A mA United States
L'-ll I A Environmental Protection
I M mAgency
Integrated Science Assessment for
Sulfur Oxides—Health Criteria
(Second External Review Draft)
December 2016
National Center for Environmental Assessment—RTP Division
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC
EP A/600/R-16/3 51
Second External Review Draft
December 2016
https ://www. epa. gov/isa
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Disclaimer
This document is an external review draft, for review purposes only. This information is distributed solely
for predissemination peer review under applicable information quality guidelines. It has not been formally
disseminated by EPA. It does not represent and should not be construed to represent any Agency
determination or policy. Mention of trade names or commercial products does not constitute endorsement
or recommendation for use.
December 2016
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Contents
Integrated Science Assessment Team for Sulfur Oxides—Health Criteria xx
Authors, Contributors, and Reviewers xxii
Clean Air Scientific Advisory Committee Sulfur Oxides NAAQS Review Panel xxvii
Acronyms and Abbreviations xxviii
Preface xxxv
Legislative Requirements for the Review of the National Ambient Air Quality Standards xxxv
Overview and History of the Reviews of the Primary National Ambient Air Quality Standard for
Sulfur Dioxide xxxvii
Table I History of the primary National Ambient Air Quality Standards for
sulfur dioxide since 1971. xxxviii
Executive Summary xlii
Purpose and Scope of the Integrated Science Assessment xlii
Sources and Human Exposure to Sulfur Dioxide xliii
Dosimetry and Mode of Action of Inhaled Sulfur Dioxide xlv
Health Effects of Sulfur Dioxide Exposure xlvii
Table ES-1 Causal determinations for relationships between sulfur dioxide
exposure and health effects from the 2008 and current draft
Integrated Science Assessment for Sulfur Oxides. xlix
Sulfur Dioxide Exposure and Respiratory Effects I
Sulfur Dioxide Exposure and Other Health Effects li
Policy-Relevant Considerations for Health Effects Associated with Sulfur Dioxide Exposure li
Chapter 1 Integrative Synthesis of the ISA 1-1
1.1 Purpose and Overview of the Integrated Science Assessment 1-1
1.2 Process for Developing Integrated Science Assessments 1-3
1.3 Organization of the Integrated Science Assessment 1-6
1.4 From Emissions Sources to Exposure to Sulfur Dioxide 1-6
1.4.1 Emission Sources and Distribution of Ambient Concentrations 1-7
1.4.2 Assessment of Human Exposure 1-9
1.5 Dosimetry and Mode of Action of Sulfur Dioxide 1-12
1.5.1 Dosimetry of Inhaled Sulfur Dioxide 1-12
1.5.2 Mode of Action of Inhaled Sulfur Dioxide 1-14
1.6 Health Effects of Sulfur Dioxide 1-16
1.6.1 Respiratory Effects 1-16
1.6.1.1 Respiratory Effects Associated with Short-Term Exposure to Sulfur Dioxide 1-16
1.6.1.2 Respiratory Effects Associated with Long-Term Exposure to Sulfur Dioxide 1-18
1.6.2 Health Effects beyond the Respiratory System 1-19
1.6.2.1 Cardiovascular Effects Associated with Short-Term Exposure to Sulfur Dioxide _ 1-19
1.6.2.2 Cardiovascular Effects Associated with Long-Term Exposure to Sulfur Dioxide _ 1-19
1.6.2.3 Reproductive and Developmental Effects 1-20
1.6.2.4 Total Mortality Associated with Short-Term Exposure to Sulfur Dioxide 1-20
1.6.2.5 Total Mortality Associated with Long-Term Exposure to Sulfur Dioxide 1-21
1.6.2.6 Cancer 1-21
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CONTENTS (Continued)
Table 1-1 Key evidence contributing to causal determinations for sulfur
dioxide exposure and health effects evaluated in the current draft
Integrated Science Assessment for Sulfur Oxides. 1-22
1.7 Policy-Relevant Considerations 1-26
1.7.1 Durations and Lag Structure of Sulfur Dioxide Exposure Associated with Health Effects 1-26
1.7.2 Concentration-Response Relationships and Thresholds 1-26
1.7.3 Regional Heterogeneity in Effect Estimates 1-27
1.7.4 Public Health Significance 1-28
1.7.4.1 Characterizing Adversity of Health Effects 1-28
1.7.4.2 At-Risk Populations and Lifestages for Health Effects Related to Sulfur
Dioxide Exposure 1-28
1.7.4.3 Summary of Public Health Significance of Health Effects Related to Sulfur
Dioxide Exposure 1-30
1.8 Summary and Health Effects Conclusions 1-30
Chapter 2 Atmospheric Chemistry and Ambient Concentrations of Sulfur Dioxide and Other
Sulfur Oxides 2-1
2.1 Introduction 2-1
2.2 Anthropogenic and Natural Sources of Sulfur Dioxide 2-2
2.2.1 U.S. Anthropogenic Sources 2-2
Figure 2-1 Sulfur dioxide emissions by sector in tons, 2011. 2-3
Figure 2-2 Distribution of electric power generating unit-derived sulfur
dioxide emissions across the U.S., based on the 2011 National
Emissions Inventory. 2-4
2.2.2 National Geographic Distribution of Large Sources 2-6
U.S. EPA Sulfur Dioxide Data Requirements Rule 2-6
Figure 2-3 Geographic distribution of (A) continental U.S. facilities emitting
more than 1,000 tpy sulfur dioxide, with (B) an enlargement of
the midwestern states, including the Ohio River Valley, where a
large number of these sources are concentrated. 2-7
Figure 2-4 Sulfur dioxide sources identified by state/local air agencies under
the U.S. Environmental Protection Agency's Data Requirements
Rule, as of July 18, 2016. 2-8
2.2.3 U.S. Anthropogenic Emission Trends 2-8
Table 2-1 Summary of 2011 U.S. Environmental Protection Agency sulfur
dioxide trends data by emissions sector. Values shown in bold
indicate increased emissions, 2001-2011. 2-9
Figure 2-5 National sulfur dioxide emissions trends by sector, 1970-2011.2-
10
2.2.4 Natural Sources 2-10
2.2.4.1 The Global Sulfur Cycle 2-11
2.2.4.2 Volcanoes as a Natural Source of Sulfur Dioxide 2-11
Figure 2-6 Sulfur dioxide released during the July 12-20, 2008 eruption of
the Okmok Volcano in Alaska's Aleutian Islands (image derived
from data collected by the Atmospheric Infrared Sounder
instrument aboard the National Aeronautics and Space
Administration Aqua satellite). 2-12
Figure 2-7 Geographic location of volcanoes and other potentially active
volcanic areas within the continental U.S. 2-13
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CONTENTS (Continued)
Figure 2-8 National Aeronautics and Space Administration/Ozone
Monitoring Instrument image of the KTIauea sulfur dioxide plume
during its March 20-27, 2008 eruption. 2-14
2.2.4.3 Wildfires as a Natural Source of Sulfur Dioxide 2-14
2.2.5 Reduced Sulfur Compounds as Indirect Sources of Sulfur Dioxide 2-15
Table 2-2 Global sulfide emissions in tpy sulfur. 2-16
2.3 Atmospheric Chemistry and Fate 2-17
2.3.1 Photochemical Removal of Atmospheric SO2 2-17
Equation 2-1 2-18
Equation 2-2 2-18
Equation 2-3 2-18
Equation 2-4 2-19
2.3.2 Heterogeneous Oxidation of Sulfur Dioxide 2-19
Equation 2-5 2-19
Equation 2-6 2-20
Equation 2-7 2-20
Figure 2-9 The effect of pH on the rates of aqueous-phase sulfur (IV)
oxidation by various oxidants. 2-21
2.4 Measurement Methods 2-22
2.4.1 Federal Reference and Equivalent Methods 2-22
2.4.1.1 Minimum Performance Specifications 2-23
Table 2-3 Minimum performance specifications for sulfur dioxide based in
40 Code of Federal Regulations Part 53, Subpart B. 2-24
2.4.1.2 Positive and Negative Interferences 2-24
2.4.2 Alternative Sulfur Dioxide Measurements 2-26
2.4.3 Ambient Sampling Network Design 2-28
Figure 2-10 Routinely operating sulfur dioxide monitoring networks: National
Core and State and Local Air Monitoring Sites, reporting 1 hour
and 5 minute sulfur dioxide concentration data. 2-29
2.5 Environmental Concentrations 2-31
2.5.1 Sulfur Dioxide Metrics and Averaging Time 2-31
Table 2-4 Summary of sulfur dioxide metrics and averaging times. 2-32
2.5.2 Spatial Variability 2-32
Table 2-5 Summary of sulfur dioxide data sets originating from the Air
Quality System database. 2-33
2.5.2.1 Nationwide Spatial Variability 2-33
2.5.2.2 Urban Spatial Variability 2-34
Table 2-6 National statistics of sulfur dioxide concentrations (parts per
billion) from Air Quality System monitoring sites, 2013-2015.®
2-35
Figure 2-11 Map of 99th percentile of 1-h daily max sulfur dioxide
concentration reported at Air Quality System monitoring sites,
2013-2015. 2-36
Figure 2-12 Map of 99th percentile of24-h avg sulfur dioxide concentration
reported at Air Quality System monitoring sites, 2013-2015. 2-37
Figure 2-13 Map of the Cleveland, OH focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015. 2-38
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CONTENTS (Continued)
Figure 2-14
Figure 2-15
Figure 2-16
Figure 2-17
Figure 2-18
Table 2-7
Table 2-8
Figure 2-19
Figure 2-20
2.5.3 Temporal Variability
2.5.3.1 Long-Term Trends
Figure 2-21
2.5.3.2 Seasonal Trends_
Figure 2-22
2.5.3.3 Diel Variability_
Map of the Pittsburgh, PA focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015. 2-39
Map of the New York City, NY focus area showing emissions
from large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015. 2-40
Map of the St Louis, MO-IL focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015. 2-41
Map of the Houston, TX focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015. 2-42
Map of the Gila County, AZ focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015. 2-43
1-h daily max sulfur dioxide concentration distribution by Air
Quality System monitoring site in six focus areas, 2013-2015.a2-
44
5-minute sulfur dioxide concentrations by Air Quality System
monitoring sites in select focus areas, 2013-2015.® 2-47
Pairwise correlations of 24-h avg sulfur dioxide versus distance
between monitoring site pairs in six focus areas, 2013-2015.
2-50
Pairwise correlations of 5-minute hourly max data versus
distance between monitoring sites in six focus areas,
2013-2015. 2-51
2-52
2-53
National sulfur dioxide air quality trend, based on the 99th
percentile of the 1-h daily max concentration for 163 sites,
1980-2015. A 76% decrease in the national average was
observed from 1990-2015. 2-53
2-54
Sulfur dioxide month-to-month variability based on 1-h daily max
concentrations at Air Quality System sites in each core-based
statistical area, 2013-2015. 2-56
2-57
Figure 2-23 Diel variability based on 1-h avg sulfur dioxide concentrations in
the six focus areas, 2013-2015. 2-58
Figure 2-24 Diel trend based on 5-minute hourly max data in the six focus
areas, 2013-2015. 2-59
Figure 2-25 Diel trend based on 5-minute hourly max data in the Cleveland,
OH and Gila County, AZ focus areas during winter and summer,
2013-2015. 2-61
2.5.4 Relationships between Hourly Mean and Peak Concentrations 2-62
Figure 2-26 Scatterplot of 5-minute hourly max versus 1-h avg sulfur dioxide
concentrations, 2013-2015. 2-63
Figure 2-27
2.5.5 Background Concentrations
Scatterplot of 5-minute hourly max versus 1-h avg sulfur dioxide
concentrations by focus area, 2013-2015. 2-64
2-65
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CONTENTS (Continued)
Table 2-9 Pearson correlation coefficient and peak-to-mean ratio for
maximum sulfur dioxide concentrations in the six focus areas,
2013-2015. 2-66
Figure 2-28 1-h daily max sulfur dioxide concentrations measured at (A) Hilo,
HI and (B) Pahala, HI. 2-67
Figure 2-29 Average 24-hour ambient sulfur dioxide concentrations during
low and high (volcanic gas) concentration study periods
(November 26, 2007 to June 6, 2008) for Ka'u District, located
downwind of KTIauea Volcano. 2-68
2.6 Atmospheric Modeling 2-68
2.6.1 Dispersion Modeling 2-69
2.6.2 Chemical Transport Models 2-75
2.7 Summary 2-77
Chapter 3 Exposure to Ambient Sulfur Dioxide 3-1
3.1 Introduction 3-1
3.2 Conceptual Overview of Human Exposure 3-1
3.2.1 Exposure Metrics 3-1
3.2.2 Conceptual Model of Personal Exposure 3-3
Equation 3-1 3-3
Equation 3-2 3-3
Equation 3-3 3-4
Equation 3-4 3-4
Equation 3-5 3-4
Equation 3-6 3-5
Equation 3-7 3-5
3.2.3 Exposure Considerations Specific to Sulfur Dioxide 3-5
3.3 Methodological Considerations for Use of Exposure Data 3-6
3.3.1 Measurements 3-6
3.3.1.1 Central Site Monitoring 3-6
3.3.1.2 Personal Monitoring Techniques 3-6
3.3.2 Modeling 3-7
3.3.2.1 Source Proximity Models 3-8
3.3.2.2 Land Use Regression Models 3-9
3.3.2.3 Inverse Distance Weighting 3-12
3.3.2.4 Dispersion Models 3-12
3.3.2.5 Chemical Transport Models 3-13
3.3.2.6 Microenvironmental Exposure Models 3-14
3.3.3 Choice of Exposure Metrics in Epidemiologic Studies 3-16
3.4 Exposure Assessment, Error, and Epidemiologic Inference 3-16
3.4.1 Relationships between Personal Exposure and Ambient Concentration 3-16
Table 3-1 Summary of exposure assignment methods, their typical use in
sulfurdioxide epidemiologic studies, strengths, limitations, and
related errors and uncertainties. 3-17
3.4.1.1 Air Exchange Rate 3-20
3.4.1.2 Indoor-Outdoor Relationships 3-22
3.4.1.3 Personal-Ambient Relationships 3-23
3.4.2 Factors Contributing to Error in Estimating Exposure to Ambient Sulfur Dioxide 3-24
3.4.2.1 Activity Patterns 3-24
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CONTENTS (Continued)
3.4.2.2
Table 3-2
Spatial Variability
Figure 3-1
Table 3-3
Figure 3-2
Table 3-4
Mean fraction of time spent in outdoor locations by various age
groups in the National Human Activity Pattern Survey study.
3-25
3-27
Map of the Cleveland, OH core-based statistical area including
National Emissions Inventory facility locations, urban sulfur
dioxide monitor locations, and distance to each facility with
respect to core-based statistical area block group population
density estimates for 2011. National Emissions Inventory facility
emissions ranged from 1,942 tons/year to 48,300 tons/year. 3-29
2011 American Community Survey population estimates of
people living within a specified distance of an urban sulfur
dioxide monitor in the Cleveland, OH core-based statistical area.
Population estimates are based on census block group
estimates. 3-30
Map of the Pittsburgh, PA core-based statistical area including
National Emissions Inventory facility locations, urban sulfur
dioxide monitor locations, and distance to each facility with
respect to core-based statistical area block group population
density estimates for 2011. National Emissions Inventory facility
emissions ranged from 1,279 tons/year to 46,467 tons/year. 3-31
2011 American Community Survey population estimates of
people living within a specified distance of an urban sulfur
dioxide monitor in the Pittsburgh, PA core-based statistical area.
Population estimates are based on census block group
estimates. 3-32
3.4.2.3
Temporal Variability
Figure 3-3 Pearson correlations between 1
3-33
h avg and 5-minute hourly max,
24-h avg and 1-h daily max, and 24-h avg and 5-minute daily
max sulfur dioxide concentrations. 3-34
3.4.2.4 Method Detection Limit, Instrument Accuracy, and Instrument Precision_
3.4.3 Copollutant Relationships
3.4.3.1 Temporal Relationships among Ambient Sulfur Dioxide and Copollutant
Exposures
Short-Term Temporal Correlations
3-35
3-36
3-38
3-38
Figure 3-4 Distribution of Pearson correlation coefficients for comparison of
24-h avg sulfur dioxide concentration from the year-round data
set with collocated National Ambient Air Quality Standards
pollutants (and sulfur in PM2.5) from Air Quality System during
2013-2015. 3-39
Figure 3-5 Distribution of Pearson correlation coefficients for comparison of
daily 1-h max sulfur dioxide concentration from the year-round
data set with collocated National Ambient Air Quality Standards
pollutants (and sulfur in PM2.5) from Air Quality System during
2013-2015. 3-40
Figure 3-6 Distribution of Pearson correlation coefficients for comparison of
daily 24-h avg sulfur dioxide ambient concentration stratified by
season with collocated National Ambient Air Quality Standards
pollutants (and PM2.5) from Air Quality System during
2013-2015. 3-41
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CONTENTS (Continued)
Figure 3-7 Distribution of Pearson correlation coefficients for comparison of
daily 1-h max sulfur dioxide ambient concentration stratified by
season with collocated National Ambient Air Quality Standards
pollutants (and PM2.5) from Air Quality System during
2013-2015. 3-42
Figure 3-8 Summary of temporal sulfur dioxide-copollutant correlation
coefficients from measurements reported in the literature, sorted
by temporal averaging period. 3-46
Figure 3-9 Trends in copollutant correlations computed using hourly
(1-h avg or 1-h daily max) concentration data. 3-48
Long-Term Correlations 3-48
3.4.3.2 Spatial Relationships among Ambient Sulfur Dioxide and Copollutants 3-49
3.4.4 Implications for Epidemiologic Studies of Different Designs 3-49
3.4.4.1 Community Time-Series Studies 3-51
3.4.4.2 Long-Term Cohort Studies 3-55
3.4.4.3 Panel Studies 3-57
3.5 Summary and Conclusions 3-58
Chapter 4 Dosimetry and Mode of Action 4-1
4.1 Introduction 4-1
4.1.1 Structure and Function of the Respiratory Tract 4-1
Figure 4-1 Diagrammatic representation of respiratory tract regions in
humans. 4-2
4.1.2 Breathing Rates and Breathing Habit 4-2
4.1.2.1 Breathing Rates 4-2
Table 4-1 Ventilation rates in humans as a function of activity. 4-3
4.1.2.2 Breathing Habit 4-4
4.2 Dosimetry of Inhaled Sulfur Dioxide 4-7
4.2.1 Chemistry 4-7
Equation 4-1 4-8
4.2.2 Absorption 4-8
4.2.3 Distribution 4-11
4.2.4 Metabolism 4-13
4.2.5 Elimination 4-14
4.2.6 Sources and Levels of Exogenous and Endogenous Sulfite 4-15
4.3 Mode of Action of Inhaled Sulfur Dioxide 4-17
4.3.1 Activation of Sensory Nerves in the Respiratory Tract 4-18
4.3.2 Injury to Airway Mucosa 4-22
4.3.3 Modulation of Airway Responsiveness and Allergic Inflammation 4-23
4.3.4 Induction of Systemic Effects 4-28
4.3.5 Role of Endogenous Sulfur Dioxide/Sulfite 4-30
4.3.6 Mode of Action Framework 4-30
Figure 4-2 Summary of evidence for the mode of action linking short-term
exposure to sulfur dioxide and respiratory effects. 4-31
Figure 4-3 Summary of evidence for the mode of action linking long-term
exposure to sulfur dioxide and respiratory effects. 4-33
Figure 4-4 Summary of evidence for the mode of action linking exposure to
sulfur dioxide and extrapulmonary effects. 4-35
Chapter 5 Integrated Health Effects of Exposure to Sulfur Oxides 5-1
5.1 Introduction 5-1
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CONTENTS (Continued)
5.1.1 Scope of the Chapter 5-1
5.1.2 Evidence Evaluation and Integration to Form Causal Determinations 5-2
5.1.2.1 Evaluation of Individual Studies 5-2
5.1.2.2 Integration of Scientific Evidence 5-3
5.1.3 Summary 5-4
5.2 Respiratory Effects 5-5
5.2.1 Short-Term Exposure 5-5
5.2.1.1 Introduction 5-5
5.2.1.2 Asthma Exacerbation 5-6
Lung Function Changes in Populations with Asthma 5-7
Controlled Human Exposure Studies 5-8
Table 5-1 Study-specific details from controlled human exposure studies of
individuals with asthma. 5-9
Table 5-2 Percentage of adults with asthma in controlled human exposure
studies experiencing sulfur dioxide-induced decrements in lung
function and respiratory symptoms. 5-15
Figure 5-1 Distribution of individual airway sensitivity to sulfur dioxide.
