD: 1.007 (0.981, 1.033) 

PERI: 1.028 (0.999, 1.059) 

Finger wounds 1.007 (0.998, 1.026) 

Single day lag models presented graphically. 

No multipollutant models run for SO2 since 

association was not observed in single-pollutant 

models. 

Michaud et al. 

(2004) 

Hilo, Hawaii 

Period of Study: 

1997-2001 

N: 1385 days 

Outcome(s) (ICD9): 

Cardiac 410-414, 425-429, 

Emergency visits, primary diagnosis. 

Study design: Time-series 

Statistical Analysis: Exponential regression, 

autocorrelation assessed by regressing square root 

of number of ED visits on covariates (DurbinWatson statistic). Newey-West procedure also 

conducted for assessment of autocorrelation. 

Covariates: Temperature, humidity, interaction 

between SO2 and PM 

Lag(s): 1-3 days 

SO2 (all hourly measurements) 

(ppb) 

Mean (SD): 1.92 (12.2) 

Min: 0 

Max: 447 

Daily SO2 

(12am-6am) (ppb) 

Mean (SD): 1.97 (7.12) 

Min: 0 

Max: 108.5 

Copollutants: PM 

Effects were presented as relative risk based on an 

increment of 10 ppb and the 24-h avg SO2

concentration. 

Cardiac 

0.92 (0.85, 1.00) lag 3 

No associations of cardiac ER visits with vog (SO2-

acidic aerosols) observed. 

Moolgavkar (2000a) 

Cook County IL, 

Los Angeles 

County, CA, 

Maricopa County, 

AZ 

Period of Study: 

1987-1995 

Outcome(s) (ICD9): CVD 

390-429; Cerebrovascular disease 430-448. 

Hospital admissions from CA department of health 

database. 

Age groups analyzed: 20-64, 65+ yrs 

Study design: Time-series 

N: 118 CVD admissions/days 

# Hospitals: NR 

Statistical analysis: Poisson regression, GAM 

Covariates: adjustment for day of wk, long-term 

temporal trends, relative humidity, temperature 

Statistical package: SPLUS 

Lag: 0-5 days 

SO2 24-h avg (ppb) 

Cook County: 

Min: 0.5; Q1: 4 

Median: 6; Q3: 8 

Max: 36 

LA County: 

Min: 0; Q1: 1 

Median: 2; Q3: 4 

Max: 16 

Maricopa County: 

Min: 0; Q1: 0.5 

Median: 2; Q3: 4 

Max: 14 

Copollutants: 

PM10 (0.11, 0.42) 

PM2.5 (0.42) (LA only) 

CO (0.35, 0.78) 

NO2 (0.02, 0.74) 

O3 (−0.37, 0.01) 

Results reported for percent change in hospital 

admissions per 10 ppb increase in SO2. T statistic in 

parentheses. 

CVD, 65+: 

Cook County: 4.0 (6.1), lag 0 

3.1 (4.5), lag 0, 2-pollutant model (CO) 

1.0 (1.4), lag 0, 2-pollutant model (NO2) 

LA County: 14.4 (15.2), lag 0 

−2.5 (−1.6), lag 0, 2-pollutant model (CO) 

7.7 (5.7), lag 0, 2-pollutant model (NO2) 

Maricopa County: 7.4 (4.5), lag 0 

3.0 (1.8), lag 0, 2-pollutant model (CO) 

3.9 (1.5), lag 0, 2-pollutant model (SO2) 

Cerebrovascular Disease, 65+: 

Cook County: 3.1 (3.3) 

LA County: 6.5 (4.9) 

Lags 1-5 also presented. Effect size generally 

diminished with increasing lag time. Increase in 

hospital admissions (10.3 for CVD and 9.0 for 

cerebrovascular) also observed for the 

20-64 age group. 

 F-66 
Study Methods Pollutant Data Findings 

Moolgavkar (2003b) 

Cook County IL, 

Los Angeles 

County, CA, 

Maricopa County, 

AZ 

Period of Study: 

1987-1995 

Outcome(s) (ICD9): CVD 

390-429; Cerebrovascular disease 430-448 was not 

considered in the reanalysis. Hospital admissions 

from CA department of health database. 

Age groups analyzed: 20-64, 65+ yrs 

Study design: Time-series 

N: 118 CVD admissions/day 

# Hospitals: NR 

Statistical analysis: Poisson regression, GAM with 

strict convergence criteria (10-8), GLM using 

natural splines 

Covariates: adjustment for day of wk, long-term 

temporal trends, relative humidity, temperature 

Statistical package: SPLUS 

Lag: 0-5 days 

See original analysis 

(Moolgavkar, 2000) above. 

Use of stringent criteria in GAM did not alter results 

substantially. However, increased smoothing of 

temporal trends attenuated results for all gases and 

effect size diminished with increasing lag. 

Results reported for incremental increase of 10 ppb 

SO2. Estimated coefficient and T statistic in 

parentheses. 

GLM with 100 df (LA County) 

13.67 (11.82), lag 0 

6.44 (5.23), lag 1 

0.23 (0.18), lag 2 

Morris et al. (1995) 

U.S. (Chicago, 

Detroit, Houston, 

LA, Milwaukee, 

NYC, Philadelphia) 

Period of Study: 

1986-1989 

N: 4 yrs 

Outcome(s) (ICD9): CHF 428. Daily Medicare 

hospital admission records. 

Study design: Time-series 

Statistical analyses: GLM, negative binomial 

distribution 

Age groups analyzed: ≥ 65 yrs 

Covariates: temperature, indicator variables for mo 

to adjust for weather effects and seasonal trends, 

day of wk, yr 

Statistical software: S-PLUS 

Lag(s): 0-7 days 

SO2 1-h max (ppm) 

Mean (SD): 

LA: 0.010 (0.005) 

Chicago: 0.025 (0.011) 

Philadelphia: 0.029 (0.015) 

New York: 0.032 (0.015) 

Detroit: 0.025 (0.013) 

Houston: 0.018 (0.009) 

Milwaukee: 0.017 (0.013) 

Copollutants: 

NO2 O3 CO 

Correlations of SO2 with other 

pollutants strong. 

Multipollutant models run. 

Results reported for RR of admission for CHF 

associated with an incremental increase in SO2 of 

0.05 ppm. 

CHF: 

LA: 1.60 (1.41, 1.82) 

Chicago: 1.05 (1.00, 1.10) 

Philadelphia: 1.01 (0.96, 1.06) 

New York: 1.04 (1.01, 1.08) 

Detroit: 1.00 (0.95, 1.06) 

Houston: 1.07 (0.97, 1.17) 

Milwaukee: 1.07 (0.99, 1.15) 

RR diminished in multipollutant (4 copollutants) 

models for all cities. 

Peel et al. (2007) 

Atlanta, GA 

Period of Study: 

Jan 1993-Aug 2000 

Outcome(s) (ICD9): IHD 410-414; dysrhythmia 427; 

CHF 428; peripheral vascular and cerebrovascular 

disease 

433-437, 440, 443, 444, 451-453. Computerized 

billing records for ED visits. 

Comorbid conditions: Hypertension 

401-405; diabetes 250; dysrhythmia 427, CHF 428; 

atherosclerosis 440; COPD 491, 492, 496; 

pneumonia 480-486; upper respiratory infection 

460-465, 466.0; asthma 493, 786.09. 

# Hospitals: 31 

N: 4,407,535 visits 

Study design: case-crossover and time-series. CVD 

outcomes among susceptible groups with comorbid 

conditions. 

Statistical analyses: Conditional logistic regression 

and Poisson GLM. 

Covariates: cubic splines for temperature and 

humidity included in models. Time independent 

variables controlled through design. 

Statistical Software: SAS 

Lag(s): 3 day avg, lagged 0-2 day 

SO2 1-h max (ppb) 

Mean (SD): 16.5 (17.1) 

10th: 2 

90th: 39 

Copollutants: 

PM10 O3 NO2 CO 

Results expressed as OR for association of CVD 

admissions with a 20 ppb incremental increase in 

SO2. 

Case-Crossover: 

All CVD: 1.009 (0.995,1.024), 0-2 avg 

IHD: 1.013 (0.988, 1.039), 0-2 avg 

Dysrhythmia: 1.003 (0.975, 1.031), 0-2 avg 

Peripheral and Cerebrovascular: 1.024 (0.993, 

1.055), 0-2 avg 

CHF: 0.993 (0.961, 1.026), 0-2 avg 

Time-series: 

Odds Ratio (95% CI) (per 20 ppb SO2) 

All cardiovascular disease: 1.007 (0.993, 1.022) 

Ischemic heart disease: 1.007 (0.981, 1.003) 

Dysrhythmia: 1.001 (0.975, 1.028) 

Peripheral and cerebrovascular disease: 1.028 

(0.999, 1.059) 

Congestive heart failure:0.992 (0.961, 1.025) 

Effect modification by comorbid conditions was not 

observed. 

 F-67 
Study Methods Pollutant Data Findings 

Schwartz and 

Morris (1995) 

Detroit, MI 

Period of Study: 

1986-1989 

Outcome(s) (ICD9): IHD 410-414; CHF 428; 

Dysrhythmia 427. Medicare data, diagnosis at 

discharge. 

Study design: Time-series 

Statistical analysis: Poisson regression, GAM 

Age groups analyzed: 65+ yrs 

Covariates: adjustments for long-term patterns, 

temperature, humidity, days of the wk, holidays, 

viral infections, etc. 

Lag(s): 0-3, cumulative up to 3 days 

SO2 24-h avg (ppb): 

Mean: 25.4 

IQR: 18 ppb 

Q2: 15 

Q3: 33 

# Stations: 6 

Copollutants: 

PM10 (0.42) 

CO (0.23) 

O3 (0.15) 

Effects were expressed as relative risk based on an 

increment of 18 ppb. 

IHD 

1.014 (1.003, 1.026) lag 0, single-pollutant 

1.009 (0.994, 1.023), 2-pollutant model with PM10 

CHF 

1.002 (0.978, 1.017), single-pollutant model 

Risks for dysrhythmia were NR for SO2. 

Schwartz (1997) 

Tuscon, AZ 

Period of Study: 

Jan 1988-Dec 1990 

Outcome(s) (ICD9): CVD 390-429. Ascertained 

from hospital discharge records. 

Study design: Time-series 

Statistical analysis: Poisson regression, GAM 

Age groups analyzed: 65+ 

Covariates: long-term and seasonal trends, day of 

the wk, temperature, dew point, 

Statistical software: S-PLUS 

SO2 24-h avg (ppb) 

Mean: 4.6 ppb 

IQR: 3.9 ppb 

10th: 0.7 

Q2: 2.0 

Median: 3.4 

Q3: 5.9 

90th: 10.1 

Copollutants: 

PM10 (0.095) NO2 (0.482) 

CO (0.395) O3 (−0.271) 

Results were expressed as percent change based 

on an increment of 3.9 ppb. 

0.14% (−1.3%, 1.6) 

No other statistically significant associations for 

cardiovascular outcomes were observed. 

Tolbert et al. (2007) 

Atlanta, GA 

Period of Study: 

1993-2004 

ED Visits. 

Outcome(s) (ICD9): Cardiovascular (410-414, 427, 

428, 433-437, 440, 443-445, 451-453); Respiratory 

(493, 786.07, 786.09, 491, 492, 496, 460-465, 477, 

480-486, 466.1, 466.11, 466.19). 

Study design: Time-series 

Statistical Analysis: Poisson Generalized Linear 

Model (GLM). 

Statistical package: SAS 

Age groups analyzed: all ages 

Covariates: adjustment for day-of-wk, hospital 

entry, holidays, time, temperature, dew point 

temperature 

# Hospitals: 41 

N: 238,360 (Cardiovascular); 

1,072,429 (Respiratory) 

Lag(s): 3-day moving avg 

1-h max (ppb): 14.9 

Range: 1.0, 149.0 

Copollutants: 

PM10 

PM2.5 

O3 

NO2 

CO 

Sulfate 

Total Carbon 

Organic Carbon 

EC 

Water-Soluble Metals 

Oxygenated Hydrocarbons 

Relative Risk (95% CI) (per 16.0 ppb SO2) 

1.003 (0.994, 1.011) 

 F-68 
Study Methods Pollutant Data Findings 

Wellenius et al. 

(2005b) 

Birmingham, 

Chicago, Cleveland, 

Detroit, 

Minneapolis, New 

Haven, Pittsburgh, 

Seattle 

Period of Study: 

Jan 1986-Nov 1999 

(varies slightly 

depending on city) 

Outcome(s) IS, primary diagnosis of acute but illdefined cerebrovascular disease or occlusion of the 

cerebral arteries; HS, primary diagnosis of 

intracerebral hemorrhage. ICD codes not provided. 

Hospital admissions ascertained from the Centers 

for Medicare and Medicaid Services. Cases 

determined from discharge data were admitted from 

the ER to the hospital. 

N IS: 155,503 

N HS: 19,314 

Study design: Time-stratified case-crossover. 

Control days chosen such that they fell in same mo 

and same day of wk. Design controls for 

seasonality, time trends, chronic and other slowly 

varying potential confounders. 

Statistical Analysis: 2-stage hierarchical model 

(random effects), conditional logistic regression for 

city effects in the first stage 

Software package: SAS 

Lag(s): 0-2, unconstrained distributed lags 

SO2 24 h avg (ppb) 

10th: 2.17 

25th: 3.57 

Median: 6.22 

75th: 10.26 

90th: 16.17 

SO2 data not available for 

Birmingham, AL 

Copollutants: 

PM10 (0.39) 

CO 

NO2

Results reported for percent increase in stroke 

admissions for an incremental increase in SO2

equivalent to one IQR (6.69). 

Ischemic Stroke: 1.35 (0.43, 2.29), lag 0 

Hemorrhagic Stroke: 0.68 (−1.77, 3.19) 

Multipollutant models not run. 

Wellenius et al. 

(2005a) 

Allegheny County, 

PA (near Pittsburgh) 

Period of Study: 

Jan 1987-Nov 1999 

Outcome(s): CHF 428. Cases are Medicare 

patients admitted from ER with discharge of CHF 

Study design: Case-crossover, control exposures 

same mo and day of wk, controlling for season by 

design. 

Statistical Analysis: Conditional logistic regression 

N: 55,019 admissions, including repeat admissions, 

86% admitted ≤ 5 times 

Age groups analyzed: 65+ yrs (Medicare recipients)

Covariates: Temperature and pressure. Effect 

modification by age, gender, secondary diagnosis 

arrhythmias, atrial fibrillation, COPD, hypertension, 

type 2 diabetes, AMI within 30 days, angina 

pectoris, IHD, acute respiratory infection. 

Statistical software: SAS 

Lag(s): 0-3 

SO2 24-h avg (ppb): 

Mean (SD): 14.78 (9.88) 

5th: 3.98 

25th: 7.70 

Median: 12.24 

75th: 18.98 

95th: 33.93 

# Stations: 10 

Copollutants: 

PM10 (0.51) 

CO (0.54) 

NO2 (0.52) 

O3 (−0.19) 

Effects were reported as percent change based on 

an increment of 11 ppb. 

CHF, single-pollutant models: 

2.36 (1.05, 3.69) lag 0, or 

2.14 (0.95, 3.35) lag 0 after adjusted to an increment 

of 10 ppb. 

CHF, 2-pollutant models: 

1.35 (−0.27, 2.99), SO2 with PM10

0.10 (−1.35, 1.57), SO2 with CO 

0.68 (−0.82, 2.21), SO2 with NO2

2.02 (0.68, 3.37), SO2 with O3

CANADA 

Burnett et al. 

(1997b) 

Metropolitan 

Toronto (East York, 

Etobicoke, North 

York, Scarborough, 

Toronto, York), 

Canada 

Period of Study: 

1992-1994, 388 

days, summers only 

Outcome(s) (ICD9): IHD 410-414; Cardiac 

Dysrhythmias 427; Heart failure 428. All Cardiac 

410-414, 427, 428. Obtained from hospital 

discharge data. 

Population: 2.6 million residents 

Study design: Time-series 

Age groups analyzed: All 

# Hospitals: NR 

Statistical analysis: relative risk regression models, 

GAMs. 

Covariates: adjusted for long- term trends, seasonal 

and subseasonal variation, day of the wk, 

temperature, dew point 

Season: summer only 

Dose response: figures presented 

Lag: 1-4 days 

SO2 daily 1-h max (ppb): 

Mean: 7.9 

CV: 64 

Min: 0 

25th percentile: 4 

50th percentile: 7 

75th percentile: 11 

Max: 26 

# of Stations: 4-6 

(Results are reported for 

additional metrics including 24-

h avg and daytime avg (day) 

Copollutants: 

H+ (0.45) SO4 (0.42) 

TP (0.55) FP (0.49) 

CP (0.44) COH (0.50) 

O3 (0.18) NO2 (0.46) 

CO (0.37) 

Effects were expressed as relative risk based on an 

increment of 7.00 ppb (IQR). T ratio in parentheses. 

All cardiac disease 

Single-pollutant model 

1.041 (2.66), daily max over 4 days, lag 0 

Multipollutant model w/ SO2, O3, NO2

Of 7.72 excess hospital admissions, 2.8% attributed 

to SO2. 

Objective of study was to evaluate the role of particle 

size and chemistry on cardiac and respiratory 

diseases. 

 F-69 
Study Methods Pollutant Data Findings 

Burnett et al. (1999) 

Metropolitan 

Toronto (East York, 

Etobicoke, North 

York, Scarborough, 

Toronto, York), 

Canada 

Period of Study: 

1980-1994 

N: 14 yrs 

Outcome(s) (ICD9): IHD 410-414; Cardiac 

Dysrhythmias 427; Heart failure 428; All cardiac 

410-414, 427, 428; Cerebrovascular Disease 

obtained from hospital discharge data 430-438; 

Peripheral Circulation Disease 440-459. 

Population: 2.13-2.42 million residents 

Study design: Time-series 

Statistical analysis: GAMs to estimate log RR per 

unit changes, stepwise regression used to select 

min number of air pollutants in multipollutant 

models. 

Covariates: long-term trends, seasonal variation, 

day of wk, temperature, and humidity. 

Statistical package: S-PLUS 

Lag(s): 0-2 days 

SO2 daily avg (ppb) 

Mean: 5.35 

5th percentile: 0 

25th percentile: 1 

50th percentile: 4 

75th percentile: 8 

95th percentile: 17 

Max: 57 

Multiple day avgs used in 

models 

Copollutants: 

PM2.5 (0.50) PM10-2.5 (0.38) 

PM10 (0.52) CO (0.55) 

SO2 (0.55) O3 (−0.04) 

Effects were reported as % change based on an 

increment of 5.35 ppb. 

Single-pollutant model 

Dysrhythmias 0.8% (−0.3, 1.9) 

Cerebrovascular 0.04% (−0.7, 0.8) 

CHF 1.93% (0.9, 2.9) 

IHD 2.32% (1.6, 3.1) 

Attributed percent increase in admissions for SO2

was determined from multipollutant models. 

IHD 

Attributed percent increase: 0.95% 

Authors note SO2 effects could be largely explained 

by other variables in the pollution mix as 

demonstrated by the multipollutant model. 

Fung et al. (2005) 

Windsor, Ontario, 

Canada 

Period of Study: 

Apr 1995-Jan 2000 

Outcome(s) (ICD9): CHF 428; IHD 410-414; 

dysrhythmias 427 and all cardiac. Hospital 

admissions from Ontario Health Insurance Plan 

records. 

Study design: Time-series 

Statistical analysis: GLM 

N: 11,632 cardiac admission, 4.4/day for 65+ age 

group 

Age groups analyzed: 65+, < 65 yrs 

Statistical Software: SPLUS 

Lag(s): lag 0, 2, 3 day avg 

SO2 1-h max (ppb) 

Mean (SD): 27.5 (16.5) 

Min: 0 

Max: 129 

IQR: 19.3 ppb 

Copollutants: 

CO (0.16) O3 (−0.02) 

PM10 (0.22) NO2 (0.22) 

Effects were expressed as percent change of cardiac 

disease hospital admissions based on an increment 

of 19.3 ppb. 

Single-pollutant model: 

< 65 yrs: 2.3% (−1.8, 6.6) lag 0; 

3.9% (−1.5, 9.6) lag 0-1; 3.4% (−3.0, 10.1) lag 0-2 

≥ 65 yrs: 2.6% (0.0, 5.3) lag 0; 

4.0% (0.6, 1.6) lag 0-1; 5.6% (1.5, 9.9) lag 0-2 

Inclusion of particulate matter and adjustment for 

meteorological variables did not change the 

association between SO2 and cardiac hospitalization. 

Stieb et al. (2000) 

Saint John, New 

Brunswick Canada 

Period of Study: 

July 1992-Mar 1996 

Outcome(s): Angina pectoris; MI; 

dysrhythmia/conduction disturbance; CHF; All 

Cardiac. ED Visits collected prospectively. 

Study design: Time-series 

Statistical analyses: Poisson regression, GAM 

N: 19,821 ER visits 

# Hospitals: 2 

Lag(s): 1-8 days 

SO2 24-h avg (ppb) 

Mean (SD): 6.7 (5.6) 

95th: 18 

Max: 60 

SO2 1-h max (ppb) 

Mean (SD): 23.8 (21.0) 

95th: 62 

Max: 161 

Copollutants: 

CO (0.31) H2S (−0.01) 

O3 (−0.02) NO2 (0.41) 

PM10 (0.36) PM2.5 (0.31) 

H+ (−0.24) SO4 (0.26) 

COH (0.31) 

Results reported for percent change in admissions 

based on a single-pollutant model for incremental 

increase in NO2 equivalent to one IQR (8.9 ppb). 

Cardiac visits (p-value in parentheses): 

4.9 (0.002), 1 day avg, lag 8, all yr 

2.8 (0.067), 5 day avg, lag 6, May-Sept 

Multi-pollutant models: 

4.9, (1.7, 8.2), 1 day avg, lag 8, all yr (O3) 

Lags 0-10 presented graphically. 

All but lag 8 in single-pollutant model approximately 

null. 

 F-70 
Study Methods Pollutant Data Findings 

Villeneuve et al. 

(2006a) 

Edmonton, Canada 

Period of Study: 

Apr 1992-Mar 2002 

Outcome(s) (ICD9): Acute ischemic stroke 434, 

436; hemorrhagic stroke 430, 432; transient 

ischemic attack (TIA) 435; Other 433, 437, 438. ED 

visits supplied by Capital Health. 

N: 12,422 

Stroke Visits Catchment area: 1.5 million people 

Study design: Case-crossover, exposure index time 

compared to referent time. Time independent 

variables controlled in the design. Index and 

referent day matched by day of wk. 

Statistical analysis: conditional logistic regression, 

stratified by season and gender. 

Covariates: temperature and humidity 

Statistical software: SAS 

Season: Warm: Apr-Sep; Cool: Oct-Mar. 

Lag(s): 0, 1, 3 day avg 

SO2 24 h avg ppb: 

All yr 

Mean (SD): 2.6 (1.9) 

Median: 2.0 

25th: 1.0 

75th: 4.0 

IQR: 3.0 

Summer 

Mean (SD): 2.1 (1.6) 

Median: 2.0 

25th: 1.0 

75th: 3.0 

IQR: 2 

Winter 

Mean (SD): 3.1 (2.0) 

Median: 3.0 

25th: 2.0 

75th: 4.0 

IQR: 2.0 

Correlation between SO2 and 

other pollutants (all yr): 

NO2 (0.42) CO (0.41) 

O3 (−0.25) PM2.5 (0.22) 

PM10 (0.19) 

Effects were reported as odds ratios based on an 

increment of 3 ppb. 

Acute Ischemic stroke, ≥ 65 yrs 

All yr OR 1.05 (0.99,1.11), lag 0 

Warm OR 1.11 (1.01, 1.22), lag 0 

Cold OR 1.00 (0.93, 1.09), lag 0 

Effect stronger among males 

Hemorrhagic stroke, ≥ 65 yrs 

All yr: 0.98 (0.90, 1.06), lag 0 

Cold: 0.94 (0.84, 1.05), lag 0 

Warm: 1.03 (0.90, 1.17) 

Effect stronger among males 

Transient Cerebral Ischemic Attack, ≥ 65 yrs 

All yr: 1.06 (1.00, 1.12), lag 0 

Cold: 1.03 (0.95, 1.11), lag 0 

Warm OR 1.11 (1.02, 1.22), lag 0 

2-pollutant models presented graphically. Association 

of SO2 with Acute Ischemic stroke diminished with 

inclusion of CO and NO2. 

EUROPE 

Anderson et al. 

(2001) 

West Midlands 

conurbation, UK 

Period of Study: 

1994-1996 

N: 832 days 

Outcome(s) (ICD9): All CVD 390-459; cardiac 

disease 390-429; IHD 410-414; stroke 430-438. 

Emergency admissions counted. 

Catchment area: 2.3 million 

Age groups analyzed: 0-14, 15-64, ≥ 65. 

Study design: Time-series, APHEA 2 methods 

Statistical analyses: GAMs for modeling non-liner 

dependence of some variables. 

Covariates: Adjusted for effects of seasonal 

patterns, temperature and humidity, influenza 

episodes, day of wk and holidays. 

Software package: S-PLUS 

Season: Interaction by warm and cool season 

investigated. 

Lag(s): 0-3 days 

SO2 24-h avg (ppb) 

Mean (SD): 7.2 (4.7) 

Min: 1.9 

10th: 3.3 

Median: 5.8 

90th: 12.3 

Max: 59.8 

# of Stations: 5 sites 

Copollutants: 

PM10 (0.55) PM2.5 (0.52) 

PM2.5-10 (0.31) BS (0.50) 

SO4 (0.19) NO2 (0.52) 

O3 (-0.22) 

Results reported for % change in admissions, 

increment = 9 ppb (10th-90th). 

All CVD all ages 

−0.4 (−2.2, 1.5), mean lags 0 + 1 

Cardiac all ages: 

0.7 (−1.3, 2.8), mean lags 0 + 1 

IHD ≥ 65 yrs 

1.5 (−2.5, 5.6), mean lags 0 + 1 

Stroke ≥ 65 yrs 

−5.1 (−9.6, −0.4), mean lags 0 + 1 

 F-71 
Study Methods Pollutant Data Findings 

Atkinson et al. 

(1999b) 

London, England 

Period of Study: 

1992-1994 

N: 1,096 days 

Outcome(s) (ICD9): 

All CVD 390-459; IHD 410-414. Emergency 

admissions obtained from the Hospital Episode 

Statistics (HES) database (complaints). 

Ages groups analyzed: 0-14 yrs, 15-64 yrs, 0-64 

yrs, 65+ yrs, 65-74 yrs, 75+ yrs 

Study design: Time-series, hospital admission 

counts 

N: 189,109 CVD admissions 

Catchment area: 7 million residing in 1,600 Km2

area of Thames basin. 

Statistical analyses: APHEA protocol, Poisson 

regression 

Covariates: adjusted long-term seasonal patterns, 

day of wk, influenza, temperature, humidity 

(compared alternative methods for modeling 

meteorological including linear, quadradic, piecewise, spline) 

Season: warm season Apr-Sep, cool season 

remaining mos, interactions between season 

investigated 

Dose response investigated: yes, bubble charts 

presented 

Statistical package: SAS 

Lag: 0-3 

Dose response: Bubble plots presented 

SO2 24 h avg (ppb): 

Mean: 21.2 

SD: 7.8 

Min: 7.4 

10th: 13 

Median: 19.8 

90th: 31 

Max: 82.2 

10th-90th percentile: 11.2 

# of Stations: 3, results 

averaged across stations 

Copollutants: 

PM10 CO SO2 O3 BS 

Correlations of SO2 with CO, 

NO2, O3, BS ranged from 0.5-

0.6 

Correlation of SO2 with O3

negative 

Results reported for % change in admissions, 

increment 10th-90th percentile (11.2 ppb). 

All CVD, all ages 

1.57 (0.22, 2.93), lag 0 

All CVD, 0-64 yrs 

2.44 (0.3, 4.63), lag 0 

All CVD, 65+ 

1.72 (0.15, 3.32), lag 0 

IHD, 0-64 yrs 

−2.03 (−5.35, 0.91), lag 2 

IHD, 65+ 

3.10 (0.61, 5.65), lag 0 

Effect size and significance diminished in models 

containing SO2 and BS. 

Ballester et al. 