The cumulative percentage of subjects is plotted as a function of
provocative concentration, which is the concentration of sulfur
dioxide that provoked a 100% increase in specific airway
resistance compared to clean air. 5-18
Table 5-3 Percent change in post-versus pre-exposure measures of forced
expiratory volume in 1 second relative to clean air control after
5-10-minute exposures to sulfur dioxide during exercise.
5-19
Table 5-4 Percent change in post- versus pre-exposure measures of
specific airway resistance relative to clean air control after
5-10-minute exposures to sulfur dioxide during exercise. 5-20
Epidemiologic Studies 5-25
Table 5-5 Recent epidemiologic studies of lung function in adults with
asthma. 5-28
Table 5-6 Recent epidemiologic studies of lung function in children with
asthma. 5-31
Summary of Lung Function Changes in Populations with Asthma 5-34
Respiratory Symptoms in Populations with Asthma 5-35
Controlled Human Exposure Studies 5-35
Table 5-7 Study-specific details from controlled human exposure studies of
respiratory symptoms. 5-36
Epidemiologic Studies 5-39
Table 5-8 Recent epidemiologic studies of respiratory symptoms in
populations with asthma. 5-41
Figure 5-2 Associations between short-term average ambient sulfur dioxide
concentrations and respiratory symptoms and asthma
medication use in children with asthma. 5-45
Summary of Respiratory Symptoms in Populations with Asthma 5-46
Hospital Admission and Emergency Department Visits for Asthma 5-47
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CONTENTS (Continued)
Figure 5-3 Percent increase in asthma hospital admissions and emergency
department visits from U.S. and Canadian studies evaluated in
the 2008 SOx ISA and recent studies in all-year and seasonal
analyses for a 10-ppb increase in 24-h avg or40-ppb increase in
1-h max sulfur dioxide concentrations. 5-48
Table 5-9 Study-specific details and mean and upper percentile
concentrations from asthma hospital admission and emergency
department visit studies conducted in the U.S. and Canada and
evaluated in the 2008 SOx ISA and studies published since the
2008 SOx ISA. 5-49
Hospital Admissions 5-55
Emergency Department Visits 5-56
Hospital Admissions and Emergency Department Visits for Respiratory Conditions
Associated with Asthma 5-59
Outpatient and Physician Visits Studies of Asthma 5-60
Examination of Seasonal Differences 5-61
Lag Structure of Associations 5-63
Exposure Assignment 5-64
Concentration-Response Relationship 5-65
Figure 5-4 Concentration-response for associations between 3-day average
(lag 0-2) sulfur dioxide concentrations and emergency
department visits for pediatric asthma at the 5th to 95th
percentile of sulfur dioxide concentrations in the Atlanta, GA
area. 5-66
Sulfur Dioxide within the Multipollutant Mixture 5-66
Figure 5-5 Rate ratio and 95% confidence intervals for single-pollutant and
joint effect models for each pollutant combination in warm and
cold season analyses for an interquartile range increase in each
pollutant at lag 0-2 days. 5-68
Figure 5-6 Rate ratio and 95% confidence interval for association between
self-organizing map-based multipollutant day type and pediatric
asthma emergency department visits at lag 1. 5-70
Summary of Asthma Hospital Admission and Emergency Department Visits 5-70
Subclinical Effects Underlying Asthma Exacerbation: Pulmonary Inflammation and
Oxidative Stress 5-72
Controlled Human Exposure Studies 5-72
Epidemiologic Studies 5-73
Animal Toxicological Studies 5-74
Table 5-10 Recent epidemiologic studies of pulmonary inflammation and
oxidative stress in populations with asthma. 5-75
Table 5-11 Study-specific details from animal toxicological studies of
subclinical effects underlying asthma. 5-77
Summary of Subclinical Effects Underlying Asthma Exacerbation 5-79
Summary of Asthma Exacerbation 5-80
5.2.1.3 Allergy Exacerbation 5-82
Lung Function in Populations with Allergy 5-82
Respiratory Symptoms and Physician Visits in Populations with Allergy 5-82
Subclinical Effects Underlying Allergy Exacerbation 5-83
5.2.1.4 Chronic Obstructive Pulmonary Disease Exacerbation 5-83
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CONTENTS (Continued)
Lung Function and Respiratory Symptoms 5-84
Hospital Admissions and Emergency Department Visits 5-84
Figure 5-7 Percent increase in chronic obstructive pulmonary disease
hospital admissions and emergency department visits from U.S.
and Canadian studies evaluated in the 2008 SOx ISA and recent
studies in all-year analyses for a 10-ppb increase in 24-h avg or
40-ppb increase in 1-h max sulfur dioxide concentrations. 5-85
Table 5-12 Study-specific details and mean and upper percentile
concentrations from chronic obstructive pulmonary disease
hospital admission and emergency department visit studies
conducted in the U.S. and Canada and evaluated in the 2008
SOx ISA and studies published since the 2008 SOx ISA. 5-86
Hospital Admissions 5-87
Emergency Department Visits 5-88
Seasonal Analyses 5-89
Lag Structure of Associations 5-90
Summary of Chronic Obstructive Pulmonary Disease Exacerbation 5-90
5.2.1.5 Respiratory Infection 5-91
Hospital Admissions and Emergency Department Visits 5-92
Hospital Admissions 5-92
Table 5-13 Study-specific details and mean and upper percentile
concentrations from respiratory infection hospital admission and
emergency department visit studies conducted in the U.S. and
Canada and evaluated in the 2008 SOx ISA and studies
published since the 2008 SOx ISA. 5-93
Figure 5-8 Percent increase in respiratory infection hospital admissions and
emergency department visits from U.S. and Canadian studies
evaluated in the 2008 SOx ISA and recent studies in all-year and
seasonal analyses for a 10-ppb increase in 24-h avg or 40-ppb
increase in 1-h max sulfur dioxide concentrations. 5-96
Emergency Department Visits 5-97
Physician/Outpatient Visits 5-97
Multiday Lags 5-98
Seasonal Analyses 5-98
Summary of Respiratory Infection 5-99
5.2.1.6 Aggregated Respiratory Conditions 5-100
Figure 5-9 Percent increase in respiratory disease hospital admissions and
emergency department visits from U.S. and Canadian studies
evaluated in the 2008 SOx ISA and recent studies in all-year and
seasonal analyses for a 10-ppb increase in 24-h avg or 40-ppb
increase in 1-h max sulfur dioxide concentrations. 5-101
Table 5-14 Study-specific details and mean and upper percentile
concentrations from respiratory disease hospital admission and
emergency department visit studies conducted in the U.S. and
Canada and evaluated in the 2008 SOx ISA and studies
published since the 2008 SOx ISA. 5-102
Hospital Admissions 5-106
Emergency Department Visits 5-107
Model Specification—Sensitivity Analyses 5-107
Lag Structure of Associations 5-108
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CONTENTS (Continued)
Examination of Seasonal Differences 5-108
Summary of Aggregate Respiratory Conditions 5-108
5.2.1.7 Respiratory Effects in General Populations and Healthy Individuals 5-109
Lung Function Changes in General Populations and Healthy Individuals 5-110
Controlled Human Exposure Studies 5-110
Epidemiologic Studies 5-111
Table 5-15 Study-specific details from controlled human exposure studies of
lung function and respiratory symptoms in healthy adults.
5-112
Table 5-16 Recent epidemiologic studies of lung function in healthy adults
and adults in the general population. 5-114
Table 5-17 Recent epidemiologic studies of lung function in healthy children
and children in the general population. 5-116
Animal Toxicological Studies 5-121
Table 5-18 Study-specific details from animal toxicological studies of lung
function. 5-122
Summary of Lung Function Changes in General Populations and Healthy Individuals...
5-123
Respiratory Symptoms in General Populations and Healthy Individuals 5-124
Controlled Human Exposure Studies 5-124
Epidemiologic Studies 5-124
Table 5-19 Recent epidemiologic studies of respiratory symptoms in healthy
adults and children and groups in the general population. 5-126
Summary of Respiratory Symptoms in General Populations and Healthy Individuals
5-128
Subclinical Respiratory Effects in Healthy Individuals 5-128
Controlled Human Exposure Studies 5-129
Epidemiologic Studies 5-129
Animal Toxicological Studies 5-129
Table 5-20 Study-specific details from animal toxicological studies of
subclinical effects. 5-131
Summary of Subclinical Respiratory Effects in Healthy Individuals 5-132
5.2.1.8 Respiratory Mortality 5-133
Figure 5-10 Percent increase in chronic obstructive pulmonary disease
mortality associated with a 10 |jg/m3 (3.62 ppb) increase in
24-h avg sulfur dioxide concentrations at various single and
multiday lags. 5-135
Figure 5-11 City-specific concentration-response curves for short-term sulfur
dioxide exposures and daily chronic obstructive pulmonary
disease mortality in four Chinese cities. 5-136
5.2.1.9 Summary and Causal Determination 5-137
Evidence for Asthma Exacerbation 5-137
Evidence for Other Respiratory Effects 5-139
Conclusion 5-140
Table 5-21 Summary of evidence for a causal relationship between
short-term sulfur dioxide exposure and respiratory effects. 5-141
5.2.2 Long-Term Exposure 5-143
5.2.2.1 Development and Severity of Asthma 5-144
December 2016
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CONTENTS (Continued)
Development of Asthma 5-144
Epidemiologic Studies 5-145
Table 5-22 Selected epidemiologic studies of long-term exposure to SO2
and the development of asthma and intervention studies/natural
experiments. 5-146
Severity of Asthma 5-152
Animal Toxicological Studies 5-153
Table 5-23 Study-specific details from animal toxicological studies. 5-154
Summary of Asthma Development and Severity 5-155
5.2.2.2 Development of Allergy 5-156
5.2.2.3 Lung Function 5-157
Epidemiologic Studies 5-157
Animal Toxicological Studies 5-159
Summary of Lung Function 5-159
5.2.2.4 Respiratory Infection 5-159
Epidemiologic Studies 5-159
Animal Toxicological Studies 5-160
Summary of Respiratory Infection 5-160
5.2.2.5 Development of Other Respiratory Diseases: Chronic Bronchitis, Chronic
Obstructive Pulmonary Disease, and Acute Respiratory Distress Syndrome 5-161
5.2.2.6 Respiratory Mortality 5-161
5.2.2.7 Summary and Causal Determination 5-161
Evidence for the Development of Asthma 5-162
Table 5-24 Summary of evidence for a suggestive of, but not sufficient to
infer, a causal relationship between long-term sulfur dioxide
exposure and respiratory effects. 5-163
Evidence for the Severity of Asthma 5-166
Evidence for the Development of Allergies 5-167
Evidence for Lung Function 5-167
Evidence for Respiratory Infection 5-167
Evidence for the Development of Other Respiratory Diseases 5-167
Evidence for Respiratory Mortality 5-168
Conclusion 5-168
5.3 Cardiovascular Effects 5-169
5.3.1 Short-Term Exposure 5-169
5.3.1.1 Introduction 5-169
5.3.1.2 Myocardial Infarction and Ischemic Heart Disease 5-170
Figure 5-12 Results of studies of short-term sulfur dioxide exposure and
hospital admissions for ischemic heart disease. 5-172
Table 5-25 Mean and upper percentile concentrations of sulfur dioxide from
ischemic heart disease hospital admission and emergency
department visit studies. 5-173
ST-Segment Changes 5-175
Summary of Ischemic Heart Disease and Myocardial Infarction 5-175
5.3.1.3 Arrhythmias and Cardiac Arrest 5-176
Table 5-26 Epidemiologic studies of arrhythmia and cardiac arrest. 5-177
5.3.1.4 Cerebrovascular Diseases and Stroke 5-179
Table 5-27 Mean and upper percentile concentrations of sulfur dioxide from
cerebrovascular disease and stroke-related hospital admission
and emergency department visit studies. 5-181
December 2016
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CONTENTS (Continued)
Figure 5-13 Results of studies of short-term sulfur dioxide exposure and
hospital admissions for cerebrovascular disease and stroke.
5-182
5.3.1.5 Blood Pressure and Hypertension 5-182
Epidemiologic Studies 5-183
Experimental Studies 5-184
Summary of Blood Pressure 5-184
5.3.1.6 Venous Thromboembolism 5-185
5.3.1.7 Heart Failure 5-185
5.3.1.8 Aggregated Cardiovascular Disease 5-186
Table 5-28 Mean and upper percentile concentrations of sulfur dioxide from
cardiovascular-related hospital admission and emergency
department visit studies: U.S. and Canadian studies from the
2008 ISA for Sulfur Oxides and recent studies. 5-186
Table 5-29 Mean and upper percentile concentrations of sulfur dioxide from
cardiovascular-related hospital admission and emergency
department visit studies. 5-191
Figure 5-14 Studies of hospital admissions and emergency department visits
for all cardiovascular disease. 5-193
5.3.1.9 Cardiovascular Mortality 5-194
Figure 5-15 Percent increase in stroke mortality associated with a 10 |jg/m3
(3.62 ppb) increase in sulfur dioxide concentrations using
different lag structures. 5-197
Figure 5-16 Pooled concentration-response curves for sulfur dioxide and
daily stroke mortality in eight Chinese cities for a 10 |jg/m3
(3.62 ppb) increase in 24-h avg concentrations at lag 0-1 days.
5-198
5.3.1.10 Subclinical Effects Underlying Cardiovascular Effects 5-199
Heart Rate and Heart Rate Variability 5-199
Epidemiology 5-200
Experimental Studies 5-201
Summary of Heart Rate and Heart Rate Variability 5-202
QT Interval Duration 5-202
Insulin Resistance 5-203
Biomarkers of Cardiovascular Risk 5-203
Table 5-30 Epidemiologic studies of biomarkers of cardiovascular effects.
5-204
Epidemiologic Studies 5-206
Experimental Studies 5-207
Summary of Blood Markers of Cardiovascular Risk 5-208
5.3.1.11 Summary and Causal Determination 5-208
Table 5-31 Summary of evidence, which is inadequate to infer a causal
relationship between short-term sulfur dioxide exposure and
cardiovascular effects. 5-210
5.3.2 Long-Term Exposure 5-214
5.3.2.1 Introduction 5-214
5.3.2.2 Ischemic Heart Disease and Myocardial Infarction 5-215
Table 5-32 Epidemiologic studies of the association of long-term exposure
to sulfur dioxide with cardiovascular disease. 5-216
5.3.2.3 Cerebrovascular Diseases and Stroke 5-220
December 2016
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CONTENTS (Continued)
Table 5-33 Epidemiologic studies of the association of long-term exposure
to sulfur dioxide with stroke. 5-222
5.3.2.4 Blood Pressure and Hypertension 5-224
Table 5-34 Epidemiologic studies of the association of long-term exposure
to sulfur dioxide with hypertension. 5-225
5.3.2.5 Other Cardiovascular Effects 5-227
5.3.2.6 Cardiovascular Mortality 5-227
5.3.2.7 Subclinical Effects Underlying Cardiovascular Diseases 5-227
5.3.2.8 Summary and Causal Determination 5-228
Table 5-35 Summary of evidence, which is inadequate to infer a causal
relationship between long-term sulfur dioxide exposure and
cardiovascular effects. 5-230
5.4 Reproductive and Developmental Effects 5-231
5.4.1 Introduction 5-231
Table 5-36 Key reproductive and developmental epidemiologic studies for
sulfur dioxide. 5-233
Table 5-37 Study specific details from animal toxicological studies of the
reproductive and developmental effects of sulfur dioxide. 5-237
5.4.2 Fertility, Reproduction, and Pregnancy 5-237
5.4.3 Birth Outcomes 5-240
5.4.3.1 Fetal Growth 5-240
5.4.3.2 Preterm Birth 5-241
5.4.3.3 Birth Weight 5-243
5.4.3.4 Birth Defects 5-245
5.4.3.5 Fetal Mortality 5-245
5.4.3.6 Infant Mortality 5-246
5.4.4 Developmental Outcomes 5-247
5.4.4.1 Respiratory Outcomes 5-247
5.4.4.2 Other Developmental Effects 5-247
5.4.5 Summary and Causal Determination 5-248
Table 5-38 Summary of evidence inadequate to infer a causal relationship
between sulfur dioxide exposure and reproductive and
developmental effects. 5-249
5.5 Mortality 5-252
5.5.1 Short-Term Exposure 5-252
5.5.1.1 Introduction 5-252
5.5.1.2 Associations between Short-Term Sulfur Dioxide Exposure and Mortality in
All-Year Analyses 5-254
Table 5-39 Air quality characteristics of multicity studies and meta-analyses
evaluated in the 2008 SOx ISA and recently published multicity
studies and meta-analyses. 5-255
Figure 5-17 Percent increase in total mortality from multicity studies and
meta-analyses evaluated in the 2008 ISA for Sulfur Oxides
(black circles) and recently published multicity studies (red
circles) for a 10-ppb increase in 24-h avg sulfur dioxide
concentrations. 5-258
5.5.1.3 Potential Confounding ofthe Sulfur Dioxide-Mortality Relationship 5-259
Figure 5-18 Percent increase in total, cardiovascular, and respiratory
mortality from multicity studies evaluated in the 2008 ISA for
Sulfur Oxides (black) and recently published multicity studies
(red) for a 10-ppb increase in 24-h avg sulfur dioxide
concentrations. 5-260
December 2016
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CONTENTS (Continued)
Examination of Potential Copollutant Confounding 5-261
Table 5-40 Percent increase in total, cardiovascular, and respiratory
mortality for a 10-ppb increase in 24-h avg sulfur dioxide
concentrations at lag 0-1 in single and copollutant models. 5-262
Modeling Approaches to Control for Weather and Temporal Confounding 5-263
Figure 5-19 Percent increase in total, cardiovascular, and respiratory
mortality associated with a 10 |jg/m3 (3.62 ppb) increase in
24-h avg sulfur dioxide concentrations, lag 0-1, in single and
copollutants models in Public Health and Air Pollution in Asia
cities. 5-264
Temporal 5-265
Figure 5-20 Percent increase in daily mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-h avg sulfur dioxide concentrations at
lag 0-1 days using various degrees of freedom per year for time
trend, China Air Pollution and Health Effects Study cities,
1996-2008. 5-265
Figure 5-21 Percent increase in total mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-h avg sulfur dioxide concentrations at
lag 0-1 in Public Health and Air Pollution in Asia cities, using
different degrees of freedom per year for time trend. 5-266
5.5.1.4 Modification of the Sulfur Dioxide-Mortality Relationship 5-267
Individual- and Population-Level Factors 5-267
Season and Weather 5-267
5.5.1.5 Sulfur Dioxide-Mortality Concentration-Response Relationship and Related
Issues 5-268
Lag Structure of Associations 5-268
Figure 5-22 Percent increase in daily mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-h avg sulfur dioxide concentrations,
using various lag structures for sulfur dioxide in the China Air
Pollution and Health Effects Study cities, 1996-2008. 5-269
Figure 5-23 Percent increase in total mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-h avg sulfur dioxide concentrations for
different lag structures in individual Public Health and Air
Pollution in Asia cities and in combined four city analyses. 5-270
Concentration-Response Relationship 5-271
Figure 5-24 Flexible ambient concentration-response relationship between
short-term sulfur dioxide (ppb) exposure (24-h avg
concentrations) and total mortality at lag 1. 5-272
Figure 5-25 Concentration-response curves for total mortality (degrees of
freedom = 3) for sulfur dioxide in each of the four Public Health
and Air Pollution in Asia cities. 5-273
5.5.1.6 Summary and Causal Determination 5-274
Table 5-41 Summary of evidence, which is suggestive of, but not sufficient
to infer, a causal relationship between short-term sulfur dioxide
exposure and total mortality. 5-275
5.5.2 Long-Term Exposure 5-279
Table 5-42 Summary of studies of long-term exposure and mortality. 5-280
5.5.2.1 U.S. Cohort Studies 5-284
5.5.2.2 European Cohort Studies 5-287
5.5.2.3 Asian Cohort Studies 5-289
5.5.2.4 Cross-Sectional Analysis Using Small Geographic Scale 5-290
December 2016
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CONTENTS (Continued)
5.5.2.5 Summary and Causal Determination 5-291
Figure 5-26 Relative risks (95% confidence interval) of sulfur
dioxide-associated total mortality. 5-293
Figure 5-27 Relative risks (95% confidence interval) of sulfur
dioxide-associated cause-specific mortality. 5-294
Table 5-43 Summary of evidence, which is inadequate to infer a causal
relationship between long-term sulfur dioxide exposure and total
mortality. 5-295
5.6 Cancer 5-296
5.6.1 Introduction 5-296
5.6.2 Cancer Incidence and Mortality 5-297
5.6.2.1 Lung Cancer Incidence and Mortality 5-297
Sulfur Dioxide Lung Carcinogenesis, Cocarcinogenic Potential, and Tumor
Promotion in Laboratory Animal Models 5-299
5.6.2.2 Bladder Cancer Incidence and Mortality 5-301
5.6.2.3 Incidence of Other Cancers 5-302
5.6.2.4 Summary of Cancer Incidence and Mortality 5-303
5.6.3 Genotoxicity and Mutagenicity 5-303
5.6.4 Summary and Causal Determination 5-304
Table 5-44 Summary of evidence, which is inadequate to infer a causal
relationship between long-term sulfur dioxide exposure and
cancer. 5-305
Annex for Chapter 5: Evaluation of Studies on Health Effects of Sulfur Oxides 5-307
Table A-1 Scientific considerations for evaluating the strength of inference
from studies on the health effects of sulfur oxides. 5-307
Chapter 6 Populations and Lifestages Potentially at Increased Risk for Health Effects Related
to Sulfur Dioxide Exposure 6-1
6.1 Introduction 6-1
6.2 Approach to Evaluating and Characterizing the Evidence for At Risk Factors 6-2
Table 6-1 Characterization of evidence for factors potentially increasing the
risk for sulfur dioxide-related health effects. 6-3
6.3 Pre-existing Disease/Condition_
Table 6-2
6.3.1 Asthma
6-4
Prevalence of respiratory diseases among adults by age and
region in the U.S. in 2012. 6-4
6-5
Table 6-3
6.4 Genetic Factors
6.5 Sociodemographic and Behavioral Factors
6.5.1 Lifestage
6.5.1.1 Children
Controlled human exposure, epidemiology, and animal
toxicology studies evaluating pre-existing asthma and sulfur
dioxide exposure. 6-1
6-4
6-4
6-4
6-5
6.5.1.2
6.5.2 Sex
Table 6-4
Older Adults
Table 6-5
6.5.3 Socioeconomic Status
Epidemiologic studies evaluating childhood lifestage and sulfur
dioxide exposure. 6-6
6-7
6-8
Epidemiologic studies evaluating older adult lifestage and sulfur
dioxide exposure. 6-9
6-11
December 2016
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CONTENTS (Continued)
Table 6-6 Epidemiologic studies evaluating effect modification by sex and
sulfur dioxide exposure. 6-12
6.5.4 Smoking 6-14
6.6 Conclusions 6-14
Table 6-7 Summary of evidence for potential increased sulfur dioxide