(2001) 

Valencia, Spain 

Period of Study: 

1992-1996 

Outcome(s) (ICD9): 

All CVD 390-459; 

heart diseases 410-414, 427, 428; cerebrovascular 

diseases 430-438. Admissions from city registry – 

discharge codes used. 

Study design: Time-series 

N: 1080 CVD admissions 

# of Hospitals: 2 

Catchment area: 376,681 inhabitants of Urban 

Valencia 

Statistical analyses: Poisson regression, GAM, 

APHEA/ Spanish EMECAM protocol. Both linear 

and nonparametric model, including a loess term 

was fitted, departure from linearity assess by 

comparing deviance of both models. 

Covariates: long-term trend and seasonality, 

temperature and humidity, weekdays, flu, special 

events, air pollution. 

Season: 

Hot season May to Oct; 

Cold season Nov to Apr 

Statistical package: SAS 

Lag: 0-4 

24 h avg (μg/m3): 

Mean: 25.6 

SD: NR 

Min: 4.4 

Max: 68.4 

Median: 25 

# of Stations: 14 manual, 5 

automatic 

Correlation among stations: 

0.3-0.62 for BS, 0.46-0.78 for 

gaseous pollutants 

Copollutants: 

CO (0.74) 

NO2 (0.22) 

O3 (−0.35) 

BS (0.63) 

2-pollutant models used to 

adjust for copollutants. 

Results expressed as relative risk, increment of 

10 μg/m3. 

All CVD 

1.0302 (1.0042, 1.0568), lag 2 

Heart disease 

1.0357 (1.0012, 1.0714), lag 2 

Cerebrovascular disease 

1.0378 (0.9844 to 1.0940), lag 5 

Digestive diseases 

1.0234 (0.9958, 1.0518), lag 1 

All CVD, hottest semester 

1.050 (1.010, 1.092), lag 2 

Effect size for all CVD and cerebrovascular disease 

diminished in 2-pollutant models. 

 F-72 
Study Methods Pollutant Data Findings 

Ballester et al. 

(2006) 

Multicity, Spain: 

(Barcelona, Bilbao, 

Castellon, Gijon, 

Granada, Huelva, 

Madrid, Oviedo, 

Seville, Valencia, 

Zaragoza) 

Period of Study: 

1995/1996-1999 

N: 1,096 days 

Outcome(s) (ICD9): 

All CVD 390-459; 

Heart diseases 410-414,427,428. Emergency 

admission from hospital records. Discharge data 

used. 

Study design: Time-series, meta-analysis to pool 

cities 

N: Daily mean admissions reported by city. 

Statistical analyses: Poisson regression and GAM, 

with stringent convergence criteria, meta-analysis 

with random effect model. Tested linearity by 

modeling pollutant in linear and non-linear way 

(spline smoothing). Linear model provided best 

results 55% of time but used in all cases to facilitate 

comparability. 

Covariates: temperature, humidity and influenza, 

day of wk unusual events, seasonal variation and 

trend of the series 

Season: Hot: May to Oct; Cold: Nov to Apr 

Statistical package: S-PLUS 

Lag: 0-3 

SO2 24-h avg (μg/m2) 

Mean, 10th, 90th 

Barcelona: 15.5, 6.6, 27.9 

Bilbao: 18.6, 10.2, 29.3 

Cartagena: 27.1, 14.6, 40.8 

Castellon: 7.7, 3.8, 12.7 

Gijon: 29.4, 10.3, 52.4 

Granada: 19.1, 8.8, 31.5 

Huelva: 11.9, 4.5, 22.6 

Madrid: 21.8, 8.7, 41.8 

Oviedo: 40.9, 16.3, 75.5 

Pamplona: 7.6, 1.8, 17.0 

Seville: 9.6, 5.6, 14.6 

Valencia: 16.6, 9.4, 24.4 

Vigo: 9.3, 2.6, 18.2 

Zaragoza: 9.3, 2.0, 19.9 

# of stations: depends on the 

city 

Correlation among stations: 

Correlations between SO2

stations within cities poor. 

Copollutants: 

CO (0.58) O3 (−0.03) 

NO2 (0.46) BS (0.24) 

TSP (0.31) PM10 (0.46) 

Correlations reported are the 

median for all cities. 

Results reported for % change in admissions, 

increment 10 (μg/m3). 

All cardiovascular 

1.33% (0.21, 2.46) lag 0-1 

Heart diseases 

1.72% (0.50, 2.95) lag 0-1 

Single day lags presented graphically. Effect size 

decreased with increasing lag. 

Multi-pollutant results presented graphically. Control 

for CO and particulates diminished SO2 effects. 

D’Ippoliti et al. 

(2003) 

Rome, Italy 

Period of Study: 

Jan 1995- June 

1997 

Outcome(s) (ICD): AMI 410 (first episode). 

Computerized hospital admission data. 

Study design: case-crossover, time stratified, 

control days within same mo falling on the same 

day. 

Statistical analyses: conditional logistic regression, 

examined homogeneity across co-morbidity 

categories 

N: 6531 cases 

Age groups analyzed: 18-64 yrs, 

65-74 yrd, ≥ 75 

Season: Cool: Oct-Mar; 

Warm: Apr-Sep 

Lag(s): 0-4 day, 0-2 day cum avg 

Dose Response: OR for increasing quartiles 

presented and p-value for trend. 

SO2 24 h avg (μg/m3) 

All yr: 

Mean (SD): 9.5 (6.0) 

25th: 5.4 

50th: 8.2 

75th: 12.6 

IQR: 7.2 

Cold season: 

Mean (SD): 12.7 (6.5) 

Warm Season: 

Mean (SD): 88.3 (15.4) 

# Stations: 5 

Copollutants: 

TSP (0.29) NO2 (0.37) 

CO (0.56) 

Results reported as odds ratios for increment equal 

to one IQR (7.2 μg/m3). 

AMI 

Quartile I (referent) 

Quartile II: 0.987 (0.894, 1.089), lag 0-2 

Quartile III: 1.008 (0.892, 1.140), lag 0-2 

Quartile IV: 1.144 (0.991, 1.321), lag 0-2 

Results at various lags NR for SO2. 

Llorca et al. (2005) 

Torrelavega, Spain 

Period of Study: 

1992-1995 

Outcome(s) (ICD): CVD (called cardiac in paper) 

390-459. Emergency admissions, excluding 

nonresidents. Obtained admissions records from 

hospital admin office. 

Study design: Time-series 

Statistical analyses: Poisson regression, APHEA 

protocol 

Covariates: rainfall, temperature, wind speed 

direction 

N: 18,137 admissions 

Statistical software: STATA 

Lag(s): NR 

SO2 24 h avg μg/m3: 

Mean (SD): 13.3 (16.7) 

Copollutants: 

TSP (−0.40) 

NO2 (0.588) 

SH2 (0.957) 

NO (0.544) 

Multipolutant models. 

Results expressed as rate ratios. 

Increment = 100 μg/m3. 

Cardiac admissions, single-pollutant model 

0.94 (0.84, 1.05) 

Five-pollutant model 

1.09 (0.83, 1.42) 

All cardiorespiratory admissions, single-pollutant 

model 

RR 0.98 (0.89, 1.07) 

Five-pollutant model 

0.98 (0.80, 1.21) 

 F-73 
Study Methods Pollutant Data Findings 

Poloniecki et al. 

(1997) 

London, UK 

Period of Study: 

Apr, 1987-Mar 

1994, 7 yrs 

Outcome(s): All CVD 390-459; MI 410; Angina 

pectoris 413; other IHD 414; ARR 427; congestive 

heart failure 428; cerebrovascular disease 430-438. 

Hospital Episode Statistics (HES) data on 

emergency hospital admissions. 

Study design: Time-series 

N: 373, 556 CVD admissions 

Statistical analyses: Poisson regression with GAM, 

APHEA protocol 

Covariates: long-term trends, seasonal variation, 

day of wk, influenza, temperature and humidity. 

Season: Warm, Apr-Sep; 

Cool, Oct-Mar 

Lag: 0-1 

SO224 h avg ppb: 

Min: 0 

10%: 2 

Median: 6 

90%: 21 

Max: 1 

Copollutants: 

BS 

CO 24 h avg 

NO2 24 h avg 

O3 8 h 

Correlations between 

pollutants high but not 

specified 

Effects were expressed as relative risk based on an 

increment of 19 ppb 

(10th-90th percentile). 

Single-pollutant models (lag 0-1): 

MI: 1.0326 (1.0133, 1.0511) 

Angina: 1.0133 (0.9907, 1.0383) 

IHD: 0.9944 (0.9651, 1.0239) 

ARR: 1.0181 (1.0000, 1.0448) 

CHF: 1.0057 (0.9846, 1.0258) 

Cerebrovascular: 1.0019 (0.9837, 1.0189) 

All circulatory: 1.0248 (1.0062, 1.0444) 

MI, 2-pollutant models, cool season: 

1.0399 (1.0171, 1.0628), SO2 only 

1.0285 (1.0019, 1.0571), SO2 withNO2

1.0380 (1.0057, 1.0704), SO2/CO 

1.0285 (1.0019, 1.0552), SO2/BS 

1.0476 (1.0209, 1.0742), SO2 with O3 

In the warm season no significant associations were 

observed in 2-pollutant models. 

Prescott et al. 

(1998) 

Edinburgh, UK 

Period of Study: 

Oct 1992-Jun 1995 

Outcome(s) (ICD9): Cardiac and cerebral ischemia 

410-414, 426-429, 

434-440. Extracted from Scottish record linkage 

system. 

Study design: Time-series 

Statistical Analysis: Poisson, log linear regression 

models 

Age groups analyzed: < 65, 65+ yrs 

Covariates: Seasonal and weekday variation, 

temperature, and wind speed. 

Lag(s): 0, 1, 3 day moving avg 

NO2 24 h avg ppb 

Mean (SD): 8.3 (5.6) 

Range: 1-50 

90th-10th 

Percentile = 12 ppb 

Copollutants: 

O3, 24 h avg PM, 24 h avg 

NO2, 24 h avg CO, 24 h avg 

Correlations NR. 

Results reported as % increase in admissions, 

increment 10 ppb. 

All CVD, ≤ 65 yrs 

4.9 (−1.0, 11.1), 3 day moving avg 

All CVD, ≥ 65 yrs 

−3.7 (−12.4, 5.9), 3 day moving avg 

Sunyer et al. (2003) 

Europe 

(Birmingham, 

London, Milan, 

Paris, Rome, 

Stockholm, the 

Netherlands) 

Period of Study: 

1990-1996 

Hospital Admissions 

Outcome(s) (ICD9): Cardiovascular diseases (390-

429); IHD (410-413); stroke (430-438) 

Study design: Time-series 

Statistical Analysis: Poisson autoregression with 

GAM 

Age groups analyzed: all ages 

Covariates: trend, seasonal patterns, 

meteorological factors 

Lag(s): 0 + 1 

24-h median (μg/m3): 

Birmingham: 19 

London: 21 

Milan: 18 

Netherlands: 9 

Paris: 15 

Rome: 9 

Stockholm: 5 

Copollutants: 

PM10 BS NO2 O3

% increase in Hospital Admissions (95% CI) (per 10 

μg/m3 SO2) 

Cardiovascular: All ages: 0.7 (0.3, 1.1); 

>65: 0.7 (0.3, 1.2) 

IHD: < 65: 0.6 (0.2, 1.1); >65: 1.2 (0.8, 1.6) 

IHD after adjustment for CO, NO2, BS < PM10: 

< 65: 0.7 (0.1, 1.3); >65: −1.4 (−8.0, 6.0) 

Stroke: >65: 0.0 (−0.5, 0.5) 

Yallop et al. (2007) 

London, England 

Period of Study: 

Jan 1998-Oct 2001 

N: >1400 days 

Outcome(s): Acute pain in Sickle Cell Disease 

(HbSS, HbSC, HbS/∃0, thalassaemia, HbS/∃+). 

Admitted to hospital for at least one night. 

Study design: Time-series 

Statistical analyses: Cross-correlation function 

N: 1047 admissions 

Covariates: No adjustment made in analysis, 

discussion includes statement that the effects of 

weather variables and copollutants are interrelated. 

Statistical package: SPSS 

Lag(s): 0-2 days 

Dose response: quartile analysis, graphs 

presented, ANOVA comparing means across 

quartiles. 

NR 

Copollutants: 

O3 

CO 

NO 

NO2

PM10

Daily avg used for all 

copollutants. 

No association for SO2 

 F-74 
Study Methods Pollutant Data Findings 

AUSTRALIA 

Jalaludin et al. 

(2006) 

Sydney, Australia 

Period of Study: 

Jan 1997-Dec 2001 

Outcome(s) (ICD9): All CVD 

390-459; cardiac disease 

390-429; IHD 410-413; and cerebrovascular 

disease or stroke 430-438; Emergency room 

attendances obtained from health department data. 

Age groups included: 65+ 

Study design: Time-series, multicity APHEA2 

Protocol. 

Statistical Analysis: GAM (with appropriate 

convergence criteria) and GLM Models. Only GLM 

presented. 

Lag: 0-3 

Covariates: Daily avg temperature and daily 

relative, humidity, long-term trends, seasonality, 

weather, day of wk, public school holidays, outliers 

and influenza epidemics. 

Dose response: quartile analysis 

Season: Separate analyses for warm (Nov-Apr) and 

cool periods (May-Oct). 

SO2 24 h avg avg (ppb) 

Mean (SD): 1.07 (0.58) 

Min: 0.09 

25th: 0.64 

Median: 1.01 

75th: 1.39 

Max: 3.94 

IQR: 0.75 

# of Stations: 14 

Copollutants: 

BS (0.21) 

PM10 (0.37) 

O3 (0.454) 

NO2 (0.52) 

CO (0.46) 

Effects were presented as percent change based on 

an increment of 0.75 ppb. 

Single-pollutant model: 

All CVD, all yr: 1.33% (0.24, 2.43) lag 0 

Cardiac: 1.62% (0.33, 2.93) lag 0 

IHD: 1.12% (−0.84, 3.12) lag 0 

Stroke: −1.41% (−3.67, 0.90) lag 0 

Cool Season 

All cardiovascular: 2.15% (0.84, 3.46) lag 0 

Cardiac: 2.48% (0.94, 4.04) lag 0 

IHD: 2.49% (0.13, 4.91) lag 0 

Stroke: −0.19% (−2.90, 2.60) lag 0 

Warm Season 

All cardiovascular: 0.06% (−1.48, 1.62) lag 0 

Cardiac: 0.38% (−1.37, 2.16) lag 0 

IHD: −0.47% (−3.08, 2.22) lag 0 

Stroke: −2.74% (−5.92, 0.55) lag 0 

Results for lags 0-3 presented. In general, effect size 

diminished with increasing lag. 

Effects of SO2 on all CVD were diminished with 

inclusion of PM and CO (graphically presented.) 

Petroeschevsky et 

al. (2001) 

Brisbane, Australia 

Period of Study: 

Jan 1987-Dec 1994 

N: 2,922 days 

Outcome(s) (ICD9): CVD 390-459. Hospital 

admissions, non-residents excluded. 

Study design: Time-series 

Statistical analyses: Poisson regression, APHEA 

protocol, linear regression and GEEs 

Age groups analyzed: 15-64, 65+ 

Covariates: temperature, humidity, rainfall. Longterm trends, season, flu, day of wk, holidays. 

Dose response: quintile analysis. 

Statistical software: SAS 

Lag(s): lag 0-4, 3 day avg, 5 day avg 

SO2 24-h avg (pphm): 

Summer: mean, min, max 

0.39, 0.0, 1.63 

Fall: mean, min, max 

0.42, 0.01, 3.55 

Winter: mean, min, max 

0.48, 0.0, 2.08 

Spring: mean, min, max 0.37, 

0.0, 6.02 

Overall: mean, min, max 

0.41, 0.0, 3.55 

SO2 1-h max (pphm): 

Summer: Mean, min, max 

0.78, 0.0, 5.5 

Fall: Mean, min, max 

0.93, 0.05, 5.95 

Winter: Mean, min, max 

1.13, 0.0, 6.68 

Spring: Mean, min, max 0.84, 

0.0, 6.01 

Overall: Mean, min, max 

0.92, 0.0, 6.68 

Copollutants:BSP O3 NO2 

Correlation between pollutants 

NR. 

Effects were expressed as relative risk based on an 

increment of 10 ppb and the 24-h avg SO2

concentrations. 

All CVD 

15 to ≥ 65 yrs: 1.028 (0.987, 1.070) lag 0 

15 to 64 yrs: 1.081 (1.010, 1.158) lag 0 

≥ 65 yrs: 1.038 (0.988, 1.091) lag 1 

Non-significant increasing risk for CVD in those 15-

64 by quintile of SO2 concentration observed. 

 F-75 
Study Methods Pollutant Data Findings 

ASIA 

Chan et al. (2006) 

Taipai, Taiwan 

Period of Study: 

Apr 1997-Dec 2002 

N: 2,090 days 

Outcome(s) (ICD9): Cerebrovascular disease 430-

437; stroke 430-434; hemorrhagic stroke 430-432; 

ischemic stroke 433-434. Emergency admission 

data collected from National Taiwan University 

Hospital. 

Ages groups analyzed: age >50 included in study 

Study design: Time-series 

N: 7341 Cerebrovascular admissions among those 

>50 yrs old 

Catchment area: 

Statistical analyses: Poisson regression, GAMs 

used to adjust for non-linear relation between 

confounders and ER admissions. 

Covariates: time trend variables: yr, mo, and day of 

wk, daily temperature difference, and dew point 

temperature. 

Linearity: investigated graphically by using the 

LOESS smoother. 

Lag: 0-3, cumulative lag up to 3 days 

SO2 24-h avg (ppb): 

Mean: 4.3 

SD: 2.4 

Min: 0.4 

Max: 17.1 

IQR: 3.1 ppb 

# of Stations: 16 

Correlation among stations: 

NR. 

Copollutants: 

PM10 (0.59) PM2.5 (0.51) 

CO (0.63) NO2 (0.64) 

O3 (0.51) 

Results reported for OR for association of 

emergency department admissions with an IQR 

increase in SO2 (3.1 ppb) 

Cerebrovascular: 

1.008 (0.969, 1.047), lag 0 

Stroke: 

0.991 (0.916, 1.066), lag 0 

Ischemic stroke: 

1.044 (0.966, 1.125), lag 0 

Hemorrhagic stroke: 

0.918 (0.815, 1.021), lag 0 

No significant associations for SO2 reported. Lag 0 

shown but similar null results were obtained for lags 

0-3. 

2-pollutant models to adjust for copollutants but not 

for SO2, which was not associated with health 

outcomes. 

Chang et al. (2005) 

Taipei, Taiwan 

Period of Study: 

1997-2001 

N: 5 yrs 

Outcome(s) (ICD9): CVD 410-429. 

Daily clinic visits or hospital admission from 

computerized records of National Health Insurance. 

Discharge data. 

Source populatioN: 2.64 million 

N: 40.8 admissions/day; 74,509/5 yrs 

# Hospitals: 41 

Study design: Case-crossover, referent day 1 wk 

before or after index day 

Statistical analyses: Conditional logistic regression. 

Covariates: same day temperature and humidity. 

Season: warm/cool (stratified by temperature 

cutpoint of 20 °C) 

Lag(s): 0-2 days 

SO2 24-h avg (ppb) 

Mean: 4.32 

Min: 0.15 

25th: 2.74 

Median: 3.95 

75th: 5.49 

Max: 14.57 

IQR: 2.75 

# of Stations: 6 

Copollutants: 

CO O3 NO2 

PM10 Correlations NR. 

2-pollutant models to adjust for 

copollutants. 

Effects were expressed as odds ratios based on an 

increment of 2.75 ppb. 

Warm (≥ 20 °C) 0.967 (0.940, 0.995) 

Cool (< 20 °C) 1.015 (0.965, 1.069) 

In 2-pollutant models with (PM10, NO2, CO, or O3) the 

effect of SO2 was attenuated for both temperature 

ranges such that it was negatively associated with 

CVD. 

≥ 20 °C: 0.874 (0.77, 0.880), w/ PM10 

< 20 °C: 0.986 (0.928, 1.048), w/ PM10 

≥ 20 °C: 0.826 (0.798, 0.854), w/ NO2 

< 20 °C: 0.922 (0.865, 0.984), w/ NO2 

≥ 20 °C: 0.903 (0.876, 0.931), w/ CO 

< 20 °C: 0.960 (0.901, 1.022), w/ CO 

≥ 20 °C: 0.953 (0.926, 0.981), w/ O3 

< 20 °C: 1.014 (0.963, 1.067), w/ O3

Lee et al. (2003b) 

Seoul, Korea 

Period of Study: 

Dec 1997-Dec 

1999, 822-days, 

N: 184 days in 

summer 

Outcome(s) (ICD10): IHD: Angina pectoris 120; 

Acute or subsequent MI 121-123; other acute IHD 

124. Electronic medical insurance data used. 

Study design: Time-series 

Statistical methods: Poisson regression, GAM with 

strict convergence criteria. 

Age groups analyzed: all ages, 64+ 

Covariates: Long-term trends LOESS smooth, 

temperature, humidity, day of wk. 

Season: Presented results for summer (Jun, Jul, 

Aug) and entire period. 

Lag(s): 0-6 

SO2 24 h avg (ppb): 

5th: 3.7 

10th: 5.1 

Median: 7.0 

75th: 9.5 

95th: 14.3 

Mean (SD): 7.7 (3.3) 

IQR: 4.4 

Copollutants: 

All yr 

NO2 (0.72) O3 (−0.30) 

CO (0.81) PM10 (0.59) 

Warm season 

NO2 (0.79) O3 (−0.56) 

CO (0.41) PM10 (0.61) 

2-pollutant models. 

Results reported for RR of IHD hospital admission 

for an incremental increase in SO2 equivalent to one 

IQR (4.4 ppb). 

Single-pollutant model: 

Entire season- IHD 

All ages 0.96 (0.92, 0.99) lag 3 

≥ 64 yrs 0.95 (0.90, 1.01) lag 3 

Summer- IHD 

All ages 1.09 (0.96, 1.24) lag 3 

≥ 64 yrs 1.32 (1.08, 1.62) lag 3 

2-pollutant model: 

Entire season; SO2 and PM10

≥ 64 yrs 0.98 (0.94, 1.03) lag 3 

 F-76 
Study Methods Pollutant Data Findings 

Tsai et al. (2003a) 

Kaohsiung, Taiwan 

Period of Study: 

1997-2000 

Outcome(s) (ICD9): All cerebrovascular 

430-438; SHS 430;PIH 431-432; IS 433-435; Other 

436-438. Ascertained from National Health 

Insurance Program computerized admissions 

records. 

Study design: Case-crossover 

Statistical Analysis: conditional logistic regression. 

N: 23,179 stroke admissions 

# Hospitals: 63 

Statistical software: SAS 

Season: warm (≥ 20 °C); cool (< 20 °C) 

Lag(s): 0-2, cumulative lag up to 2 previous days 

SO2 (ppb) 

Min: 1.25 

25th: 6.83 

Median: 9.76 

75th: 13.00 

Max: 26.80 

Mean: 10.08 

# Station: 6 

Copollutants: 

PM10 SO2 

CO O3

Results reported as OR for the association of 

admissions with an incremental increase of SO2

equivalent to the IQR of 6.2 ppb 

PIH admissions: Warm: 1.06 (0.95, 1.18), lag 0-2; 

Cool: 0.85 (0.58, 1.26), lag 0-2 

IS admissions: Warm: 1.06 (1.00, 1.13), lag 0-2; 

Cool: 1.11 (0.83, 1.48), lag 0-2 

2-pollutant models: PIH 0.91 (0.80, 1.03) w/ NO2 

IS 0.93 (0.87, 1.00) w/ NO2; 

PIH 0.94 (0.83, 1.06), w/ CO 

IS 0.94 (0.88, 1.02), w/ CO 

PIH 1.08 (0.96, 1.20) w/ O3 

IS 1.08 (1.01, 1.15) w/ O3 

PIH 0.99 (0.88, 1.11) w/ PM 

IS 1.01 (0.95-1.08) w/ PM 

Wong et al. (1999) 

Hong Kong, China 

Period of Study: 

1994-1995 

Outcome(s) (ICD9): CVD: 

410-417, 420-438, 440-444; CHF 428; IHD 410-

414; Cerebrovascular Disease 430-438. Hospital 

admissions through ER departments via Hospital 

Authority (discharge data). 

Study design: Time-series 

Statistical analyses: Poisson regression, APHEA 

protocol 

# Hospitals: 12 

Covariates: daily temperature, relative humidity day 

of wk, holidays, influenza, long-term trends (yr and 

seasonality variables). Interaction of pollutants with 

cold season examined. 

Season: cold (Dec-Mar) 

Lag(s): 0-3 days 

SO2 24-h avg (μg/m3) 

Mean: 20.2 

IQR: 10 

Copollutants: 

PM10

SO2

O3

Results reported for RR associated with incremental 

increase in NO2 equal to 10 μg/m3. 

All CVD, All ages 

1.016 (1.006, 1.026) lag 0-1 

All CVD, 5-65 yrs 

1.004 (0.989, 1.020) lag 0-1 

All CVD, >65 yrs 

1.021 (1.010, 1.032) lag 0-1 

CHF 

1.036 (1.013, 1.059) lag 0 

IHD 

1.010 (0.995, 1.025) lag 0-1 

Cerebrovascular 

0.990 (0.978, 1.002) lag 3 

2-pollutant model results not presented for SO2

Wong et al. (2002a) 

Hong Kong, London 

Period of Study: 

1995-1997 (Hong 

Kong), 

1992-1994 

(London) 

Outcome(s) (ICD9): Cardiac disease 390-429; IHD 

410-414. Patients admitted to hospitals from 

emergency departments, out patient departments 

or directly to inpatient wards. 

Statistical Analysis: Poisson regression, GAMs 

Covariates: smooth functions of time, temperature, 

humidity (up to 3 days before admission) day of wk, 

holidays and unusual events. 

Statistical software: 

S-PLUS 

Season: warm/cold 

Lag(s): 0-3, cumulative 0-1 

SO2 24-h avg (μg/m3) 

Hong Kong 

Mean, all yr: 17.7 (12.3) 

Mean, warm: 18.3 

Mean, cold: 17.2 

Min: 1.1 

10th: 6.2 

50th: 14.5 

90th: 32.8 

Max: 90 

London 

Mean, all yr: 23.7 (12.3) 

Mean, warm: 22.2 

Mean, cold: 25.3 

Min 6.2 

10th: 13.2 

50th: 20.6 

90th: 38.1 

Max: 113.6 

Copollutants: 

Hong Kong 

NO2 (0.37) PM10 (0.30) 

O3 (−0.18) 

London 

NO2 (0.71) PM10 (0.64) 

O3 (−0.25) 

Effects expressed as % change, increment was 

10 μg/m3 

Cardiac (all ages) 

Hong Kong: All yr: 2.1% (1.3, 2.8) lag 0-1 

Warm: 1.0% (0.0, 2.0) lag 0-1 

Cold: 1.9% (1.2, 2.7) lag 0-1 

London: All yr: 1.6% (1.0, 2.2) lag 0-1 

Warm: 0.6% (−0.6, 1.7) lag 0-1 

Cold: 1.9% (1.2, 2.7) lag 0-1 

IHD (all ages) 

Hong Kong: All yr: 0.1% (−1.1, 1.2) lag 0-1 

Warm: −0.6% (−2.0, 0.8) lag 0-1 

Cold: 1.0% (−0.8, 2.8) lag 0-1 

London: All yr: 1.7% (0.8, 2.6) lag 0-1 

Warm: 1.0% (−0.6, 2.6) lag 0-1 

Cold: 2.0% (0.9, 3.1) lag 0-1 

Multipollutant model 

Cardiac (all ages) 

Hong Kong: SO2 alone 2.1% (1.3, 2.8) 

SO2 withNO2 1.4% (0.4, 2.3) 

SO2 with O3 2.1% (1.4, 2.9) 

SO2 with PM10 2.0% (1.1, 2.8) 

London: SO2 1.6% (1.0, 2.2) 

SO2 with NO2 1.4% (0.6, 2.3) 

SO2 with O3 1.6% (0.9, 2.2) 

SO2 with PM10 2.2% (1.2, 3.2) 

 F-77 
Study Methods Pollutant Data Findings 

Yang et al. (2004b) 

Kaohsiung, Taiwan 

Period of Study: 

1997-2000 

Outcome(s) (ICD9): All CVD: 410-429 * (All CVD 

typically defined to include ICD9 codes 390-459) 

N: 29,661 

Study design: Case-crossover 

Statistical Analysis: Poisson Time-series regression 

models, APHEA protocol 

# of Hospitals: 63 

Season: Authors indicate not considered because 

the Taiwanese climate is tropical with no apparent 

seasonal cycle 

Covariates: Stratified by warm (≥ 25°) and cold (< 

25°) days, temperature, and humidity 

measurements included in the model 

Statistical package: SAS 

Lag: 0-2 days 

SO2 24-h avg (ppb) 

Min: 1.25 

25%: 6.83 

50%: 9.76 

75%: 13.00 

Max: 26.80 

Mean: 10.08 

# of Stations: 6 

Correlation among stations: 

NR 

Copollutants: 

PM10 CO SO2 O3 8 

2-pollutant models used to 

adjust for copollutants 

Correlations NR 

OR’s for the association of one IQR (17.08 ppb) 

increase in SO2 with daily counts of CVD hospital 

admissions are reported 

All CVD (ICD9: 410-429) 

One-pollutant model: ≥ 25°: 0.999 (0.954, 1.047); 

< 25°: 1.187 (1.092, 1.291) 

2-pollutant models: 

Adjusted for PM10: ≥ 25°: 0.961 (0.917, 1.008) 

< 25°: 1.048 (0.960, 1.145) 

Adjusted for NO2:≥ 25°: 0.921 (0.875, 0.969) 

< 25°: 0.711 (0.641, 0.789) 

Adjusted for CO: ≥ 25°: 0.831 (0.785, 0.879) 

< 25°: 0.996 (0.910, 1.089) 

Adjusted for O3: ≥ 25°: 1.034 (0.987, 1.084) 

< 25°: 1.194 (1.098, 1.299) 

MIDDLE EAST 

Hosseinpoor et al. 

(2005) 

Tehran, Iran 

Period of Study: 

Mar 1996-Mar 

2001, 5 yrs 

Outcome(s) (ICD9): Angina pectoris 413. Primary 

discharge diagnosis from registry databases or 

records. 

Study design: Time-series 

Statistical methods: Poisson regression 

# Hospitals: 25 

Covariates: Long-term trends, seasonality, 

temperature, humidity, holiday, post-holiday, day of 

wk. 

Lag(s): 0-3 

SO2 24-h avg (μg/m3) 

Mean (SD): 73.74 (33.30) 

Min: 0.30 

25th: 48.23 

Median: 74.05 

75th: 98.64 

Max: 499.26 

Copollutants: 

NO2 CO O3 PM10

Correlations NR 

Results reported for relative risk in hospital 

admissions per increment of 10 μg/m3 SO2. 

Angina 

0.99995 (0.99397, 1.00507), lag 1 

In a multipollutant model only CO (lag 1) was 

significantly associated with angina pectoris related 

hospital admissions. 

Table F-5. Short-term exposure to SO2 and mortality. 

Study Methods Pollutant Data Outcome Findings 

META ANALYSIS 

Stieb et al. (2002), 

(reanalysis 2003) 

Meta-analysis of estimates 

from various countries. 

The lags and multiday 

averaging used varied 

Meta-analysis of time-series 

study results. 

24-h avg ranged from 0.7 ppb 

(San Bernardino) to 75 ppb 

(Shenyang) 

“Representative” 

concentration: 9.4 ppb 

Copollutants: PM10, O3, NO2, 

CO 

All cause Single-pollutant model 

(29 estimates): 1.0% (0.6, 1.3) 

Multipollutant model estimates 

(10 estimates): 0.9% (0.3, 1.4) 

UNITED STATES 

Chock et al. (2000) 

Pittsburgh, PA 

Period of Study: 

1989-1991 

Lags: 0, 1, 2, 3 

Poisson GLM. Time-series 

study. Numerous results 

Mean NR 

Copollutants: PM10, O3, NO2, 

CO; 2-, 5-, and 6-pollutant 

models 

All cause; age < 75 yrs; age 

75+ yrs 

All cause: 

Age 0-75 yrs: 

Lag 1: 0.7% (−0.7, 2.2) 

Age 75+ yrs: 

Lag 1: −0.2% (−1.6, 1.3) 

 F-78 
Study Methods Pollutant Data Outcome Findings 

De Leon et al. (2003) 

New York City 

Period of Study: 

1985-1994 

Lags: 0 or 1 

Poisson GAM with 

Stringent convergence 

Criteria; Poisson GLM. 

Time-series study. 

24-h avg: 14.64 ppb 

Copollutants: PM10, O3, NO2, 

CO; 2-pollutant models 

Circulatory and cancer with 

and without contributing 

respiratory causes 

Gaseous pollutants results 

were given only in figures. 

Circulatory: 

Age < 75 yrs: ~2% 

Age 75+ yrs: ~2% 

Dockery et al. (1992) 

St. Louis, MO and 

Eastern Tennessee 

Period of Study: 

1985-1986 

Lag: 1 

Poisson with GEE. 

Time-series study. 

24-h avg: 

St. Louis: 8.9 ppb 

Eastern Tennessee: 

5.1 ppb 

Copollutants: PM10, PM2.5, 

SO4

2−, H+, O3, NO2

All cause All cause: 

St. Louis, MO: 0.8% (−1.7, 3.2)

Eastern Tennessee: 0.4% 

(−0.4, 1.1) 

Gamble (1998) 

Dallas, TX 

Period of Study: 

1990-1994 

Lag: 0 

Poisson GLM. 

Time-series study. 

24-h avg: 3 ppb 

Copollutants: PM10, O3, NO2, 

CO; 2-pollutant models 

All cause; respiratory; 

cardiovascular 

All cause: −0.8% (−3.8, 2.4) 

Respiratory: −1.0% (−5.8, 4.1)

Cardiovascular: −0.5% (−11.4, 

11.8) 

Gwynn et al. (2000) 

Buffalo, NY 

May 1998-Oct 1990 

Lag: 0, 1, 2, 3 

Poisson GAM with 

Default convergence criteria. 

Time-series study. 

24-h avg: 12.2 ppb 

Copollutants: PM10, CoH, 

SO4

2- , O3, NO2, CO, H+ 

All cause; respiratory; 

circulatory 

All cause: 

Lag 0: −0.1% (−1.8, 1.7) 

Circulatory: Lag 3: 1.3% (−2.9, 

5.6) 

Respiratory: Lag 0: 6.4% 

(−2.5, 16.2) 

Kelsall et al. (1997) 

Philadelphia, PA 

Period of Study: 

1974-1988 

Lag: 0 

(AIC presented for 0 through 

5) 

Poisson GAM. 

24-h avg: 17.3 ppb 

Copollutants: TSP, CO, NO2, 

O3

All cause; respiratory; 

cardiovascular 

All cause: 

Single-pollutant: 

0.8% (0.3, 1.4) 

With all other pollutants: 

0.8% (0.1, 1.6) 

Kinney and Özkaynak (1991) 

Los Angeles County, CA 

Period of Study: 

1970-1979 

Lag: 1 

OLS (ordinary least squares) 

on high-pass filtered variables. 

Time-series study. 

24-h avg: 15 ppb 

Copollutants: KM (particle 

optical reflectance), Ox, NO2, 

CO; multipollutant models 

All cause; respiratory; 

circulatory 

All cause: 

Exhaustive multipollutant 

model: 0.0% (−1.1, 1.2) 

Klemm and Mason (2000) 

Atlanta, GA 

Period of Study: 

Aug 1998-Jul 1999 

Lag: 0-1 

Poisson GLM using quarterly, 

monthly, or biweekly knots for 

temporal smoothing. Timeseries study. 

1-h max: 18.7 ppb 

Copollutants: PM2.5, 

PM10-2.5, EC, OC, SO4

2- , NO3−, 

O3, NO2, CO 

All cause; respiratory; 

cardiovascular; cancer; other; 

age < 65 yrs; age 65+ yrs 

All cause - Age 65+ yrs: 

Quarterly knots: 

4.7% (−2.6, 12.5) 

Monthly knots: 

3.4% (−4.1, 11.5) 

Bi-weekly knots: 

1.0% (−6.7, 9.3) 

Klemm et al. (2004) 

Georgia (Fulton; DeKalb 

counties) 

Period of Study: 

1998-2000 

Lags: 2-day avg (avg of lag 0 

and lag 1) 

Poisson with GLM. Timeseries study. 

1-h max (ppb): 19.4 (13.42) 

Copollutants: PM2.5, Coarse 

mass, O3, NO2, CO, Acid, 

Ultrafine surface area, 

Ultrafine count, EC, Organic 

carbon, SO4,Oxygenated 

hydrocarbons, Nonmethane 

hydrocarbons, NO3

All-cause ≥ 65 yrs old 

Quarterly knots (SE) 

β = 0.00115 (0.00092) t = 1.24 

Monthly knots (SE) 

β = 0.00084 (0.00096) t = 0.87

Biweekly knots (SE) 

β = 0.00024 (0.00101) t = 0.24

Lipfert et al. (2000a) 

Seven counties in 

Philadelphia, PA area 

Period of Study: 

May 1992-Sep 1995 

Lag: 0-1 

Linear with 19-day weighted 

avg Shumway filters. Timeseries study. Numerous 

results. 

24-h avg: ~8 ppb 

Copollutants: PM10, PM2.5, 

PM10-2.5, SO4

2−, other PM 

indices, O3, NO2, CO; 2-

pollutant models 

All cause; respiratory; 

cardiovascular; all ages; age 

65+ yrs; age 

< 65 yrs; various subregional 

boundaries 

All-cause: 

Philadelphia: 

0.7% (p > 0.05) 

 F-79 
Study Methods Pollutant Data Outcome Findings 

Lippmann et al. (2000); 

reanalysis Ito, (2003) 

Detroit, MI 

Period of Study: 

1985-1990 

1992-1994 

Lags: 0, 1, 2, 3, 0-1, 0-2, 0-3 

Poisson GAM, reanalyzed with 

stringent convergence criteria; 

Poisson GLM. Numerical SO2

risk estimates were not 

presented in the re-analysis. 

Time-series study. 

24-h avg: 

1985-1990: 9.8 ppb 

1992-1994: 7 ppb 

Copollutants: PM10, PM2.5, 

PM10-2.5, SO4

2−, H+, O3, NO2, 

CO; 

2-pollutant models 

All cause; respiratory; 

circulatory; cause-specific 

Poisson GAM: 

All cause: 

1985-1990: 

Lag 1: 0.5% (−1.5, 2.4) 

1992-1994: 

Lag 1: 1.1% (−1.4, 3.6) 

Mar et al. (2000; 2003) 

Phoenix, AZ. 

Period of Study: 

1995-1997 

Lags: 0 for all cause; 0, 1, 2, 3, 

4 for cardiovascular 

Poisson GAM with default 

convergence criteria (only 

cardiovascular deaths were 

reanalyzed in 2003). Timeseries study. 

24-h avg: 3.1 ppb 

Copollutants: PM2.5, PM10, 

PM10-2.5, CO, NO2, O3, and 

selected trace elements, ions, 

EC, OC, TOC, and factor 

analysis components 

All cause, cardiovascular Poisson GAM: 

All cause: 

Lag 0: 11.2% (−1.5, 25.6) 

Poisson GLM: 

Cardiovascular: 

Lag 1: 7.4% (−13.1, 32.6) 

Moolgavkar et al. (1995) 

Philadelphia, PA 

Period of Study: 

1973-1988 

Lag: 1 

Poisson GLM. Time-series 

study. 

24-h avg: 

Spring: 16.8 ppb 

Summer: 15.7 ppb 

Fall: 17.8 ppb 

Winter: 25.4 ppb 

Copollutants: TSP, O3; 

2-pollutant models 

All cause All yr: 1.3% (0.8, 1.8) 

Spring: 1.7% (0.6, 2.9) 

Summer: 0.9% (−0.7, 2.5) 

Fall: 1.3% (0.0, 2.6) 

Winter: 2.0% (0.9, 3.0) 

Moolgavkar (2000b; 2003b) 

Cook County, IL; Los Angeles 

County, CA; and Maricopa 

County, AZ 

Period of Study: 

1987-1995 

Lags: 0, 1, 2, 3, 4, 5 

Poisson GAM with default 

convergence criteria in the 

original Moolgavkar (2000); 

GAM with stringent 

convergence criteria and GLM 

with natural splines in the 2003 

re-analysis. The 2000 analysis 

presented total death risk 

estimates only in figures. 

24-h avg median: 

Cook County: 6 ppb 

Los Angeles: 2 ppb 

Maricopa County: 2 ppb 

Copollutants: PM2.5, PM10, O3, 

NO2, CO; 2- and 3-pollutant 

models 

Cardiovascular; 

cerebrovascular; 

COPD 

GLM (re-analysis): 

Cook County: 

All-cause: 

Lag 1: 2.6% (1.4, 3.8) 

Cardiovascular: 

Lag 1: 2.9% (1.0, 4.8) 

Los Angeles: Cardiovascular: 

Lag 1: 5.9% (3.0, 9.0) 

Moolgavkar (2003a) 

Cook County, IL and Los 

Angeles County, CA 

Period of Study: 

1987-1995 

Lags: 0, 1, 2, 3, 4, 5 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg median: 

Cook County: 6 ppb 

Los Angeles: 2 ppb 

Copollutants: PM2.5, PM10, O3, 

NO2, CO; 2-pollutant models 

All cause; cardiovascular All cause: Cook County 

Single-pollutant: 

Lag 1: 2.6% (1.5, 3.7) 

With PM10: 

Lag 1: 1.9% (0.6, 3.2) 

Los Angeles 

Single-pollutant: 

Lag 1: 6.9% (5.4, 8.4) 

With PM2.5: 

Lag 1: 7.6% (3.4, 12.0) 

Samet et al. (2000a; 2000b); 

reanalysis Dominici et al. 

(2003) 

90 U.S. cities (58 U.S.cities 

with SO2 data) 

Period of Study: 1987-1994 

Lags: 0, 1, 2 

Poisson GAM, reanalyzed with 

stringent convergence criteria; 

Poisson GLM. Time-series 

study. 

24-h avg ranged from 0.4 ppb 

(Riverside) to 14.2 ppb 

(Pittsburgh) 

Copollutants: PM10, O3, NO2, 

CO; multipollutant models 

All cause; cardiopulmonary Posterior means: 

All cause: 

Single-pollutant: 

Lag 1: 0.6% (0.3, 1.0) 

With PM10 and NO2: 

Lag 1: 0.4% (−0.6, 1.4) 

Schwartz (1991) 

Detroit, MI 

Period of Study: 

1973-1982 

Lags: 0, 1, 0-1 

Poisson GEE. Time-series 

study. 

24-h avg: 12 ppb 

Copollutants: TSP (predicted 

from extinction coefficient); 2-

pollutant models 

All cause Poisson regression coefficient 

Single-pollutant: 

Lag 1: 0.863 (SE = 0.323) 

With TSP: 

Lag 1: 0.230 (SE = 0.489) 

(Though SO2 levels were 

reported in ppb, these 

coefficients must have been 

for SO2 in ppm.) 

 F-80 
Study Methods Pollutant Data Outcome Findings 

Schwartz (2000) 

Philadelphia, PA 

Period of Study: 

1974-1988 

Lag: 0 

Poisson GAM model in 15 

winter and 15 summer periods. 

The second stage regressed 

the TSP and SO2 risk 

estimates on SO2/TSP 

relationships. 

24-h avg summer mean 

declined from 20 ppb in 1974 

to 9 ppb in 1988; winter mean 

declined from 35 ppb in 1974 

to 17 in 1988 

Copollutants: TSP, extinction 

coefficient 

All cause Single-pollutant: 

2.3% (1.6, 3.0) 

With TSP: 

0.4% (−2.2, 3.1) 

Schwartz (2004) 

14 U.S. cities that had daily 

PM10 data 

Period of Study: 

1986-1993 

Lag: 1 

Case-crossover design, 

estimating PM10 risks by 

matching by the levels of 

gaseous pollutants. 

24-h avg median ranged from 

2.2 ppb (Spokane, WA) to 39.4 

ppb (Pittsburgh, PA) 

Copollutants: PM10 risk 

estimates computed, matched 

by the levels of SO2, CO, NO2, 

and O3

All cause SO2 risk estimates not 

computed. PM10 risk estimates 

showed the largest risk 

estimate when matched for 

SO2. 

CANADA 

Burnett et al. (2004) 

12 Canadian cities 

 Period of Study: 

1981-1999 

Lag: 1 

Poisson GLM. Time-series 

study. 

24-h avg ranged from 1 ppb 

(Winnipeg) to 10 ppb (Halifax) 

Copollutants: PM2.5, PM10-2.5, 

O3, NO2, CO 

All cause Single-pollutant: 

0.7% (0.3, 1.2) 

With NO2: 

0.4% (0.0, 0.8) 

Burnett et al. (1998a) 

11 Canadian cities 

Period of Study: 

1980-1991 

Lags: 0, 1, 2, 0-1, 0-2 

examined but the best 

lag/averaging for each city 

chosen 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg ranged from 1 ppb 

(Winnipeg) to 11 ppb 

(Hamilton) 

Copollutants: O3, NO2, CO 

All cause Single-pollutant: 

3.4% (2.0, 4.7) 

With all gaseous pollutants: 

2.6% (1.3, 3.9) 

Burnett et al. (1998b) 

Toronto 

Period of Study : 

1980-1994 

Lags: 0, 1, 0-1 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg: 5 ppb 

Copollutants: O3, NO2, CO, 

TSP, COH, estimated PM10, 

estimated PM2.5

All cause Single-pollutant: 

Lag 0: 1.0% (0.3, 1.8) 

With CO: 

Lag 0: 0.6% (−0.4, 1.5) 

Goldberg et al. (2003) 

Montreal, Quebec 

Period of Study: 

1984-1993 

Lags: 0, 1, 0-2 

Poisson GLM with natural 

splines. Time-series study. 

24-h avg: 6 ppb 

Copollutants: PM2.5, coefficient 

of haze, SO42−, O3, NO2, CO 

Congestive heart failure (CHF) 

as underlying cause of death 

versus those classified as 

having CHF 1 yr prior to death 

CHF as underlying cause of 

death: Lag 1: −0.1% (−8.9, 

9.6) 

Having CHF 1 yr prior to 

death: Lag 1: 5.4% (1.3, 9.5) 

Vedal et al. (2003) 

Vancouver, British Columbia 

Period of Study: 

1994-1996 

Lags: 0, 1, 2 

Poisson GAM with stringent 

convergence criteria. Timeseries study. By season. 

24-h avg: 3 ppb 

Copollutants: PM10, O3, NO2, 

CO 

All cause; respiratory; 

cardiovascular 

Results presented in figures 

only. 

All cause: 

Summer: Lag 0: ~3% 

Winter: Lag 1: ~1% 

Villeneuve et al. (2003) 

Vancouver, British Columbia 

Period of Study: 

1986-1999 

Lags: 0, 1, 0-2 

Poisson GLM with natural 

splines. Time-series study. 

24-h avg: 5 ppb 

Copollutants: PM2.5, PM10, 

PM10-2.5, TSP, coefficient of 

haze, SO42−, O3, NO2, CO 

All cause; respiratory; 

cardiovascular; cancer; SES 

All yr: 

All cause: 

Lag 1: 1.7% (−1.1, 4.5) 

Cardiovascular: 

Lag 1: 1.1% (−3.1, 5.4) 

Respiratory: 

Lag 1: 8.3% (0.6, 16.6) 

EUROPE 

Anderson et al. (1996) 

London, England 

Period of Study: 

1987-1992 

Lag: 1 

Poisson GLM. Time-series 

study. 

24-h avg: 11 ppb 

Copollutants: BS, O3, NO2; 

2-pollutant models 

All cause; respiratory; 

cardiovascular 

All cause: 1.0% (0.0, 2.0) 

Respiratory: 1.7% (−1.3, 4.9) 

Cardiovascular: 0.2% (−1.4, 

1.8) 

 F-81 
Study Methods Pollutant Data Outcome Findings 

Anderson et al. (2001) 

West Midlands region, 

England 

Period of Study: 

1994-1996 

Lag: 0-1 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg: 7.2 ppb 

Copollutants: PM10, PM2.5, 

PM10-2.5, BS, SO42−, O3, 

NO2, CO 

All cause; respiratory; 

cardiovascular 

All cause: −0.2% (−2.5, 2.1) 

Respiratory: −2.2% (−7.4, 3.2) 

Cardiovascular: −0.2% (−3.5, 

3.1) 

Ballester et al. (2002) 

13 Spanish cities 

Period of Study: 

1990-1996 

Lags: 0-1 for 24-h avg SO2; 0 

for 1-h max SO2

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg SO2 ranged from 

2.8 ppb (Sevilla) to 15.6 ppb 

(Oviedo) 

Copollutants: TSP, BS, PM10

All cause, cardiovascular, 

respiratory 

All cause: 

Lag 0-1: 1.4% (0.2, 2.7) 

Cardiovascular: 

Lag 0-1: 1.4% (−0.4, 3.3) 

Respiratory: 

Lag 0-1: 3.5% (1.0, 6.0) 

Biggeri et al. (2005) 

8 Italian cities 

Period of study variable 

between 1990-1999 

Lag: 0-1 

Poisson GLM. Time-series 

study. 

24-h avg ranged from 2 ppb 

(Verona) to 14 ppb (Milan) 

Copollutants: O3, NO2, CO, 

PM10

All cause; respiratory; 

cardiovascular 

All cause: 4.1% (1.1, 7.3) 

Respiratory: 7.4% (−3.6, 19.6)

Cardiovascular: 4.9% (0.4, 

9.7) 

Bremner et al. (1999) 

London, England 

Period of Study : 

1992-1994 

Lags: Selected best from 0, 1, 

2, 3, (all cause); 0, 1, 2, 3, 0-1, 

0-2, 0-3 (respiratory, 

cardiovascular) 

Poisson GLM. Time-series 

study. 

24-h avg: 7 ppb 

Copollutants: BS, PM10, O3, 

NO2, CO; 2-pollutant models 

All cause; respiratory; 

cardiovascular; all cancer; all 

others; all ages; age specific 

(0-64, 65+, 65-74, 75+ yrs) 

All cause: Lag 1: 1.6% (−0.5, 

3.7) 

Respiratory: Lag 2: 4.8% 

(−0.2, 10.0) 

Cardiovascular Lag 1: 

1.3% (−1.7, 4.3) 

Clancy et al. (2002) 

Dublin, Ireland 

Period of Study:` 

1984-1996 

NA 

Comparing standardized 

mortality rates for 72 mos 

before and after the ban on 

coal sales in Sep 1990. 

24-h avg: 

1984-1990: 11.7 ppb 

1990-1996: 7.7 ppb 

Copollutants: BS 

All cause, cardiovascular, and 

respiratory 

BS mean declined by a larger 

percentage (70%) than SO2

(34%) between the two 

periods. 

All cause death rates reduced 

by 5.7% (4, 7); respiratory 

deaths by 15.5% (12, 19); 

cardiovascular deaths by 

10.3% (8, 13). 

Dab et al. (1996) 

Paris, France 

Period of Study : 

1987-1992 

Lag: 1 

Poisson autoregressive. Timeseries study. 

24-h avg: 11.2 ppb 

1-h max: 22.5 ppb 

Copollutants: BS, PM13, O3, 

NO2, CO 

Respiratory Lag 1: 2.3% (−0.9, 5.5) 

Díaz et al. (1999) 

Madrid, Spain 

Period of Study: 

1990-1992 

Lag: 1 

Autoregressive OLS 

regression. Time-series study. 

24-h avg: Levels NR. 

Copollutants: TSP, O3, NO2, 

CO 

All cause; respiratory; 

cardiovascular 

Only significant regression 

coefficients were shown, but 

description of the table was 

not clear enough to derive risk 

estimates. 

Fischer et al. (2003) 

The Netherlands 

Period of Study: 

1986-1994 

Lags: 0-6 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg median: 3.5 ppb 

Copollutants: PM10, BS, O3, 

NO2, CO 

All-cause, cardiovascular, 

COPD, and pneumonia in age 

groups < 45, 45-64, 65-74, 

75+ 

Cardiovascular: Age < 45 yrs: 

4.3% (−4.6, 13.9); Age 45-64 

yrs: −0.5% (−3.6, 2.7); Age 65-

74 yrs: 1.6% (−0.8, 4.2); Age 

75+ yrs: 2.8% (1.3, 4.3) 

 F-82 
Study Methods Pollutant Data Outcome Findings 

Garcia-Aymerich et al. (2000) 

Barcelona, Spain 

Period of Study: 

1985-1989 

Selected best averaged lag 

Poisson GLM. Time-series 

study. 

Levels NR. 

Copollutants: BS, O3, NO2

All cause; respiratory; 

cardiovascular; general 

population; patients with 

COPD 

All cause: 

General population: 

Lag 0-3: 4.4% (2.3, 6.5); 

COPD patients: 

Lag 0-2: 2.6% (−5.0, 10.7) 

Respiratory: 

General population: Lag 0-1: 

3.5% (−0.6, 7.8) 

COPD patients: 

Lag 0-2: 2.3% (−8.9, 15.0) 

Cardiovascular: 

General population: Lag 0-3: 

5.1% (2.3, 8.0) 

COPD patients: Lag 0-2: 2.0% 

(−11.5, 17.5) 

Hoek et al. (1997) 

Rotterdam, the Netherlands 

Period of Study: 

1983-1991 

Lag: 1 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg median: 

7.7 ppb 

Copollutants: TSP, BS, Fe, O3, 

CO 

All cause Single-pollutant:1.5% (0.0, 3.0)

With TSP and O3: 0.5% (−1.2, 

2.3) 

Hoek et al. (2001; reanalysis 

2003) 

The Netherlands: Entire 

country, four urban areas 

Period of Study: 

1986-1994 

Lag: 1, 0-6 

Poisson GAM, reanalyzed with 

stringent convergence criteria; 

Poisson GLM. 

Time-series study. 

24-h avg median: 3.5 ppb in 

the Netherlands; 5.6 ppb in the 

four major cities 

Copollutants: PM10, BS, SO4

2-, 

NO3−, O3, NO2, CO; 

2-pollutant models 

All cause; COPD; pneumonia; 

cardiovascular 

Poisson GLM: 

All cause: 

Lag 1: 1.3% (0.7, 1.9) 

Lag 0-6: 1.8% (0.9, 2.7) With 

BS: 1.1% (−0.3, 2.4) 

Cardiovascular: 

Lag 0-6: 2.7% (1.3, 4.1) 

COPD: 

Lag 0-6: 3.6% (−0.3, 7.7) 

Pneumonia: 

Lag 0-6: 6.6% (1.2, 12.2) 

Hoek et al. (2001); reanalysis 

Hoek (2003) 

The Netherlands 

Period of Study: 

1986-1994 

Lag: 0-6 

Poisson GAM, reanalyzed with 

stringent convergence criteria; 

Poisson GLM. Time-series 

study. 

24-h avg median: 3.5 ppb in 

the Netherlands; 5.6 ppb in the 

four major cities 

Copollutants: PM10, O3, NO2, 

CO 

Total cardiovascular; 

myocardial infarction; 

arrhythmia; heart failure; 

cerebrovascular; thrombosisrelated 

Poisson GLM: 

Total cardiovascular: 2.7% 

(1.3, 4.1) 

Myocardial infarctioN: 0.8% 

(−1.2, 2.8) 

Arrhythmia: 2.3% (−3.9, 8.8) 

Heart failure: 7.1% (2.6, 11.7)

Cerebrovascular: 4.4% 

(1.4, 7.5) 

Thrombosis-related: 9.6% (3.1, 

16.6) 

Katsouyanni et al. (1997) 

12 European cities 

Period of Studys vary by city, 

ranging from 1977 to 1992 

“Best” lag variable across 

cities from 0 to 3 

Poisson autoregressive. Timeseries study. 