exposure and increased risk of sulfur dioxide-related health
effects. 6-15
References R-1
December 2016
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Integrated Science Assessment Team for Sulfur
Oxides — Health Criteria
Executive Direction
Dr. John Vandenberg (Director, RTP Division)—National Center for Environmental
Assessment—Office of Research and Development, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Ms. Debra Walsh (Deputy Director, RTP Division)—National Center for Environmental
Assessment, Office of Research and Development, U.S. Environmental Protection
Agency, Research Triangle Park, NC
Dr. Reeder Sams II (Acting Deputy Director, RTP Division)—National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Mary Ross (Branch Chief)—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Steven J. Dutton (Branch Chief)—National Center for Environmental Assessment,
Office of Research and Development, U.S. Environmental Protection Agency, Research
Triangle Park, NC
Dr. Ellen Kirrane (Acting Branch Chief)—National Center for Environmental Assessment,
Office of Research and Development, U.S. Environmental Protection Agency, Research
Triangle Park, NC
Scientific Staff
Dr. Tom Long (Team Leader, Integrated Science Assessment for Sulfur Oxides)—National
Center for Environmental Assessment, Office of Research and Development,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. James Brown—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Barbara Buckley—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Steven J. Dutton—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Brooke L. Hemming—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Erin Hines—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Ellen Kirrane—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
December 2016
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Dr. Dennis Kotchmar—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Thomas Luben—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Ms. Connie Meacham—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Elizabeth Oesterling Owens—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Molini M. Patel—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Joseph P. Pinto—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Mr. Jason Sacks—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. David Svendsgaard—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Lisa Vinikoor-Imler—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Technical Support Staff
Ms. Marieka Boyd—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Mr. Kenneth J. Breito—Senior Environmental Employment Program, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Eleanor Jamison—Senior Environmental Employment Program, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Mr. Ryan Jones—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Ms. Olivia Philpott—Senior Environmental Employment Program, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Mr. Samuel S. Thacker—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
December 2016
xxi
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Mr. Richard N. Wilson—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Authors, Contributors, and Reviewers
Authors
Dr. Tom Long (Team Leader, Integrated Science Assessment for Sulfur Oxides)—National
Center for Environmental Assessment, Office of Research and Development,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Michael Breen—National Exposure Research Laboratory, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. James Brown—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Barbara Buckley—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Mr. Evan Coffman—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Laura Datko-Williams—Oak Ridge Institute for Science and Education, National Center
for Environmental Assessment, Office of Research and Development,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Steven J. Dutton—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Rachelle Duvall—National Exposure Research Laboratory, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Brooke L. Hemming—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Erin Hines—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Ellen Kirrane—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Dennis Kotchmar—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Thomas Luben—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Jennifer Nichols—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
December 2016
xxii
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Dr. Michelle Oakes—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Elizabeth Oesterling Owens—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Molini M. Patel—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Steven Perry—National Exposure Research Laboratory, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Joseph P. Pinto—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Kristen Rappazzo—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Jennifer Richmond-Bryant—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Mr. Jason Sacks—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Tina Stevens—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. George Thurstonf—Department of Environmental Medicine, New York University
School of Medicine, Tuxedo, NY
Dr. Lisa Vinikoor-Imler—National Center for Environmental Assessment, Office of
Research and Development, U.S. Environmental Protection Agency, Research Triangle
Park, NC
Dr. Gregory Welleniusf—Department of Community Health (Epidemiology Section),
Brown University, Providence, RI
fUnder Sub-contract, through ICF International
Contributors
Ms. Breanna Alman—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Mr. Adam Benson—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Candis Edwards—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
December 2016
xxiii
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Ms. Charlene Finley—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Beth Gatling—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Mr. William Griffin—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Rachel Housego—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Mr. Satoru Ito—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Emily Lau—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Meagan Madden—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. April Maxwell—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Mr. Ihab Mikati—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Danielle Moore—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Mr. Kyle Painter—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Dr. Adam Reff—Office of Air Quality Planning and Standards, Office of Air and Radiation,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Ms. Alexandra Ross—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Kaylyn Siporin—Curriculum for the Environment and Ecology, University of North
Carolina, Chapel Hill, NC
Mr. Doug Solomon—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
December 2016
xxiv
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Ms. Adrien Wilkie—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Ms. Brianna Young—Oak Ridge Institute for Science and Education, National Center for
Environmental Assessment, Office of Research and Development, U.S. Environmental
Protection Agency, Research Triangle Park, NC
Reviewers
Dr. Sara Adar—School of Public Health, University of Michigan, Ann Arbor, MI
Mr. Ed Avol—Keck School of Medicine, University of Southern California,
Los Angeles, CA
Dr. Philip Bromberg—School of Medicine, University of North Carolina, Chapel Hill, NC
Dr. Jeffrey Brook—Environment Canada, Toronto, ON
Mr. Matthew Davis—Office of Children's Health Protection, U.S. Environmental Protection
Agency, Washington, DC
Dr. Russell Dickerson—Department of Atmospheric and Oceanic Science, University of
Maryland, College Park, MD
Dr. Tyler Fox—Office of Air Quality Planning and Standards, Office of Air and Radiation,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Nicole Hagan—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Douglas Johns—Division of Respiratory Disease Studies, National Institute for
Occupational Safety and Health, Morgantown, WV
Mr. William Keene—Department of Environmental Sciences, University of Virginia,
Charlottesville, VA
Dr. James Kelly—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Leila Lackey—Office of Science Policy, Office of Research and Development,
U.S. Environmental Protection Agency, Research Triangle Park, NC
Mr. John Langstaff—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Ms. Connie Meacham—National Center for Environmental Assessment, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Qingyu Meng—School of Public Health, Rutgers University, Piscataway, NJ
Dr. Deirdre Murphy—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. Jennifer Peel—Colorado School of Public Health, Colorado State University, Fort
Collins, CO
Dr. Edward Schelegle—School of Veterinary Medicine, University of California—Davis,
Davis, CA
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Dr. Michael Stewart—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. James Thurman—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, Office of Research
and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Dr. James Wagner—College of Veterinary Medicine, Michigan State University,
East Lansing, MI
Dr. Lewis Weinstock—Office of Air Quality Planning and Standards, Office of Air and
Radiation, U.S. Environmental Protection Agency, Research Triangle Park, NC
Ms. Debra Walsh—National Center for Environmental Assessment, Office of Research and
Development, U.S. Environmental Protection Agency, Research Triangle Park, NC
Ms. Melina Williams—Air and Radiation Law Office, Office of General Counsel,
U.S. Environmental Protection Agency, Washington, DC
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Clean Air Scientific Advisory Committee Sulfur
Oxides NAAQS Review Panel
Chair of the Sulfur Oxides Review Panel
Dr. AnaDiez-Roux*, Drexel University, Philadelphia, PA
Sulfur Oxides Review Panel Members
Mr. George A. Allen**—Northeast States for Coordinated Air Use Management
(NESCAUM), Boston, MA
Dr. John R. Balmes—University of California, San Francisco, CA
Dr. James Boylan—Georgia Department of Natural Resources, Atlanta, GA
Dr. Judith Chow**—Desert Research Institute, Reno, NV
Dr. Aaron Cohen—Health Effects Institute, Boston, MA
Dr. Alison Cullen—University of Washington, Seattle, WA
Dr. Delbert Eatough—Brigham Young University, Provo, UT
Dr. Christopher Frey***—North Carolina State University, Raleigh, NC
Dr. William Griffith—University of Washington, Seattle, WA
Dr. Steven Hanna—Hanna Consultants, Kennebunkport, ME
Dr. Jack Harkema**—Michigan State University, East Lansing, MI
Dr. Daniel Jacob—Harvard University, Cambridge, MA
Dr. Farla Kaufman—California Environmental Protection Agency, Sacramento, CA
Dr. David Peden—University of North Carolina at Chapel Hill, Chapel Hill, NC
Dr. Richard Schlesinger—Pace University, New York, NY
Dr. Elizabeth A. (Lianne) Sheppard**—University of Washington, Seattle, WA
Dr. Frank Speizer—Harvard Medical School, Boston, MA
Dr. James Ultman—Pennsylvania State University, University Park, PA
Dr. Ronald Wyzga**—Electric Power Research Institute, Palo Alto, CA
* Chair of the statutory Clean Air Scientific Advisory Committee (CASAC) appointed by the
U.S. EPA Administrator
**Members of the statutory CASAC appointed by the U.S. EPA Administrator
** immediate Past CASAC Chair
Science Advisory Board Staff
Mr. Aaron Yeow (Designated Federal Officer)—U.S. Environmental Protection Agency,
Science Advisory Board (1400R), 1200 Pennsylvania Avenue, NW, Washington, DC
20460-0001, Phone: 202-564-2050, Fax: 202-565-2098, (yeow.aaron@epa.gov)
(FedEx: 1300 Pennsylvania Avenue, NW, Suite 31150, Washington, DC 20004)
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Acronyms and Abbreviations
Acronym/
Abbreviation
Meaning
a
alpha, exposure factor
A4
not classifiable for humans or
animals
AA
adenine-adenine genotype
ACS
American Cancer Society
AER
air exchange rate; Atmospheric
and Environmental Research
AERMOD
American Meteorological
Society/U.S. EPA Regulatory
Model
ag
agriculture
AG
adenine-guanine genotype
AGL
above ground level
AHR
airway hyperresponsiveness
AIRS
Aerometric Information
Retrieval System; Atmospheric
Infrared Sounder
AL
Alabama
ALRI
acute lower respiratory infection
a.m.
ante meridiem (before noon)
APEX
Air Pollution Exposure model
APHEA
Air Pollution and Health:
A European Approach study
APIMS
atmospheric pressure ionization
mass spectrometry
AQCD
air quality criteria document
AQS
air quality system
ARIES
Aerosol Research Inhalation
Epidemiology Study
ARP
Acid Rain Program
ASM
airway smooth muscle
AT
Atascadero
ATD
atmospheric transport and
dispersion
ATS
American Thoracic Society
avg
average
AZ
Arizona
P
beta
BAL
bronchoalveolar lavage
Acronym/
Abbreviation
Meaning
BALF
bronchoalveolar lavage fluid
B[a]P
benzo[a]pyrene
bax
B-cell lymphoma 2-like
protein 4
BC
black carbon
Bcl-2
B-cell lymphoma 2
BHR
bronchial hyperreactivity
BK
Bangkok
BMA
Bayesian Model Averaging
BMI
body mass index
BP
blood pressure
BrO
bromine oxide
BS
black smoke
C
degrees Celsius; the product of
microenvironmental
concentration; carbon
CI
sulfur dioxide + nitrogen
dioxide
C2
sulfur dioxide + PMio
C3
sulfur dioxide + ozone
CA
California
Ca
central site ambient SO2
concentration
Ca,csm
ambient concentration at a
central site monitor
CAA
Clean Air Act
CAIR
Clean Air Interstate Rule
CAPES
China Air Pollution and Health
Effects Study
CASAC
Clean Air Scientific Advisory
Committee
CBS A
core-based statistical area
CCN
cloud condensation nuclei
CDC
Centers for Disease Control and
Prevention
CFR
Code of Federal Regulations
cGMP
cyclic guanosine
monophosphate
CHsSH
methyl mercaptan
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Acronym/
Abbreviation
Meaning
CH3-S-CH3
dimethyl sulfide
CH3-S-S-CH3
dimethyl disulfide
(CH3)2SO
dimethyl sulfoxide
CH3S03H
methanesulfonic acid
CHAD
Consolidated Human Activity
Database
CHD
coronary heart disease
CHF
congestive heart failure
CI(s)
confidence interval(s)
cIMT
carotid intima-media thickness
Cj
airborne SO2 concentration at
micro environment j
CI
chlorine radical
CMAQ
Community Multiscale Air
Quality
CO
carbon monoxide; Colorado
CO2
carbon dioxide
COH
coefficient of haze
Cone
concentration
Cong.
congress
COPD
chronic obstructive pulmonary
disease
COX-2
cyclooxygenase-2
C-R
concentration-response
(relationship)
CRDS
cavity ring-down spectroscopy
CRP
c-reactive protein
CS2
carbon disulfide
CT
Connecticut
CTM
chemical transport models
CVD
cardiovascular disease
D.C. Cir
District of Columbia Circuit
d
day
DBP
diastolic blood pressure
DC
District of Columbia
DEcCBP
diesel exhaust particle
extract-coated carbon black
particles
DEP
diesel exhaust particles
df
degrees of freedom
Acronym/
Abbreviation
Meaning
DFA
detrended fluctuation analysis
DL
distributed lag
DMDS
dimethyl disulfide
DMS
dimethyl sulfide
DNA
deoxyribonucleic acid
DO AS
differential optical absorption
spectroscopy
DVT
deep vein thrombosis
eg-
exempli gratia (for example)
Ea
exposure to SO2 of ambient
origin
EBC
exhaled breath condensate
EC
elemental carbon
ECG
electrocardiographic
ECRHS
European Community
Respiratory Health Survey
ED
emergency department
EGF
epidermal growth factor
EGFR
epidermal growth factor receptor
EGU
electric power generating unit
EIB
exercise-induced bronchospasm
EKG
electrocardiogram
ELF
epithelial lining fluid
EMSA
electrophoretic mobility shift
assay
Ena
exposure to SO2 of nonambient
origin
eNO
exhaled nitric oxide
EP
entire pregnancy
EPA
U.S. Environmental Protection
Agency
Et
total exposure over a time
period of interest
EWPM
emission-weighted proximity
model
Exp(B)
odds ratio of bivariate
associations
F
female
FB
fractional bias
FC
fuel combustion
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Acronym/
Abbreviation Meaning
FEF25-75% forced expiratory flow at
25-75% of exhaled volume
FEF50% forced expiratory flow at 5 0% of
forced vital capacity
FEF75% forced expiratory flow at 75% of
forced vital capacity
FEFmax maximum forced expiratory
flow
FEM federal equivalent method
FeNO fractional exhaled nitric oxide
FEV forced expiratory volume
FEVi forced expiratory volume in
1 second
FL Florida
FOXp3 forkhead box P3
FPD flame photometric detection
FR Federal Register
FRC functional residual capacity
FRM federal reference method
func functional residual capacity
FVC forced vital capacity
g gram
GA Georgia
GALA II Genes-Environments and
Admixture in Latino Americans
GG guanine-guanine genotype
GIS geographic information system
GM geometric mean
GP general practice
GPS global positioning system
GSD geometric standard deviation
GSTM1 glutathione S-transferase Mu 1
GSTP glutathione S-transferase P
GSTP1 glutathione S-transferase Pi 1
h hour(s)
H+ hydrogen ion
H2O water
H2O2 hydrogen peroxide
H2S hydrogen sulfide
H2SO3 sulfurous acid
Acronym/
Abbreviation
Meaning
H2SO4
sulfuric acid
HERO
Health and Environmental
Research Online
HF
high frequency component
of HRV
HI
Hawaii
HK
Hong Kong
HO2
hydroperoxyl radical
HR
hazard ratio(s); heart rate
HRV
heart rate variability
HS
hemorrhagic stroke
HSO3-
bisulfite
HSC
Harvard Six Cities
i.p.
intraperitoneal
IARC
International Agency for
Research on Cancer
i.e.
id est (that is)
ICAM-1
intercellular adhesion
molecule 1
ICC
intraclass correlation coefficient
ICD
International Classification of
Diseases; implantable
cardioverter defibrillators
IDW
inverse distance weighting
IFN-y
interferon gamma
IgE
immunoglobulin E
IgG
immunoglobulin G
IHD
ischemic heart disease
IKKP
inhibitor of nuclear factor
kappa-B kinase subunit beta
IL
Illinois
IL-4
interleukin-4
IL-5
interleukin-5
IL-6
interleukin-6
IL-8
interleukin-8
He
isoleucine
IQR
interquartile range
IS
ischemic stroke
ISA
Integrated Science Assessment
ISAAC
International Study of Asthma
and Allergies in Children
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Acronym/
Abbreviation Meaning
IUGR intrauterine growth restriction
IkBoi nuclear factor of kappa light
polypeptide gene enhancer in
B-cells inhibitor, alpha
j microenvironment
JE joint model estimate
k reaction rate; decay constant
derived from empirical data; rate
of SO2 loss in the
microenvironment
Katp adenosine triphosphate
(ATP)-sensitive potassium
channel
kg kilogram(s)
km kilometer(s)
KS Kansas
L liter(s)
LBW low birth weight
LED light-emitting diode
LF low-frequency component of
HRV
LF/HF ratio of LF and HF components
of HRV
LIF laser induced fluorescence
In natural logarithm
LOD limit of detection
LOESS locally weighted scatterplot
smoothing
Lp-PLA2 lipoprotein-associated
phospholipase A2
LUR land use regression
LX lung adenoma-susceptible
mouse strain
|x mu; micro
|xg/m3 micrograms per cubic meter
m meter
M male
MA Massachusetts
Ml Month 1
M2 Month 2
M3 Month 3
M12 average of Ml and M2
Acronym/
Abbreviation Meaning
max maximum
MAX-DO AS multiaxis differential optical
absorption spectroscopy
MCh methacholine
MD Maryland
MDL method detection limit
ME Maine
med median
mg milligram
MI myocardial infarction ("heart
attack"); Michigan
min minimum; minute
MINAP Myocardial Ischaemia National
Audit Project
MISA Meta-analysis of the Italian
studies on short-term effects of
air pollution
mL milliliter(s)
mm millimeters
MMEF maximum midexpiratory flow
MMFR Maximal midexpiratory flow
rate
mmHg millimeters of mercury
MN Minnesota
MN micronuclei formation
MNPCE polychromatophilic
erythroblasts of the bone
marrow
mo month(s)
MO Missouri
MOA mode(s) of action
MODIS Moderate Resolution Imaging
Spectroradiometer
mRNA messenger ribonucleic acid
MS Mississippi
MSA methane sulfonic acid
MSE mean standardized error
MUC5AC mucin 5AC glycoprotein
n sample size; total number of
microenvironments that the
individual has encountered
N population number
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Acronym/
Abbreviation
Meaning
N2
molecular nitrogen
N/A
not applicable
NA
not available
NAAQS
National Ambient Air Quality
Standards
NaCl
sodium chloride
NALF
nasal lavage fluid
NBP
NOx Budget Program
NC
North Carolina
NCore
National Core network
NEI
National Emissions Inventory
NFkB
nuclear factor kappa-
light-chain-enhancer of
activated B cells
NH
New Hampshire
NH3
ammonia
nh4+
ammonium ion
NHAPS
National Human Activity
Pattern Survey
NHLBI
National Heart, Lung, and Blood
Institute
NJ
New Jersey
NLCS
Netherlands Cohort Study on
Diet and Cancer
nm
nanometer
NMMAPS
The National Morbidity
Mortality Air Pollution Study
NO
nitric oxide
NO2
nitrogen dioxide
no3~
nitrate
NOs
nitrate radical
non-HS
non-hemhorragic stroke
NOx
the sum of NO and NO2
NR
not reported
NY
New York
O3
ozone
obs
observations
OC
organic carbon
OCS
carbonyl sulfide
OH
hydroxide; Ohio
Acronym/
Abbreviation Meaning
OHCA out-of-hospital cardiac arrests
OMI Ozone Monitoring Instrument
OR odds ratio(s)
OVA ovalbumin
p probability
P Pearson correlation
P53 tumor protein 53
PA Pennsylvania
PAH(s) polycyclic aromatic
hydrocarbon(s)
PAPA Public Health and Air Pollution
in Asia
Pb lead
PC(S02) provocative concentration of
SO2
PE pulmonary embolism
PEF peak expiratory flow
Penh enhanced pause
PEFR peak expiratory flow rate
PM particulate matter
PM10 In general terms, particulate
matter with a nominal
aerodynamic diameter less than
or equal to 10 |xm; a
measurement of thoracic
particles (i.e., that subset of
inhalable particles thought small
enough to penetrate beyond the
larynx into the thoracic region
of the respiratory tract). In
regulatory terms, particles with
an upper 50% cutpoint of
10 ± 0.5 |xm aerodynamic
diameter (the 50% cutpoint
diameter is the diameter at
which the sampler collects 50%
of the particles and rejects 50%
of the particles) and a
penetration curve as measured
by a reference method based on
Appendix J of 40 CFR Part 50
and designated in accordance
with 40 CFR Part 53 or by an
equivalent method designated in
accordance with 40 CFR
Part 53.
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Acronym/
Abbreviation Meaning
PMio-2.5 In general terms, particulate
matter with a nominal
aerodynamic diameter less than
or equal to 10 |xm and greater
than a nominal 2.5 |xm; a
measurement of thoracic coarse
particulate matter or the coarse
fraction of PMio. In regulatory
terms, particles with an upper
50% cutpoint of 10 |xm
aerodynamic diameter and a
lower 50% cutpoint of 2.5 |xm
aerodynamic diameter (the 50%
cutpoint diameter is the diameter
at which the sampler collects
50% of the particles and rejects
50%) of the particles) as
measured by a reference method
based on Appendix O of 40 CFR
Part 50 and designated in
accordance with 40 CFR Part 53
or by an equivalent method
designated in accordance with
40 CFR Part 53.
PM2.5 In general terms, particulate
matter with a nominal
aerodynamic diameter less than
or equal to 2.5 |xm; a
measurement of fine particles.
In regulatory terms, particles
with an upper 50% cutpoint of
2.5 |xm aerodynamic diameter
(the 50%o cutpoint diameter is
the diameter at which the
sampler collects 50% of the
particles and rejects 50% of the
particles) and a penetration
curve as measured by a
reference method based on
Appendix L of 40 CFR Part 50
and designated in accordance
with 40 CFR Part 53, by an
equivalent method designated in
accordance with 40 CFR
Part 53, or by an approved
regional method designated in
accordance with Appendix C of
40 CFR Part 58.
PMR peak-to-mean ratio
PNC particle number concentration
PR prevalence ratio
PRB policy-relevant background
PWEI Population Weighted Emissions
Index
Q2 2nd quartile or quintile
Acronym/
Abbreviation
Meaning
Q3
3rd quartile or quintile
Q4
4th quartile or quintile
Q5
5th quintile
QT interval
time between start of Q wave
and end of T wave in ECG
R2
square of the correlation
coefficient
RI
Rhode Island
RMB
renminbi
rMSSD
root-mean-square of successive
differences
RR
risk ratio(s), relative risk
RSP
respirable suspended particles
RT
total respiratory resistance
sec
second(s)
S2O
disulfur monoxide
S. Rep
Senate Report
SDCCE
simulated downwind coal
combustion emissions
SE
standard error
SEARCH
Southeast Aerosol Research
Characterization
Sess.
session
SGA
small for gestational age
SH
Shanghai
SHEDS
Stochastic Human Exposure and
Dose Simulation
SHEEP
Stockholm Heart Epidemiology
Programme
SLAMS
state and local air monitoring
stations
SO2
sulfur dioxide
SO32-
sulfite
SO3
sulfur trioxide
SO4
sulfur tetroxide
SO42-
sulfate
SOx
sulfur oxides
SPE
single-pollutant model estimate
SPM
source proximity model;
suspended particulate matter
sRaw
specific airway resistance
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Acronym/
Abbreviation Meaning
ST segment segment of the
electrocardiograph between the
end of the S wave and beginning
of the T wave
STN Speciation Trends Network
subj subject
t fraction of time spent in a
microenvironment across an
individual's microenvironmental
exposures, time
TBARS thiobarbituric acid reactive
substances (species)
T1 first trimester
T2 second trimester
T3 third trimester
Tl-Tl correlation between 1st trimester
SO2 and copollutants
TC total hydrocarbon
Tg teragram(s)
Thl T-helper 1
Th2 T-helper 2
TIA transient ischemic attack
TN Tennessee
TNF - a tumor necro sis factor alpha
TX Texas
U.S.C. U.S. Code
U.K. United Kingdom
U.S. United States of America
UT Utah
Vmax5o maximal expiratory flow rate
at 50%
Vmax75 maximal expiratory flow rate
at 75%
Vmax25 maximal expiratory flow rate
at 25%
VA Virginia
Val valine
VOC volatile organic compound
VSGA very small for gestational age
VTE venous thromboembolism
WBC white blood cell
WH Wuhan
Acronym/
Abbreviation Meaning
wk week
WHI Women's Health Initiative
WI Wisconsin
yr year(s)
Z* the true concentration
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Preface
Legislative Requirements for the Review of the National Ambient
Air Quality Standards
Two sections of the Clean Air Act (CAA) govern the establishment, review, and revision
of the National Ambient Air Quality Standards (NAAQS). Section 108 [42 U.S. Code
(U.S.C.) 7408] directs the Administrator to identify and list certain air pollutants and then
to issue air quality criteria for those pollutants. The Administrator is to list those air
pollutants that in her "judgment, cause or contribute to air pollution which may
reasonably be anticipated to endanger public health or welfare," "the presence of which
in the ambient air results from numerous or diverse mobile or stationary sources," and
"for which ... [the Administrator] plans to issue air quality criteria ..." [42 U.S.C.
7408(a)(1); (CAA. 1990a)1. Air quality criteria are intended to "accurately reflect the
latest scientific knowledge useful in indicating the kind and extent of all identifiable
effects on public health or welfare, which may be expected from the presence of [a]
pollutant in the ambient air ..." [42 U.S.C. 7408(b)]. Section 109 [42 U.S.C. 7409;
(CAA. 1990b) I directs the Administrator to propose and promulgate "primary" and
"secondary" NAAQS for pollutants for which air quality criteria are issued.
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 Administrator, based on
such criteria, is requisite to protect the public welfare from any known or anticipated
adverse effects associated with the presence of [the] air pollutant in the ambient air."2
The requirement that primary standards provide 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
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).
2 Section 302(h) of the Act [42 U.S.C. 7602(h)] provides that all language referring to effects on welfare includes,
but is not limited to, "effects on soils, water, crops, vegetation, man-made 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 ..." (CAA. 2005).
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reasonable degree of protection against hazards that research has not yet identified.1 Both
kinds of uncertainty 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 provide 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. The CAA does not require the Administrator to establish a primary NAAQS at a
zero-risk level or at background concentration levels, but rather at a level that reduces
risk sufficiently so as to protect public health with an adequate margin of safety.2 In so
doing, protection is provided for both the population as a whole and those groups and
lifestages potentially at increased risk for health effects from exposure to the air pollutant
for which each NAAQS is set.
In addressing the requirement for an adequate margin of safety, the U.S. Environmental
Protection Agency (U.S. EPA) considers such factors as the nature and severity of the
health effects involved, the size of the sensitive group(s), and the kind and degree of the
uncertainties. The selection of any particular approach to providing an adequate margin
of safety is a policy choice left specifically to the Administrator's judgment.3
In setting standards that are "requisite" to protect public health and welfare as provided in
Section 109(b), the U.S. EPA's task is to establish standards that are neither more nor less
stringent than necessary for these purposes. In so doing, the U.S. EPA may not consider
the costs of implementing the standards.4 Likewise, "Attainability and technological
feasibility are not relevant considerations in the promulgation of national ambient air
quality standards."5
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
1 See Lead Industries Association v. EPA, 647 F.2d 1130, 1154 [District of Columbia Circuit (D.C. Cir.) 1980];
American Petroleum Institute v. Costle, 665 F.2d 1176, 1186 (D.C. Cir. 1981)', American Farm Bureau Federation
v. EPA, 559 F. 3d 512, 533 (D.C. Cir. 2009); Association of Battery Recyclers v. EPA, 604 F. 3d 613, 617-18
(D.C. Cir. 2010).
2 See Lead Industries v. EPA, 647 F.2d at 1156 n.5V, Mississippi v. EPA, 744 F. 3d 1334, 1339, 1351, 1353 (D.C.
Cir. 2013).
3 See Lead Industries Association v. EPA, 647 F.2d at 1161-62; Mississippi v. EPA, 744 F. 3d at 1353.
4 See generally, Whitman v. American Trucking Associations, 531 U.S. 457, 465-472, 475-476 (2001).
5 See American Petroleum Institute v. Costle, 665 F. 2d at 1185.
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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).1
Overview and History of the Reviews of the Primary National
Ambient Air Quality Standard for Sulfur Dioxide
NAAQS are defined by four basic elements: indicator, averaging time, level, and form.
The indicator defines the pollutant to be measured in the ambient air for the purpose of
determining compliance with the standard. The averaging time defines the time period
over which air quality measurements are to be obtained and averaged or cumulated,
considering evidence of effects associated with various time periods of exposure.
The level of a standard defines the air quality concentration used (i.e., an ambient
concentration of the indicator pollutant) in determining whether the standard is achieved.
The form of the standard defines the air quality statistic that is compared to the level of
the standard in determining whether an area attains the standard. For example, the form
of the current primary 1-hour sulfur dioxide (SO2) standard is the 3-year average of the
99th percentile of the annual distribution of 1-hour daily maximum SO2 concentrations.
The Administrator considers these four elements collectively in evaluating the protection
to public health provided by the primary NAAQS.
The U.S. EPA considers the term sulfur oxides to refer to multiple gaseous oxidized
sulfur species such as SO2 and sulfur trioxide (SO3). SO2 was chosen as the indicator for
sulfur oxides because as in previous reviews, the presence of other sulfur oxides in the
atmosphere has not been demonstrated, and SO2 has a large body of health effects
evidence associated with it. The atmospheric chemistry, exposure, and health effects
associated with sulfur compounds present in particulate matter (PM) were most recently
considered in the U.S. EPA's review of the NAAQS for PM. Some of the welfare effects
resulting from deposition of sulfur oxides (e.g., effects associated with ecosystem
loading) are being considered in a separate assessment as part of the review of the
secondary NAAQS for nitrogen dioxide and SO2 (U.S. EPA. 2013d).
The U.S. EPA completed the initial review of the air quality criteria for sulfur oxides in
1969 [34 Federal Register (FR) 1988; (HEW. 1969)1. Based on this review, the U.S. EPA
promulgated NAAQS for sulfur oxides in 1971, establishing the indicator as SO2 [36 FR
1 Lists of CASAC members and of members of the CASAC Augmented for Sulfur Oxides Panel are available at:
http://vosemite.epa.gov/sab/sabproduct.nsfAVebCASAC/CommitteesandMembership7QpeiiDocument.
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8186; (U.S. EPA. 1971)1. The 1971 primary standards were set at 365 |ig/m3 [equal to
0.14 parts per million (ppm)] averaged over a 24-hour period, not to be exceeded more
than once per year, and at 80 |ig/m3 (equal to 0.03 ppm) annual arithmetic mean.1 Since
then, the Agency has completed multiple reviews of the air quality criteria and standards,
as summarized in Table I.
Table I History of the primary National Ambient Air Quality Standards for
sulfur dioxide since 1971.
Final Rule/
Decisions
Indicator
Averaging Time Level
Form
1971
SO2
24 h 140 ppba
One allowable exceedance
36 FR 8186
1 yr 30 ppba
Annual arithmetic average
Apr 30, 1971
1996
Both the 24-h and annual average standards retained without revision.
61 FR 25566
May 22, 1996
2010
75 FR 35520
June 22, 2010
SO2
1 h 75 ppb
3-yr average of the 99th percentile of the
annual distribution of daily maximum 1-h
concentrations
24-h and annual SO2 standards revoked.
FR = Federal Register; S02 = sulfur dioxide.
aThe initial level of the 24-h S02 standard was 365 |jg/m3 which is equal to 0.14 parts per million (ppm) or 140 parts per billion
(ppb). The initial level of the annual S02 standard was 80 |jg/m3 which is equal to 0.03 ppm or 30 ppb. The units for the standard
level were officially changed to ppb in the final rule issued in 2010 (75 FR 35520).
In 1982, the U.S. EPA published the Air Quality Criteria for Particulate Matter and
Sulfur Oxides (U.S. EPA. 1982a) along with an addendum of newly published controlled
human exposure studies, which updated the scientific criteria upon which the initial
standards were based (U.S. EP A. 1982b). In 1986, a second addendum was published
presenting newly available evidence from epidemiologic and controlled human exposure
studies (U.S. EP A. 1986a). In 1988, the U.S. EPA published a proposed decision not to
revise the existing standards (53 FR 14926). However, the U.S. EPA specifically
requested public comment on the alternative of revising the current standards and adding
a new 1-hour primary standard of 0.4 ppm to protect against short-term peak exposures.
1 Note that 0.14 parts per million (ppm) is equivalent to 140 parts per billion (ppb) and 0.03 ppm is equivalent
to 30 ppb.
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As a result of public comments on the 1988 proposal and other post-proposal
developments, the U.S. EPA published a second proposal 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 the respiratory effects of
short-term SO2 exposures in individuals with asthma (U.S. EPA. 1994). As in the 1988
proposal, the U.S. EPA proposed to retain the existing 24-hour and annual standards.
The U.S. EPA also solicited comment on three regulatory alternatives to further reduce
the health risk posed by exposure to high 5-minute 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-minute 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 with
one expected exceedance; and (3) augmenting implementation of existing standards by
focusing on those sources or source types likely to produce high 5-minute concentrations
of S02.
In assessing the regulatory options mentioned above, the Administrator concluded that
the likely frequency of 5-minute concentrations of concern should also be a consideration
in assessing the overall public health risks. Based upon an exposure analysis conducted
by the U.S. EPA, the Administrator concluded that exposure of individuals with asthma
to SO2 at levels that can reliably elicit adverse health effects was likely to be a rare event
when viewed in the context of the entire population of individuals with asthma. Thus, the
Administrator judged that high 5-minute SO2 concentrations did not pose a broad public
health problem when viewed from a national perspective, and a 5-minute standard was
not promulgated. In addition, no other regulatory alternative was finalized, and the
24-hour and annual average primary SO2 standards were retained in 1996 (61 FR 25566).
The American Lung Association and the Environmental Defense Fund challenged the
U.S. EPA's decision not to establish a 5-minute standard. On January 30, 1998, the Court
of Appeals for the District of Columbia ("D.C. Circuit") found that the U.S. EPA had
failed to adequately explain its determination that no revision to the SO2 NAAQS was
appropriate and remanded the decision back to the U.S. EPA for further explanation.1
Specifically, the court found that the U.S. EPA had failed to provide adequate rationale to
support the Agency judgment that exposures to high 5-minute concentrations of SO2 do
not pose a public health problem from a national perspective even though these peaks
will likely cause adverse health impacts in a subset of individuals with asthma. Following
the remand, the U.S. EPA requested that states voluntarily submit 5-minute SO2
monitoring data to be used to conduct air quality analyses in order to gain a better
1 See American LungAss'n v. EPA, 134 F. 3d 388 (D.C. Cir. 1998).
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understanding of the magnitude and frequency of high, 5-minute peak SO2
concentrations. The data submitted by states and the analyses based on this data helped
inform the last review of the SO2 NAAQS, which ultimately addressed the issues raised
in the 1998 remand.
The last review of the health-related air quality criteria for sulfur oxides and the primary
SO2 standard was initiated in May 2006 (71 FR 28023).1,2 The Agency's plans for
conducting the review were presented in the Integrated Review Plan (IRP) for the
Primary National Ambient Air Quality Standards for Sulfur Oxides ("U.S. EPA. 2007a').
which included consideration of comments received during a CASAC consultation as
well as public comment on a draft IRP. The science assessment for the review was
described in the 2008 ISA for Sulfur Oxides—Health Criteria (U.S. EPA. 2008d).
multiple drafts of which received review by CASAC and the public. The U.S. EPA also
conducted quantitative human risk and exposure assessments after having consulted with
CASAC and receiving public comment on a draft analysis plan (U.S. EPA. 2007b). These
technical analyses were presented in the Risk and Exposure Assessment (REA) to
Support the Review of the SO2 Primary National Ambient Air Quality Standards (U.S.
EPA. 2009b). multiple drafts of which were reviewed by CASAC and the public.
On June 22, 2010, the U.S. EPA revised the primary SO2 NAAQS to provide requisite
protection of public health with an adequate margin of safety (75 FR 35520).
Specifically, after concluding that the then-existing 24-hour and annual standards were
inadequate to protect public health with an adequate margin of safety, the U.S. EPA
established a new 1-hour SO2 standard at a level of 75 parts per billion (ppb), based on
the 3-year average of the annual 99th percentile of 1-hour daily maximum concentrations.
This standard was promulgated to provide substantial protection against S02-related
health effects associated with short-term exposures ranging from 5 minute to 24 hours.
More specifically, U.S. EPA concluded that a 1-hour SO2 standard at 75 ppb would
substantially limit exposures associated with the adverse respiratory effects
(e.g., decrements in lung function and/or respiratory symptoms) reported in exercising
asthmatics following 5-10 minute exposures in controlled human exposure studies, as
well as the more serious health associations (e.g., respiratory-related emergency
department visits and hospitalizations) reported in epidemiologic studies that mostly used
daily metrics (1-h daily max and 24-h avg). In the last review, the U.S. EPA also revoked
the then-existing 24-hour and annual primary standards based largely on the recognition
1 Documents related to reviews completed in 2010 and 1996 are available at: https://www.epa.gov/naaas/sulfur-
dio.\idc-so2 -pri marv -ai r-ci ualit v-staiidards.
2 The U.S. EPA conducted a separate review of the secondary SO2 NAAQS jointly with a review of the secondary
NO2 NAAQS. The Agency retained those secondary standards, without revision, to address the direct effects on
vegetation of exposure to oxides of nitrogen and sulfur (77 FR 20218).