24-h avg median of the 

median across the cities was 

14 ppb, ranging from 5 ppb 

(Bratislava) to 26 ppb 

(Cracow) 

Copollutants: BS, PM10

All cause All cities: 1.1% (0.9, 1.4) 

Western cities: 2.0% (1.2, 2.8) 

Central eastern cities: 0.5% 

(−0.4, 1.4) 

 F-83 
Study Methods Pollutant Data Outcome Findings 

Keatinge and Donaldson 

(2006) 

London, United Kingdom 

Period of Study: 

1991-2002 

Lags: Mean of 0, −1, −2 

Graphic analysis and GAM. 

Time-series study. 

24-h avg: Levels NR 

Copollutants: O3, PM10

All-cause Relative Risk for a 106

Increase in Mortality (per 10 

ppb SO2) 

SO2 + Temp: 3.1 (0.6, 5.5) 

SO2 + Temp + Acclim.: 2.2 

(−0.1, 4.6) 

SO2 + Temp + Acclim. + 

Acclim. x Temp: 2.5 (0.2, 4.8) 

SO2 + Temp + Acclim. + 

Acclim. x Temp + Sun: 

2.3 (−0.03, 4.5) 

SO2 + Temp + Acclim. + 

Acclim. x Temp + Sun + Wind: 

1.6 (−0.7, 3.8) 

SO2 + Temp + Acclim. + 

Acclim. x Temp + Sun + Wind 

+ Abs. Humidity: 1.7 (−0.6, 

3.9) 

SO2+ Temp + Acclim. + 

Acclim. x Temp + Sun + Wind 

+ Abs. Humidity + Rain: 1.8 

(−0.4, 4.1) 

SO2 + Temp. + Abs. Humidity: 

2.5 (0.03, 4.9) 

Kotesovec et al. (2000) 

Northern Bohemia, Czech 

Republic 

Period of Study: 

1982-1994 

Lags: 0, 1, 2, 3, 4, 5, 6, 0-6 

Poisson GLM, time-series 

study 

24-h avg: 34.9 ppb 

Copollutants: TSP 

All cause, cardiovascular (only 

age = < 65 presented), cancer 

All cause: 

Lag 1: 0.1% (−0.1, 0.4) 

Le Tertre et al. (2002) 

Bordeaux , Le Havre, Lille, 

Lyon, Marseille, Paris, Rouen, 

Strasbourg, France 

Period of Study varies by city, 

ranging from 1990-1995 

Lags: 0-1 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg ranged from 3 ppb 

(Bordeaux) to 9 ppb (Rouen) 

Copollutants: BS, O3, NO2

All cause; respiratory; 

cardiovascular 

8-city pooled estimates: 

All cause: 

2.0% (1.2, 2.9) 

Respiratory: 3.2% (0.1, 6.3) 

Cardiovascular: 3.0% (1.5, 

4.5) 

Michelozzi et al. (1998) 

Rome, Italy 

Period of Study: 

1992-1995 

Lags: 0, 1, 2, 3, 4 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg: 5.7 ppb 

Copollutants: PM13, NO2, O3, 

CO 

All-cause Lag 1: −2.0% (−4.4, 0.5); 

(negative estimates at all lags 

examined) 

Peters et al. (2000b) 

NE Bavaria, Germany 1982-

1994 

Coal basin in Czech Republic 

Period of Study: 

1993-1994 

Lags: 0, 1, 2, 3 

Poisson GLM. Time-series 

study. 

24-h avg: 

Czech Republic: 35 ppb 

Bavaria, Germany: 14 ppb 

Copollutants: TSP, PM10, O3, 

NO2, CO 

All cause; respiratory; 

cardiovascular; cancer 

Czech Republic: 

All cause: 

Lag 1: 0.8% (−0.2, 1.8) 

Bavaria, Germany: 

All cause: 

Lag 1: 0.3% (−0.3, 0.9) 

Pönkä et al. (1998) 

Helsinki, Finland 

Period of Study: 

1987-1993 

Lags: 0, 1, 2, 3, 4, 5, 6, 7 

Poisson GLM. Time-series 

study. 

24-h avg median: 

3.5 ppb 

Copollutants: TSP, PM10, O3, 

NO2

All cause; cardiovascular; age 

< 65 yrs, age 65+ yrs 

No risk estimate presented for 

SO2. PM10 and O3 were 

reported to have stronger 

associations. 

 F-84 
Study Methods Pollutant Data Outcome Findings 

Prescott et al. (1998) 

Edinburgh, Scotland 

Period of Study: 

1992-1995 

Lag: 0 

Poisson GLM. Time-series 

study. 

24-h avg: 1981-1995: 15 ppb 

1992-1995: 8 ppb 

Copollutants: BS, PM10, O3, 

NO2, CO; 2-pollutant models 

All cause; respiratory; 

cardiovascular; all ages; age < 

65 yrs; age 65+ yrs 

Results presented as figures 

only. Essentially no 

associations in all categories. 

Very wide confidence intervals. 

Rahlenbeck and Kahl (1996) 

East Berlin, Germany 

Period of Study: 

1981-1989 

Lags: 0, 1, 2, 3, 4, 5 

OLS, with log of SO2, Timeseries study. 

24-h avg: 61.9 ppb 

“SP” (beta absorption) 

All cause Single-pollutant: 

Lag 1: 4.4% (0, 8.7); 

With SP: 

Lag 1: 2.9% (−2.7, 8.5) 

Roemer and van Wijinen 

(2001) 

Amsterdam, the Netherlands 

Period of Study:` 

1987-1998 

Lags: 1, 2, 0-6 

Poisson GAM with default 

convergence criteria (only one 

smoother). Time-series study. 

24-h avg: 

Background sites: 3.1 ppb 

Traffic sites: 4.2 ppb 

Copollutants: BS, PM10, O3, 

NO2, CO 

All cause Total population using 

background sites: 

Lag 1: 2.6% (−0.6, 5.8) 

Traffic population using 

background sites: 

Lag 1: 0.6% (−6.9, 8.6) 

Total population using traffic 

sites: Lag 1: 2.4% (−0.3, 5.1) 

Saez et al. (1999) 

Barcelona, Spain 

Period of Study: 

1986-1989 

Lags: 0-1 

Poisson with GEE. Timeseries study. 

Levels NR. 

Copollutants: BS, O3, NO2, 

Asthma mortality; age 2-45 yrs RR = 1.9 (0.7, 4.4) 

Saez et al. (2002) 

Seven Spanish cities 

Variable periods of study 

between 1991 and 1996 

Lags: 0-3 

Poisson GAM with default 

convergence criteria. Timeseries study. 

Values for SO2 NR. 

Copollutants: O3, PM, NO2, 

CO 

All cause; respiratory; 

cardiovascular 

Risk estimates for SO2 was 

NR. Including SO2 in 

regression model did not 

appear to reduce NO2 risk 

estimates. 

Spix and Wichman (1996) 

Koln, Germany 

Period of Study: 

1977-1985 

Lags: 0, 1, 0-3 

Poisson GLM. Time-series 

study. 

24-h avg: 15 ppb 

1-h max: 32 ppb 

Copollutants: TSP, PM7, NO2

All-cause Lag 1: 0.8% (0.2, 1.4) 

Sunyer et al. (2002) 

Barcelona, Spain 

Period of Study:` 

1986-1995 

Lags: 0-2 

Conditional logistic (casecrossover) 

24-h avg median: 

6.6 ppb 

Copollutants: PM10, BS, NO2, 

O3, CO, pollen 

All cause, respiratory, and 

cardiovascular mortality in a 

cohort of patients with severe 

asthma 

Odds ratio: Patients with 1 

asthma admission: 

All cause: 14.8% (−19.8, 64.4) 

Patients with more than 1 

asthma adm: All cause: 

50.4% (−48.6, 340.4) 

Patients with more than 1 

asthma or COPDadm: All 

cause: 20.2% (−17.5, 75.0) 

NO2 and O3 were more 

strongly associated with 

outcomes than SO2. 

Sunyer et al. (1996) 

Barcelona, Spain 

Period of Study: 

1985-1991 

Selected best single-day lag 

Autoregressive Poisson. Timeseries study. 

24-h avg median: 

Summer: 13 ppb 

Winter: 16 ppb 

Copollutants: BS, NO2, O3

All cause; respiratory; 

cardiovascular; all ages; age 

70+ yrs 

All yr, all ages: All cause: 

Lag 1: 3.5% (1.9, 5.1) 

Respiratory: 

Lag 0: 3.5% (−0.2, 5.0) 

Cardiovascular: 

Lag 1: 2.2% (0.5, 3.9) 

Verhoeff et al. (1996) 

Amsterdam, the Netherlands 

Period of Study: 

1986-1992 

Lags: 0, 1, 2 

Poisson GLM. Time-series 

study. 

24-h avg: 4.5 ppb 

Copollutants: BS, PM10, O3, 

CO; multipollutant models 

All cause; all ages; age 65+ 

yrs 

Single-pollutant: 

Lag 1: 1.4% (−1.4, 4.2) 

With BS: 

−3.7% (−8.1, 0.9) 

 F-85 
Study Methods Pollutant Data Outcome Findings 

Zeghnoun et al. (2001) 

Rouen and Le Havre, France 

Period of Study: 

1990-1995 

Lags: 0, 1, 2, 3, 0-3 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg: Rouen: 10 ppb 

Le Havre: 12 ppb 

Copollutants: NO2, BS, PM13,

O3

All cause; respiratory; 

cardiovascular 

All cause: 

Rouen: Lag 1: 2.3% (−1.1, 5.9)

Le Havre: Lag 1: 1.1% (−0.3, 

2.5) 

Zmirou et al. (1996) 

Lyon, France 

Period of Study : 

1985-1990 

Lags: Selected best from 0, 1, 

2, 3 

Poisson GLM. Time-series 

study. 

24-h avg: 16 ppb 

Copollutants: PM13, NO2, O3

All cause; respiratory; 

cardiovascular; digestive 

All cause: 

Lag 0: 3.4% (1.4, 5.4) 

Respiratory: 

Lag 3: 2.8% (0.9, 4.8) 

Cardiovascular: 

Lag 0-3: 4.5% (2.0, 7.0) 

Zmirou et al. (1998) 

10 European cities 

Period of Studys vary by city, 

ranging from 1985-1992 

Lags: 0, 1, 2, 3, 0-1, 

0-2, 0-3 (best lag selected for 

each city) 

Poisson GLM. Time-series 

study. 

24-h avg: Cold Season: 

Ranged from 12 ppb (London) 

to 87 ppb (Milan) ppb 

Warm Season: Ranged from 5 

ppb (Bratislava) to 21 ppb 

(Cracow) in warm season 

Copollutants: BS, TSP, NO2, 

O3

Respiratory; cardiovascular Western cities: 

Respiratory: 2.8% (1.7, 4.0) 

Cardiovascular: 2.3% (0.9, 

3.7) 

Central eastern cities: 

Respiratory: 0.6% (−1.1, 2.3) 

Cardiovascular: 0.6% (0.0, 

1.1) 

AUSTRALIA 

Simpson et al. (1997) 

Brisbane, Australia 

Period of Study: 

1987-1993 

Lag: 0 

Autoregressive Poisson with 

GEE. Time-series study. 

24-h avg: 4.2 ppb 

1-h max: 9.6 ppb 

Copollutants: PM10, bsp, O3, 

NO2, CO 

All cause; respiratory; 

cardiovascular 

All cause: 

All yr: Lag 0: −2.8% (−2.7, 8.6)

Summer: 

Lag 0: 2.8% (−8.3, 15.2) 

Winter: 

Lag 0: 2.8% (−3.9, 9.8) 

LATIN AMERICA 

Borja-Aburto et al. (1998) 

SW Mexico City 

Period of Study: 

1993-1995 

Lags: 0, 1, 2, 3, 4, 5, and 

multiday avg. 

Poisson GAM with default 

convergence criteria (only one 

smoother). Time-series study. 

24-h avg: 5.6 ppb 

Copollutants: PM2.5, O3, NO2; 

2-pollutant models 

All cause; respiratory; 

cardiovascular; other; all ages; 

age >65 yrs 

SO2 risk estimates NR. PM2.5

and O3 were associated with 

mortality. 

Borja-Aburto et al. (1997) 

Mexico City 

Period of Study: 

1990-1992 

Lags: 0, 1, 2 

Poisson iteratively weighted 

and filtered least-squares 

method. Time-series study. 

24-h avg median: 

5.3 ppb 

Copollutants: 

TSP, O3 CO; 

2-pollutant models 

All cause; respiratory; 

cardiovascular; all ages; age < 

5 yrs; age >65 yrs 

All-cause: 

Lag 0: 0.2% (−1.1, 1.5) 

Cardiovascular: 

Lag 0: 0.7% (−1.6, 3.0) 

Respiratory: 

Lag 0: −1.0% (−5.0, 3.2) 

Cakmak et al. (2007b) 

7 Chilean urban centers 

Period of Study: 

1997-2003 

Lags: 0, 1, 2, 3, 4, 5, 0-5 

Poisson GLM with random 

effects between cities. Timeseries study. 

24-h avg ranged from 9.12 ppb 

(Las Condes) to 64.06 ppb 

(Independencia) 

Population-weighted avg 

concentration: 14.08 ppb 

Copollutants: PM10, O3, CO 

All cause; respiratory; 

cardiovascular; all ages; age 

< 65 yrs; age 

65-74 yrs; age 75-84 yrs; age 

85+ yrs 

All cause: All ages: 

Single-pollutant: 

Lag 1: 4.0% (2.4, 5.6) 

Lag 0-5: 6.5% (4.5, 8.5) 

Multipollutant: 

Lag 1: 3.2% (1.3, 5.1) 

< 65 yrs: 

Lag 0-5: 3.0% (0.6, 5.5) 

65-74 yrs: 

Lag 0-5: 5.1% (1.2, 9.1 

75-84 yrs: 

Lag 0-5: 7.8% (4.1, 11.6) 

85+ yrs: 7.8% (4.2, 11.5) 

Warm Season: 

Lag 0-5: 7.2% (4.1, 10.3) 

Cool Season: Lag 0-5: 3.0% (-

0.4, 6.5) 

 F-86 
Study Methods Pollutant Data Outcome Findings 

Cifuentes et al. (2000) 

Santiago, Chile 

Period of Study: 

1988-1966 

Lags: 1-2 

Poisson GAM with default 

convergence criteria; 

Poisson GLM. Time-series 

study. 

24-h avg: 18.1 ppb 

Copollutants: PM2.5, PM10-2.5, 

CO, NO2, O3

All cause Poisson GLM: Single-pollutant:

Lag 1-2: 0.2% (−0.9, 1.3) 

With other pollutants: Lag 1-2: 

−0.6% (−1.7, 0.5) 

Conceição et al. (2001) 

São Paulo, Brazil 

Period of Study: 

1994-1997 

Lag: 2 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg: 7.4 ppb 

Copollutants: PM10, CO, O3

Child mortality (age under 5 

yrs) 

Single-pollutant: 

Lag 2: 17.0% (7.0, 28.0); 

With all other pollutants: 

Lag 2: 13.7% (−1.1, 30.8) 

Loomis et al. (1999) 

Mexico City 

Period of Study: 

1993-1995 

Lags: 0, 1, 2, 3, 4, 5, 3-5 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg: 5.6 ppb 

Copollutants: PM2.5, O3

Infant mortality SO2 risk estimates NR. PM2.5

and O3 were associated with 

mortality. 

Ostro et al. (1996) 

Santiago, Chile 

Period of Study: 

1989-1991 

Lag: 0 

OLS, Poisson. Time-series 

study. 

1-h max: 60 ppb 

Copollutants: 

PM10, O3, NO2; 

2-pollutant models 

All cause Lag 0: 0.7% (−0.3, 1.7) 

Pereira et al. (1998) 

São Paulo, Brazil 

Period of Study: 

1991-1992 

Lag: 0 

Poisson GLM. Time-series 

study. 

24-h avg: 6.6 ppb 

Copollutants: PM10, O3, NO2, 

CO 

Intrauterine mortality Single-pollutant model: 11.5% 

(−0.3, 24.7) 

With other pollutants: 8.6% 

(−8.7, 29.3) 

Saldiva et al. (1994) 

São Paulo, Brazil 

Period of Study: 

1990-1991 

Lags: 0-2 

OLS of raw or transformed 

data. Time-series study. 

24-h avg: 6.0 ppb 

Copollutants: PM10, O3, NO2, 

CO; multipollutant models 

Respiratory; age < 5 yrs −1.0% (−47.1, 45.1) 

Saldiva et al. (1995) 

São Paulo, Brazil 

Period of Study: 

1990-1991 

Lag: 0-1 

OLS; Poisson with GEE. Timeseries study. 

24-h avg: 6.5 ppb 

Copollutants: PM10, O3, NO2, 

CO; 2-pollutant models 

All cause; age 65+ yrs Single-pollutant: 8.5% 

(1.3, 15.6) 

With other pollutants: 

−3.1% (−13.0, 6.9) 

ASIA 

Ha et al. (2003) 

Seoul, Korea 

Period of Study: 

1995-1999 

Lag: 0 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg: 11.1 ppb 

Copollutants: PM10, O3, NO2, 

CO 

All cause; respiratory; 

postneonatal (1 mo to 1 yr); 

age 

2-64 yrs; age 65+ 

All cause: 

Postneonates: 11.3% (4.0, 

19.1) 

Age 65+ yrs: 3.2% (3.1, 3.3) 

Hong et al. (2002) 

Seoul, Korea 

Period of Study: 

1995-1998 

Lag: 2 

GAM with default convergence 

criteria. Time-series study. 

24-h avg (ppb): 12.1 (7.4) 

Copollutants: PM10, NO2

CO, O3

Stroke % increase (per 5.7 ppb SO2) 

2.9% (0.8, 5.0) lag 2 

Stratified by PM10

(Median: 47.4 μg/m3) 

<Med: 1.3% 

≥ Med: 3.8% 

Hong et al. (2002) 

Seoul, Korea 

Period of Study: 

1995-1998 

Lag: 2 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg: 12.1 ppb 

Copollutants: PM10, O3, NO2, 

CO 

Acute stroke mortality 5.2% (1.4, 9.0) 

 F-87 
Study Methods Pollutant Data Outcome Findings 

Kwon et al. (2001) 

Seoul, Korea 

Period of Study: 

1994-1998 

Lag: 0 

Poisson GAM with default 

convergence criteria; casecrossover analysis using 

conditional logistic regression. 

24-h avg: 13.4 ppb 

Copollutants: PM10, O3, NO2, 

CO 

Mortality in a cohort of patients 

with congestive heart failure 

Odds ratio in general 

population: 1.0% (−0.1, 2.1) 

Congestive heart failure 

cohort: 6.9% (−3.4, 18.3) 

Lee et al. (2000) 

Seoul, Korea 

Period of Study: 

2000-2004 

Lag: 1 

GAM with stringent 

convergence criteria. Timeseries study. 

24-h avg (ppb): 5.20 (2.17) 

Copollutants: PM10, CO, NO2, 

O3

Non-accidental % Increase (per 3.06 ppb SO2)

2.7 (1.8, 3.5) lag 1 

Lee et al. (1999) 

Seoul and Ulsan, Korea 

Period of Study: 

1991-1995 

Lags: 0-2 

Poisson with GEE. Timeseries study. 

1-h max: Seoul: 26 ppb 

Ulsan: 31 ppb 

Copollutants: TSP, O3

All cause Seoul: 1.5% (1.1, 1.9) 

Ulsan: 1.0% (−0.2, 2.2) 

Lee and Schwartz (1999) 

Seoul, Korea 

Period of Study: 

1991-1995 

Lags: 0-2 

Conditional logistic regression. 

Case-crossover with 

bidirectional control sampling. 

1-h max: 26 ppb 

Copollutants: TSP, O3

All cause Two controls, ± 1 wk: 

0.3% (−0.5, 1.0) 

Four controls, ± 2 wks: 

1.0% (0.3, 1.6) 

Lee et al. (2007) 

7 Korean cities 

Period of Study: 

1991-1997 

Lags: 0-1 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg SO2 ranged from 

12.1 ppb (Kwangju) to 31.4 

ppb (Taegu) 

Copollutants: TSP, NO2, O3, 

CO 

All cause Single-pollutant : 

Lag 0-1 : 0.6% (0.3, 0.8) 

Multipollutant : 

Lag 0-1 : 0.6% (0.2, 0.9) 

Qian et al. (2007) 

Wuhan, China 

Period of Study : 

2000-2004 

Lag: 0 

Poisson GAM with stringent 

convergence criteria. Timeseries study. 

24-h avg (μg/m3): 44.1 (25.3) 

Copollutants: 

PM10

NO2

O3

Non-accidental, 

cardiovascular, stroke, cardiac, 

respiratory, cardiopulmonary 

Mean % change (per 10 μg/m3

SO2) 

Non-accidental: 

All Ages: 0.01 (−0.46, 0.47) 

< 65: −0.55 (−1.33, 0.23) 

≥ 65: 0.22 (−0.32, 0.76) 

Cardiovascular: 

All Ages: 0.20 (−0.45, 0.86) 

< 65: −0.63 (−1.96, 0.72) 

≥ 65: 0.41 (−0.31, 1.14) 

Stroke: 

All Ages: −0.27 (−1.04, 0.51) 

< 65: −1.35 (−3.01, 0.33) 

≥ 65: 0.01 (−0.87, 0.88) 

Cardiac: 

All Ages: 0.88 (−0.22, 1.99) 

< 65: 0.29 (−2.11, 2.75) 

≥ 65: 1.01 (−0.18, 2.21) 

Respiratory: 

All Ages: 1.13 (−0.28, 2.56) 

< 65: −0.59 (−4.24, 3.19) 

≥ 65: 1.36 (−0.05, 2.80) 

Cardiopulmonary: 

All Ages: 0.29 (−0.33, 0.92) 

< 65: −0.80 (−2.07, 0.49) 

≥ 65: 0.53 (−0.15, 1.20) 

HEI (2004) 

East Asian cities 

The lags and multi-day 

averaging used in varied 

Meta-analysis of time-series 

study results 

The levels of SO2 in these 

Asian cities were generally 

higher than those in the U.S. 

or Canadian cities, with more 

than half of these studies 

reporting the mean SO2 levels 

higher than 10 ppb. 

Copollutants considered varied 

across studies. 

All-cause The estimates were found to 

be heterogeneous across 11 

studies. Random-Effects 

Estimate: 1.49% (95% CI: 

0.86, 2.13); Fixed-Effects 

Estimate: 1.01% (95% CI: 

0.73, 1.28). 

 F-88 
Study Methods Pollutant Data Outcome Findings 

Tsai et al. (2003b) 

Kaohsiung, Taiwan 

Period of Study : 

1994-2000 

Lags: 0-2 

Conditional logistic regression. 

Case-crossover analysis. 

24-h avg: 11.2 ppb 

Copollutants: PM10, NO2, O3, 

CO 

All cause; respiratory; 

cardiovascular; tropical area 

Odds ratios: 

All cause: 1.1% (−4.4, 6.8) 

Respiratory: 

3.5% (−17.6, 29.9) 

Cardiovascular: 

2.4% (−9.1, 15.4) 

Venners et al. (2003) 

Chonqing, China 

Period of Study : 

1995 

Lags: 0, 1, 2, 3, 4, 5 

Poisson GLM, time-series 

study 

24-h avg: 74.5 ppb 

Copollutants:PM2.5

All cause, cardiovascular, 

respiratory, cancer, and other 

All cause: 

Lag 2: 1.1% (−0.1, 2.4) 

Cardiovascular: 

Lag 2: 2.8% (0.4, 5.2) 

Respiratory: 

Lag 2: 3.0% (0.4, 5.7) 

Wong et al. (2001a) 

Hong Kong 

Period of Study : 

1995-1997 

Lags: 0, 1, 2 

Poisson GAM with default 

convergence criteria. Timeseries study. 

24-h avg: 

Warm Season: 6.4 ppb 

Cool Season: 6.0 ppb 

Copollutants: PM10, O3, NO2; 

2-pollutant models 

All cause; respiratory; 

cardiovascular 

All cause: Lag 1: 3.2% (1.1, 

5.3) 

Respiratory: Lag 0: 5.3% (2.2, 

8.6) 

Cardiovascular: Lag 1: 4.3% 

(1.1, 7.5) 

Wong et al. (2002b) 

Hong Kong 

Period of Study : 

1995-1998 

Lags: 0, 1, 2, 0-1, 0-2 

Poisson GLM. Time-series 

study. 

24-h avg: 29 ppb 

Copollutants: PM10, O3, NO2; 

2-pollutant models 

Respiratory; cardiovascular; 

COPD; pneumonia and 

influenza; ischemic heart 

disease; cerebrovascular 

Respiratory: Lag 0-1: 2.6% 

(0.2, 5.1) 

Cardiovascular: Lag 0-1: 1.2% 

(−1.0, 3.5) 

Yang et al. (2004a) 

Taipei, Taiwan 

Period of Study: 

1994-1998 

Lags: 0-2 

Conditional logistic regression. 

Case-crossover analysis. 

24-h avg: 5.5 ppb 

Copollutants: PM10, NO2, O3, 

CO 

All cause; respiratory; 

cardiovascular; subtropical 

area 

Odds ratios: 

All cause: −0.5% (−7.0, 6.6) 

Respiratory: −1.8% (−23.1, 

25.3); 

Cardiovascular: −3.4% (−15.2, 

10.0) 

 

 F-89 
Table F-6. Long-term exposure to SO2 and respiratory morbidity. 

Study Methods Pollutant Data Findings 

UNITED STATES AND CANADA 

Dockery et al. (1989) 

Kingston-Harriman, 

TN; Portage, WI; St. 

Louis, MO; 

Steubenville, OH; 

Topeka, KS; 

Watertown, MA 

Period of Study: 

1980-1981 school yr 

Cross-sectional assessment of the association between 

air pollution and chronic respiratory health of 5,422 (10-

12 yrs) white children examined in the 1980-1981 school 

yr. Children were part of the cohort of children in the Six 

Cities Study of Air pollution and Health. Symptoms were 

analyzed using logistic regression that included sex, age,

indicators of parental education, maternal smoking, 

indicator for gas stove, and an indicator for city. 

Respiratory symptoms investigated were bronchitis, 

chronic cough, chest illness, persistent wheeze, asthma. 

The logarithm of pulmonary function was fitted to a 

multiple linear regression model that included sex, sexspecific log of height, age, indicators of parental 

education, maternal smoking, a gas stove indicator, and 

city indicator. Annual means of the 24 h avg air pollutant 

concentration for the 12 mos preceding the examination 

of each child was calculated for each city. 

Daily mean 

concentrations, 

averaging hourly 

concentrations for each 

day with at least 

18 hourly values 

Portage: 4.2 ppb 

Topeka: 3.5 

Watertown: 10.5 

Kingston: 6.5 

St. Louis: 13.5 

Steubenville: 27.8 

No significant associations between SO2 and any 

pulmonary function measurements. No significant 

association between SO2 and symptoms. 

Relative odds and 95% CI between most/least polluted 

cities: 

Bronchitis: 1.5 (0.4, 5.8) 

Chronic cough: 1.8 (0.3, 12.5) 

Chest illness: 1.5 (0.4, 5.9) 

Persistent wheeze: 0.9 (0.4, 1.9) 

Asthma: 0.6 (0.3, 1.2) 

Reference symptoms: 

Hay fever: 0.6 (0.2, 1.7) 

Ear ache: 1.2 (0.3, 5.3) 

Nonrespiratory illness: 1.0 (0.6, 1.5) 

Analysis stratified by asthma or persistent wheeze 

bronchitis 

No wheeze or asthma 1.5 (0.5, 4.3) 

Yes wheeze or asthma 2.0 (0.3, 14.3) 

Chronic cough 

No wheeze or asthma 2.4 (0.5, 11.7) 

Yes wheeze or asthma 1.9 (0.1, 44.1) 

Chest illness 

No wheeze or asthma 1.5 (0.4, 5.6) 

Yes wheeze or asthma 1.9 (0.3, 13.0) 

Dockery et al. (1996) 

18 sites in U.S. 

6 sites in Canada 

Period of Study: 

1988-1991 

Study of the respiratory health effects of acid aerosols in 

13,369 white children aged 8 to 12 yrs old from 24 

communities in the U.S. and Canada between 1988 and 

1991. Information was gathered by questionnaire and a 

pulmonary function. 

SO2 avg 4.8 ppm 

SD 3.5 

Range 0.2, 12.9 

With the exception of the gaseous acids (nitrous and 

nitric acid), none of the particulate or gaseous 

pollutants, including SO2, were associated with 

increased asthma or any asthmatic symptoms. Stronger

associations with particulate pollutants were observed 

for bronchitis and bronchitic symptoms. 