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that the new 1-hour standard at 75 ppb would generally maintain 24-hour and annual SO2
concentrations well below the NAAQS, so that retaining the corresponding standards
would not provide additional public health protection (75 FR 35550). The decision to set
a 1-hour standard at 75 ppb—in part to substantially limit exposure to 5-minute
concentrations of SO2 resulting in adverse respiratory effects in exercising
asthmatics—also satisfied the remand by the D.C. Circuit in 1998.
As mentioned above, the U.S. EPA's last review placed considerable weight on
substantially limiting health effects associated with high 5-minute SO2 concentrations.
Thus, as part of the final rulemaking, the U.S. EPA for the first time required the states to
report either the highest 5-minute concentration for each hour of the day, or all twelve
5-minute concentrations for each hour of the day. The rationale for this requirement was
that this additional monitored data could then be used in future reviews to evaluate the
extent to which the 1-hour SO2 NAAQS at 75 ppb provides protection against 5-minute
concentrations of concern.
After publication of the final rule, a number of industry groups and states filed petitions
for review arguing that the U.S. EPA failed to follow notice-and-comment rulemaking
procedures, and that the decision to establish the 1-hour SO2 NAAQS at 75 ppb was
arbitrary and capricious because it was lower than statutorily authorized. The D.C.
Circuit rejected these challenges, thereby upholding the standard in its entirety [.National
Environmental Development Association's Clean Air Project v. EPA, 686 F. 3d 803
(D.C. Cir. 2012), cert, denied Asarco LLC v. EPA, 133 S. Ct. 983 (Jan. 22, 2013)].
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Executive Summary
Purpose and Scope of the Integrated Science Assessment
This Integrated Science Assessment (ISA) is a comprehensive evaluation and synthesis of
policy-relevant science aimed at characterizing exposures to ambient sulfur oxides (SOx)
and the health effects associated with these exposures.1 Thus, this ISA serves as the
scientific foundation for the review of the primary (health-based) National Ambient Air
Quality Standard (NAAQS) for sulfur dioxide (SO2). The indicator2 for the current
standard is SO2 because it is the most prevalent species of SOx (a group of closely related
gaseous compounds including SO2 and SO3) in the atmosphere and has health effects for
which there is a large body of scientific evidence. The health effects of sulfate and other
sulfur aerosols are considered as part of the review of the NAAQS for particulate matter
[e.g., in the 2009 Integrated Science Assessment for Particulate Matter (U.S. EPA.
2009a)].3 Some of the welfare effects resulting from deposition of sulfur oxides
(e.g., effects associated with ecosystem loading) are being considered in a separate
assessment as part of the review of the secondary (welfare-based) NAAQS for oxides of
nitrogen and sulfur (U.S. EPA. 2013d').
In 2010, the U.S. Environmental Protection Agency (U.S. EPA) established a new 1-hour
SO2 primary standard of 75 parts per billion (ppb) based on the 3-year average of the 99th
percentile of each year's 1-hour daily maximum concentrations (75 FR 35520).4
The 1-hour standard was established to protect against a broad range of respiratory
effects associated with short-term exposures in potential at-risk populations, such as
people with asthma. This standard was based on clear evidence of S02-related effects in
controlled human exposure studies of exercising individuals with asthma, as well as
epidemiologic evidence of associations between ambient SO2 concentrations and
respiratory-related emergency department visits and hospitalizations. The U.S. EPA also
revoked the existing 24-hour and annual primary SO2 standards of 140 and 30 ppb,
respectively, based largely on the recognition that the new 1-hour standard would
generally maintain 24-hour and annual SO2 concentrations well below the NAAQS, and
thus retaining these standards would not provide additional public health protection (75
1 The general process for developing an ISA, including the framework for evaluating weight of evidence and
drawing scientific conclusions and causal judgments, is described in a companion document, Preamble to the
Integrated Science Assessments (U.S. EPA. 2015b'). https://www.epa.gov/isa.
2 The four components to a NAAQS are: (1) indicator (e.g., SO2), (2) level (e.g., 75 ppb), (3) averaging time
(e.g., 1 h), and (4) form (e.g., 3 yr avg of the 99th percentile of each year's daily 1 -h max concentrations).
3 In this ISA, the blue electronic links can be used to navigate to cited chapters, sections, tables, figures, and studies.
4 The legislative requirements and history of the SO2 NAAQS are described in detail in the Preface to this ISA.
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FR 35550). The U.S. EPA also began requiring states to report 5-min avg SO2
concentrations in light of evidence from controlled human exposure studies of health
effects associated with 5-minute SO2 exposures.
This ISA updates the 2008 ISA for Sulfur Oxides RU.S. EPA. 2008d) hereafter referred
to as the 2008 SOx ISA] with studies and reports published from January 2008 through
August 2016. The U.S. EPA conducted in-depth searches to identify peer-reviewed
literature on relevant topics such as health effects, atmospheric chemistry, ambient
concentrations, and exposure. Information was also solicited from subject-matter experts
and the public during a kick-off workshop held at the U.S. EPA in June 2013 and at a
public meeting of the Clean Air Scientific Advisory committee held in January 2015.
To fully describe the state of available science, The U.S. EPA also included in this ISA
the most relevant studies from previous assessments.
As in the 2008 SOx ISA, this ISA determines the causal nature of relationships with
health effects only for SO2 Chapter 5). Health effects of other SOx species are not
considered, because their presence in the atmosphere has not been demonstrated,
(Chapter 2). transformation products of SOx such as sulfate are considered in the ISA for
Particulate Matter (U.S. EPA. 2009a'). and the health literature is focused on SO2. Key to
interpreting the health effects evidence is understanding the sources, chemistry, and
distribution of SO2 in the ambient air (Chapter 2) that influence exposure, (Chapter 3).
the uptake of inhaled SO2 in the respiratory tract, and what biological mechanisms may
subsequently be affected (Chapter 4). Further, the ISA aims to characterize the
independent effect of SO2 on health (Chapter 5). The ISA also informs policy-relevant
issues (Chapter 1 and Chapter 6). such as (1) exposure durations and patterns associated
with health effects; (2) concentration-response relationship(s), including evidence of
potential thresholds for effects; and (3) populations or lifestages at increased risk for
health effects related to SO2 exposure (Section 1.7.4 and Chapter 6).
Sources and Human Exposure to Sulfur Dioxide
The main objective of the ISA is to characterize health effects related to ambient SO2
exposure. This requires understanding the factors that affect both the exposure to ambient
SO2 and the uncertainty in estimating exposure. These factors include spatial variability
in SO2 concentrations, exposure to copollutants, and uncharacterized time-activity
patterns.
Emissions of SO2 have decreased by approximately 72% from 1990 to 2011 due to
several federal air quality regulatory programs. Coal-fired electricity generation units are
the dominant sources, emitting 4.6 million tons of SO2 in 2011, nearly 10 times more
than the next largest source (coal-fired boilers for industrial fuel combustion;
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Section 2.2). In addition to emission rate, important factors that affect ambient SO2
concentrations at downwind locations include local meteorology (e.g., wind, atmospheric
stability, humidity, and cloud/fog cover) and chemistry in the plume (Section 2.3).
The national average daily 1-hour max SO2 concentration reported during 2013-2015
was 5.4 ppb with a 99th percentile concentration of 64 ppb (Section 2.5). However,
1-hour daily max SO2 concentrations were 75 ppb or higher at some monitors located
near point sources, such as power plants or metals processing facilities, or natural
sources, such as volcanoes. The national average of 5-minute hourly max concentrations
during 2013-2015 was 2.1 ppb, with a 99th percentile concentration of 24 ppb. Hourly
5-minute max concentrations tracked closely with their corresponding 1-h avg
concentrations, with 75% of sites having a correlation above 0.9, indicating that
fluctuations in 5-minute hourly max concentrations are well represented by changes in
1-h avg concentrations. The ratio of 5-minute hourly max concentrations to their
corresponding 1-h avg concentrations was generally in the range of 1-3, although higher
ratios were also observed during some hours. Background SO2 concentrations due to
natural sources and man-made sources located outside the U.S. are very low across most
of the U.S. (less than 0.03 ppb) except in areas affected by volcanoes, such as Hawaii and
the West Coast.
Air quality models are used to estimate SO2 concentrations in locations without ambient
SO2 monitors (Section 2.6). As part of the implementation program for the 2010 primary
SO2 NAAQS, air quality modeling may be used to characterize air quality for
determining compliance with the standard where existing ambient SO2 monitors may not
capture peak 1-hour concentrations (75 FR 35520). The widely used dispersion model
American Meteorological Society/U.S. EPA Regulatory Model (AERMOD) is based on
Gaussian dispersion models with enhancements to improve modeling of SO2 plumes.
AERMOD is relatively unbiased in estimating upper-percentile 1-hour concentration
values over averaging times from 1 hour to 1 year. Lagrangian puff dispersion models,
such as CALPUFF, have been developed as an alternative to Gaussian dispersion models.
Uncertainties in model predictions are influenced by uncertainties in model inputs,
particularly emissions data and meteorological conditions.
Correlations between ambient concentrations of SO2 and copollutants are generally low
(<0.4), although they vary across location, study, and SO2 averaging time and are greater
than 0.7 at some monitoring sites (Section 3.4.3). Median correlations of
1-hour daily maximum and 24-h avg SO2 concentrations with particulate matter, nitrogen
dioxide (NO2), and carbon monoxide (CO) during 2013-2015 ranged from 0.2-0.4,
while for ozone (O3) the median daily copollutant correlation with SO2 was less than 0.1
(Figure 3-5).
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Estimating exposure to ambient SO2 for use in epidemiologic studies can be done in
multiple ways. Common techniques include using air quality monitoring data, personal
SO2 monitoring, and modeling. Air quality monitoring data from central site monitors
(rather than near-source monitors), which are assumed to represent population exposure,
are frequently used, but these monitors may not capture the spatial variation in ambient
SO2 concentrations across an urban area, which can be relatively high in areas affected by
large point sources. Modeling approaches combining air quality data with geographic
information or time-activity patterns, or both, can provide estimates of local ambient
concentration or exposure concentration, although more complex approaches need more
detailed inputs and have the potential for uncertainty related to missing sources, overly
smooth concentration gradients, and other factors.
"Exposure error," which refers to the bias and uncertainty associated with using exposure
metrics to represent the actual exposure of an individual or population, can contribute to
error in health effect estimates in epidemiologic studies (Section 3.4.4). Several
exposure-related factors (including uncharacterized time-activity patterns, spatial and
temporal variability of SO2 concentrations, and distance of individuals and populations
from air quality monitors used in the statistical analyses) contribute to error in estimating
exposure to ambient SO2. Variation in activity patterns across individuals and overtime
results in corresponding variations in exposure concentration. Uncharacterized spatial
variability in SO2 concentrations can contribute to exposure error that tends to add
uncertainty and reduce the magnitude of effect estimates in daily time-series
epidemiologic studies. For long-term (e.g., annual) studies, the effect estimate may be
increased or reduced by using central site monitoring data, depending on the relative
locations of sources, monitors, and exposed people. The exposure error associated with
using central site monitors is generally expected to widen confidence intervals beyond the
nominal coverage of those intervals that would be produced had the true exposure been
used for all study types.
Dosimetry and Mode of Action of Inhaled Sulfur Dioxide
Understanding the absorption and fate of SO2 in the body (dosimetry) and the biological
pathways that potentially underlie health effects (mode of action) is crucial to provide
biological plausibility for linking SO2 exposure with observed health effects.
Inhaled SO2 is readily absorbed in the nasal passages of resting humans and laboratory
animals (Section 4.2). As physical activity increases, there is an increase in breathing rate
and a shift to breathing through the mouth, resulting in greater SO2 penetration into the
lower airways. Relative to healthy adults, children, and individuals with asthma or
allergic rhinitis have an increased amount of oral breathing, and thus, may be expected to
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have greater SO2 penetration into the lungs. Children also generally have a greater intake
dose of SO2 per body mass than adults.
The distribution and clearance of inhaled SO2 from the respiratory tract involves several
chemical transformations, particularly the formation of sulfite and S-sulfonates. Sulfite is
metabolized into sulfate, which is rapidly excreted through the urine, while S-sulfonates
are cleared more slowly from the circulation over a period of days. Although SC>2-derived
products have been found in the blood and urine within minutes of an inhalation
exposure, a substantial portion of these products appear to be retained within the upper
airways, particularly during nasal breathing, with only slow absorption into the blood.
Although inhaled SO2 produces sulfite that is distributed through the circulation, overall
sulfite levels are heavily influenced by production within the body (endogenous
production) and by eating food with sulfur-containing amino acids or sulfite itself
(Section 4.2.6). For both adults and children, metabolism of sulfur-containing amino
acids produces much more sulfite than is ingested as food additives. Sulfite produced
endogenously generates levels two or more orders of magnitude higher than
inhalation-derived sulfite levels for both children and adults, even for full-day exposures
to 75 ppb SO2 (i.e., the level of the 1-hour NAAQS). Sulfite ingestion from food
additives varies widely, but is generally expected to exceed sulfite intake from inhalation
in both adults and children, even for full-day exposures to 75 ppb SO2. However, an
important distinction is that inhalation-derived SO2 products can accumulate in the
respiratory tract, whereas sulfite from ingestion or endogenous production does not.
SO2 inhalation produces bronchoconstriction in both healthy adults and those with
asthma (Section 4.3). but the underlying processes are somewhat different. The response
to SO2 in healthy adults occurs primarily from activation of sensory nerves in the
respiratory tract resulting in neural reflex responses through the vagus nerve. In adults
with asthma, the response is only partly due to this neural reflex response, with
inflammatory mediators also being involved. Inhalation of SO2 increases allergic
inflammation in adults with asthma and in animals with allergic airways disease, which
shares many features with asthma. Furthermore, SO2 inhalation increases allergic
sensitization in animals not already allergic, and once allergic, these animals respond to
an allergen challenge with greater allergic inflammation and airway obstruction (likely
due to bronchoconstriction) compared to animals who were not exposed to SO2. These
findings suggest that allergic inflammation and increased airway responsiveness due to
short-term SO2 exposure (minutes up to 1 month) may be linked to asthma exacerbation
seen in epidemiologic studies.
For long-term SO2 exposure (more than 1 month to years), animal studies provide
additional evidence of airway inflammation, airway remodeling, AHR, and allergic
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sensitization. In animals that are not allergic, SO2 inhalation leads to airway inflammation
and allergic sensitization. In animals with allergic airway disease, SO2 exposure increases
airway responsiveness and airway remodeling. Thus, inhalation of SO2 may lead to the
development and worsening of allergic airway disease. The development of AHRmay
link long-term exposure to SO2 to the epidemiologic outcome of physician-diagnosed
asthma (new onset asthma).
While there is some evidence for extrapulmonary effects of inhaled SO2, the mode of
action underlying these responses is uncertain. Controlled human exposure studies
provide evidence suggesting activation of sensory nerves in the respiratory tract resulting
in a neural reflex response by SO2 exposure, which could lead to changes in heart rate or
heart rate variability. Additionally, the transport of sulfite into the circulation could result
in redox stress, but this is likely to only occur at elevated or prolonged exposures due to
the body's efficient metabolism of sulfite to sulfate.
Health Effects of Sulfur Dioxide Exposure
This ISA integrates information on SO2 exposure and health effects from controlled
human exposure, epidemiologic, and toxicological studies to form conclusions about the
causal nature of relationships between SO2 exposure and health effects. For most health
effect categories, with the exception of reproductive and developmental effects, effects
are evaluated separately for short-term exposures and long-term exposures. Health effects
are considered in relation to the full range of SO2 concentrations relevant to ambient
conditions. Based on upper-percentile ambient concentrations (Section 2.5) and the ISA's
emphasis on ambient-relevant exposures within one to two orders of magnitude of current
conditions I Preamble to the ISAs (U.S. EPA. 2015b). Section 5c], SO2 concentrations up
to 2,000 ppb1 are defined to be ambient-relevant. A consistent and transparent framework
I Preamble to the ISAs (U.S. EPA. 2015b). Table II] is applied to classify the health
effects evidence according to a five-level hierarchy:
1. Causal relationship
2. Likely to be a causal relationship
3. Suggestive of, but not sufficient to infer, a causal relationship
4. Inadequate to infer the presence or absence of a causal relationship
5. Not likely to be a causal relationship
1 The 2,000-ppb upper limit applies mostly to animal toxicological studies and also a few controlled human
exposure studies. Experimental studies examining SO2 exposures greater than 2,000 ppb were included if they
provided information on the uptake of SO2 in the respiratory tract or on potential biological mechanisms.
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The causal determinations presented in Table ES-1 are informed by recent findings and
whether these recent findings, integrated with information from the 2008 SOx ISA,
support a change in causal conclusions. Important considerations include: (1) determining
whether laboratory studies of humans and animals demonstrate an independent health
effect of SO2 exposure and what the potential underlying biological mechanisms are;
(2) determining whether there is consistency in epidemiologic evidence across various
methods used to estimate SO2 exposure; (3) examining epidemiologic studies of the
potential influence of factors that could bias associations observed with SO2 exposure;
(4) determining the coherence of findings integrated across controlled human exposure,
epidemiologic, and toxicological studies; and (5) making judgments regarding error and
uncertainty in the collective body of available studies.
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Table ES-1 Causal determinations for relationships between sulfur dioxide
exposure and health effects from the 2008 and current draft
Integrated Science Assessment for Sulfur Oxides.
Health Effect Category3 and
Exposure Duration
Causal Determination
2008 SOx ISA
Current Draft ISA
Respiratory effects—Short-term
exposure
Section 5.2.1. Table 5-21
Causal relationship
Causal relationship
Respiratory effects—Long-term exposure
Section 5.2.2, Table 5-24
Inadequate to infer the presence or
absence of a causal relationship
Suggestive of, but not sufficient to
infer, a causal relationship
Cardiovascular effects—Short-term
exposure
Section 5.3.1. Table 5-34
Inadequate to infer the presence or
absence of a causal relationship
Inadequate to infer the presence or
absence of a causal relationship
Cardiovascular effects—Long-term
exposure
Section 5.3.2. Table 5-35
Not included
Inadequate to infer the presence or
absence of a causal relationship
Reproductive and developmental effects'5
Section 5.4. Table 5-38
Inadequate to infer the presence or
absence of a causal relationship
Inadequate to infer the presence or
absence of a causal relationship
Total mortality—Short-term exposure
Section 5.5.1, Table 5-41
Suggestive of, but not sufficient to
infer, a causal relationship
Suggestive of, but not sufficient to
infer, a causal relationship
Total mortality—Long-term exposure
Section 5.5.2. Table 5-43
Inadequate to infer the presence or
absence of a causal relationship
Inadequate to infer the presence or
absence of a causal relationship
Cancer—Long-term exposure
Section 5.6. Table 5-44
Inadequate to infer the presence or
absence of a causal relationship
Inadequate to infer the presence or
absence of a causal relationship
ISA = Integrated Science Assessment; SOx = sulfur oxides.
Previous causal determinations taken from the 2008 SOx ISA (U.S. EPA. 2008d').
aAn array of outcomes is evaluated as part of a broad health effect category: physiological measures (e.g., airway
responsiveness), clinical outcomes (e.g., hospital admissions), and cause-specific mortality. Total mortality includes all
nonaccidental causes of mortality and is informed by findings for the spectrum of morbidity effects (e.g., respiratory,
cardiovascular) that can lead to mortality. The sections and tables referenced include a detailed discussion of the evidence that
supports the causal determinations and the S02 concentrations with which health effects have been associated.
bReproductive and developmental effects studies consider a wide range of exposure durations.
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Sulfur Dioxide Exposure and Respiratory Effects
As in the 2008 SOx ISA (U.S. EPA. 2008(1). the current ISA concludes that there is a
causal relationship between short-term SO2 exposure and respiratory effects, particularly
in individuals with asthma (Section 5.2.IV This determination is based on consistent,
coherent, and biologically plausible evidence for asthma exacerbation due to SO2
exposure. The clearest evidence for this conclusion comes from controlled human
exposure studies available at the time of the 2008 SOx ISA showing lung function
decrements and respiratory symptoms in adults with asthma exposed to SO2 for
5-10 minutes at elevated breathing rates. The effects observed in these studies are
consistent with the processes leading to asthma exacerbation described in the mode of
action section (Section 4.3). Epidemiologic evidence, including recent studies not
available at the time of the 2008 SOx ISA, also supports a causal relationship, primarily
due to studies reporting positive associations for asthma hospital admissions and
emergency department visits with short-term SO2 exposures, specifically for children.
This is coherent with studies showing that children have increased airway responsiveness
to a trigger and have greater oral breathing and body-mass-adjusted intake dose relative
to adults, suggesting they will have a greater response to SO2 exposure than adults.
Hospital admissions and emergency department visits studies that examined potential
copollutant confounding reported associations were generally unchanged in copollutant
models. Additional support comes from studies reporting positive associations between
short-term SO2 exposures and respiratory symptoms in children with asthma, although
the evidence from respiratory symptoms studies in adults with asthma is less consistent.
Finally, epidemiologic studies that report consistent positive associations between
short-term SO2 concentrations and respiratory mortality indicate a potential continuum of
effects.
For long-term SO2 exposure and respiratory effects the evidence is suggestive of, but not
sufficient to infer, a causal relationship (Section 5.2.2). The strongest evidence is
provided by coherence among findings of epidemiologic studies showing associations
between long-term SO2 exposure and increases in asthma incidence among children and
results of animal toxicological studies that provide a pathophysiologic basis for the
development of asthma. Some evidence regarding respiratory symptoms and/or
respiratory allergies among children provides limited support for a possible relationship
between long-term SO2 exposure and the development of asthma. This represents a
change in the causal determination made in the 2008 SOx ISA from inadequate to
suggestive, based on a limited body of new evidence.
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Sulfur Dioxide Exposure and Other Health Effects
There is more uncertainty regarding relationships between SO2 exposure and health
effects outside of the respiratory system. SO2 itself is unlikely to enter the bloodstream;
however, its reaction products, such as sulfite, may do so. The amount of circulating
sulfite due to inhalation of ambient-relevant concentrations of SO2 is far less than the
contribution from metabolism of sulfur-containing amino acids.
For short-term SO2 exposure and total mortality, the current ISA reaches the same
conclusion as the 2008 SOx ISA (U.S. EPA. 2008d); that the evidence is suggestive of,
but not sufficient to infer, a causal relationship (Section 5.5. IV This conclusion is based
on previous and recent multicity epidemiologic studies providing consistent evidence of
positive associations. While recent multicity studies have analyzed some key
uncertainties and data gaps identified in the 2008 SOx ISA, questions remain regarding
the potential for SO2 to have an independent effect on mortality, considering issues such
as the limited number of studies that examined copollutant confounding, evidence for a
decrease in the size of S02-mortality associations in copollutant models with NO2 and
PM10, and the lack of a potential biological mechanism for mortality following short-term
exposures to SO2.
For the remaining health effect categories (short-term and long-term SO2 exposure and
cardiovascular effects, long-term exposure and total mortality, reproductive and
developmental effects, and long-term exposure and cancer), the evidence is inadequate to
infer the presence or absence of a causal relationship, mainly due to inconsistent evidence
across specific outcomes and uncertainties regarding exposure measurement error,
copollutant confounding, and potential modes of action. These conclusions are consistent
with those made in the 2008 SOx ISA, as illustrated in Table ES-1.
Policy-Relevant Considerations for Health Effects Associated
with Sulfur Dioxide Exposure
This section describes issues relevant for considering the potential importance of impacts
of ambient SO2 exposure on public health, including exposure durations observed to
cause health effects, the shape of the concentration-response relationship, regional
differences, and at-risk populations and lifestages.
Evidence from controlled human exposure studies of respiratory effects after exposures
of 5-10 minutes indicates a rapid onset of S02-related effects and provides support for
the 1-h avg time used in the primary SO2 NAAQS (Section 5.2.1). Epidemiologic studies
of asthma hospital admissions and emergency department visits using daily exposure
metrics (24-h avg and 1-h daily max) show positive associations that are generally
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unchanged in copollutant models, although these associations could be due to very short
duration exposures (5-10 minutes) experienced during the day. The rapid onset of effects
is also coherent with the limited number of epidemiologic studies that examined lag
structures and reported associations within the first few days of exposure.
Substantial interindividual variability was observed in controlled human exposure studies
of SO2 and respiratory effects, but there was a clear increase in the magnitude of
respiratory effects with increasing exposure concentrations between 200 and 1,000 ppb
during 5-10 minute SO2 exposures (Section 5.2.1.2). Both the number of affected
individuals with asthma and the severity of the response increased as SO2 concentrations
increased. Epidemiologic studies evaluating the shape of the concentration-response
function have found no evidence for a population-level threshold or nonlinearity,
although the evidence is limited.
SO2 concentrations are highly spatially heterogeneous, with SO2 concentrations at some
monitors possibly not highly correlated with the community average concentration
(Section 3.4.2.2). The predominance of point sources results in an uneven distribution of
SO2 concentrations across an urban area. This spatial and temporal variability in SO2
concentrations can contribute to exposure error in epidemiologic studies, whether the
studies rely on central site monitor data or concentration modeling for exposure
assessment.
Consistent with the findings of the 2008 SOx ISA (U.S. EPA. 2008d). this ISA concludes
there is adequate evidence that people with asthma, particularly children, are at increased
risk for SC>2-related health effects compared with those without asthma (Chapter 6). This
conclusion is based on the evidence for short-term SO2 exposure and respiratory effects
(specifically lung function decrements), for which a causal relationship has been
determined. The ISA concludes there is suggestive evidence that children are at increased
risk for SCh-related health effects, based on their increased ventilation rates relative to
body mass and increased oral breathing, together with some epidemiologic evidence of
increased associations between SO2 and respiratory effects relative to adults, even though
recent epidemiologic evidence is less consistent. There is also evidence suggestive of
increased risk of SCh-related health effects for older adults relative to other lifestages.
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Chapter 1 Integrative Synthesis of the ISA
1.1 Purpose and Overview of the Integrated Science
Assessment
The Integrated Science Assessment (ISA) is a comprehensive evaluation and synthesis of
the policy-relevant science "useful in indicating the kind and extent of all identifiable
effects on public health or welfare which may be expected from the presence of [a]
pollutant in the ambient air," as described in Section 108 of the Clean Air Act (CAA.
1990a).1 This ISA communicates critical science judgments of the health-related air
quality criteria for the broad category of sulfur oxides (SOx). As such, this ISA serves as
the scientific foundation for the review of the current primary (health-based) National
Ambient Air Quality Standard (NAAQS) for sulfur dioxide (SO2). SOx include several
related gaseous compounds such as SO2 and sulfur trioxide (SO3) (Section 23). SO2 was
chosen as the indicator2 for the NAAQS because as in previous reviews, the presence of
other sulfur oxides in the atmosphere has not been demonstrated (U.S. EPA. 1996b;
HEW. 1969).3 and there is a large body of evidence on health effects following exposure
to SO2. In addition, the 2010 Final Rule concluded that "measures leading to reductions
in population exposures to SO2 can generally be expected to lead to reductions in
population exposures to SOx." (75 FR 35536). Health effects of particulate
sulfur-containing species (e.g., sulfate) are being considered in the current review of the
NAAQS for particulate matter (PM) and were previously evaluated in the 2009 ISA for
PM (U.S. EPA. 2009a). Some of the welfare effects resulting from deposition of SOx
(e.g., effects associated with ecosystem loading) are being evaluated in a separate
assessment conducted as part of the review of the secondary (welfare-based) NAAQS for
oxides of nitrogen (NOx) and SOx (U.S. EPA. 2013d).
This ISA evaluates relevant scientific literature published since the 2008 ISA for Sulfur
Oxides rOJ.S. EPA. 2008d). or 2008 SOx ISA], integrating key information and
judgments contained in the 2008 SOx ISA and the 1982 Air Quality Criteria Document
(AQCD) for Particulate Matter and Sulfur Oxides (U.S. EPA. 1982a) and its Addenda
(U.S. EPA. 1994. 1986a. 1982b). Thus, this ISA updates the state of the science that was
1 The general process for developing an ISA, including the framework for evaluating weight of evidence and
drawing scientific conclusions and causal judgments, is described in a companion document, Preamble to the
Integrated Science Assessments (U.S. EPA. 2015b).
2 The four components to a NAAQS are: (1) indicator (e.g., SO2); (2) level (e.g., 75 ppb); (3) averaging time
(e.g., 1 h), and (4) form (e.g., 3 yr avg of the 99th percentile of each year's 1-h daily max concentrations).
3 In this ISA, the blue electronic links can be used to navigate to cited chapters, sections, tables, figures, and studies.
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available for the 2008 SOx ISA, which informed decisions on the primary SO2 NAAQS
in the review completed in 2010. In 2010, the U.S. Environmental Protection Agency
(U.S. EPA) established a new 1-hour standard of 75 parts per billion (ppb) SO2 based on
the 3-yr avg of the 99th percentile of each year's 1-hour daily max concentrations.1
The 1-hour standard was established to protect against a broad range of respiratory
effects associated with short-term exposures in potential at-risk populations such as
people with asthma. This standard was based on clear evidence of SCh-related effects in
controlled human exposure studies of exercising individuals with asthma, as well as
epidemiologic evidence of associations between ambient SO2 concentrations and
respiratory-related emergency department visits and hospitalizations. The U.S. EPA also
revoked the existing 24-hour and annual primary SO2 standards of 140 and 30 ppb,
respectively. The 24-hour and annual primary standards were revoked largely based on
the recognition that the new 1-hour standard at 75 ppb would generally maintain 24-hour
and annual SO2 concentrations well below the NAAQS, and thus, retaining these
standards would not provide additional public health protection (75 FR 35550). In light of
considerable weight being placed on health effects associated with 5-minute peak SO2
concentrations, the U.S. EPA for the first time required state reporting of either the
highest 5-minute concentration for each hour of the day, or all twelve 5-minute
concentrations for each hour of the day (U.S. EPA. 2010b).
This new review of the primary SO2 NAAQS is guided by several policy-relevant
questions that are identified in The Integrated Review Plan for the Primary National
Ambient Air Quality Standard for Sulfur Dioxide (U.S. EPA. 2014a). To address these
questions and update the scientific judgments in the 2008 ISA for Sulfur Oxides (U.S.
EPA. 2008d). this ISA aims to:
• Characterize the evidence for health effects associated with short-term (minutes
up to 1 month) and long-term (more than 1 month to years) exposure to SOx by
integrating findings across scientific disciplines and across related health
outcomes and by considering important uncertainties identified in the
interpretation of the scientific evidence, including the role of SO2 within the
broader ambient mixture of pollutants.
• Inform policy-relevant issues related to quantifying health risks, such as exposure
concentrations, durations, and patterns associated with health effects;
concentration-response (C-R) relationships and existence of thresholds below
which effects do not occur; and populations and lifestages potentially with
increased risk of health effects related to exposure to SOx.
Sulfur dioxide is the most abundant species of SOx in the atmosphere, while the presence
of other SOx species in the atmosphere has not been demonstrated (Section 2.1). Most
studies on the health effects of SOx focus on SO2. In evaluating the health evidence, this
1 The legislative requirements and history of the SO2 NAAQS are described in detail in the Preface to this ISA.
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ISA considers possible influences of other atmospheric pollutants, including interactions
of SO2 with co-occurring pollutants such as PM, NOx, carbon monoxide (CO), and ozone
(O3).
In addressing policy-relevant questions, this ISA aims to characterize the independent
health effects of SO2. As described in this ISA, recent evidence continues to support a
causal relationship between short-term SO2 exposure and respiratory effects based on the
consistency of findings, coherence among evidence from controlled human exposure,
epidemiologic, and toxicological studies, and biological plausibility for effects
specifically related to asthma exacerbation. The information summarized in this ISA will
serve as the scientific foundation for the review of the current primary 1-hour SO2
NAAQS.
1.2 Process for Developing Integrated Science Assessments
The U.S. EPA uses a structured and transparent process for evaluating scientific
information and determining the causal nature of relationships between air pollution
exposures and health effects [details provided in the Preamble to the Integrated Science
Assessments (U.S. EPA. 2015bYI. The ISA development process describes approaches for
literature searches, criteria for selecting and evaluating relevant studies, and a framework
for evaluating the weight of evidence and forming causal determinations. As part of this
process, the ISA is reviewed by the Clean Air Scientific Advisory Committee (CASAC),
which is a formal independent panel of scientific experts, and by the public. As this ISA
informs the review of the primary SO2 NAAQS, it integrates and synthesizes information
characterizing exposure to SO2 and potential relationships with health effects. Relevant
studies include those examining atmospheric chemistry, spatial and temporal trends, and
exposure assessment, as well as U.S. EPA analyses of air quality and emissions data.
Relevant health research includes epidemiologic, controlled human exposure, and
toxicological studies on health effects, as well as studies on dosimetry and modes of
action.
The U.S. EPA initiated the current review of the primary NAAQS for SO2 in August
2013 with a call for information from the public (U.S. EPA. 2013d). Thereafter, the
U.S. EPA routinely conducted literature searches to identify relevant peer-reviewed
studies published since the previous ISA (i.e., from January 2008 through August 2016).
Multiple search methods were used I Preamble to the IS As (U.S. EPA. 2015b'). Section 2],
including searches in the PubMed and Web of Science databases. Subject-area experts
and the public were also able to recommend studies and reports during a science/policy
issue "kick-off' workshop held at the U.S. EPA in June 2013. The U.S. EPA identified
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additional studies considered to be the definitive work on particular topics from previous
assessments to include in this ISA. Studies that did not address a topic described in the
preceding paragraph based on title were excluded. Studies that were judged to be
potentially relevant based on review of the abstract or full text and "considered" for
inclusion in the ISA are documented and can be found at the Health and Environmental
Research Online (HERO) website. The HERO project page for this ISA
(https://hero.epa.gov/hero/sulfur-oxides') contains the references that are cited in the ISA,
the references that were considered for inclusion but not cited, and electronic links to
bibliographic information and abstracts.
Categories of health effects were considered for evaluation in this ISA if they were
examined in previous U.S. EPA assessments for SOx or in multiple recent studies. For
other categories of health effects, literature searches were conducted to determine the
extent of available health evidence. These searches identified a few recently published
epidemiologic studies on outcomes such as migraine/headache, depression, suicide, eye
irritation/conjunctivitis, rheumatic disease, and gastrointestinal disorders [Supplemental
Table 5S-1 (U.S. EPA. 20161)1. Literature searches have also identified a few recently
published toxicological studies on hematological effects, mRNA and protein expression
in the brain, sensory symptoms, and effects in other organs (e.g., liver, spleen)
[Supplemental Table 5S-2 (U.S. EPA. 2015e)l. These health effects are not evaluated in
the current draft ISA because of the lack of relationship between the toxicological and
epidemiological health effects examined, as well as a large potential for publication bias
(i.e., a greater likelihood of publication for studies showing effects compared with those
showing no effect).
The Preamble to the ISAs (U.S. EPA. 2015b) describes the general framework for
evaluating scientific information, including criteria for assessing study quality and
developing scientific conclusions. Aspects specific to evaluating studies of SOx are
described in the Annex for Chapter 5. For epidemiologic studies, emphasis is placed on
studies that (1) characterize quantitative relationships between SO2 and health effects,
(2) examine exposure metrics that well represent the variability in concentrations in the
study area, (3) consider the potential influence of other air pollutants and factors
correlated with SO2, (4) examine potential at-risk populations and lifestages, or
(5) combine information across multiple cities. With respect to the evaluation of
controlled human exposure and toxicological studies, emphasis is placed on studies that
examine effects relevant to humans and SO2 concentrations relevant to ambient
exposures. Based on peak ambient concentrations (Section 2.5) and the ISA's emphasis
on ambient-relevant exposures within one to two orders of magnitude of current ambient
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concentrations, SO2 concentrations of 2,000 ppb1 or less are defined to be
ambient-relevant. Experimental studies with higher exposure concentrations were
included if they contributed to an understanding of dosimetry or potential modes of
action. For the evaluation of human exposure to ambient SO2, emphasis is placed on
studies that examine the quality of data sources used to assess exposures, such as central
site monitors, personal exposure monitors, and dispersion models. The ISA also
emphasizes studies that examine factors that influence exposure such as time-activity
patterns and building ventilation characteristics.
Integrating information across scientific disciplines and related health outcomes and
synthesizing evidence from previous and recent studies, the ISA draws conclusions about
relationships between SO2 exposure and health effects. Determinations are made about
causation, not just association, and are based on judgments of aspects such as the
consistency, coherence, and biological plausibility of observed effects (i.e., evidence for
effects on key events in the mode of action) as well as related uncertainties. The ISA uses
a formal causal framework [Table II of the Preamble to the ISAs (U.S. EPA. 2015b)! to
classify the weight of evidence according to the five-level hierarchy summarized below.
• Causal relationship: the consistency and coherence of evidence integrated
across scientific disciplines and related health outcomes are sufficient to rule out
chance, confounding, and other biases with reasonable confidence.
• Likely to be a causal relationship: there are studies in which results are not
explained by chance, confounding, or other biases, but uncertainties remain in the
evidence overall. For example, the influence of other pollutants is difficult to
address, or evidence across scientific disciplines may be limited or inconsistent.
• Suggestive of, but not sufficient to infer, a causal relationship: evidence is
generally supportive but not entirely consistent or is limited overall. Chance,
confounding, and other biases cannot be ruled out.
• Inadequate to infer the presence or absence of a causal relationship: there is
insufficient quantity, quality, consistency, or statistical power of results from
studies.
• Not likely to be a causal relationship: several adequate studies, examining the
full range of anticipated human exposure concentrations and potential at-risk
populations and lifestages, consistently show no effect.
1 The 2,000-ppb upper limit applies largely to animal toxicological studies but also a few controlled human
exposure studies.
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1.3
Organization of the Integrated Science Assessment
This ISA comprises the Preface (legislative requirements of the NAAQS and history of
the primary SO2 NAAQS), Executive Summary, and six chapters. This chapter
(Chapter 1) synthesizes the scientific evidence that best informs policy-relevant questions
that frame this review of the primary SO2 NAAQS. Chapter 2 characterizes the sources,
atmospheric processes involving SOx, and trends in ambient concentrations. Chapter 3
describes methods to estimate human exposure to SOx and the impact of error in
estimating exposure on relationships with health effects. Chapter 4 describes the
dosimetry and modes of action for SO2. Chapter 5 evaluates and integrates
epidemiologic, controlled human exposure, and toxicological evidence for health effects
related to short-term and long-term exposure to SOx. Chapter 6 evaluates information on
potential at-risk populations and lifestages. In addition, the Preamble to the ISAs (U.S.
EPA. 2015b) describes the general process for developing an ISA.
The purpose of this chapter is not to summarize each of the aforementioned chapters but
to synthesize the key findings for each topic that informed the characterization of SO2
exposure and relationships with health effects. This chapter also integrates information
across the ISA to inform policy-relevant issues such as SO2 exposure metrics associated
with health effects, concentration-response relationships, and the public health impact of
S02-related health effects (Section 1.7). A key consideration in the health effects
assessment is the extent to which evidence indicates that SO2 exposure independently
causes health effects. To that end, this chapter draws upon information about the sources,
distribution, and exposure to ambient SO2 and identifies pollutants and other factors
related to the distribution of or exposure to ambient SO2 that can potentially influence
epidemiologic associations observed between health effects and SO2 exposure
(Section 1.4). The chapter also summarizes information on the dosimetry and mode of
action of inhaled SO2 that can provide biological plausibility for observed health effects
(Section 1.5). The discussions of the health effects evidence and causal determinations
(Section 1.6) describe the extent to which epidemiologic studies accounted for factors
that may influence epidemiologic study results and the extent to which findings from
controlled human exposure and animal toxicological studies support independent
relationships between SO2 exposure and health effects.
1.4 From Emissions Sources to Exposure to Sulfur Dioxide
Characterizing human exposure is key to understanding the relationships between
ambient SO2 exposure and health effects. The sources of SOx and the transformations
that occur in ambient air influence the spatial and temporal pattern of SO2 concentrations
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in the air. These patterns have implications for variation in exposure in the population,
the adequacy of methods used to estimate exposure, and in turn, the strength of inferences
that can be drawn about health effects related to SO2 exposure.
1.4.1 Emission Sources and Distribution of Ambient Concentrations
Emissions of SO2 have declined by approximately 72% for all sources from 1990 to 2011
as a result of several U.S. air quality regulatory programs. Coal-fired electricity
generation units (EGUs) remain the dominant sources by nearly an order of magnitude
above the next highest source (industrial fuel combustion), emitting 4.6 million tons of
SO2 annually, according to the 2011 National Emissions Inventory (NEI; Section 2.2).