Odds Ratio (95% CI) for 12.7 ppb range of SO2

pollution 

Asthma 1.05 (057, 1.93) 

Attacks of Wheeze 1.07 (0.75, 1.55) 

Persistent Wheeze 1.19 (0.80, 1.79) 

Any asthmatic symptoms 1.16 (0.80, 1.68) 

Bronchitis 1.56 (0.95, 2.56) 

Chronic cough 1.02 (0.66, 1.58) 

Chronic phlegm 1.55 (1.01, 2.37) 

Any Bronchitic symptoms 1.29 (0.98, 1.71) 

 F-90 
Study Methods Pollutant Data Findings 

Euler et al. (1987) 

California, USA 

Cross-sectional study of 7,445 (25 yrs or older) SeventhDay Adventists who lived in their 1977 residential areas 

(Los Angeles and it border counties, San Francisco, and 

San Diego) for at least 10 yrs to determine the effect of 

long-term cumulative exposure to ambient levels of TSP 

and SO2 on COPD symptoms. Study population is 

subgroup of NCI-funded ASHMOG study that enrolled 

36,805 Seventh-Day Adventists in 1974. Each 

participant’s cumulative exposure to the pollutant 

exceeding 4 different threshold levels were estimated 

using moly residence ZIP code histories and interpolated 

dosages from state monitoring stations. Participants 

completed a questionnaire on respiratory symptoms, 

smoking history, occupational history, and residence 

history. 

None provided Study reported that SO2 exposure was not associated 

with symptoms of COPD until concentrations exceeded 

4 ppm. The correlation coefficient of SO2 (above 4 ppm) 

with TSP (above 200 μg/m3) the highest exposure 

levels for these two pollutants was 0.30; thus, the 

authors believed that it was possible to separate the 

effects of SO2 from TSP. Multiple regressions used in 

the analysis. No significant effect at exposures levels 

below 4 ppm or above 8 ppm. 

Relative risk estimate (based on 1,003 cases) 

SO2 exposure above 2 ppm during 11 yrs of study 

 2000 h/yr: 1.09 1000 h/yr : 1.04 

 500 h/yr: 1.03 

SO2 exposure above 4 ppm 

 500 h/yr : 1.18 250 h/yr: 1.09 

 100 h/yr: 1.03 

SO2 above 8 ppm 

 60 h/yr: 1.07 30 h/yr: 1.03 

 15 h/yr: 1.02 

SO2 above 14 ppm 

 10 h/yr: 1.03 5 h/yr: 1.01 

 1 h/yr: 1.00 

Goss et al. (2004) 

U.S. nationwide 

Period of Study: 

1999-2000 

Cohort study of 18,491 cystic fibrosis patients over 6 yrs 

of age who were enrolled in the Cystic Fibrosis 

Foundation National Patient Registry in 1999 and 2000. 

Mean age of patients was 18.4 yrs; 92% had pancreatic 

insufficiency. Air pollution from the Aerometric 

Information Retrieval System linked with patient’s home 

ZIP code. Air pollutants studied included O3, NO2, SO2, 

CO, PM10, and PM2.5. Health endpoints of interest were 

pulmonary exacerbations, lung function, and mortality. 

However, study did not have enough power to assess 

the outcome of mortality. Logistic regression and 

polytomous regression models that adjusted for sex, 

age, weight, race, airway colonization, pancreatic 

function, and insurance status were used. 

Mean (SD): 4.91 

(2.6) ppb 

Median: 4.3 ppb 

IQR: 2.7-5.9 ppb 

With the single-pollutant model, no significant 

association between SO2 and pulmonary 

exacerbations. 

Odds ratio per 10 ppb increase in SO2: 

0.83 (95% CI: 0.71, 1.01), p = 0.068 

No clear association between pulmonary function and 

SO2. No effect estimates provided. 

McDonnell et al. 

(1999) 

California, U.S. 

Period of Study: 

1973-1992 

Prospective study (over 15 yrs) of 3,091 nonsmokers 

aged 27-87 yrs that evaluated the association between 

long-term ambient O3 exposure and the development of 

adult-onset asthma. Cohort consisted of nonsmoking, 

non-Hispanic white, California Seventh Day Adventists 

who were enrolled in 1977 in the AHSMOG study. 

Logistic regression used to assess the association 

between the 1973-1992 mean 8-h avg ambient O3

concentration and the 1977-1992 incidence of doctor-told

asthma. Levels of PM10, NO2, and SO4 were measured 

but no effect estimates were given. 

Mean: SO2 6.8 μg/m3

Range: 0.0-10.2 μg/m3

Correlation coefficient 

r = 0.25 with O3

No significant positive association between SO2 and 

asthma for males or females. Addition of a second 

pollutant to the O3 model for the male subjects, did not 

result in a decrease of more than 10% in the magnitude 

of the regression coefficient for O3, and for the females 

addition did not cause the coefficient for O3 to become 

significantly positive 

Schwartz (1989) 

United States 

Period of Study: Feb

1976-Feb 1980 

Cross-sectional study using data from the Second 

National Health and Nutrition Examination Survey 

(NHANES II) to examine the relation between air 

pollution and lung function growth in 4,300 children and 

youths 6-24 yrs old. A two-staged analysis was 

performed that consisted of (1) regression equations 

including factors known to affect lung function and (2) a 

regression of the residuals of the first regression on air 

pollution. 

Annual percentiles 

(ppm): 

10th: 0.0060 

25th: 0.0106 

50th: 0.0131 

75th: 0.0159 

90th: 0.0193 

The study did not find an association between SO2 and 

any of the lung function growth measurements (i.e., 

FVC, FEV1, and Peak flow). 

 F-91 
Study Methods Pollutant Data Findings 

EUROPE 

Ackermann-Liebrich 

et al. (1997) 

8 communities in 

Switzerland 

Aarau, Basel, Davos,

Geneva, Lugano, 

Montana, Payerne, 

and Wald 

Period of Study: 

1991-1993 

Cross-sectional population based study of 9,651 adults 

(18-60 yrs) in 8 areas in Switzerland (SAPALDIA), to 

evaluate the effect of long-term exposure of air pollutants 

on lung function. Examined the effects of SO2, NO2, O3, 

TSP, and PM10. Participants were given a medical exam 

that included questionnaire data, lung function tests, skin 

prick testing, and end-expiratory CO concentration. 

Subjects had to reside in the area for at least 3 yrs to be 

in the study. 

Avg SO2 in 1991 (μg/m3)

Mean: 11.7 

SD: 7.1 

Range: 2.5, 25.5 

Mean values of SO2, PM10, and NO2 were significantly 

associated with reduction in pulmonary function. SO2

was correlated with Pm3

0 (r = 0.78), PM10 (r = 0.93) and 

NO2 (r = 0.86). Authors stated that the association with 

SO2 disappeared after controlling for PM10 but no data 

was shown. 

Regression coefficients and 95% CI in healthy never 

smokers (per 10 μg/m3 increase in annual avg SO2) 

FVC: −0.0325 (−0.0390, −0.0260) 

FEV1: −0.0125 (−0.0192,−0.0058) 

Braun-Fahrlander 

et al. (1997) 

10 communities in 

Switzerland 

Anieres, Bern, Biel, 

Geneva, Langnau, 

Lugano, Montana, 

Payerne, Rheintal, 

Zurich 

Period of Study: 

1992-1993 

Cross-sectional study of 4,470 children (6-15 yrs) living 

in 10 different communities in Switzerland to determine 

the effects of long term exposure to PM10, NO2, SO2, and 

O3 on respiratory and allergic symptoms and illnesses. 

Part of the Swiss Study on Childhood Allergy and 

Respiratory Symptoms with Respect to Air Pollution 

(SCARPOL). 

Annual avg SO2 (μg/m3)

Lugano: 23 

Geneva: 13 

Zurich: 16 

BerN: 11 

Anieres: 4 

Biel: 15 

Rheintal: 8 

Langnau: NA 

Payerne: 3 

MontaN: 2 

This study reported that the annual mean SO2, PM10, 

and NO2 were positively and significantly associated 

with prevalence rates of chronic cough, nocturnal dry 

cough, and bronchitis and conjunctivitis symptoms. 

Strongest association found with PM10. However, there 

was no significant association between SO2 and 

asthma or allergic rhinitis. 

Adjusted relative odds between the most/least polluted 

community 2-23 μg/m2 (0.8, 8.8 ppb) 

Chronic cough: 1.57 (1.02, 2.42) 

Nocturnal dry cough: 1.66 (1.16, 2.38) 

Bronchitis: 1.48 (0.98, 2.24) 

Wheeze: 0.88 (0.54, 1.44) 

Asthma (ever): 0.74 (0.45, 1.21) 

Sneezing during pollen Season: 1.07 (0.67, 1.70) 

Hay fever: 0.84 (0.55, 1.29) 

Conjunctivitis symptoms: 1.74 (1.22, 2.46) 

Diarrhea: 1.02 (0.75, 1.39) 

Charpin et al. (1999) 

Etang de Berre area 

of France: Arles, 

Istres, Port de Bouc, 

Rognac-Velaux, 

Salon de Provence, 

Sausset, Vitrolles 

Period of Study : 

Jan-Feb 1993 

Cross-sectional cohort study of 2,073 children (10-11 

yrs) from 7 communities in France (some with the 

highest photochemical exposures in France) to test the 

hypothesis that atopy is greater in towns with higher 

photochemical pollution levels. Mean levels of SO2, NO2, 

and O3 were measured for 2 mos in 1993. Children 

tested for atopy based on skin prick test (house dust 

mite, cat dander, grass pollen, cypress pollen, and 

Alternaria). To be eligible for the study, subjects must 

have resided in current town for at least 3 yrs. 

Questionnaire filled out by parents that included 

questions on SES passive smoking at home. Two-mo 

mean level of air pollutants used in logistic regression 

analysis. 

24-h avg (SD) 

SO2 (μg/m3) 

Arles: 29.7 (15.5) 

Istres: 23.8 (12.7) 

Port de Bouc: 32.3 

(24.5) 

Rognanc-Velaux: 

39.5 (21.8) 

Salon de Provence: 

17.3 (11.6) 

Sausset: 29.0 (28.7) 

Vitrolles : 57.4 (32.0) 

Study did not demonstrate any association between air 

pollution and atopic status of the children living in the 

seven communities, some with high photochemical 

exposures. A limitation of study is that authors did not 

consider short-term variation in air pollution and did not 

have any indoor air pollution measurements. 

Frischer et al. (1999) 

Nine communities in 

Austria 

Period of Study: 

1994-1996 

Longitudinal cohort study of 1150 children (mean age 

7.8 yrs) to investigate the long-term effects of O3 on lung 

growth. Children were followed for 3 yrs and lung 

function was recorded biannually, before and after 

summertime. The dependant variables were change in 

FVC, FEV1, and MEF50. The 9 sites were selected to 

represent a broad range of O3 exposures. GEE models 

adjusted for baseline function, atopy, gender, site, 

environmental tobacco smoke exposure, season, and 

change in height. Other pollutants studied included PM10, 

SO2, and NO2. 

Annual mean SO2 (ppb) 

in 1994 

Amstetten: 3.75 

St. Valentin: 3.00 

Krems: 3.75 

Heidenreichstein: 4.13 

Ganserndorf: 5.63 

Mistelbach: 5.25 

Wiesmath: 6.00 

Bruck: 4.88 

Pollau: 2.25 

No consistent association observed between lung 

function and SO2, NO2 and PM10. A negative effect 

estimate was observed during the summer and a 

positive estimate during the winter. 

Change in lung function (per ppb SO2): 

FEV1 (mL/day): 

Summer: −0.018 (0.004), p < 0.001 

Winter : 0.003 (0.001), p < 0.001 

FVC (mL/day): 

Summer: −0.009 (0.004), p = 0.02 

Winter: 0.002 (0.001), p = 0.03 

MEF50 (mL/s/day): 

Summer: −0.059 (0.010), p < 0.001 

Winter: 0.003 (0.003), p = 0.26 

 F-92 
Study Methods Pollutant Data Findings 

Frischer et al. (2001) 

Nine communities in 

Austria 

Period of Study: 

Sep-Oct 1997 

Cross-sectional cohort study of 877 children (mean age 

11.2 yrs) living in 9 sites with different O3 exposures. 

Urinary eosinophil protein U-EPX) measured as a marker

of eosinophil activation. U-EPX determined from a single 

spot urine sample analyzed with linear regression 

models. 

1⁄2-h avg SO2: 

30-day mean 2.70 ppb 

IQR 2.1 ppb 

No significant association between SO2 and U-EPX 

Regression coefficient and SE −10.57 (0.25) per ppb 

SO2

Frye et al. (2003) 

Zerbst, Hettstedt, 

Bitterfeld,East 

Germany 

Period of Study: 

1992-93, 1995-1996, 

1998-1999 

Three consecutive cross-sectional surveys of children 

(11-14 yrs) from three communities in East Germany. 

Parents of 3,155 children completed a questionnaire on 

symptoms. Lung function tests performed on 

2,493 children. Study excluded children if they lived for 

less than 2 yrs in current home and if their previous 

home was more than 2 km away. The log-transformed 

lung function parameters were used as the response 

variables in a linear regression analysis that controlled 

for sex, height, season of examination, lung function 

equipment, parental education, parental atopy, and 

environmental tobacco smoke. Used avg of annual 

means of pollutants 2 yrs preceding each survey. 

Used avg of annual 

means of pollutants 2 yrs

preceding health 

measurement 

High of 113 μg/m3 (in 

Bitterfeld) to a low of 

6 μg/m3. (Pollution 

values only described in 

figure) 

The annual mean TSP declined from 79 to 25 μg/m3

and SO2 from 113 to 6 μg/m3 and the mean FVC and 

FEV1 increased from 1992-1993 to 1998-1999. Study 

concluded that reduction of air pollution in a short time 

period may improve children’s lung function. 

Percent change of lung function for a 100-μg/m3

decrease in SO2 2 yrs before the investigation 

(N: 1,911) 

FVC: 4.9 (0.7, 9.3) 

FEV1: 3.0 (−1.1, 7.2) 

FEV1/FVC: −1.5 (−3.0, 0.1) 

Garcia-Marcos et al. 

(1999) 

Cartagena, Spain 

Period of Study: 

winter 1992 

A total of 340 children (10-11 yrs) living in and attending 

schools within a polluted and a relatively nonpolluted 

area were included in this study which aimed to establish 

the relative contribution socioeconomic status, parental 

smoking, and air pollution on asthma symptoms, 

spirometry, and bronchodilator response. Parents 

completed questionnaire on respiratory symptoms and 

risk factors including, living in polluted area, maternal 

smoking, paternal smoking, number of people living in 

the house, proximity to heavy traffic roads. Spirometry 

was performed before and after an inhaled 0.2 mg 

fenoterol was delivered to determine bronchodilator 

response. Bronchodilator response was considered 

positive if the FVC after fenoterol was increased by at 

least 10% or PEF by 12%. Logistic regression included 

as independent variables all the risk factors. 

Annual mean SO2

(μg/m3) Polluted areas 

75 μg/m3

Nonpolluted areas: 

20 μg/m3

This study found that living in the polluted areas 

reduced the risk of a positive bronchodilator response 

(RR = 0.61, p = 004). 

Gokirmak et al. 

(2003) 

Malatya, Turkey 

Study on occupational exposure to SO2 in apricot 

sulfurization workers that investigated the role of 

oxidative stress resulting exposure to high 

concentrations of SO2 on bronchoconstriction. Forty 

workers (mean age: 28 yrs, range 16-60 yrs) who have 

been working in apricot sulfurization for 20-25 days each 

yr and 20 controls (mean age: 29 yrs, range 17-42) who 

had no SO2 exposure participated in the study. Activities 

of antioxidant enzymes (glutathione peroxidase (GSHPx), superoxide dismutase (SOD) and catalase) 

malondialdehyde (MDA) concentrations (marker of lipid 

peroxidation), and pulmonary function test measured in 

subjects. 

SO2 conc ranged from 

106.6 to 639.2 ppm in 

9 apricot farms. 

Mean conc around 

sulfurization chamber: 

324.1 (35.1) ppm 

SOD, GSH-Px, and catalase activities were lower and 

malondialdehyde concentrations were higher in the 

apricot sulfurization workers compared to controls. 

Pulmonary function decreased after SO2 exposure 

among the apricot sulfurization workers. Authors 

concluded that occupational exposure to high 

concentrations of SO2 enhances oxidative stress and 

that lipid peroxidation may be a mechanism of SO2

induced bronchoconstriction. 

Apricot sulfurization workers vs. controls 

Mean (SD) 

SOD (U/mL): 2.2 (0.6) vs. 3.2 (0.7) U/m, 

p < 0.0001 

Glutathione peroxidase (U/mL): 0.6 (0.3) vs. 1.1 (0.3), 

p < 0.0001 

Catalase (L/L): 107.6 (27.4) vs. 152.6 (14.3), 

p < 0.0001 

MDA (nmol/L): 4.1 (0.9) vs. 1.9 (5.3) , p < 0.0001 

Before vs. after SO2 exposure among apricot 

sulfurization workers 

Mean (SD) 

FVC (% predicted) 88 (17) vs. 84 (16) , p < 0.001 

FEV1 (% predicted) 98 (14) vs. 87 (14), p < 0.001 

FEV1/FVC: 92 (7) vs. 86 (9), p < 0.001 

FEF25-75% (% predicted) 108 (19) vs. 87 (23) , p < 

0.001 

 F-93 
Study Methods Pollutant Data Findings 

Heinrich et al. (2002) 

Reunified Germany 

Bitterfeld, Hettstedt, 

Zerbst 

Period of Study: 

1992-1993, 1995-

1996, 1998-1999 

Three cross-sectional surveys of children (5-14 yrs) from 

3 areas that were formerly part of East Germany to 

investigate the impact of declines in TSP and SO2 on 

prevalence of nonallergic respiratory disorders in 

children. Study excluded children if they lived for less 

than 2 yrs in current home and if their previous home 

was more than 2 km away. GEE used for analysis. 

SO2 concentration in 

μg/m3

Yr /Zerbst/Bitterf/Hettst

1991/78/113/ 84 

1992/ 58/ 75/ 46 

1993/ 42/ 60/ 49 

1994/ 29/ 35/ 38 

1995/ 21/ 30/ 26 

1996 25 24 25 

1997/ 13/ 13/ 13 

1998/ 8/ 9/ 6 

Study found that SO2 exposure was significantly 

associated with prevalence of bronchitis, frequent 

colds, and febrile infections. While results are reported 

as risk for an increase in air pollutant, the respiratory 

health of children improved with declines in TSP and 

SO2. Authors concluded that exposure to combustionderived air pollution is causally related to nonallergic 

respiratory health in children. 

Odds ratio and 95% CI: (per 100 μg/m3 in 2 yr mean 

SO2) 

All children: 

Bronchitis: 2.72 (1.74, 4.23) 

Otitis media: 1.42 (0.94, 2.15) 

Sinusitis: 2.26 (0.85, 6.04) 

Frequent colds: 1.81 (1.23, 2.68) 

Febrile infections: 1.76 (1.02, 3.03) 

Cough in morning: 1.10 (0.73, 1.64) 

Shortness of breath: 1.31 (0.84, 2.03) 

Children without indoor exposures (living in damp 

houses with visible molds, ETS in the home, gas 

cooking emissions, and contact with cats): 

Bronchitis: 4.26 (2.15, 8.46) 

Otitis media: 1.43 (0.73, 2.81) 

Sinusitis: 2.95 (0.52, 16.6) 

Frequent colds: 2.29 (1.15, 4.54) 

Febrile infections: 1.75 (0.78, 3.91) 

Cough in morning: 1.00 (0.38, 2.64) 

Shortness of breath: 2.07 (0.90, 4.75) 

Herbarth et al. 

(2001) 

East Germany 

Period of Study: 

1993-1997 

Meta-analysis of three cross-sectional studies: (1) Study 

on Airway Diseases and Allergies among Kindergarten 

Children (KIGA), (2) the Leipzig Infection, Airway 

Disease and Allergy Study on School starters (LISS), 

and (3) KIGA-IND, which was based on the KIGA design 

but conducted in 3 differentially polluted industrial areas. 

A total of 3,816 children participated in the three studies. 

Analysis of data from parent-completed questionnaires to

determine the effect of life time exposure to SO2 and 

TSP on the occurrence of acute bronchitis. Total lifetime 

exposure burden corresponds to the exposure duration 

from birth to time of the study. The LISS study was 

divided in to LISS-U for the urban area and LISS-R for 

the rural area. Logistic regression analysis used that 

adjusted for predisposition in the family (mother or father 

with bronchitis), ETS, smoking during pregnancy or in 

the presence of the pregnant women. 

Avg lifetime exposure 

burden of SO2 (μg/m3) 

KIGA: 142 

LISS: 48 

LIISS: R 47 

KIGA-IND: 59 

This study found the highest bronchitis prevalence in 

the KIGA cohort and the lowest in the LISS cohort, 

which is consistent with the SO2 concentrations in these

cohorts. Study found a correlative link between SO2 and

bronchitis (R = 0.96, p < 0.001) but not TSP (R = 0.59). 

Results of study suggest that SO2 may be a more 

important factor than TSP in the occurrence of 

bronchitis in these study areas. 

Odds ratio for bronchitis adjusted for parental 

predisposition, smoking, and lifetime exposure to SO2

and TSP (2-pollutant model). 

SO2: 3.51 (2.56, 4.82) 

TSP: 0.72 (0.49, 1.04) 

Hirsch et al. (1999) 

Dresden, Germany 

Cross sectional study to relate the prevalence of 

respiratory and allergic diseases in childhood to 

measurements of outdoor air pollutants. 5,421 children 

ages 5-7 yrs and 9-11 yrs were evaluated by 

questionnaires, skin-prick testing, venipuncture for (Ig)E, 

lung function, and bronchial challenge test. 

Mean (μg/m3): 48.3 

Range: 29.0-69.3 

25-75 percentile 42.7-

54.3 

SOX was positively associated with current morning 

cough but not with bronchitis. 

Prevalence odds ratio (95% CI) for symptoms within 

past 12 mos, +10 μg/m3: 

Wheeze: Atopic 103 (0.79, 1.35) μg/m3; 

Nonatopic 1.36 (1.01, 1.84) 

Morning Cough: Atopic 1.22 (0.92, 1.61) 

Nonatopic 1.32 (1.07, 1.63) 

Prevalence odds ratio (95% CI) for doctor’s diagnosis, 

+10 μg/m3: 

Asthma: Atopic 1.07 (0.79, 1.45) 

Nonatopic 1.35 (1.00, 1.82) 

Bronchitis: Atopic 1.04 (0.87, 1.25) 

Nonatopic 0.99 (0.88, 1.12) 

 F-94 
Study Methods Pollutant Data Findings 

Horak et al. (2002) 

Eight communities in 

Austria 

Period of Study: 

1994-1997 

Longitudinal cohort study that continued the work of 

Frischer et al. (1999) by adding one more yr of data and 

analyzing the effects of PM10 in addition to SO2, NO2, 

and O3. At the beginning of the study 975 children (mean 

age 8.11 yrs) were recruited for the study, but only 80.6%

of the children performed all 6 lung function tests (twice a

yr). The difference for each lung function parameter 

between two subsequent measures was divided by the 

days between measurements and presents as difference 

per day (dpd) for that parameter. 860 children were 

included in the GEE analysis that controlled for sex, 

atopy, passive smoking, initial height, height difference, 

site, and initial lung function. 

Seasonal avg SO2

μg/m3: 

Winter: 

Mean: 16.8 

Range: 7.5, 37.4 

Summer: 

Mean : 6.9 μg/m3

Range: 3.1, 11.7 

Moderate correlation between PM10 and SO2 in the 

winter (r = 0.52). In a one-pollutant model for SO2, long 

term seasonal mean concentration of SO2 was had a 

positive association with FVC dpd and FEV1 dpd in the 

winter, but no effect on MEF25-75 dpd. In a two-pollutant 

model with PM10, wintertime SO2 had a positive 

association with FEV1 dpd. 

Single-pollutant model 

FVC dpd: 

Summer: 0.009, p = .336; Winter: 0.006, p = .009 

FEV1 dpd: Summer : 0.005, p = 0.576; 

Winter: 0.005, p = 0.013 

MEF25-75: Summer: 0.015, p = 0.483; 

Winter: 0.003, p = 0.637 

Two-pollutant model: 

SO2 + PM10

FVC dpd: Summer: 0.008, p = 0.395; 

Winter: 0.004, p = 0.225 

FEV1 dpd: Summer : 0.010 (0.271); Winter: 0.007 

(0.025) 

MEF25-75 dpd: Summer : 0.037, p = 0.086; 

Winter: 0.007, p = 0.429 

Jedrychowski et al. 

(1999) 

Krakow, Poland 

Period of Study: 

1995 (Mar-Jun) and 

1997 (Mar-Jun) 

Cohort prospective study consisting of 1,001 

preadolescent children (9 yrs old) from two areas of 

Krakow, Poland. The study examined lung function 

growth using FVC and FEV1 measurements taken in 

1995 and then again two yrs later, 1997. Used a twostage analysis that consisted of (1) multivariate linear 

regression analyses to determine body variables that are 

significant predictors of lung function growth, and then 

(2) multivariate logistic regression to examine the relation

between air pollution and lung function growth. 

Annual avg: 

City Center (μg/m3): 

43.87 (32.69) 

Control Area (μg/m3): 

31.77 (21.93) 

The study did not provide individual estimates for SO2. 

Koksal et al. (2003) 

Malatya, Turkey 

Study on occupational exposure to high concentrations 

of SO2 on respiratory symptoms and pulmonary function 

on apricot sulfurization workers. Apricot sulfurization 

workers (N: 69) from 15 apricot farms who have been 

working in sulfurization of apricots for 20-25 days a yr 

during each summer were recruited for the study. 

Subjects rated symptoms (itchy eyes, runny nose, stuffy 

nose, itchy or scratchy throat, cough, shortness of 

breath, phlegm, chest pain, and fever) before during and 

1-h after each exposure. 

SO2 conc ranged from 

106.6 to 721.0 ppm 

SO2 exposure at high concentrations increased 

symptoms of itchy eyes, shortness of breath, cough, 

running and/or stuffy nose, and itchy or scratchy throat 

during exposure (p < 0.05). Inhalation of high 

concentrations of SO2 for 1-h caused significant 

decreases in pulmonary function. Difference in 

pulmonary function measured before and after 

exposure: 

FVC (L) 0.16 (0.42), p < 0.05 

FEV1 (L) 0.39 (0.36), p < 0.001 

FEV1/FVC: 5.22 (6.75), p < 0.001 

PEF (L/s) 1.39 (1.06), p < 0.001 

FEF25-75% (L/s) 0.82 (0.70), p < 0.001 

 F-95 
Study Methods Pollutant Data Findings 

Kopp et al. (2000) 

Ten communities in 

Austria and SW 

Germany 

Longitudinal cohort study of 797 children (mean age 

8.2 yrs) from 2nd and 3rd grades of 10 schools in Austria 

and SW Germany to assess the effects of ambient O3 on 

lung function in children over a 2-summer period. Study 

also examined the association between avg daily lung 

growth and SO2, NO2, and PM10. Each child performed 4 

lung function tests during spring 1994 and summer 1995.

ISAAC questionnaire used for respiratory history. Linear 

regression models used to assess effect of air pollutants 

on FVC and FEV1, which were surrogates of lung growth.

Mean SO2 (95% CI) ppb

Apr-Sep 1994 

Amstetten: 3.7 (0.7, 3.9)

St Valentin: 2.6 (1.5, 5.2)

Krems: 3.7 (0.7, 7.5) 

Villingen: 0.7 (0 , 3.0) 

Heindenreichstein: 3.7, 

(0.7, 7.5) 

Ganserndorf: 3.7 (0.7, 

11.2) 

Mistelbach: 3.7 (0.7, 7.5)

Wiesmath: 6.3 (3.4, 9.4)

Bruck: 1.5 (0.7, 4.1) 

Freudenstadt: 0.7 (0, 

3.0) 

Oct 1994-Mar 1995 

Amstetten: 3.7 (0.7, 7.5)

St Valentin: 3.0 (1.1, 9.4)

Krems: 3.7 (0.7, 11.0) 

Villingen: 1.9 (0 , 3.0) 

Heindenreichstein: 3.7 

(0.7, 15.0) 

Ganserndorf: 3.7 (0.7, 

22.5) 

Mistelbach: 3.7 (0.7, 

22.5) 

Wiesmath: 2.23 (0.7, 

10.1) 

Bruck: 15 (1.1, 7.9) 

Freudenstadt: 1.57 (0.4, 

5.3) 

Apr-Sep 1995 

Amstetten: 3.7 (0.7, 3.8)

St Valentin: 2.6 (1.1, 6.8)

Krems: 3.7 (0.5, 3.8) 

Villingen: 0.7 (0 , 2.6) 

Heindenreichstein: 0.7 

(0.5, 0.9) 

Ganserndorf: 3.7 (0.7, 

7.5) 

Mistelbach: 3.7 (0.7, 7.5)

Wiesmath: 7.5 (0.7, 

14.9) 

Bruck: 3.7 (0.4, 4.9) 

Freudenstadt: 0.7 (0, 

3.4) 

Lower FVC and FEV1 increases observed in children 

exposed to high ambient O3 levels vs. those exposed to 

lower levels in the summer. This study found no effect 

of SO2 and PM10 on FVC increase during the summer of

1995 and winter 1994/1995, however, SO2 was 

negatively associated with FVC during the summer of 

1994. 

Change in FVC (per ppb SO2) 

Summer 1994: −0.044, p = 0.006 

Winter 1994/95: 0.007, p = 0.243 

Summer 1995: 0.045, p = 0.028 

 F-96 
Study Methods Pollutant Data Findings 

Kramer et al. (1999) 

East and West 

Germany 

Period of Study: 

1991 to 1995 

Repeated cross-sectional studies between 1991 and 

1995 on 7-yr-old children in East Germany and between 

1991 and 1994 in West Germany. Comparison of 

prevalence of airway diseases and allergies in East and 

West Germany during the first five yrs after reunification. 

A total of 19,090 children participated in the study. 

Logistic regression used to assess the effect of SO2 and 

TSP on airway diseases and allergies. Analysis 

performed on 14,144 children with information on all 

covariates of interest. 

East Germany 2-yr avg 

concentration ranged 

from 45 to 240 μg/m3

West Germany 2-yr avg 

concentration ranged 

from 18-33 

All infectious airway diseases and irritation of the airway

was associated with either SO2 or TSP in East 

Germany in 1991. The decrease of pollution between 

1991 and 1995 had a favorable effect on the prevalence

of these illnesses. SO2 was significantly associated with 

more than 5 colds in the last 12 mos, tonsillitis, dry 

cough in the last 12 mos, and frequent cough in 1991-

1995. 

Odds ratio and 95% CI: (per 200 μg/m3 SO2) in East 

Germany areas, 1991-1995 for children living at least 2 

yrs in the areas, adjusted for time trend: 

Infectious airway diseases: 

Pneumonia ever diagnosed: 1.17 (0.85, 1.62) 

Bronchitis ever diagnosed: 0.85 (0.68, 1.05) 

∃5 colds in last 12 mos: 1.55 (1.18, 2.04) 

Tonsillitis in the last 12 mos: 1.89 (1.49, 2.39) 

Dry cough in the last 12 mos: 1.46 (1.12, 1.91) 

Frequent cough ever: 2.51 (1.79. 3.53) 

Allergic diseases and symptoms: 

Irritated eyes in the last 12 mos: 1.06 (0.66, 1.70) 

Irritated nose in the last 12 mos: 1.26 (0.96, 1.66) 

Wheezing ever diagnosed: 0.68 (0.46, 1.01) 

Bronchial asthma ever diagnosed: 2.73 (1.24, 6.04) 

Hay fever ever diagnosed: 0.60 (0.24, 1.52) 

Eczema ever diagnosed: 0.87 (0.65, 1.18) 

Allergy ever diagnosed: 0.93 (0.67, 1.29) 

Liebhart et al. (2007) 

Poland (Bialystok, 

Bydgoszcz, Gdansk, 

Krakow, Lublin, 

Lodz, Poznan, 

Rabka, Warszawa, 

Wroclaw, Zabrze) 

Period of Study: 

1998-1999 

The Polish Multicentre Study of Epidemiology of Allergic 

Diseases (PMSEAD), which consisted of a cohort of 

16,238 individuals aged 3-80 yrs old from 33 areas in 11 

regions of Poland. Asthma diagnosis was determined 

through household questionnaires. Conducted 

multivariate and univariate logistic regression analyses to

examine the prevalence of and risk factors for asthma. 

Range (μg/m3): 

4.0-35.0 

In multivariate logistic regression models, BS was found

to be a significant risk factor for asthma for both 

children and adults. SO2 was found to be a significant 

risk factor for asthma in both children and adults, but 

only in a univariate logistic regression. 

Adjusted Odds Ratio (95% CI) 

Univariate logistic regression 

Children: 1.34 (1.04, 1.72) 

Adults: 1.19 (1.02, 1.38) 

Multivariate logistic regression 

Children: 1.20 (0.91, 1.59) 

Adults: 1.01 (0.85, 1.20) 

Kohlhammer et al. 

(2007) 

Hettstedt, Germany 

Period of Study: 

1992-1999 

Three repeated cross-sectional studies of 5,360 children 

ages 5-14 examining health impacts (lifetime 

pneumonia) of social and environmental factors 

--- No relationship between SO2 and pneumonia was 

observed. 

 F-97 
Study Methods Pollutant Data Findings 

Penard-Morand et al.

(2006) 

Six communities in 

France: Bordeaux, 

Clermont-Ferrand, 

Creteil, Marseille, 

Strasbourg and 

Reims 

Period of Study: Mar 

1999-Oct 2000 

Cross-sectional study of 4,901 children 

(9-11 yrs) form 108 randomly selected schools in 6 cities 

to assess the association between long-term exposure to

background air pollution (NO2, SO2, PM10, O3) and atopy 

and respiratory outcomes. Analysis restricted to children 

who had lived at least the last 3 yrs in their house at the 

time of the examination. Analysis used three yr avgd air 

pollutant concentrations at the children’s schools. 

Parents completed questionnaire on respiratory and 

allergic disorders (asthma, allergic rhinitis (AR), and 

atopic dermatitis) and children underwent examination 

that included a skin prick test to assess allergic 

sensitization, evidence of visible flexural dermatitis and 

measure of exercise-induced bronchial reactivity (EIB). 

Estimated 3-yr avg 

concentrations at 108 

schools 

Low conc: 4.6 μg/m3 

(Range: 1.3, 7.4), 

High conc: 9.6 μg/m3 

(range 7.7, 13.7) 

Increased concentrations of SO2 were significantly 

associated with an increased risk of EIB, lifetime 

asthma and lifetime AR. Past yr wheeze and asthma 

were also associated with SO2. In a two-pollutant model 

with PM10, significant associations were observed 

between SO2 and EIB and past yr wheeze. 

Odds ratio and 95% CI (per 5 μg/m3 SO2) 

EIB: 1.39 (1.15, 1.66), p < 0.001 

Flexural dermatitis: 0.86 (0.73, 1.02), p < 0.10 

Past yr wheeze: 1.23 (1.0, 1.51), p < 0.05 

Past yr asthma: 1.28 (1.00, 1.65), p < 0.01 

Past yr rhinoconjunctivitis: 1.05 (0.89, 1.24) 

Past yr atopic dermatitis: 1.01 (0.86, 1.18) 

Lifetime asthma: 1.19 (1.00, 1.41), p < 0.10 

Lifetime allergic rhinitis: 1.16 (1.01, 1.32), 

p < 0.05 

Lifetime atopic dermatitis: 0.93 (0.82, 1.05) 

Two-pollutant model with PM10

EIB: 1.46 (1.12, 1.90) 

Past yr wheeze: 1.45 (1.09, 1.93) 

Pikhart et al. (2001) 

Czech Republic, 

Poland, 

Period of Study: 

1993-1994 

Part of the small-area variation in air pollution and health 

(SAVIAH) study to assess long-term effects of air 

pollution on respiratory outcomes. Analysis on data from 

two centers of the multicenter study: Prague, Czech 

Republic, and Poznan, Poland. Both cities had wide 

variation in air pollution levels. Parents/guardians of 

6,959 children (7-10 yrs) completed a questionnaire 

about the socioeconomic situation of the family, type of 

housing, family history of atopy, parental smoking, family 

composition, and health of the child. SO2 was measured 

at 80 sites in Poznan and 50 sites in Prague during 2-wk 

campaigns. From these data GIS was used to estimate 

pollutant concentrations at a small area level. Logistic 

regression used to assess effect of air pollution on the 

prevalence of respiratory outcomes. 

Mean SO2 (μg/m3) 

Prague: 83.9 

Range: 65.8-96.6 

PoznaN: 79.7 

Range: 44.2-140.2 

SO2 levels (mean of home and school) were associated 

with the prevalence of wheezing/whistling in the past 

12 mos. There was a marginal association between 

SO2 and lifetime prevalence of wheezing and physician 

diagnosed asthma. Fully adjusted model controlled for 

age, gender, maternal education, number of siblings, 

dampness at home, heating and cooking on gas, 

maternal smoking, and family history of atopy and 

center. Authors noted SO2 is strongly spatially 

correlated with particles in the Czech Republic and 

probably Poland, so SO2 may be proxy for exposure to 

other pollutants. Not other pollutants measured in study.

Odds ratio (per 50 μg/m3) SO2

Wheezing/whistling in past 12 mos: 1.32 (1.10, 1.57) 

Wheezing/whistling ever: 1.13 (0.99, 1.30) 

Asthma ever diagnosed by doctor: 1.39 (1.01, 1.92) 

Dry cough at night: 1.06 (0.89, 1.27) 

Ramadour et al. 

(2000) 

Seven towns in SE 

France 

Period of Study: JanFeb 1993 

Cross-sectional cohort study of 2,445 children (age 13-

14 yrs) who had lived for at least 3 yrs in their current 

residence to compare the levels of O3, SO2, and NO2 to 

the prevalence rates of rhinitis, asthma, and asthmatic 

symptoms. Some of the communities had the heaviest 

photochemical exposure in France. Subjects completed 

ISAAC survey of asthma and respiratory symptoms. 

Analysis conducted with logistic regression models that 

controlled for family history of asthma, personal history of

early -life respiratory diseases, and SES. Also performed 

simple univariate linear regressions. 

Mean (SD) μg/m3 of SO2

during 2-mo period 

Port de Bouc: 32.3 

(24.5) 

Istres: 23.8 (12.7) 

Sausset: 29.0 (28.7) 

Rognanc-Velaux: 39.5 

(21.8) 

Vitrolles: 57.4 (32.0) 

Arles: 29.7 (15.5) 

SaloN: 17.3 (11.6) 

Study found no relationship between mean levels of 

SO2, NO3, or O3 and rhinitis ever, 12-mo rhinitis, 

rhinoconjunctivitis, and hay fever or asthmatic 

symptoms. Simple regression analyses of respiratory 

outcomes vs. mean SO2 levels in the 7 towns indicated 

that nocturnal dry cough was associated with mean SO2

levels (r = 0.891). Potential confounding across towns. 

Soyseth et al. 

(1995b) 

Ardal and Laerdal, 

Norway 

Period of Study: 

winter seasons 

1989-92 

Cross-sectional study of 529 children 

(aged 7-13 yrs) to determine whether exposure to SO2

during infancy is related to the prevalence of bronchial 

hyperresponsiveness (BHR). A sulfur dioxide emitting 

aluminum smelter is present in Ardal, but there is no air 

polluting industry in Laerdal. Parents filled out 

questionnaire regarding family history of asthma, type of 

housing, respiratory symptoms and parent’s smoking 

habits. Spirometry was performed on each child and 

bronchial hyperactivity was determined by methacholine 

challenge or reversibility test. Skin prick test done to 

assess atopy. Also examined, the effects of fluoride. 

Median SO2 

37.1 μg/m3 at ages 

0-12 mos 

 

37.9 μg/m3 at 

ages 13-36 mos 

This study found that the risk of BHR was associated 

with SO2 exposure at 0-12 mos 

Odds ratio for BHR (per 10 μg/m3 SO2) for various ages 

at exposure 

0-12 mos: 1.62 (1.11, 2.35) 

13-36 mos: 1.40 (0.90, 2.21) 

37-72 mos: 1.19 (0.77, 1.82) 

73-108 mos: 1.19 (0.63, 2.22) 

 F-98 
Study Methods Pollutant Data Findings 

Studnicka et al. 

(1997) 

Austria (8 nonurban 

communities) 

Period of Study: 

1991-1993 

Longitudinal study of 843 children 7 yrs old from 8 

nonurban Austrian communities. A logistic regression 

was used to examine the association between SO2

concentrations and asthma and respiratory symptoms by 

comparing low, regular, and high SO2 communities with 

very low SO2 communities. 

Range: 

Jan. 1991-Dec. 1993 

(ppb): 6.0 (Krems), 12.0 

(Mistel. and Gäns) 

SO2 was significantly associated with bronchial asthma 

in the last 12 mos and positively associated with parentreported “ever asthma” when comparing low SO2

concentration communities with very low SO2

communities. 

Adjusted Prevalence Odds Ratio 

Wheeze last 12 mos: 

Low: 0.68. Regular: 0.88. High: 0.42 

Cough apart from colds last 12 mos: 

Low: 0.75. Regular: 0.85. High: 0.72 

Bronchitis last 12 mos: 

Low: 0.21. Regular: 0.45. High: 0.56 

Bronchial asthma last 12 mos: 

Low: 2.35. Regular: 0.22. High: 0.33 

Parent-reported “ever asthma”: 

Low: 1.70. Regular: 0.23. High: 0.67 

von Mutius et al. 

(1995) 

Leipzig, East 

Germany, 

Period of Study: Oct 

1991-Jul 1992 

The effects of high to moderate levels of air pollution 

(SO2, NOX, and PM) on the incidence of upper 

respiratory were investigated in 1,500 schoolchildren (9-

11 yrs) in Leipzig, East Germany. Logistic regression 

models controlled for paternal education, passive smoke 

exposure, number of siblings, temperature, and humidity.

During winter mos, SO2

daily max concentrations 

ranged from 40-1283 

μg/m3. 

During high pollution 

period, avg 

concentration of SO2

was 188 μg/m3 and 

during low pollution avg 

was 57 μg/m3. 

The daily mean values of SO2 and NOX were 

significantly associated with increased risk of 

developing upper respiratory illnesses during the high 

concentration period. In the low concentration period, 

only NOX daily mean values were associated with 

increased risks. In a two-pollutant model with PM, 

similar estimates to the single-pollutant model were 

obtained, thus collinearity of data may not account for 

the effects of high mean concentrations of SO2. 

Odds ratio and 95% CI: (did not indicate per what level 

of SO2 increase) 

Daily mean SO2 : High period: 1.72 (1.19, 2.49); 

Low period: 1.40 (0.95, 2.07) 

Daily max SO2 : High period: 1.26 (0.80, 1.96); 

Low period: 0.99 (0.66, 1.47) 

 F-99 
Study Methods Pollutant Data Findings 

LATIN AMERICA 

Solé et al. (2007) 

São Paulo, Brazil 

(São Paulo West 

(SPW), São Paulo 

South (SPS), Santo 

André (SA), Curitiba 

(CR), Porto Alegre 

(PoA) 

Cohort of 16,209 adolescents (13-14 yrs old) from the 21 

centers involved in the International Study of Asthma and 

Allergies in Childhood (ISAAC). Each participant was 

given a questionnaire to identify various allergy-related 

symptoms that occurred in the last 12 mos. The 

relationship between affirmative answer to a question, 

socioeconomic status, and air pollutants was analyzed 

by the Spearman correlation coefficient. The location 

with the lowest level of a specific air pollutant was 

defined as the reference and the risk of an affirmative 

answer to a question was presented as an odds ratio for 

each location. 

NR In the analysis of the risk of allergy-related symptoms 

due to SO2 levels in relation to the center with the 

lowest annual mean SO2 concentrations SPW was 

significantly associated with every symptom. Other 

significant associations were observed in SA for current 

wheezing; in CR for rhinitis and rhinoconjunctivitis; and 

in PoA for current wheezing, nighttime cough, rhinitis, 

and eczema. 

Odds Ratio (95% CI)-Reference Center: São Paulo 

South (SPS) 

Current Wheezing: SPW: 1.21 (1.08, 1.38); 

SA: 1.31 (1.16, 1.48); CR: 1.02 (0.90, 1.15) 

PoA: 1.68 (0.85, 1.10) 

Severe Asthma: SPW: 2.01 (1.56, 2.60); 

SA: 1.04 (0.78, 1.40); CR: 1.08 (0.81, 1.42); 

PoA: 1.01 (1.29, 2.20) 

Nighttime Cough: SPW: 1.14 (1.03, 1.26); 

SA: 0.94 (0.85, 1.04); CR: 0.93 (0.84, 1.02); 

PoA: 1.25 (0.91, 1.12) 

Rhinitis Last Yr: SPW: 1.14 (1.02, 1.27); 

SA: 1.05 (0.94, 1.18); CR: 1.71 (1.54, 1.90); 

PoA: 1.36 (1.12, 1.40) 

Rhinoconjunctivitis: SPW: 1.78 (1.55, 2.04); 

SA: 1.15 (0.99, 1.33); CR: 1.50 (1.31, 1.72); 

PoA: 1.48 (1.18, 1.57) 

Severe Rhinitis: SPW: 1.50 (1.32, 1.71); 

SA: 1.08 (0.94, 1.24); CR: 1.52 (1.34, 1.73); 

PoA: 0.97 (1.30, 1.69) 

Eczema: SPW: 1.40 (1.17, 1.68); SA: 1.00 (0.83, 1.21);

CR: 0.88 (0.72, 1.06); PoA: 1.40 (0.80, 1.18) 

Flexural Eczema: SPW: 2.00 (1.58, 2.52); 

SA: 0.95 (0.73, 1.24); CR: 1.02 (0.79, 1.31); 

PoA: 2.41 (1.09, 1.80) 

Severe Eczema: SPW: 2.58 (1.94, 3.44); 

SA: 0.92 (0.65, 1.30); CR: 0.71 (0.50, 1.02); 

PoA: NR (1.80, 3.22) 

ASIA 

Ho et al. (2007) 

Taiwan 

Period of Study: 

1995-1996 

Survey of 69,367 children ages 12-15 by questionnaire. 

The max likelihood estimation was carried out with 

Fisher’s scoring algorithm and GEE. 

NR SO2 not significant in both genders. However, SO2

showed a reversal effect on monthly asthma attack rate.

(Authors state that this reversal effect could be caused 

by the interaction of sulfur dioxide with the lowest 5% 

monthly temperature avg) 

Hwang et al. (2005) 

Taiwan 

Period of Study: 

2001 

A cross-sectional study consisting of 32,672 Taiwanese 

school children aged 6-15 yrs old. Using a modified 

Chinese version of the International Study of Asthma and 

Allergies in Childhood (ISAAC) questionnaire collected 

information on each participant’s health, environmental 

exposures, and other variables. A two-stage hierarchical 

model consisting of logistic and linear regression 

analyses was used to account for, in the first stage, 

variation among subjects, and, in the second stage, 

variation among municipalities. 

2000 (ppb): 3.53 (2.00) Increased annual levels of NOX, CO, and O3 were 

associated with an increased risk of childhood asthma 

levels. In both single- and co-pollutant models SO2 was 

not found to be associated with the risk of asthma. 

Odds Ratio (95% CI) (per 10 ppb SO2) 

Single-pollutant model 

0.874 (0.729, 1.054) 

Two-pollutant model 

NOX + SO2: 0.724 (0.545, 0.963) 

CO + SO2: 0.689 (0.542, 0.875) 

SO2 + O3: 0.826 (0.674, 1.014) 

 F-100 
Study Methods Pollutant Data Findings 

Peters et al. (1996) 

Hong Kong (Kwai 

Tsing; Southern) 

Period of Study: 

1989-1991 

Cohort of 3,521 children from two districts in Hong Kong 

with good and poor air quality prior to the 1990 

legislation to reduce fuel sulfur levels. Analyses 

consisted of multivariate methods using logistic 

regression along with generalized estimating equations 

(GEE) to examine the effect of legislation implemented to

reduce fuel sulfur levels on respiratory health. 

Annual avg (μg/m3) 

Southern 

1989: 11 

1990: 8 

1991: 7 

Kwai Tsing 

1989: 111 

1990: 67 

1991: 23 

SO2 emissions were reduced by 80% after institution of 

the legislation. The study does not provide effect 

estimates for individual pollutants. 

Wang et al. (1999) 

Taiwan (Kaohsiung; 

Pintong) 

Period of Study: 

1995-1996 

A cross-sectional study consisting of 165,173 high school

students aged 11-16 yrs old residing in the communities 

of Kaohsiung and Pintong in Taiwan from Oct 1995 to 

Jun 1996. Used a video and questionnaire developed by 

the International Study of Asthma and Allergies in 

Childhood (ISAAC). The association between air 

pollution and asthma was examined using logistic 

regression. In addition, the study performed a multiple 

logistic regression to examine the independent effects of 

risk factors of asthma after adjusting for age, sex, 

parents’ education, and area of residence. The multiple 

logistic regression included pollutant concentrations to 

examine the combined effect. 

Median: 

1996 (ppm): 0.013 

In the univariate analysis, increasing concentrations of 

TSP, SO2, NO2, CO, O3, and airborne dust were all 

found to be significantly associated with asthma. These 

univariate estimates are associated with concentrations 

above a cutoff (i.e., the median concentrations of each 

pollutant). In the multivariate analysis increasing 

concentrations of TSP, NO2, CO, O3, and airborne dust 

were significantly associated with asthma. 

Odds Ratio (95% CI) (per 0.013 ppm SO2) 

Univariate analysis 

≥ 0.013 ppm: 1.05 (1.02, 1.09) 

Adjusted Odds Ratio (95% CI) 

Multivariate analysis 

0.98 (0.95, 1.02) 

MIDDLE EAST 

Dubnov et al. (2007) 

Israel 

(Hadera, PardesHanna) 

Period of Study: 

1996 and 1999 

Cohort of 1,492 schoolchildren (7-14 yrs old) living near 

a major coal-fired power station. Subjects underwent 

pulmonary function tests (PFT) for forced vital capacity 

(FVC) and forced expiratory volume during the first 

second (FEV1) to examine the association between 

pulmonary function and long-term exposure to air 

pollution. Using stepwise multiple regression (SMR) and 

ordinary leas squares regression (OLS) examined the 

multiplicative effect of NOX and SO2 on pulmonary 

function. 

1996 and 1999 avg (SD) 

(ppm): 

12.9 (11.3) 

Using an integrated concentration value (ICV), which 

equals the product of NOX concentration and SO2

concentration when both concentrations individually 

exceed the half-hour reference level, found significant 

associations between exposure to air pollution and 

decrements in pulmonary function. 

All Children: 

NOX x SO2

ΔFVC (%) 

β = −0.004, p < 0.001 

ΔFEV1 (%) 

β = −0.004, p < 0.001 

Children in zone of highest concentration of air 

pollution: 

NOX x SO2

ΔFVC (%) 

β = −0.005, p < 0.001 

ΔFEV1(%) 

β = −0.005, p < 0.001 

AFRICA 

Houssaini et al. 

(2007) 

Morocco 

Cross-sectional study of 1,318 children with a mean age 

of 12 yrs. Used a questionnaire and medical 

diagnosis/reporting for asthma, and evaluated using 

Student’s t-test, Chi-square, odds ratios, and CochranArmitage tests. 

Annual Avg: 

2000-2001: 60.2 μg/m3

2001-2002: 50.2 μg/m3

2002-2003: 49.6 μg/m3

2003-2004: 36.8 μg/m3

Significant prevalence for respiratory diseases, asthma, 

and infectious disease, when combined with TSP. 

 F-101 
Table F-7. Long-term exposure to SO2 and lung cancer incidence and mortality. 

Study Methods Pollutant Data Findings 

UNITED STATES 

Abbey et al. (1999) 

Three California air 

basins: San 

Francisco, South 

Coast (Los Angeles 

and eastward), San 

Diego 

Period of Study: 

1977-1992 

Prospective cohort study of 6,338 nonsmoking nonHispanic white adult members of the Adventist Health 

Study followed for all cause, cardiopulmonary, 

nonmalignant respiratory, and lung cancer mortality. 

Participants were aged 27-95 yrs at enrollment in 

1977. 1,628 (989 females, 639 males) mortality 

events followed through 1992. All results were 

stratified by gender. Used Cox proportional hazards 

analysis, adjusting for age at enrollment, past 

smoking, environmental tobacco smoke exposure, 

alcohol use, education, occupation, and body mass 

index. Analyzed mortality from all natural causes, 

cardiopulmonary, nonmalignant respiratory, and lung 

cancer. 

Mean SO2 Levels: 

24-h avg SO2: 5.6 ppb 

Copollutants: 

PM10

SO4

O3

NO2

Lung cancer mortality showed large risk estimates 

for most of the pollutants in either or both sexes, but 

the number of lung cancer deaths in this cohort was 

very small (12 for female and 18 for male) Generally 

wide confidence intervals (relative to other U.S. 

cohort studies). 

Adjusted Mortality Relative Risk 

(95% CI) (per 3.72 ppb SO2) 

Lung Cancer 

Males: 1.99 (1.24, 3.20) 

Females: 3.01 (1.88, 4.84) 

Beeson et al. 

(1998) 

Three California air 

basins: San Diego, 

San Francisco, 

South Coast (Los 

Angeles and 

eastward) 

Period of Study: 

1977-1992 

Prospective cohort study of 6,338 nonsmoking nonHispanic white adult members of the Adventist Health 

Study aged 27-95 yrs at time of enrollment. 

36 (20 females, 16 males) histologically confirmed 

lung cancers were diagnosed through 1992. 

Extensive exposure assessment, with assignment of 

individual long-term exposures to O3, PM10, SO4

2−, 

and SO2, was a unique strength of this study. All 

results were stratified by gender. Used Cox 

proportional hazards analysis, adjusting for age at 

enrollment, past smoking, education, and alcohol use.

24-h avg SO2: 5.6 ppb Lung cancer incidence relative risk: 

Male: RR = 3.72 (95%CI: 1.91, 7.28); 

Female: RR = 2.78 (95%CI: 1.51, 5.12) per 5 ppb 

increase in SO2 

Case number very small (16 for male, 20 for female).

Krewski et al. 

(2000) 

Re-analysis and sensitivity analysis of Dockery et al. 

(1993) Harvard Six Cities study. 

Mean SO2 Levels: 24-h avg 

SO2 ranged from 1.6 

(Topeka) to 24.0 

(Steubenville) ppb 

Copollutants: Fine Particles, 

Sulfates 

SO2 showed positive associations with lung cancer 

deaths (1.03 (95% CI: 0.91, 1.16), but in this dataset, 

SO2 was highly correlated with PM2.5 (r = 0.85), 

sulfate (r = 0.85), and NO2

(r = 0.84) 

EUROPE 

Beelen et al. (2008) 

The Netherlands 

Period of Study: 

1987-1996. 

Cohort study on diet and cancer with 120,852 

subjects who were followed from 1987 to 1996. BS, 

NO2, SO2, and PM2.5 and traffic-exposure estimates 

were analyzed. Cox regression model adjusted for 

age, sex, smoking, and area-level socioeconomic 

status. 

Mean SO2 Levels: 

Mean: 4.8 ppb, with a range 

of 1.5 to 11.8 ppb. 

Copollutants: 

PM2.5 BS NO2

Traffic intensity on the nearest road was not 

associated with exposure SO2. Background SO2

levels were not associated with lung cancer mortality.

Adjusted RR 

(per 20 μg/m3 SO2) 

1.00 (0.79, 1.26) 

Filleul et al. (2005) 

Seven French cities 

Period of Study: 

1975-2001 

Cohort study of 14,284 adults who resided in 24 

areas from seven French cities when enrolled in the 

PAARC survey (air pollution and chronic respiratory 

diseases) in 1974. Daily measurements of SO2, TSP, 

BS, NO2, and NO were made in 24 areas for three yrs 

(1974-1976). Cox proportional hazards models 

adjusted for smoking, educational level, BMI, and 

occupational exposure. Models were run before and 

after exclusion of six area monitors influenced by 

local traffic as determined by the NO/NO2 ratio >3. 