In addition to emission rate, the two important variables that determine the concentration
of SO2 downwind of the source are the photochemical and other removal processes (e.g.,
formation of particle-phase reduced sulfur compounds) occurring in the emissions plume
and the local meteorology, including wind, atmospheric stability, humidity, and cloud/fog
cover (Section 2.3). The primary gas-phase photochemical SO2 oxidation mechanism
requires the hydroxyl radical (OH). Another oxidation mechanism involves a Criegee
intermediate biradical that participates in converting SO2 to SO3, which rapidly reacts
with water vapor to form sulfuric acid (H2SO4). The Criegee-based SO2 oxidation
mechanism may amplify the rate of SO2 removal and formation of organosulfur
compounds in areas with high concentrations of Criegee precursors (i.e., low-molecular-
weight organic gases, such as biogenic compounds and unsaturated hydrocarbons present
downwind of industrial sites and refineries). Aqueous-phase oxidation of SO2 is also an
important removal mechanism. Clouds and fog can reduce local SO2 concentrations by
converting it to H2SO4 in the droplet phase.
Changes were undertaken to the existing U.S. EPA monitoring network as a result of the
new 1-hour primary NAAQS standard promulgated in 2010 (Section 2.4V First, the
automated pulsed ultraviolet fluorescence (UVF) method, the method most commonly
used by state and local monitoring agencies for NAAQS compliance, was designated as a
federal reference method (FRM). Second, new SO2 monitoring guidelines require states
to report either the highest 5-minute concentration for each hour of the day or all twelve
5-minute concentrations for each hour of the day in light of health effects evidence on
respiratory effects among exercising individuals with asthma following a 5-10-minute
exposure to SO2. Analysis of environmental concentrations of SO2 data reported in
Section 25. reflect the monitoring network changes, particularly the analysis of the recent
5-minute data.
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On a nationwide basis, the average 1-h daily max SO2 reported during 2013-2015 is
5.4 ppb with a 99th percentile concentration of 64 ppb (Section 2.5). However, peak
concentrations (99th percentile) of 1-h daily max SO2 concentrations can be greater than
75 ppb at some monitoring sites located near large anthropogenic sources (e.g., power
plants or metal processing facilities) or natural sources (e.g., volcanoes). The mean
5-minute hourly max concentration across the U.S. in 2013-2015 was 2.1 ppb, with a
99th percentile concentration of 24.0 ppb. Correlations between hourly 5-minute max
SO2 concentrations and their corresponding 1-h avg concentrations are high, with
approximately 75% of sites having correlations greater than 0.9. Peak-to-mean ratios
(PMRs) between the two metrics are generally less than 3, although higher PMRs are
observed during some hours (Section 2.5.4). Background concentrations of SO2 from
natural sources and sources outside the U.S. are very low across most of the country (less
than 0.03 ppb), accounting for less than 1% of ambient SO2 concentrations except in
areas where volcanic emissions are important, such as Hawaii and the West Coast
(Section 2.5.5).
SO2 concentrations are highly variable across urban spatial scales, exhibiting moderate to
poor correlations between SO2 measured at different monitoring sites across a
metropolitan area. This high degree of urban spatial variability may not be fully captured
by central site monitors used in epidemiologic studies, and thus, has implications for the
interpretation of human exposure and health effects data (Section 2.5.2.2 and
Section 3.4.4).
Air quality models, including dispersion models and chemical transport models, can be
used to estimate SO2 concentrations in locations where monitoring is not practical or
sufficient (Section 2.6). Because existing ambient SO2 monitors may not be sited in
locations to capture peak 1-hour concentrations, the implementation program for the 2010
primary SO2 NAAQS allows for air quality modeling to be used to characterize air
quality for informing designation decisions (75 FR 35520). In addition, modeling is
critical to the assessment of the impact of future sources or proposed modifications where
monitoring cannot inform, and for the design and implementation of mitigation
techniques. Dispersion models have also been used to estimate SO2 exposure
concentrations in epidemiologic studies, particularly in long-term studies
(Section 3.3.2.4. Chapter 5). The widely used dispersion model American Meteorological
Society/U.S. EPA Regulatory Model (AERMOD) is based on Gaussian dispersion
models but includes advancements such as boundary layer scaling formulations, surface
and elevated emission points, interactions of plumes with buildings and terrain, and
source geometry. Several evaluations of the performance of AERMOD against field
study data over averaging times from 1 hour to 1 year found the model was relatively
unbiased in estimating upper-percentile 1-hour concentration values. Lagrangian puff
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dispersion models, such as CALPUFF, have been developed as an alternative to Gaussian
dispersion models. CALPUFF models SO2 as a tracer and then uses a Lagrangian step
algorithm to model non-steady-state dynamics, using time-varying winds specified by
meteorological models. CALPUFF simulations were found to improve in accuracy with
increasing integration times. Uncertainties in model predictions are influenced by
uncertainties in model input data, particularly emissions and meteorological conditions
(e.g., wind).
1.4.2 Assessment of Human Exposure
Multiple techniques can be used to assign exposure for epidemiologic studies, including
evaluation of data from central site monitoring, personal SO2 monitoring, and using
various modeling approaches (Section 3.3). Each has strengths and limitations, as
summarized in Table 3-1. Central site monitors are intended to represent population
exposure, in contrast to near-source monitors, which are intended to capture high
concentrations in the vicinity of a source and are not typically used as the primary data
source in urban-scale epidemiologic studies. Central site monitors may provide a
continuous record of SO2 concentrations over many years, but they do not fully capture
the relatively high spatial variability in SO2 concentration across an urban area. Personal
SO2 monitors can capture the study participants' activity-related exposure across different
microenvironments, but low ambient SO2 concentrations often result in a substantial
fraction of the samples below the limit of detection for averaging times of 24 hours or
less. The time and expense involved to deploy personal monitors make them suitable for
panel epidemiologic studies and exposure validation studies. Models can be used to
estimate exposure for individuals and large populations when personal exposure
measurements are unavailable. Modeling approaches include estimating concentration
surfaces and time-activity patterns and running microenvironment-based models that
combine air quality data with time-activity patterns. In general, more complex
approaches provide more detailed exposure estimates but require additional input data,
assumptions, and computational resources. Depending on the model type, there is the
potential for bias and reduced precision due to model misspecification, missing sources,
smoothing of concentration gradients, and complex topography. Evaluation of model
results helps demonstrate the suitability of that approach for particular applications.
New studies of the relationship between indoor and outdoor SO2 concentrations have
focused on publicly owned buildings rather than residences (Section 3.4.1.2). The results
of these studies are consistent with results of previous studies showing that
indoor:outdoor ratios and slopes cover an extremely wide range, from near zero to near
one. Differences in results among studies are due to building characteristics (e.g., forced
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ventilation, building age, and building type), personal activities such as opening windows
and doors, and SO2 measurement limitations. When reported, correlations between indoor
and outdoor concentrations were relatively high (>0.75), suggesting that variations in
outdoor concentration drive indoor concentrations, particularly considering the lack of
indoor SO2 sources. These high correlations were observed across seasons and
geographic locations. The bulk of the evidence for personal-ambient SO2 relationships
was available at the time of the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) and
showed a wide range of correlations between ambient concentration and personal
exposure, in part due to a large fraction of samples below the method detection limit
(MDL) in several studies (Section 3.4.1.3). When nearly all of the personal samples are
below the MDL, no correlation can be observed. However, when the bulk of the personal
samples are above the MDL, personal exposure is moderately correlated with ambient
concentration.
"Exposure error" refers to the bias and uncertainty associated with using concentration
metrics to represent the actual exposure of an individual or population lYLipfert and
Wvzga. 1996) Section 3.21. Exposure error has two components: (1) exposure
measurement error derived from uncertainty in the metric being used to represent
exposure, and (2) use of a surrogate target parameter of interest in the epidemiologic
study in lieu of the true exposure, which may be unobservable (Section 3.2.1). Factors
that could contribute to error in estimating exposure to ambient SO2 include
time-location-activity patterns, spatial and temporal variability in SO2 concentrations, and
proximity of populations to monitoring sites and sources (Section 3.4.2V Activity patterns
vary both among and within individuals, resulting in corresponding variations in
exposure across a population and overtime. Variation in SO2 concentrations among
different microenvironments means that the amount of time spent in each location, as
well as exertion level, will influence an individual's exposure to ambient SO2. Time spent
in different locations has also been found to vary by age, with younger and older age
groups spending a greater percentage of time outdoors than adults of typical working age
(18-64 years). These variations in activity pattern contribute to differences in exposure
and, if uncharacterized, introduce error into population-averaged exposure estimates.
Uncharacterized spatial and temporal variability in SO2 concentrations can contribute to
exposure error in epidemiologic studies. SO2 has low to moderate spatial correlations
among ambient monitoring sites across urban geographic scales; thus, using central site
monitor data for epidemiologic exposure assessment introduces exposure error into the
resulting health effect estimate. Spatial variability in the magnitude of concentrations
may affect cross-sectional and large-scale cohort studies by assigning exposures from one
or a small number of sites that do not capture all of the spatial variability within a city.
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This issue may be less important for time-series studies, which rely on day-to-day
temporal variability in concentrations to evaluate health effects.
Proximity of populations to ambient monitoring sites may influence how well human
exposure is represented by measurements at the monitors, although factors other than
distance play an important role as well. While many SO2 monitoring sites are located
near dense population centers, other sites are located near sources and may not fully
represent SO2 concentrations experienced by populations in epidemiologic studies. Use
of these near-source monitoring sites introduces exposure error into health effect
estimates, although this error can be mitigated by using average concentrations across
multiple sites in an urban area.
Exposure to copollutants, such as other criteria pollutants, may result in confounding of
health effect estimates. For SO2, daily concentrations generally exhibit low correlations
(median <0.4) with other daily NAAQS pollutant concentrations at collocated monitors
(Figure 3-5. Section 3.4.3V However, a wide range of copollutant correlations has been
observed across different monitoring sites, from moderately negative to moderately
positive. In studies in which daily SO2 correlations with NO2 and CO were observed to be
high, it is possible the data were collected before a rule to reduce sulfur content in diesel
fuel (66 FR 5002) took effect in 2006 and 2007. The minority of sites with stronger
correlations may introduce a greater degree of confounding into epidemiologic results.
A similar impact is expected for epidemiologic studies of long-term SO2 exposure, which
also report a wide range of copollutant correlations.
Exposure error can influence epidemiologic study results by biasing effect estimates
either toward or away from the null and widening confidence intervals beyond the
nominal coverage that would be produced if the true exposure had been used
(Section 3.4.4). The exposure error varies according to the study design, especially
regarding the study's spatial and temporal aspects. For example, in time-series and panel
studies, low personal-ambient correlations tend to bias the effect estimate toward the null,
while spatial variation in personal-ambient correlations across an urban area contributes
to widening of the confidence interval around the effect estimate beyond the nominal
coverage of the confidence intervals that would be produced if the true exposure had been
used. For long-term studies, bias of the health effect estimate may occur in either
direction depending on whether the monitor is over- or underestimating exposure for the
population of interest. In all study types, use of central site monitors is expected to
decrease precision of the health effect estimate because spatial variation in
personal-ambient correlations across an urban area contributes to widening of the
confidence interval around the effect estimate beyond the nominal coverage that would
be produced if the true exposure had been used.
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Choice of exposure estimation method also influences the impact of exposure error on
epidemiologic study results. Central site monitors offer a convenient source of time-series
data, but fixed-site measurements do not account for the effects of spatial variation in
SO2 concentration, differences between indoor and outdoor exposure to ambient SO2, and
varying activity patterns on personal exposure to SO2. Personal exposure measurements,
such as those made in panel epidemiologic studies, provide accurate and specific
exposure estimates, but sample size is often small and only a limited set of health
outcomes can be studied. Modeled concentrations or exposures offer alternatives to
measurements, with the advantage of estimating exposures over a wide range of scales,
populations, and scenarios, particularly for locations lacking monitoring data. However,
depending on the model type, there is the potential for bias and reduced precision due to
model misspecification, missing sources, smoothing of concentration gradients, and
complex topography. Model estimates are most informative when compared to an
independent set of measured concentrations or exposures. The various sources of
exposure error and their potential impact are considered in the evaluation of
epidemiologic study results in this ISA.
This ISA summarizes information on the dosimetry of inhaled SO2, including the
processes of absorption, distribution, metabolism, and elimination, as well as information
on the mode of action of inhaled SO2, covering the processes by which inhaled SO2
initiates a cascade of molecular and cellular responses and the organ-level responses that
follow. (Chapter 4). Together, these sections provide the foundation for understanding
how exposure to inhaled SO2 may lead to health effects. This understanding may provide
biological plausibility for effects observed in the epidemiologic studies.
Dosimetry of inhaled SO2 refers to the measurement or estimation of the amount of SO2
and its reaction products reaching and/or persisting at specific sites within the respiratory
tract and systemically after exposure. Factors affecting the transport and fate of SO2 in
the respiratory tract include respiratory tract morphology, respiratory functional
parameters, and physicochemical properties of SO2 and of epithelial lining fluid (ELF).
Health effects may be due to inhaled SO2 or its chemical reaction products, including
sulfite and S-sulfonates. Few studies have investigated SO2 dosimetry since the 2008 ISA
for Sulfur Oxides (U.S. EPA. 2008d). with most studies conducted prior to the 1982
AQCD (U.S. EPA. 1982a) and the 1986 Second Addendum (U.S. EPA. 1986b).
1.5
Dosimetry and Mode of Action of Sulfur Dioxide
1.5.1
Dosimetry of Inhaled Sulfur Dioxide
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Because SO2 is highly soluble in water, it is readily absorbed in the nasal passages of
both humans and laboratory animals under resting conditions (Section 4.2.2). 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 that during
exercise. With increasing physical activity, there is an increase in ventilatory rate and a
shift from nasal to oronasal breathing, resulting in greater SO2 penetration into the lower
respiratory tract. Even at rest, differences have been observed by age, sex, disease status,
and body mass index in the fraction of oral versus nasal breathing (Section 4.1.2V
Children inhale a larger fraction of air through their mouth than adults, and males tend
inhale a larger fraction of air through their mouth than females (across all ages).
Individuals with allergies or upper respiratory infections experience increased nasal
resistance, and thus, increased fraction of oral breathing. Obesity, especially in boys, also
contributes to increased nasal resistance and an increased oral fraction of breathing
relative to normal weight children. Due to their increased amount of oral breathing, these
individuals may be expected to have greater SO2 penetration into the lower respiratory
tract than healthy, normal weight adults. Children may also be expected to have a greater
intake dose of SO2 per body mass than adults.
Following absorption in the respiratory tract, SO2 rapidly forms a mixture of bisulfite and
sulfite, with the latter predominating. As much as 15-18% of the absorbed SO2 may be
desorbed and exhaled following cessation of exposure. Although some SO2 products
rapidly move from the respiratory tract into the blood and are distributed about the body,
experiments using radiolabeled 35S indicate that the majority of sulfur in SC>2-derived
products in the body at any given time following exposure is found in the respiratory tract
and may be detected there for up to a week following inhalation (Section 4.2.3V
The distribution and clearance of inhaled SO2 from the respiratory tract may involve
several intermediate chemical reactions and transformations, particularly the formation of
sulfite and S-sulfonates. Sulfite is metabolized into sulfate, primarily in the liver, which
has higher sulfite oxidase levels than the lung or other body tissues (Section 4.2.4V
Sulfite oxidase activity is highly variable among species with liver sulfite oxidase activity
in rats being 10-20 times greater than in humans. Urinary excretion of sulfate is rapid
and proportional to the concentration of SO2 products in the blood (Section 4.2.5).
S-sulfonates are cleared more slowly from the circulation with a clearance half-time of
days.
Sulfite levels in the body are predominately influenced by endogenous production and
ingestion of sulfite in food (Section 4.2.6). The primary endogenous contribution of
sulfite is from the catabolism of sulfur-containing amino acids (namely, cysteine and
methionine). Endogenous sulfite from ingested sulfur-containing amino acids far exceeds
exogenous sulfite from ingestion of food additives [by 140 and 180 times in adult
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(19-50 years) females and males, respectively, and by 500 times or more in young
children (1-3 years)]. Endogenous sulfite production is two or more orders of magnitude
higher than inhalation-derived sulfite levels for both children and adults, even for full day
exposures to 75 ppb SO2 (the level of the 1-hour NAAQS). Ingestion rates of sulfite
added to foods vary widely; however, in general, sulfite ingestion is expected to exceed
sulfite intake from inhalation in adults and children even for full day exposures to 75 ppb
SO2. However, inhalation-derived SO2 products accumulate in respiratory tract tissues,
whereas sulfite and sulfate from ingestion or endogenous production do not.
1.5.2 Mode of Action of Inhaled Sulfur Dioxide
Mode of action refers to a sequence of key events, endpoints, and outcomes that result in
a given toxic effect. The mode of action discussion in Section 43 of this ISA updates the
basic concepts derived from the SO2 literature presented in the 1982 AQCD (U.S. EPA.
1982a) and the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) and introduces the recent
relevant literature. The main effects of SO2 inhalation are seen at the sites of absorption
(i.e., the respiratory tract) and include (1) activation of sensory nerves in the respiratory
tract resulting in a neural reflex response, (2) injury to airway mucosa, and (3) increased
airway hyperreactivity and allergic inflammation. Effects outside the respiratory tract
may occur at very high concentrations of inhaled SO2.
Reactive products formed as a result of SO2 inhalation are responsible for a variety of
downstream key events, which may include activation of sensory nerves in the
respiratory tract, release of inflammatory mediators, and modulation of allergic
inflammation or sensitization. These key events may collectively lead to several
endpoints, including bronchoconstriction and airway hyper-responsiveness (AHR).
A characteristic feature of individuals with asthma is an increased propensity of their
airways to narrow in response to bronchoconstrictive stimuli relative to nonatopic
individuals without asthma. Thus, bronchoconstriction is characteristic of an asthma
attack. However, individuals without asthma may also experience bronchoconstriction in
response to SO2 inhalation; generally this occurs at higher concentrations (>1,000 ppb)
than in an individual with asthma. Additionally, SO2 exposure may increase airway
responsiveness to subsequent exposures of other stimuli such as allergens or
methacholine. These pathways may be linked to the epidemiologic outcome of asthma
exacerbation.
The strongest evidence for the mode of action for respiratory effects following short-term
exposure comes from controlled human exposure studies. SO2 exposure resulted in
increased airway resistance due to bronchoconstriction in in adults, both with and without
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asthma. In adults without asthma, this response occurred primarily as a result of
activation of sensory nerves in the respiratory tract resulting in neural reflex responses
(Section 4.3.1). This is mediated by cholinergic parasympathetic pathways involving the
vagus nerve. However, in adults with asthma, evidence indicates that the response is only
partially due to vagal pathways and that inflammatory mediators such as histamine and
leukotrienes also play an important role. Studies in experimental animals also
demonstrate that SO2 exposure activates reflexes that are mediated by cholinergic
parasympathetic pathways involving the vagus nerve. However, noncholinergic
mechanisms (i.e., neurogenic inflammation) may also be involved.
Evidence demonstrates that SO2 exposure modulates allergic inflammatory responses
(Section 4.3.2). Enhancement of allergic inflammation (i.e., leukotriene-mediated
increases in numbers of sputum eosinophils) has been observed in adults with asthma
who were exposed for 10 minutes to 750 ppb SO2. In an animal model of allergic airway
disease, repeated exposure to 2,000 ppb SO2 led to an enhanced inflammatory response,
including allergic inflammation. In naive animals, repeated exposure to SO2 (as low as
100 ppb) over several days promoted allergic sensitization, inflammation, and AHR when
animals were subsequently sensitized and challenged with an allergen. Thus, allergic
inflammation and increased airway responsiveness may also link short-term SO2
exposure to asthma exacerbation.
Evidence for the mode of action for respiratory effects due to long-term SO2 exposure
comes from studies in both naive and allergic experimental animals, which demonstrate
allergic sensitization, allergic inflammation, AHR, and morphologic changes suggestive
of airway remodeling following exposure to SO2 (i.e., 2,000 ppb) over several weeks
(Section 4.3.3). These changes, however, are mild compared to histopathological
changes, such as mucous cell metaplasia and intramural fibrosis, which are generally
observed following chronic exposure of naive animals to SO2 concentrations of 10 ppm
(10,000 ppb) and higher. However, in allergic animals, exposure to SO2 over several
weeks leads to morphologic responses indicative of airway remodeling and to AHR.