Mean SO2 Levels: 

24-h avg SO2 ranged from 

17 mg/m3 (“Area 3” in Lille) 

to 85 mg/m3 (“Area 3” in 

Marseille) in the 24 areas in 

seven cities during 1974-

1976. Median levels during 

1990-1997 ranged from 8.5 

mg/m3 (Bordeaux) to 23.4 

mg/m3 (Rouen) in the five 

cities where data were 

available. 

Copollutants: TSP 

BS NO2 NO 

The authors noted that inclusion of air monitoring 

data from stations directly influenced by local traffic 

could overestimate the mean population exposure 

and bias the results. It should be noted that the table 

describing air pollution levels in Filleul et al.’s report 

indicates that the SO2 levels in these French cities 

declined markedly from 1974-1976 and 1990-1997 

period, by a factor of 2 to 3, depending on the city, 

whereas NO2 levels between the two periods were 

variable, increased in some cities, and decreased in 

others. These changes in air pollution levels over the 

study period complicate interpretation of reported 

risk estimates. 

Relative Risk (95% CI) for lung cancer mortality (per 

10 mg/m3 multi-year average) 

All 24 areas: 0.99 (0.92, 1.07) 

18 areas:1.00 (0.91, 1.11) 

 F-102 
Study Methods Pollutant Data Findings 

Nafstad et al. 

(2003) 

Oslo, Norway 

Period of Study: 

1972-1998 

Retrospective study associating cardiovascular risk 

factors to a national cancer register among 16,209 

men ages 10-49 yrs. Survival analyses and Cox 

proportional hazards regression were used to 

estimate associations. 

Estimated for each person 

each yr from 1974 to 1998 

Five-yr median avg levels 

SO2 participants home 

address, 1974-1978: 9.4 

μg/m3 (range 0.2 to 55.8) 

Median levels within the 

quartiles: 

2.5 μg/m3. 6.2 μg/m3

14.7 μg/m3. 31.3 μg/m3

Copollutants: 

NOX

Adjusted risk ratios (95% CI) of developing lung 

cancer: 

Model 1: 

0-9.99 μg/m3: Ref 

10-19.99 μg/m3: 1.05 (0.81, 1.35) 

20-29.99 μg/m3: 0.95 (0.72, 1.27) 

30+ μg/m3: 1.06 (0.79, 1.43) 

Model 2: Per 10 μg/m3: 1.01 (0.94, 1.08) 

Adjusted risk ratios (95% CI) of developing non-lung 

cancer: 

Model 1: 0-9.99 μg/m3: Ref. 

10-19.99 μg/m3: 1.07 (0.96, 1.19) 

20-29.99 μg/m3: 0.90 (0.80, 1.02) 

30+ μg/m3: 0.98 (0.86, 1.10) 

Model 2: Per 10 μg/m3: 0.99 (0.96, 1.02) 

Nafstad et al. 

(2004) 

Oslo, Norway 

Period of Study: 

1972-1998 

Cohort study of 16,209 Norwegian men 40-49 yrs of 

age living in Oslo, Norway, in 1972-1973. Data from 

the Norwegian Death Register were linked with estimates of avg yearly air pollution levels at the participants’ home addresses from 1974 to 1998. NOX, 

rather than NO2 was used. Exposure estimates for 

NOX and SO2 were constructed using models based 

on the subject’s address, emission data for industry, 

heating, and traffic, and measured concentrations. 

Addresses linked to 50 of the busiest streets were 

given an additional exposure based on estimates of 

annual avg daily traffic. Cox proportional-hazards 

regression was used to estimate associations 

between exposure and total and cause-specific 

mortality, adjusting for age strata, education, 

occupation, smoking, physical activity level, and risk 

groups for cardiovascular diseases 

Mean SO2 Levels: 

The yearly avg of 24-h avg 

SO2 were reduced with a 

factor of 7 during the study 

period from 5.6 ppb in 1974 

to 0.8 ppb in 1995. 

Copollutants: 

NOX

SO2 did not show any associations with lung cancer, 

e.g., 1.00 (0.93, 1.08) per 10 μg/m3 increase 

mortality in SO2. No association was also observed 

when including SO2 in the model as a categorical 

variable. Note the very low levels of SO2. 

Nyberg et al. (2000) 

Stockholm County, 

Sweden 

Period of Study: 

Jan 1, 1985-Dec 

31, 1990 

Case-control study of men 40-70 yrs, with 1,042 

cases of lung cancer and 1,274 controls, to evaluate 

the suitability of an indicator of air pollution from 

heating. 

Annual levels computed for 

each yr between 1950 and 

1990, but not provided 

herein 

NOX/NO2

Little effect of SOx in any time window, but highest 

correlations in early yrs. 

SOX RR (CI 95%) from heating (per 10 μg/m3) for 

30-yr avg 

< 41.30 μg/m3: 1 

≥ 41.30 to < 52.75: 1.06 (0.83, 1.35) 

≥ 52.75 to < 67.14: 0.98 (0.77, 1.24) 

≥ 67.14 to < 78.20: 0.90 (0.68, 1.19) 

≥ 78.20: 1.00 (0.73, 1.37) 

SOX RR (CI 95%) from heating (per 10 μg/m3) for 

10-yr avg 

< 66.20 μg/m3: 1 

≥ 66.20 to < 87.60: 1.16 (0.91, 1.47) 

≥ 87.60 to < 110.30: 1.00 (0.79, 1.27) 

≥ 110.30 to < 129.10: 0.92 (0.70, 1.21) 

≥ 129.10: 1.21 (0.89, 1.66) 

 F-103 
Table F-8. Long-term exposure to SO2 and prenatal and neonatal outcomes. 

Study Methods Pollutant Data Findings 

UNITED STATES 

Bell et al. (2007) 

Connecticut and 

Massachusetts 

Period of Study: 

1999-2002 

Outcome(s): LBW 

Study design: Case-control 

N: 358,504 live singleton births 

Statistical Analysis: Linear models and logistic 

regression 

Covariates: Gestational length, prenatal care, type of 

delivery, child’s sex, birth order, weather, yr, and 

mother’s race, education, marital status, age, and 

tobacco use. 

Gestational exposure (ppb) 

Mean: 4.7 

SD: 1.2 

IQR: 1.6 

Copollutants: 

NO2

CO 

PM10

PM2.5

No relationship between gestational exposure to 

SO2 and birth weight. First trimester exposure to 

SO2 was associated with low birth weight. No 

statistical difference n the effect estimates of SO2 for 

infants of black and white mothers. 

Increment: 1.6 ppb (IQR) 

Change in birth weight: 

Entire pregnancy: −0.9 g (−4.4, 2.6) 

Black mother: 1.2 (−6.5, 8.8) 

White mother: −1.4 (−5.1, 2.3) 

1st trimester: −3.7 to −3.3 grams 

LBW: OR 1.003 (0.961, 1.046) 

Gilboa et al. (2005) 

Seven Texas 

Counties 

Period of Study: 

1997-2000 

Outcome(s): Selected birth defects 

Study design: Case-control 

N: 4,570 cases and 3,667 controls 

Statistical Analysis: Logistic regression 

Covariates: Maternal education, maternal 

race/ethnicity, season of conception, plurality, 

maternal age, maternal illness 

Statistical package: SAS vs. 8.2 

Levels NR 

Copollutants 

PM10

O3

NO2

CO 

When the fourth quartile of exposure was compared 

with the first, SO2 was associated with increased 

risk of isolated ventricular septal defects. Inverse 

associations were noted for SO2 and risk of isolated 

atrial septal defects and multiple endocardial 

cushion defects. 

Aortic artery and valve defects: < 1.3 ppb: 1.00; 

1.3 to < 1.9: NA; 1.9 to < 2.7: 1.06 (0.34, 3.29); 

≥ 2.7: 0.83 (0.26, 2.68) 

Atrial septal defects: < 1.3 ppb: 1.00; 

1.3 to < 1.9: 1.22 (0.79, 1.88); 

1.9 to < 2.7: 0.76 (0.47, 1.23); 

≥ 2.7: 0.42 (0.22, 0.78) 

Pulmonary artery and valve defects: < 1.3 ppb: 1.00;

.3 to < 1.9: 0.63 (0.23, 1.74); 

1.9 to < 2.7: 0.93 (0.36, 2.38); 

≥ 2.7: 1.07 (0.43, 2.69) 

Ventricular septal defects: < 1.3 ppb: 1.00; 

1.3 to < 1.9: 1.02 (0.68, 1.53); 

1.9 to < 2.7: 1.13 (0.76, 1.68); 

≥ 2.7: 2.16 (1.51, 3.09) 

Conotruncal defects: < 1.3 ppb: 1.00; 

1.3 to < 1.9: 0.71 (0.46, 1.09); 

1.9 to < 2.7: 0.71 (0.46, 1.09); 

≥ 2.7: 0.58 (0.37, 0.91) 

Endocardial cushion and mitral valve defects: 

< 1.3 ppb: 1.00; 1.3 to < 1.9: 0.89 (0.50, 1.61); 

1.9 to < 2.7: 0.89 (0.49, 1.62); 

≥ 2.7: 1.18 (0.68, 2.06) 

Cleft lip with or without cleft palate: < 1.3 ppb: 1.00;

1.3 to < 1.9: 0.79 (0.52, 1.20); 

1.9 to < 2.7: 0.95 (0.64, 1.43); 

≥ 2.7: 0.75 (0.49, 1.15) 

Cleft palate: < 1.3 ppb: 1.00; 

1.3 to < 1.9: 0.89 (0.40, 1.97); 

1.9 to < 2.7: 1.49 (0.72, 3.06); 

≥ 2.7: 1.22 (0.56, 2.66) 

 F-104 
Study Methods Pollutant Data Findings 

Lipfert et al. (2000c) 

United States 

Period of Study: 

1990 

Mortality 

Outcome(s): SIDS Study design: Cohort 

Statistical Analysis: Three logistic regression 

analyses to examine the relation between annual avg 

air pollutant values and various infant mortality 

endpoints (i.e., all causes, SIDS, respiratory, and 

other causes 

Statistical package: NR 

Age groups analyzed: 0-1 

Covariates: Altitude, degree days (°F), median 

income (U.S. $), population density 

Lag(s): N/A 

Mean (SD) SO2: 4.57 (2.6) ppb

Range: 0.3, 9.9 

Copollutants: 

PM10 (r = 0.04) 

CO (r = -0.15) 

SO4

2- (r = 0.36) 

NSPM10 (r = -0.13) 

In a model that included states that lacked data on 

maternal education and smoking along with various 

personal and ecological variables, SO2 was not 

found to be a significant predictor of SIDS mortality 

in infants with birth weights >2,500 g. 

β = −0.0118 (0.0094) 

Mean Risk (95% CI) 

0.95 (0.87, 1.03) 

Maisonet et al. 

(2001) 

6 Northeastern 

cities of U.S. 

Period of Study: 

1994-1996 

Outcome(s): Term LBW 

Study design: Case-control 

N: 89,557 live singleton births 

Statistical Analysis: Logistic regression models linear 

regression models 

Covariates: Maternal age, race, season of the yr, 

smoking and alcohol use during pregnancy, firstborn, 

gender, marital status, and previous terminations, 

prenatal care (ordinal variable), weight gain, and 

gestational age 

Stratified by race/ethnicity 

Statistical package: STATA 

Exposure distribution 

(< 25th, 25th to < 50th, 50th to 

< 75th, 75th to < 95th, ≥ 95th) 

First trimester: < 7.09, 7.090 to 

8.906, 8.907 to 11.969, 11.970 

to 18.447, ≥18.448 

Second trimester: < 6.596, 

6.596 to 8.896, 8.897 to 

11.959, 11.960 to 18.275, ≥

18.276 

Third trimester: < 5.810, 

5.810 to 8.453, 

8.454 to 11.777, 11.778 to 

18.134, ≥ 18.135 

Copollutants: 

CO 

PM10

This study provides evidence of an increased risk 

for term LBW in relation to increased ambient air 

levels of SO2 at concentrations well below the 

established standards. Higher risk estimates among 

whites when stratified by race/ethnicity 

First trimester: 

< 25th: Referent 

25th-50th: 1.04 (0.88, 1.23) 

50th-75th: 1.04 (0.94, 1.15) 

75th-95th: 0.98 (0.81, 1.17) 

> 95th: 0.88 (0.73, 1.07) 

Increment (10 ppm): 0.98 (0.93, 1.03) 

Second trimester: 

25th-50th: 1.18 (1.12, 1.25) 

50th-75th: 1.12 (1.07, 1.17) 

75th-95th: 1.13 (1.05, 1.22) 

> 95th: 0.87 (0.80, 0.95) 

Increment (10 ppm): 1.01 (0.93, 1.10) 

Third trimester: 

25th-50th: 1.04 (0.92, 1.18) 

50th-75th: 1.02 (0.87, 1.18) 

75th-95th: 1.04 (0.84, 1.28) 

> 95th: 1.06 (0.76, 1.47) 

Increment (10 ppm): 1.01 (0.86, 1.20) 

Sagiv et al. (2005) 

4 Pennsylvania 

counties 

Period of Study: 

1997-2001 

Outcome(s): Pre-term birth 

Study design: Time-series 

N: 187,997 births 

Study design: Poisson-regression models 

Covariates: Long-term trends, copollutants, 

temperature, dew point temperature, and day of wk. 

Lag: Daily lags ranging from 1-7 days 

Mean SO2 Levels: 

6-wk Mean: 7.9 ± 3.5 ppb 

Range: 0.8, 17 

Median: 8.1 

Daily Mean: 7.9 ± 6.2 

Range: 0, 54.1 

Median: 6.4 

Copollutants: 

PM10; r = 0.46 

CO 

NO2

This study found an increased risk for preterm 

delivery during the last 6 wks of pregnancy with 

exposure to SO2. 

Increment: 15 ppb 

Mean: 6-wk SO2: 

RR = 1.15 (1.00, 1.32) 

< 4.9 ppb: Referent 

4.9 to 8.1 ppb: 1.02 (0.97, 1.06) 

8.1 to 10.6 ppb: 1.04 (0.98, 1.10) 

10.6 to 17.0 ppb: 1.06 (0.99, 1.14) 

Mean: Daily SO2: 

RR = 1.07 ( 0.99, 1.15) lag 3 

CANADA 

Dales et al. (2004) 

12 Canadian cities 

Period of Study: 

1984-1999 

Outcome(s): SIDS 

Study design: Time-series 

N: 1556 SIDS deaths 

Statistical Analysis: Random effects regression model

Covariates: Temperature, humidity, barometric 

pressure, season 

Lag: 0-5 days 

Mean SO2 Levels: 

24-h avg: 5.51 ppb 

IQR: 4.92 

Copollutants: 

CO NO2 O3 PM10

PM2.5 PM10-2.5

SIDS was associated with air pollution, with the 

effects of SO2 seeming to be independent of 

sociodemographic factors, temporal trends, and 

weather. 

Increment: 4.92 ppb (IQR) 

Increase in SIDS incidence: 8.49%; 

p = 0.0079 lag 1 

 F-105 
Study Methods Pollutant Data Findings 

Dales et al. (2006) 

11 Canadian cities 

Period of Study: 

1986-2000 

Outcome(s): Hospitalization for respiratory disease in 

the neonatal period 

Study design: Time-series 

N: 9,542 

Statistical Analysis: Random effects regression 

model; Poisson using fixed- or random-effects model 

Covariates: Fay of wk, temperature, humidity, 

pressure 

Lag: 0-5 days 

Statistical package: 

S-PLUS vs. 6.2 

Mean SO2 Levels: 

24-h avg: 4.3 ppb 

IQR: 3.8 

Copollutants: 

NO2 (r = 0.20, 0.67) 

CO (r = 0.19, 0.66) 

O3 (r = −0.41, 0.13) 

PM10 (r = −0.09, 0.61) 

SO4

This study detected a significant association for 

respiratory disease among neonates and gaseous 

air pollutants. 

Increment: 3.8 ppb (IQR) 

Increase in neonatal respiratory 

hospital admissions: 

SO2 alone: 2.06% (1.04, 3.08) 

Multipollutant model: 1.66% (0.63, 2.69) 

Multipollutant model restricted to days with PM10 

measures: 1.41% (0.35, 2.47) 

Dugandzic et al. 

(2006) 

Nova Scotia, 

Canada 

Period of Study: 

1988-2000 

Outcome(s): Term LBW 

Study design: Retrospective cohort study 

N: 74,284 term, singleton births 

Statistical Analysis: Logistic regression models 

Covariates: Maternal age, parity, prior fetal death, 

prior neonatal death, and prior low birth weight infant, 

smoking during pregnancy, neighborhood family 

income, infant gender, gestational age, weight 

change, and yr of birth. 

Statistical package: 

SAS vs. 8.0 

Mean: SO2 10 ppb 

Median: 10 

25th%: 7 

75th%: 14 

Max: 38 

Copollutants: 

O3

PM10

In the analyses unadjusted for birth yr, first trimester 

exposures in the highest quartile for SO2 associated 

with increased risk of LBW. After adjusting for birth 

yr, RR attenuated and not statistically significant. 

There was a linear concentration-response effect 

with increasing levels of SO2 during the first 

trimester. 

First Trimester 

25th-50th: 0.96 (0.73, 1.28) 

51st-75th: 1.18 (0.88, 1.58) 

>75th: 1.36 (1.04, 1.78) 

Increment (7 ppb): 1.20 (1.05, 1.38) 

Second Trimester 

25th-50th: 1.12 (0.86, 1.46) 

51st-75th: 1.13 (0.85, 1.50) 

>75th: 1.04 (0.79, 1.37) 

Increment (7 ppb): 0.99 (0.87, 1.13) 

Third Trimester 

25th-50th: 1.04 (0.80, 1.34) 

51st-75th: 0.85 (0.63, 1.15) 

>75th: 0.88 (0.67, 1.15) 

Increment (7 ppb): 0.93 (0.81, 1.06) 

Liu et al. (2003) 

Vancouver, Canada 

Period of Study: 

1985-1998 

Outcomes: Preterm birth, LBW, IUGR 

Study design: Case-control 

N: 229,085 singleton live births 

Statistical Analysis: Multiple logistic regressions 

Covariates: Maternal age, parity, infant sex, 

gestational age or birth weight and season of birth 

24-h avg: 4.9 ppb, 

5th: 1.5 

25th: 2.8 

50th: 4.3 

75th: 6.3 

95th: 10.5 

100th: 30.5 

1-h max: 13.4 ppb, 

5th: 4.3 

25th: 7.8 

50th: 11.7 

75th: 16.8 

95th: 28.3 

100th: 128.5 

Copollutants: 

NO2 (r = 0.61) 

CO (r = 0.64) 

O3 (r = −0.35) 

LBW and IUGR were associated with maternal 

exposure to SO2 during the first mo of pregnancy 

and preterm birth was associated with SO2 during 

the last mo. These results were robust to adjustment 

for copollutants. 

Increment: 5 ppb 

Low birth weight 

First mo: OR 1.11 (1.01, 1.22) 

Last mo: OR 0.98 (0.89, 1.08) 

Preterm birth 

First mo: OR 0.95 (0.88, 1.03) 

Last mo: OR 1.09 (1.01, 1.19) 

IUGR 

First mo: OR 1.07 (1.01, 1.13) 

Last mo: OR 1.00 (0.94, 1.06) 

First trimester: OR 1.07 (1.00, 1.14) 

Second trimester: 0.98 (0.91, 1.04) 

Third trimester: 1.03 (0.96, 1.10) 

 F-106 
Study Methods Pollutant Data Findings 

Liu et al. (2006) 

Calgary, Edmonton 

and Montreal, 

Canada 

Period of Study: 

1986-2000 

Outcome(s): IUGR 

Study design: Case-control 

N: 386,202 singleton live births 

Statistical Analysis: Multiple logistic regression 

Covariates: Maternal age, parity, infant sex, season of 

birth, city of residence 

24-h avg: 3.9 ppb, 25% 2.0 

ppb 

50% 3.0 ppb 

75% 5.0 ppb 

95% 10.0 ppb 

1-h max: 10.8 ppb, 25% 5.0 

ppb 

50% 8.6 ppb 

75% 14.0 ppb 

95% 28.0 ppb 

Copolllutants: 

NO2 (r = 0.34) 

CO (r = 0.21) 

O3 (r = −0.30) 

PM2.5 (r = 0.44) 

IUGR did not increase with maternal exposure to 

SO2. Risk decreased during first 3 mos. 

Increment: 3.0 ppb 

ORs estimated from graph: 

1st mo: OR ~0.966 (0.94, 0.99) 

2nd mo: OR ~0.97 (0.95, 0.995) 

3rd mo: OR ~0.97 (0.95, 0.995) 

1st trimester: OR ~0 .96 (0.93, 0.99) 

Bobak (2000) 

Czech Republic 

Period of Study: 

1991 

Outcomes: LBW, preterm birth 

Study design: Case-control 

N: 108,173 live singleton births 

Statistical Analysis: Logistic regression 

Covariates: Temperature, humidity, day of wk, 

season, residential area, maternal age, gender 

Statistical package: STATA 

Mean trimester exposures 

25th: 17.5 μg/m3

50th: 32.0 μg/m3

75th: 55.5 μg/m3

Copollutants: 

TSP; r = 0.68 0.73 

NOX; r = 0.53, 0.63 

LBW and preterm birth were associated with 

maternal exposure to SO2, though the association 

between SO2 and LBW was explained to a large 

extent by low gestational age. 

Increment: 50 μg/m3

LBW (adjusted for sex, parity, maternal age group, 

education, marital status, and nationality, and mo of 

birth): 

1st trimester: 1.20 (1.11, 1.30) 

2nd trimester: 1.14 (1.06, 1.22) 

3rd trimester: 1.14 (1.06, 1.23) 

LBW (also adjusted for gestational age): 

1st trimester: 1.01 (0.88, 1.17) 

2nd trimester: 0.95 (0.82, 1.10) 

3rd trimester: 0.97 (0.85, 1.10) 

Preterm birth (AOR): 

1st trimester: 1.27 (1.16, 1.39) 

2nd trimester: 1.25 (1.14, 1.38) 

3rd trimester: 1.24 (1.13, 1.36) 

Reduction in mean birth weight: 

1st trimester: 11.4 g (5.9, 16.9) 

EUROPE 

Mohorovic (2004) 

Labin, Istra, Croatia 

Period of Study: 

1987-1989 

Outcomes: LBW and preterm delivery 

Study design: Cross-sectional 

N: 704 births 

Statistical Analysis: Multiple correlation analyses, 

factor analyses, chi-square 

Statistical package: DBASE IV, SPSS 

Monthly ground levels of SO2: 

Range: 34.1, 252.9 μg/m3 

The results show an association between SO2

exposure at the end of the first and second mo of 

pregnancy and a negative correlation between 

length of gestations and lower birth weight of 

newborns. 

Correlation coefficients: 

1st mo: Gestation length: −0.09, p = 0.008 

Birthweight: −0.08, p = 0.016 

2nd mo: Gestation length: −0.08, p = 0.016 

Birthweight: −0.07, p = 0.026 

3rd mo: Gestation length: −0.04, p = 0.147 

Birthweight: −0.04, p = 0.135 

6th mo: Gestation length: −0.02, p = 0.266 

Birthweight: −0.04, p = 0.151 

Whole pregnancy: 

Gestation length: −0.09, p = 0.007 

Birthweight: −0.04, p = 0.153 

Weekly avg during whole pregnancy: 

Gestation length: −0.05, p = 0.086 

Birthweight: −0.06, p = 0.069 

 F-107 
Study Methods Pollutant Data Findings 

Pereira et al. (1998) 

São Paulo, Brazil 

Period of Study: 

1991-1992 

Outcome(s): Intrauterine mortality 

Study design: Time-series 

Statistical Analysis: Poisson regression models 

Covariates: Mo, day of wk, min daily temperature, 

relative humidity 

Lag: 2 to 14 days 

24-h avg SO2: 

18.90 (8.53) mg/m3 

Range: 3.80, 59.70 

Copollutants: 

PM10 (r = 0.45) 

NO2 (r = 0.41) 

O3 (r = 0.17) 

CO (r = 0.24) 

SO2 exhibited a marginal association with 

intrauterine mortality, but only when Poisson 

regression was employed. A concentrationresponse relationship was found. 

Estimated regression coefficients and standard 

errors: 

SO2 alone: 0.0038 (0.0020) 

SO2 + NO2 + CO + PM10 + O3: 0.0029 (0.0031) 

LATIN AMERICA 

Gouveia et al. 

(2004) 

São Paulo, Brazil 

Period of Study: 

1997 

Outcome(s): LBW 

Study design: Time-series 

N: 179,460 live singleton births 

Statistical Analysis: Logistic and linear regression with 

GAM 

Covariates: Gender, gestational age, maternal age, 

maternal education, antenatal care, parity, delivery 

method 

Statistical package: S-Plus 2000 

Annual Mean: SO2 (μg/m3) 

Mean: 19.6 

SD: 10.3 

Range: 3.4, 56.9 

Jan-Mar: 22.3 (7.7) 

Apr-June: 28.1 (10.1) 

Jul-Aug: 17.9 (8.7) 

Oct-Dec: 10.3 (3.9) 

Copollutants: 

PM10

CO 

NO2

O3

First and second trimester exposures to SO2 had a 

significant association with birth weight, though in 

different directions. When air pollutants were 

divided into quartiles and the lowest quartile was 

used as the referent exposure category, SO2

during the second trimester was marginally 

associated with low birth weight. 

Increment: 10 μg/m3 

Reduction in birth weight 

First trimester: −24.2 g (−55.5, 7.1) 

Second trimester: 33.7 g (1.6, 65.8) 

Third trimester: 9.7 g (−25.6, 44.9) 

First trimester: 

2nd: 0.902 (0.843, 0.966) 

3rd: 0.911 (0.819, 1.013) 

4th: 0.906 (0.793, 1.036) 

Second trimester: 

2nd: 0.986 (0.922, 1.053) 

3rd: 1.005 (0.904, 1.117) 

4th: 1.017 (0.883, 1.173) 

Third trimester: 

2nd: 1.203 (0.861, 1.68) 

3rd: 1.225 (0.872, 1.722) 

4th: 1.145 (0.749, 1.752) 

ASIA 

Ha et al. (2001) 

Seoul, Korea 

Period of Study: 

1996-1997 

Outcome(s): LBW 

Study design: Time-series 

N: 276,763 

Statistical Analysis: Multiple regression, GAM 

Covariates: Gestational age, maternal age, parental 

education level, infant’s birth order, gender 

24-h avg: 

1st trimester: 

25th: 10.0 ppb 

50th: 13.2 ppb 

75th: 16.2 ppb 

3rd trimester: 

25th: 8.4 ppb 

50th: 12.2 ppb 

75th: 16.3 ppb 

Copollutants: 

CO (r = 0.83) NO2 (r = 0.70) 

TSP (r = 0.67) O3 (r = −0.29) 

Ambient SO2 concentrations during the first 

trimester of pregnancy were associated with LBW 

Increment: 1st trimester: 6.2 ppb; 

3rd trimester: 7.9 ppb 

1st trimester: RR 1.06 (1.02, 1.10) 

3rd trimester: RR 0.93 (0.88, 0.98) 

Reduction in birth weight (1st trimester): 8.06 g 

(5.59, 10.53) 

Lee et al. (2003a) 

Seoul, Korea 

Period of Study: 

1996-1998 

Outcome(s): Term LBW 

Study design: Time series 

N: 388,105 full-term singleton births 

Statistical Analysis: GAM 

Covariates: Infant sex, birth order, maternal age, 

parental education level, time trend, and gestational 

age. 

Avg concentration (ppb) 

Mean: 12.1 

SD: 7.4 

Range: 3, 46 

25th: 6.8 

50th: 9.8 

75th: 15.6 

Copollutants: 

PM10; r = 0.78, 0.85 

CO; r = 0.79, 0.86 

NO2; r = 0.75, 0.76 

Second trimester exposures to SO2 as well as 

during the entire pregnancy were associated with 

LBW. Reduction in birth weight was 14.6 g for IQR 

increase in SO2 in the second trimester. When the 

exposure for each mo of pregnancy was evaluated 

separately, SO2 exposure during 3 to 5 mos of 

pregnancy associated with LBW. Increment: 8.8 ppb 

(IQR) 

First trimester: 1.02 (0.99, 1.06) 

Second trimester: 1.06 (1.02, 1.11) 

Third trimester: 0.96 (0.91, 1.00) 

All trimesters: 1.14 (1.04, 1.24) 

 F-108 
Study Methods Pollutant Data Findings 

Leem et al. (2006) 

Incheon, Korea 

Period of Study: 

2001-2002 

Outcome(s): Preterm delivery 

Study design: Time series 

N: 52,113 singleton births 

Statistical Analysis: Log-binomial regression 

Covariates: Maternal age, parity, sex, season, 

maternal education, paternal education 

Mean: SO2 concentrations by 

trimester: 

1st trimester: 

Min: 7.86 μg/m3

25th: 17.61. 50th: 22.74 

75th: 45.85. Max: 103.96 

3rd trimester: 

Min: 6.55 μg/m3

25th: 17.03. 50th: 25.62 

75th: 46.53. Max: 103.15 

Copollutants: 

NO2 (r = 0.54) CO (r = 0.31) 

PM10 (r = 0.13) 

This study found the highest SO2 concentrations 

during the first trimester to be significantly 

associated with elevated risks of preterm delivery. 

1st trimester: 7.86 to 17.61 μg/m3: referent 

17.62 to 22.74: 1.13 (0.99, 1.28) 

22.75 to 45.85: 1.13 (0.98, 1.30) 

45.86 to 103.96: 1.21 (1.04, 1.42) 

3rd trimester: 6.55 to 17.03 μg/m3: referent 

17.04 to 25.62: 0.87 (0.76, 1.01) 

25.63 to 46.53: 0.97 (0.83, 1.13) 

46.54 to 103.15: 1.11 (0.94, 1.31) 

Lin et al. (2004a) 

Kaohsiung and 

Taipei, Taiwan 

Period of Study: 

1995-1997 

Outcome(s): LBW 

Study design: Case-control 

N: 92,288 live births 

Statistical Analysis: Multiple logisistic regression 

Covariates: Gestational period, gender, birth order, 

maternal age, maternal education, season of birth 

24-h avg: 

Kaohsiung: 

Range: 10.07, 25.36 ppb 

Taipei: 

Range: 5.65, 9.33 ppb 

Copollutants: 

CO NO2 O3 PM10

Few women living in Taipei were exposed to high 

levels of SO2. In Kaohsiung, almost all women were 

exposed to high levels of SO2. Women living in 

Kaohsiung had significantly higher risk of term LBW 

compared with women living in Taipei. 

OR for Kaoshiung births (compared to Taipei births) 

All births: OR: 1.13 (1.03, 1.24) 

Female births only: OR: 1.14 (1.01, 1.28) 

Lin et al. (2004b) 

Kaohsiung and 

Taipei, Taiwan 

Period of Study: 

1995-1997 

Outcome(s): Term LBW 

Study design: Cohort 

N: 92,288 live births 

Statistical Analysis: Multiple logisistic regression 

Covariates: Gestational period, gender, birth order, 

maternal age, maternal education, season of birth 

24-h avg: 

Kaohsiung: 

Range: 10.07, 25.36 ppb 

Taipei: 

Range: 5.65, 9.33 ppb 

Copollutants: 

CO 

NO2

O3

PM10

This study found a 26% higher risk of term LBW 

delivery for mothers exposed to mean SO2

concentrations exceeding 11.4 ppb during the entire 

pregnancy, as compared with mothers exposed to 

mean concentrations less than 7.1 ppb. Trimester 

specific analysis showed a significant association 

only for the third trimester. Lowest quartile of 

exposure = referent 

Entire pregnancy: 25th-75th: 1.16 (1.02, 1.33) 

>75th: 1.26 (1.04, 1.53) 

1st trimester: 25th-75th: 1.02 (0.90, 1.16) 

>75th: 1.11 (0.94, 1.33) 

2nd trimester: 25th-75th: 1.09 (0.96, 1.24) 

>75th: 1.17 (0.99, 1.37) 

3rd trimester:25th-75th: 1.13 (0.99, 1.28) 

>75th: 1.20 (1.01, 1.41) 

Wang et al. (1997) 

Four residential 

areas: Congwen, 

Dongcheng, 

Xicheng, Xuanwu 

Beijing, China 

Period of Study: 

1988-1991 

Outcome(s): Term LBW 

Study design: Cohort study 

N: 74,671 first parity live births 

Statistical Analysis: Multiple linear regression and 

logistic regression 

Covariates: Gestational age, residence, yr of birth, 

maternal age, and infant gender. 

Mean pollution concentrations 

provided in graph 

TSP; r = 0.92 

Exposure-response relationship between SO2 during 

the third trimester of pregnancy and low birth weight.

3rd trimester: 9 to 18 μg/m3 (reference) 

18 to 55: 1.09 (0.94, 1.26) 

55 to 146: 1.12 (0.97, 1.29) 

146 to 239: 1.16 (1.01, 1.34) 

239 to 308: 1.39 (1.22, 1.60) 

SO2 as continuous variable: Odds ratio per 100 

μg/m3: 1.11 (1.06, 1.16) 

 F-109 
Study Methods Pollutant Data Findings 

Xu et al. (1995) 

Four residential 

areas: 

Congwen, 

Dongchen, Xichen, 

Xuanwu 

Beijing, China 

Period of Study: 

1988 

Outcome(s): Preterm delivery 

Study design: Prospective cohort study 

N: 25,370 singleton first live births 

Statistical Analysis: Multiple linear and logistic 

regression 

Covariates: Temperature, humidity, day of wk, 

season, residential area, maternal age, and gender of 

child. 

2 monitors for SO2: 

Dongcheng and Xicheng 

Dongcheng annual mean: 108 

μg/m3 SD: 141 μg/m3) 

Xicheng annual mean: 

93 μg/m3 (SD: 122 μg/m3) 

Copollutants: 

TSP 

Exposure response relationship between quartiles of 

SO2 and crude incidence rates of preterm birth. 

Dose dependent relationship between SO2 and 

gestational age. The estimated reduced length of 

gestation was 0.075 wks or 12.6 h per 100/m3

increase in SO2. When TSP and SO2 included in a 

multipollutant model, the effect of SO2 was reduced 

by 32%. Effect on gestational age (wk) per 

100 μg/m3 regression coef and SE for lagged 

moving avg of SO2. 

lag 0: −0.016 (0.021). lag 1: −0.022 (0.021) 

lag 6: −0.067 (0.024), p < 0.01 

lag 7: −0.075 (0.024) , p < 0.01 

lag 8: −0.075 (0.025), p < 0.01 

OR for each quartile of SO2

1st: 1.00. 2nd: 1.70 (1.15, 2.52) 

3rd: 1.74 (1.03, 2.92). 4th: 1.58 (0.87, 2.86) 

Adjusted OR for preterm delivery: 

1.21 (1.01, 1.46) per ln μg/m3 increase in SO2

Yang et al. (2003a) 

Kaohsiung, Taiwan 

Period of Study: 

1995-1997 

Outcome(s): Term LBW 

Study design: Cohort 

N: 13,396 first parity singleton live births 

Statistical Analysis: Multiple linear regression 

Covariates: Maternal age, season, marital status, 

maternal education, gender 

Statistical package: SAS 

Mean: trimester exposure 

(μg/m3) 

1st trimester 

33rd: 26.02 

67th: 36.07 

2nd trimester 

33rd: 25.76 

67th: 35.63 

3rd trimester 

33rd: 25.39 

67th: 36.96 

Copollutants: 

PM10 (r = 0.45, 0.46) 

A significant exposure-response relationship 

between maternal exposures to SO2 and birth 

weight was found first trimester of pregnancy. 

Reduction in birth weight: 

1st trimester: 

33rd-67th: 3.68 g (−12.45, 19.21) 

>67th: 18.11 g (1.88, 34.34) 

Continuous: 0.52 g (0.09, 2.63) 

2nd trimester: 

33rd-67th: 1.78 g (−17.91, 14.35) 

>67th: 13.53 g (−2.62, 29.68) 

Continuous: 0.19 g (−0.78, 1.8) 

3rd trimester: 

33rd-67th: 0.43 g (−16.56, 15.70) 

>67th: 1.97 g (−18.24, 14.30) 

Continuous: 0.03 g (−1.21, 1.37) 

 F-110 
Table F-9. Long-term exposure to SO2 and mortality. 

Study Pollutant Data Methods Findings 

UNITED STATES 

Abbey et al. (1999) 

Three California air 

basins: San Diego, 

San Francisco, South 

Coast (Los Angeles 

and eastward) 

Period of Study: 1977-

1992 

24-h avg SO2: 5.6 ppb Prospective cohort study of 6,338 nonsmoking 

non-Hispanic white adult members of the 

Adventist Health Study followed for all cause, 

cardiopulmonary, nonmalignant respiratory, and 

lung cancer mortality. Participants were aged 27-

95 yrs at enrollment in 1977. 1,628 (989 females, 

639 males) mortality events followed through 

1992. All results were stratified by gender. Used 

Cox proportional hazards analysis, adjusting for 

age at enrollment, past smoking, environmental 

tobacco smoke exposure, alcohol use, education, occupation, and body mass index. Analyzed 

mortality from all natural causes, 

cardiopulmonary, nonmalignant respiratory, and 

lung cancer. 

SO2 was not associated with total RR = 1.07 (95% 

CI: 0.92, 1.24) for male and 1.00 (95% CI: 0.88, 

1.14) for female per 5 ppb increase in multiyear avg 

SO2 , cardiopulmonary, or respiratory mortality for 

either sex. Lung cancer mortality showed large risk 

estimates for most of the pollutants in either or both 

sexes, but the number of lung cancer deaths in this 

cohort was very small (12 for female and 18 for 

male) Generally wide confidence intervals (relative 

to other U.S. cohort studies). 

Dockery et al. (1993) 

and reanalysis by 

Krewski et al. (2000) 

Harriman, TN; Portage, 

WI; 

St. Louis, MO; 

Steubenville, OH; 

Topeka, KS; 

Watertown, MA; 

Period of Study: 1974-

1991. 

24-h avg SO2 ranged from 1.6 

(Topeka) to 24.0 

(Steubenville) ppb. 

A prospective cohort study to study the effects of 

air pollution with main focus on PM components 

in six U.S. cities, which were chosen based on 

the levels of air pollution (Portage, WI, the least 

polluted to Steubenville, OH, the most polluted). 

Cox proportional hazards regression was 

conducted with data from a 14-to-16-yr follow-up 

of 8,111 adults in the six cities, adjusting for 

smoking, sex, BMI, occupational exposures, etc. 

PM2.5 and sulfate were associated with these 

causes of deaths. 

SO2 result presented only graphically. Fine particles 

and sulfate showed better fit than SO2. 

In the reanalysis by Krewski et al (2000), SO2

showed positive associations with total RR = 1.05 

(95% CI: 1.02, 1.09) per 5 ppb increase in the avg 

SO2 over the study period, cardiopulmonary (1.05 

(95% CI: 1.00, 1.10)), and lung cancer deaths (1.03 

(95% CI: 0.91, 1.16)), but in this dataset, SO2 was 

highly correlated with PM2.5 (r = 0.85), sulfate (r = 

0.85), and NO2 (r = 0.84) 

 F-111 
Study Pollutant Data Methods Findings 

Krewski et al. (2000); 

Jerrett et al. (2003); 

Krewski et al. (2003); 

Re-analysis/sensitivity 

analysis of Pope et al. 

(1995) study. 

Multiyear avg of 24-h avg 9.3 

ppb. 

Re-analysis of Pope et al. (1995) study. 

Extensive sensitivity analysis with ecological 

covariates and spatial models to account of 

spatial pattern in the ACS data. 

In the Jerret et al. reanalysis the relative risk 

estimates for total mortality was 1.06 (95% CI: 1.05, 

1.07) per 5 ppb increase in the annual avg SO2. In 

the spatial filtering model (this was the model that 

resulted in the largest reduction of SO2 risk estimate 

when sulfate was included), the SO2 total mortality 

risk estimate was 1.07 (95% CI: 1.03, 1.11) in the 

single-pollutant model and 1.04 (95% CI: 1.02, 1.06) 

with sulfate in the model. The risk estimates for 

PM2.5 and sulfate were diminished when SO2 was 

included in the models. 

In the sensitivity analysis conducted by Krewski 

et al. SO2 was significantly associated with all-cause 

mortality in a single-pollutant model: 1.30 (1.23, 

1.38). In the spatial analysis, SO2 was found to be 

the only gaseous pollutant strongly associated with 

all-cause and cardiopulmonary disease mortality: 

Relative Risk (95% CI) (per 19.9 μg/m3 Sulfates) 

Spatial Analysis, All-Cause: 

Independent Observations: 

Sulfates + SO2: 1.05 (0.98, 1.12) 

Fine particles + SO2: 1.03 (0.95, 1.13) 

Independent Cities: 

Sulfates + SO2: 1.13 (1.02, 1.25) 

Fine particles + SO2: 1.14 (0.98, 1.32) 

Regional Adjustment: 

Sulfates + SO2: 1.10 (0.97, 1.24) 

Fine particles + SO2: 1.11 (0.93, 1.33) 

Spatial Filtering: Sulfates + SO2: 1.05 (0.97, 1.14) 

Cardiopulmonary Disease: 

Independent Observations: 

Sulfates + SO2: 1.13 (1.03, 1.24) 

Fine particles + SO2: 1.17 (1.03, 1.33) Independent 

Cities: 

Sulfates + SO2: 1.18 (1.04, 1.34) 

Fine particles + SO2: 1.25 (1.05, 1.49) 

Regional Adjustment: 

Sulfates + SO2: 1.12 (0.96, 1.32) 

Fine particles + SO2: 1.23 (0.97, 1.55) 

Spatial Filtering: Sulfates + SO2: 1.10 (0.99, 1.22) 

Lung Cancer: 

Independent Observations: 

Sulfates + SO2: 1.37 (1.08, 1.73) 

Independent Cities: 

Sulfates + SO2: 1.39 (1.08, 1.81) 

Lipfert et al. 

(2006) 

32 Veterans 

Administration 

hospitals nationwide 

in the U.S. 

Period of Study: 1976-

1996 

SO2 mean levels NR. Cohort study of approximately 50,000 U.S. 

veterans (all males) diagnosed with 

hypertension. Mean age at recruitment was 51 

yrs. Exposure to O3 during four periods (1960-

1974, 1975-1981, 1982-1988, 1989-1996) 

associated with mortality over three periods 

(1976-1981, 1982-1988, 1989-1996). Long-term 

exposures to TSP, PM15, PM10, PM2.5, PM15-2.5, 

SO4

2−, NO2, and CO also analyzed. Used Cox 

proportional hazards regression, adjusting for 

race, smoking, age, systolic and diastolic blood 

pressure, body mass index, and socioeconomic 

factors. 

SO2 andd Pb were considered less thoroughly. The 

authors presented only qualitative results for SO2

from the “screening regressions” which indicated 

negatively significant risk estimate in the univariate 

model and non-significant positive estimate in the 

multivariate model. 

 F-112 
Study Pollutant Data Methods Findings 

Lipfert et al. (2000b) 

32 Veterans 

Administration 

hospitals nationwide in 

the U.S. 

Period of Study: 1976-

2001 

Mean of the 95th percentile of 

the 24-h avg SO2 for 1997-

2001 period: 15.8 ppb. 

Update of the Lipfert et al. (2000a) study, with 

follow-up period extended to 2001. Study 

focused on the traffic density data. The countylevel traffic density was derived by dividing 

vehicle-km traveled by the county land area. 

Because of the wide range of the traffic density 

variable, log-transformed traffic density was used 

in their analysis. They reported that traffic density 

was a better predictor of mortality than ambient 

air pollution variables, with the possible 

exception of O3. The log-transformed traffic 

density variable was weakly correlated with SO2

(r = 0.32) in this data set. 

RR using the 1997-2001 air quality data period: 0.99 

(95% CI: 0.97, 1.01) per 5 ppb increase; in a singlepollutant model. 

The 2-pollutant model with the traffic density 

variable: 0.99 (95% CI: 0.96, 1.01) per 5 ppb. 

Lipfert et al. (2006b) 

32 Veterans 

Administration 

hospitals nationwide in 

the U.S. 

Period of Study: 1997-

2001 

Mean of the 95th percentile of 

the 24-h avg SO2 for 1999-

2001 period: 16.3 ppb. 

Update of the Lipfert et al. (2000a) study, 

examined PM2.5 chemical constituents data. The 

analysis used county-level air pollution data for 

the period 1999-2001 and cohort mortality data 

for 1997-2001. 

Traffic density was the most important predictor of 

mortality, but associations were also seen for EC, V, 

nitrate, and Ni. NO2, O3, and PM10 also showed 

positive but weaker associations. The risk estimate 

for SO2 was essentially the same as that reported in 

the 2006a Lipfert et al. analysis (0.99 (95% CI: 0.96, 

1.01) per 5 ppb) in a single-pollutant model. 

Multipollutant model results were not presented for 

SO2. 

Pope et al. (1995) 

U.S. nationwide 

Period of Study: 1982-

1989 

Not analyzed/ reported. Investigated associations between long-term 

exposure to PM and the mortality outcomes in 

the American Cancer Society cohort. Ambient air 

pollution data from 151 U.S. metropolitan areas 

in 1981 were linked with individual risk factors in 

552,138 adults who resided in these areas when 

enrolled in the prospective study in 1982. Death 

outcomes were ascertained through 1989. Cox 

proportional hazards model adjusted for 

smoking, education, BMI, and occupational 

exposures. PM2.5 and sulfate were associated 

with total, cardiopulmonary, and lung cancer 

mortality, but not with mortality for all other 

causes. 

Gaseous pollutants not analyzed. 

Pope et al. (2002) 

U.S. nationwide 

Period of Study: 1982-

1998 

24-h avg mean of 118 MSA’s 

in1980: 9.7 ppb; mean of 

126 MSA’s during 

1982-1998: 6.7 ppb. 

Prospective cohort study of approximately 

500,000 members of American Cancer Society 

cohort enrolled in 1982 and followed through 

1998 for all cause, cardiopulmonary, lung cancer, 

and all other cause mortality. Age at enrollment 

was 30+ yrs. Air pollution concentrations in urban 

area of residence at time of enrollment assessed 

from 1982 through 1998. Other pollutants 

considered include TSP, PM15, PM10, PM2.5, PM15-

2.5, SO4

2−, SO2, NO2, and CO. 

PM2.5 was associated with total, cardiopulmonary, 

lung cancer mortality, but not with deaths for all 

other causes. SO2 was associated with all the 

mortality outcomes, including all other causes of 

deaths. SO2’s risk estimate for total mortality was 

1.03 (95% CI: 1.02, 1.05) per 5 ppb increase (1982-

1998 average).Residential location was known only 

at enrollment to study in 1982. Thus, exposure 

misclassification possible. 

Willis et al. (2003) 

Re-analysis/ sensitivity 

analysis of Pope et al. 

(1995) study. 

Multiyear average of 

24-h avg using MSA scales: 

9.3 ppb; using county scales: 

10.7 ppb. 

Investigation of the effects of geographic scale 

over which the air pollution exposures are 

averaged. Exposure estimates were averaged 

over the county scale, and compared the original 

ACS results in which MSA scale avg exposures 

were used. Less than half of the cohort used in 

the MSA-based study were used in the county 

scale based analysis because of the limited 

availability of sulfate monitors and because of 

the loss of subjects from the use of five-digit zip 

codes 

In the analysis comparing the 2-pollutant model with 

sulfate and SO2, they found that, in the MSA-scale 

model, the inclusion of SO2 reduced sulfate risk 

estimates substantially (>25%), but not substantially 

(< 25%) in the county-scale model. In the MSA-level 

anlaysis (with 113 MSA’s), SO2 relative risk estimate 

was 1.04 (95% CI: 1.02, 1.06) per 5 ppb increase, 

with sulfate in the model. In the county-level anaysis 

(91 counties) with sulfate in the model, the 

corresponding estimate was smaller (RR = 1.02 

[95% CI: 1.00, 1.05]). The correlations between 

covariates are different between the MSA-level data 

and county-level data. 

 F-113 
Study Pollutant Data Methods Findings 

CANADA 

Finkelstein et al. 

(2003) 

Ontario, Canada 

Period of Study: 1992-

1999 

24-h avg (ppb): 4.9 (1.0) Cohort consisting of 5,228 people >40 yrs old 

that were referred for pulmonary function testing 

between 1985 and 1999. Within the cohort 

identified nonaccidental deaths that occurred 

from 1992 through 1999. Used air quality data for 

TSP from 1992-1994 and SO2 for 1993-1995. 

Analyzed the association between TSP or SO2

and socioeconomic status and mortality using a 

Cox proportional hazards model stratified by sex 

and 5-yr age groups. 

Using the high income-low pollutant level as the 

reference the following results were reported for 

each of the mortality endpoints: 

Relative Risk (95% CI) All causes 

High income-high pollutant level: 1.35 (1.05, 1.73) 

Low income-low pollutant level: 1.64 (1.21, 2.24) 

Low income-high pollutant level: 2.40 (1.61, 3.58) 

Interaction with age group: 0.97 (0.95, 0.99) 

Cardiopulmonary causes 

High income-high pollutant level: 1.54 (1.13, 2.10) 

Low income-low pollutant level: 2.05 (1.45, 2.91) 

Low income-high pollutant level: 3.36 (2.12, 5.32) 

Interaction with age group: 0.95 (0.92, 0.97) 

EUROPE 

Beelen et al. (2008) 

The Netherlands 

Period of Study: 1987-

1996. 

Cohort study on diet and 

cancer with 120,852 subjects 

followed from 1987 to 1996. 

BS, NO2, SO2, and PM2.5 and 

traffic-exposure estimates 

were analyzed. Cox 

regression model adjusted for 

age, sex, smoking, and arealevel socioeconomic status. 

Mean SO2 Levels: 

Mean: 4.8 ppb, with a range of 1.5 to 11.8 ppb. 

Copollutants: 

PM2.5

BS 

NO2

Traffic intensity on the nearest road was not 

associated with exposure SO2. Background SO2

levels were not associated with mortality. 

Adjusted RR 

(per 20 μg/m3 SO2) 

All cause: 0.97 (0.90,1.05) 

Cardiovascular: 0.94 (0.82, 1.06) 

Respiratory: 0.88 (0.64, 1.22) 

Elliott et al. (2007) 

Great Britain; 1966-

1994 air pollution; 

1982-1998 mortality in 

four periods. 

24-h avg SO2 levels declined 

from 41.4 ppb in 1966-1970 

to 12.2 ppb in 1990-1994 

A small area analysis of mortality rates in 

electoral ward, with the mean area of 7.4 km2

and the mean population of 5,301 per electoral 

ward. Deaths rates were computed for four 

successive 4-yr periods from 1982 to 1994. The 

number of wards in these four periods ranged 

from 118 in the 1994-1998 period to 393 in the 

1982-1986 period. Poisson model was fit to 

model observed deaths for each ward with a 

linear function for pollutant and random intercept, 

with and without adjustment for social 

deprivation. 

They observed associations for both BS and SO2

and mortality outcomes. The estimated effects were 

stronger for respiratory illness than other causes of 

mortality for the most recent exposure periods and 

most recent mortality period (pollution levels were 

lower). The adjustment for social deprivation 

reduced the risk estimates for both pollutants. The 

adjusted risk estimates for SO2 for the pooled 

mortality periods using the most recent exposure 

windows were: 1.021 (95% CI: 1.018, 1.024) for allcause; 1.015 (95% CI: 1.011, 1.019) for 

cardiovascular; and 1.064% (95% CI: 1.056, 1.072) 

for respiratory causes per 5 ppb increase in SO2. 

The risk estimates for the most recent mortality 

period using the most recent exposure windows 

were larger. 

Filleul et al. (2005) 

Seven French cities 

Period of Study: 1975-

2001 

24-h avg SO2 ranged from 5.9 

ppb (“Area 3” in Lille) to 29.7 

ppb (“Area 3” in Marseille) in 

the 24 areas in seven cities 

during 1974-1976. Median 

levels during 1990-1997 

ranged from 3.0 ppb 

(Bordeaux) to 8.2 ppb 

(Rouen) in the five cities 

where data were available. 

Cohort study of 14,284 adults who resided in 24 

areas from seven French cities when enrolled in 

the PAARC survey (air pollution and chronic 

respiratory diseases) in 1974. Daily 

measurements of SO2, TSP, BS, NO2, and NO 

were made in 24 areas for three yrs (1974-76). 

Cox proportional hazards models adjusted for 

smoking, educational level, BMI, and 

occupational exposure. Models were run before 

and after exclusion of six area monitors 

influenced by local traffic as determined by the 

NO/NO2 ratio >3. 

Before exclusion of the six areas, none of the air 

pollutants were associated with mortality outcomes. 

After exclusion of these areas, analyses showed 

associations between total mortality and TSP, BS, 

NO2, and NO, but not SO2 (1.01 (95% CI: 0.97, 

1.06) per 5 ppb multi-yr average). From these 

results, the authors noted that inclusion of air 

monitoring data from stations directly influenced by 

local traffic could overestimate the mean population 

exposure and bias the results. It should be noted 

that the table describing air pollution levels in Filleul 

et al.’s report indicates that the SO2 levels in these 

French cities declined markedly between 1974-76 

and 1990-1997 period, by a factor of 2 to 3, 

depending on the city, whereas NO2 levels between 

the two periods were variable, increased in some 

cities, and decreased in others. This change in air 

pollution levels over the study period complicates 

interpretation of reported risk estimates. 

 F-114 
Study Pollutant Data Methods Findings 

Lepeule et al. (2006) 

Bordeaux, France 

Period of Study : 

1988–1997 

24-h avg (μg/m3): 10.3 (6.6) Identified 439 non-accidental deaths and 158 

cardiorespiratory deaths from the Personnes 

Agées QUID (PAQUID) cohort. Used a Cox 

proportional hazards model with time dependent 

covariates to examine the association between 

BS and sulfur dioxide-strong acidity (SO2-AF) 

and non-accidental and cardiorespiratory 

mortality. 

Relative Risk (per 10 μg/m3 SO2-AF) 

Non-accidental Mortality: 

1.03 (0.86, 1.24) lag 0. 0.96 (0.88, 1.06) lag 1 

0.96 (0.85, 1.09) lag 2. 1.03 (0.94, 1.12) lag 3 

1.17 (0.99, 1.39) lag 4. 1.16 (0.86, 1.55) cumulative 

Cardiorespiratory mortality: 

0.84 (0.65, 1.10) lag 0. 1.06 (0.94, 1.19) lag 1 

1.19 (1.03, 1.37) lag 2. 1.19 (1.03, 1.37) lag 3 

1.07 (0.95, 1.19) lag 4. 0.85 (0.66, 1.10) lag 5 

1.15 (0.75, 1.77) cumulative 

Nafstad et al. (2004) 

Oslo, Norway 

Period of Study: 1972-

1998. 

The yearly averages of 24-h 

avg SO2 were reduced with a 

factor of 7 during the study 

period from 5.6 ppb in 1974 to 

0.8 ppb in 1995. 

Cohort study of 16,209 Norwegian men 40-49 

yrs of age living in Oslo, Norway, in 1972-1973. 

Data from the Norwegian Death Register were 

linked with estimates of avg yearly air pollution 

levels at the participants’ home addresses from 

1974 to 1998. NOX, rather than NO2 was used. 

Exposure estimates for NOX and SO2 were 

constructed using models based on the subject’s 

address, emission data for industry, heating, and 

traffic, and measured concentrations. Addresses 

linked to 50 of the busiest streets were given an 

additional exposure based on estimates of 

annual avg daily traffic. Cox proportional-hazards 

regression was used to estimate associations 

between exposure and total and cause-specific 

mortality, adjusting for age strata, education, 

occupation, smoking, physical activity level, and 

risk groups for cardiovascular diseases. 

NOX was associated with total, respiratory, lung 

cancer, and ischemic heart disease deaths. SO2 did 

not show any associations with mortality (e.g., 0.97 

(95% CI: 0.94, 1.01) per 5 ppb multi-yr average). 

The risk estimates presented for categorical levels 

of these pollutants showed mostly monotonic 

exposure-response relationships for NOX, but not for 

SO2. Note the very low levels of SO2. 

 F-115 
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