Thus, repeated exposure to SO2 may lead to the development of allergic airway disease,
which shares many features with asthma, and to the worsening of the allergic airway
disease. The development of AHR may link long-term exposure to SO2 to the
epidemiologic outcome of new onset asthma.
Although there is some evidence that SO2 inhalation results in extrapulmonary effects,
there is uncertainty regarding the mode of action underlying these responses
(Section 4.3.4). Evidence from controlled human exposure studies points to SO2
exposure-induced activation/sensitization of neural reflexes, possibly leading to altered
heart rate (HR) or heart rate variability (HRV). Evidence also points to transport of sulfite
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into the circulation. Sulfite is highly reactive and may be responsible for redox stress
(possibly through autoxidation or peroxidase-mediated reactions to produce free radicals)
in the circulation and extrapulmonary tissues. However, this stress is likely to occur only
at very high SO2 concentrations or during prolonged exposures because circulating sulfite
is efficiently metabolized to sulfate in a reaction catalyzed by hepatic sulfite oxidase.
1.6 Health Effects of Sulfur Dioxide
This ISA evaluates relationships between an array of health effects and short-term and
long-term exposures to SO2 as examined in epidemiologic, controlled human exposure,
and animal toxicological studies. Short-term exposures are defined as those with
durations of minutes up to 1 month, with most studies examining effects related to
exposures in the range of 1 hour to 1 week. Long-term exposures are defined as those
with durations of more than 1 month to years. Drawing from the health effects evidence
described in detail in Chapter 5. information on dosimetry and modes of action presented
in Chapter 4. as well as issues regarding exposure assessment and potential confounding
described in Chapter 3 and Section L4, the subsequent sections and Table 1-1 present the
key evidence that informed the causal determinations for relationships between SO2
exposure and health effects.
1.6.1 Respiratory Effects
1.6.1.1 Respiratory Effects Associated with Short-Term Exposure to Sulfur Dioxide
Strong scientific evidence indicates that there is a causal relationship between short-term
SO2 exposure and respiratory morbidity, particularly in individuals with asthma, which is
consistent with the conclusions of the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d).
This determination is based on the consistency of findings within disciplines, coherence
among evidence from controlled human exposure, epidemiologic, and toxicological
studies, and biological plausibility for effects specifically related to asthma exacerbation
(Table 5-21).
This conclusion is primarily based on controlled human exposure studies included in the
2008 SOx ISA (U.S. EPA. 2008d) that showed lung function decrements and respiratory
symptoms in adults with asthma exposed to SO2 for 5-10 minutes under increased
ventilation conditions; no new controlled human exposure studies have been conducted to
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evaluate the effect of SO2 on respiratory morbidity among individuals with asthma. These
studies consistently demonstrated that individuals with asthma experience a moderate or
greater decrement in lung function, defined as a >100% increase in specific airway
resistance (sRaw) or >15% decrease in forced expiratory volume in 1 sec (FEVi),
frequently accompanied by respiratory symptoms, following peak exposures of
5-10 minutes with elevated ventilation rates at concentrations of 400-600 ppb
(Section 5.2.1.2). A fraction of individuals with asthma (-5-30%) was observed in these
studies to have moderate decrements in lung function at lower SO2 concentrations
(200-300 ppb; Table 5-2). Lung function decrements at these lower concentrations are
less likely to be accompanied by respiratory symptoms. Some studies have evaluated the
influence of asthma severity on response to SO2, but the most severe asthmatics have not
been tested, and thus, their response is unknown. Adults with moderate to severe asthma
demonstrated larger absolute changes in lung function during exercise in response to SO2
than adults with mild asthma, although this difference was attributed to a larger response
to the exercise component of the protocol rather than to SO2 itself. While adults with
moderate to severe asthma may have similar responses to SO2 as healthy adults (although
at lower concentrations), they have less reserve capacity to deal with an insult compared
with individuals with mild asthma; therefore, the impact of SC>2-induced decrements in
lung function is greater in individuals with asthma than healthy adults. Although there are
no laboratory studies of children exposed to SO2, a number of studies have evaluated
airway responsiveness of children and adults to a bronchoconstrictive stimulus. These
studies indicate that school-aged children, particularly boys and perhaps obese children,
are expected to have greater responses (i.e., greater lung function decrements) following
exposure to SO2 than adolescents and adults.
These findings are consistent with the current understanding of dosimetry and modes of
action (Section 1.5). Due to their increased fraction of oral breathing, individuals with
asthma may be expected to have greater SO2 penetration into the lower respiratory tract
than healthy adults. Reactive products formed as a result of SO2 inhalation, particularly
sulfites and S-sulfonates, are responsible for a variety of downstream key events, which
may include activation of sensory nerves in the respiratory tract resulting in a neural
reflex response, release of inflammatory mediators, and modulation of allergic
inflammation. These key events may lead to several endpoints including
bronchoconstriction and AHR, resulting in the outcome of asthma exacerbation.
Epidemiologic evidence also provides support for a causal relationship, including
additional studies that add to the evidence provided by the 2008 ISA for Sulfur Oxides
(U.S. EPA. 2008d). Studies of asthma hospital admissions and emergency department
(ED) visits report positive associations with short-term SO2 exposures, particularly for
children (i.e., <18 years of age), with additional evidence from studies that examine
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potential copollutant confounding that associations are generally unchanged in
copollutant models involving PM and other criteria pollutants (Section 5.2.1.2.
Figure 5-2). There is also some supporting evidence for positive associations between
short-term SO2 exposures and respiratory symptoms among children with asthma
(Section 5.2.1.2). Epidemiologic evidence of associations between short-term SO2
exposures and lung function or respiratory symptoms among adults with asthma is less
consistent (Section 5.2.1.2). Epidemiologic studies of cause-specific mortality that report
consistent positive associations between short-term SO2 exposures and respiratory
mortality provide support for a potential continuum of effects (Section 5.2.1.8).
There is some support for other SC>2-related respiratory effects including exacerbation of
chronic obstructive pulmonary disease (COPD) in individuals with COPD and other
respiratory effects including respiratory infection, aggregated respiratory conditions, and
respiratory mortality in the general population (Section 5.2.1.3. Section 5.2.1.4.
Section 5.2.1.5. and Section 5.2.1.6). The limited and inconsistent evidence for these
nonasthma-related respiratory effects does not contribute heavily to the causal
determination.
1.6.1.2 Respiratory Effects Associated with Long-Term Exposure to Sulfur Dioxide
Overall, the evidence is suggestive of, but not sufficient to infer, a causal relationship
between long-term SO2 exposure and respiratory effects, mainly the development of
asthma in children (Section 5.2.2). This represents a change from the conclusion in the
2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) that the evidence was "inadequate to infer
a causal association." There is a limited number of recent longitudinal epidemiologic
studies that evaluate associations between asthma incidence among children and
long-term SO2 exposures, with the overall body of evidence lacking consistency.
The evidence from longitudinal studies showing increases in asthma incidence is
coherent with findings from animal toxicological studies that provide a pathophysiologic
basis for the development of asthma. In naive newborn animals, repeated SO2 exposure
over several weeks resulted in immune responses and airway inflammation, key steps in
allergic sensitization. In allergic newborn animals, studies with several days or several
weeks of repeated SO2 exposure found enhanced airway inflammation and some evidence
of airway remodeling and AHR. The combined epidemiologic and animal toxicological
evidence provides support for an independent effect of long-term exposure to SO2 on the
development of asthma in children, but key uncertainties remain, including exposure
measurement error and the potential for copollutant confounding. Some evidence of a
link between long-term exposure to SO2 and respiratory symptoms and/or respiratory
allergies among children further supports a possible relationship between long-term SO2
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exposure and the development of asthma. Details of the causal determination are
provided in Table 5-24.
1.6.2 Health Effects beyond the Respiratory System
1.6.2.1 Cardiovascular Effects Associated with Short-Term Exposure to Sulfur
Dioxide
Overall, the available evidence is inadequate to infer the presence or absence of a causal
relationship between short-term exposure to SO2 and cardiovascular health effects
(Table 5-34. Section 5.3. IV This conclusion is consistent with that of the 2008 ISA for
Sulfur Oxides (U.S. EPA. 2008d). which concluded "the evidence as a whole is
inadequate to infer a causal relationship." Although multiple epidemiologic studies report
positive associations between short-term exposure to SO2 and a variety of cardiovascular
outcomes, the results are inconsistent across the specific cardiovascular outcomes, and
the associations are generally attenuated after copollutant adjustment. There is some
experimental evidence in humans and animals for S02-induced effects on the autonomic
nervous system and inflammation and other effects in tissues distal to the absorption site.
However, the limited and inconsistent evidence from the available experimental studies
does not demonstrate potentially biologically plausible mechanisms for, and is not
coherent with, cardiovascular effects such as triggering a myocardial infarction. Evidence
for other cardiovascular and related metabolic effects is inconclusive.
1.6.2.2 Cardiovascular Effects Associated with Long-Term Exposure to Sulfur
Dioxide
Overall, the evidence is inadequate to infer the presence or absence of a causal
relationship between long-term exposure to SO2 and cardiovascular health effects
Table 5-35. Section 5.3.2). The relationship between long-term SO2 exposure and
cardiovascular outcomes was not evaluated in the 2008 ISA for Sulfur Oxides (U.S. EPA.
2008d). Despite a number of epidemiologic studies that report positive associations
between long-term exposure to SO2 concentrations and cardiovascular disease and stroke,
the evidence for any one endpoint is limited and inconsistent. Exposure measurement
error and the potential for copollutant confounding are uncertainties in the interpretation
of the evidence. Additionally, there is insufficient experimental evidence to provide
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coherence or biological plausibility for an independent effect of long-term exposure to
SO2 on cardiovascular health.
1.6.2.3 Reproductive and Developmental Effects
Overall the evidence is inadequate to infer the presence or absence of a causal
relationship between exposure to SO2 and reproductive and developmental outcomes
(Table 5-38. Section 5.4). consistent with the conclusion reached in the 2008 ISA for
Sulfur Oxides (U.S. EPA. 2008d).
There are several recent well-designed, well-conducted studies that indicate an
association between SO2 and reproductive and developmental health outcomes, including
fetal growth metrics, preterm birth, birth weight, and fetal and infant mortality. However,
a number of uncertainties are associated with the observed relationship between exposure
to SO2 and birth outcomes, such as timing of exposure windows, exposure error, and
spatial and temporal heterogeneity. Few studies have examined other health outcomes,
such as fertility, effects on pregnancy (e.g., pre-eclampsia, gestational diabetes), and
developmental effects, and there is little coherence or consistency among epidemiologic
and toxicological studies for these outcomes. There is limited toxicological evidence at
relevant dose ranges of SO2, making it difficult to evaluate the potential modes of action
for reproductive and developmental effects of ambient SO2. Studies published since the
2008 SOx ISA (U.S. EPA. 2008d) have not substantially reduced any of the uncertainties
identified in the previous ISA, including exposure measurement error and the potential
for copollutant confounding; therefore, the evidence is inadequate to infer the presence or
absence of a causal relationship between exposure to SO2 and reproductive and
developmental outcomes.
1.6.2.4 Total Mortality Associated with Short-Term Exposure to Sulfur Dioxide
Multicity studies evaluated since the completion of the 2008 ISA for Sulfur Oxides
continue to provide consistent evidence of positive associations between short-term SO2
exposures and total mortality (Section 5.5.1). Although the body of evidence is larger
than at the time of the last review, key uncertainties and data gaps still remain, which
contribute to the conclusion that the evidence for short-term SO2 exposures and total
mortality is suggestive of, but not sufficient to infer, a causal relationship (Table 5-41).
This conclusion is consistent with that reached in the 2008 SOx ISA (U.S. EPA. 2008d).
Overall, recent multicity studies evaluated have further informed key uncertainties and
data gaps in the S02-mortality relationship identified in the 2008 SOx ISA including
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confounding, modification of the SCh-mortality relationship, potential seasonal
differences in SCh-mortality associations, and the shape of the S02-mortality C-R
relationship. However, questions remain regarding whether SO2 has an independent
effect on mortality, and these lingering questions can be attributed to the limited number
of studies that examined potential copollutant confounding, the relative lack of
copollutant analyses with PM2 5, and the evidence indicating attenuation of S02-mortality
associations in copollutant models with NO2 and PM10. Additionally, a biological
mechanism has not been characterized to date that could lead to mortality as a result of
short-term SO2 exposures.
1.6.2.5 Total Mortality Associated with Long-Term Exposure to Sulfur Dioxide
The overall evidence is inadequate to infer the presence or absence of a causal
relationship between long-term exposure to SO2 and total mortality among adults
(Table 5-43. Section 5.5.2). consistent with the conclusion reached in the 2008 ISA for
Sulfur Oxides (U.S. EPA. 2008d). Recent evidence is generally consistent with the
evidence included in the ISA, although some recent cohort epidemiologic studies provide
evidence for improved consistency in the association between long-term exposure to SO2
and both respiratory and total mortality. However, none of these recent studies help to
resolve the uncertainties identified in the 2008 SOx ISA related to exposure measurement
error, copollutant confounding, or the geographic scale of the analysis.
1.6.2.6 Cancer
The overall evidence for long-term SO2 exposure and cancer is inadequate to infer the
presence or absence of a causal relationship (Table 5-44. Section 5.6). the same
conclusion reached in the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d). Recent studies
include evidence on lung cancer as well as other cancer types. Although some studies of
SO2 concentrations and lung cancer mortality have reported null results, other studies that
included various cofounders and copollutants reported positive associations. Positive
associations were also observed in a study of SO2 concentrations and bladder cancer
mortality but not in ecological studies of bladder cancer incidence. Limited supportive
evidence for mode of action is available from genotoxicity and mutagenicity studies, but
animal toxicological studies provide no coherence with epidemiologic findings.
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Table 1-1 Key evidence contributing to causal determinations for sulfur dioxide exposure and health effects
evaluated in the current draft Integrated Science Assessment for Sulfur Oxides.
Health Effect Category3 and Causal Determination
SO2 Concentrations
Associated with Effects
Respiratory Effects and Short-Term Exposure (Section 5.2.1): Causal relationship
No change in causal determination from the 2008 SOx ISA (U.S. EPA. 2008d): new evidence is consistent with previous determination.
Key evidence
(Table 5-21)
Strongest evidence is for effects on asthma exacerbation. There is consistent evidence from multiple
high-quality controlled human exposure studies ruling out chance, confounding, and other biases.
These studies show decreased lung function and increased respiratory symptoms following peak
exposures of 5-10 min in exercising individuals with asthma. Additional consistent evidence from
multiple high quality epidemiologic studies at relevant SO2 concentrations shows an increase in asthma
hospital admissions and ED visits in single- and multicity studies and in studies examining individuals of
all ages, including children and older adults. These associations are generally unchanged in copollutant
models involving PM and other criteria pollutants. Additionally, there is some supporting epidemiologic
evidence of associations with respiratory symptoms among children with asthma. Evidence is available
for activation of sensory nerves in the respiratory tract resulting in a neural reflex and/or inflammation
leading to bronchoconstriction and allergic inflammation leading to increased airway responsiveness.
Enhanced allergic sensitization, allergic inflammation, and airway responsiveness was observed in
guinea pigs exposed to SO2 repeatedly over several days and subsequently sensitized and challenged
with an allergen. This evidence represents key events orendpoints in the proposed mode of action
linking short-term SO2 exposure and asthma exacerbation.
Overall study ambient
means:
Controlled human exposure
studies of decreased lung
function: 200-600 ppb, with
a subset analysis of
responders showing
statistically significant
responses at 300 ppb
Controlled human exposure
studies of increased
respiratory symptoms:
400-1,000 ppb
Epidemiologic studies:
1-h max: 9.6-11 ppb
24-h avg: 1.0-37 ppb
Animal studies:
100 ppb
Respiratory Effects and Long-Term Exposure (Section 5.2.2): Suggestive of. but not sufficient to infer, a causal relationship
Change in causal determination from the 2008 SOx ISA (U.S. EPA. 2008d) (inadequate to infer a causal relationship) due to new, but limited, evidence.
Key evidence15
(Table 5-24)
Evidence from epidemiologic studies is generally supportive but not entirely consistent for increases in
asthma incidence and prevalence related to SO2 exposure. Uncertainty remains regarding potential
copollutant confounding, so chance, confounding, and other biases cannot be ruled out. The limited
animal toxicological evidence provides biological plausibility and coherence across lines of evidence.
There is some evidence for a mode of action involving inflammation and allergic sensitization.
Overall epidemiologic study
ambient means:
2-4 ppb
Animal toxicological studies:
2,000 ppb
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Table 1-1 (Continued): Key evidence contributing to causal determinations for sulfur dioxide exposure and health
effects evaluated in the current draft Integrated Science Assessment for Sulfur Oxides.
SO2 Concentrations
Health Effect Category3 and Causal Determination Associated with Effects
Cardiovascular Effects and Short-Term Exposure (Section 5.3.1) Inadequate to infer a causal relationship
No change in causal determination from the 2008 SOx ISA (U.S. EPA. 2008d); new evidence is consistent with previous determination.
Key evidence15 There is some evidence of increased hospital admissions and ED visits among adults for IHD, Ml, and Overall epidemiologic study
(Table 5-34) a" CVD, coherence with ST-segment depression in adults with pre-existing coronary heart disease, and ambient 24-h avg means:
increased risk of cardiovascular mortality. However, there is inconsistency in results across outcomes, <| 2-30 ppb
and the associations are generally attenuated after copollutant adjustment. There is insufficient
evidence from epidemiologic panel studies and experimental studies for clinical cardiovascular effects
and to identify key events in a mode of action linking short-term SO2 exposure and cardiovascular
effects.
Cardiovascular Effects and Long-Term Exposure (Section 5.3.2) Inadequate to infer a causal relationship
Not included in the 2008 SOx ISA (U.S. EPA. 2008d).
Results of epidemiologic studies of long-term SO2 concentrations and Ml, CVD, and stroke events are Overall epidemiologic study
limited and inconsistent. There is limited coherence with evidence for cardiovascular mortality and weak ambient means: 1.3-1.7 ppb
evidence to identify key events in a mode of action linking long-term SO2 exposure and cardiovascular
effects.
Reproductive and Developmental Effects and Exposure (Section 5,4) Inadequate to infer a causal relationship
No change in causal determination from the 2008 SOx ISA (U.S. EPA. 2008d); new evidence is consistent with previous determination.
Consistent positive associations are observed with near-birth exposures to SO2 and preterm birth. Overall epidemiologic study
Although limited evidence is available, positive associations are also reported for fetal growth metrics, ambient means: 1.9-13 ppb
birth weight, and infant and fetal mortality. There is insufficient evidence from epidemiologic studies to
support an association of SO2 exposure with detrimental effects on fertility or pregnancy. Thus, the
available studies are of insufficient consistency across outcomes. Recent studies have not reduced
uncertainties identified in the previous ISA, including exposure measurement error and copollutant
confounding. Limited evidence is available for an understanding of key reproductive and developmental
events in mode of action.
Key evidence15
(Table 5-35)
Key evidence15
(Table 5-38)
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Table 1-1 (Continued): Key evidence contributing to causal determinations for sulfur dioxide exposure and health
effects evaluated in the current draft Integrated Science Assessment for Sulfur Oxides.
Health Effect Category3 and Causal Determination
SO2 Concentrations
Associated with Effects
Total Mortality and Short-Term Exposure (Section 5.5.1) Suggestive of. but not sufficient to infer, a causal relationship
No change in causal determination from the 2008 SOx ISA (U.S. EPA. 2008d): new evidence is consistent with previous determination.
Key evidence15
(Table 5-41)
There is consistent epidemiologic evidence from multiple high-quality studies at relevant SO2
concentrations demonstrating increases in mortality in multicity studies conducted in the U.S., Canada,
Europe, and Asia. There is limited coherence and biological plausibility with cardiovascular and
respiratory morbidity evidence and uncertainty regarding a biological mechanism that would explain the
continuum of effects leading to S02-related mortality; thus, chance, confounding, and other biases
cannot be ruled out.
Overall epidemiologic study
ambient 24-h avg means:
U.S., Canada, South
America, Europe:
0.4-28 ppb
Asia:
0.7->200 ppb
Total Mortality and Long-Term Exposure (Section 5.5.2) Inadeguate to infer a causal relationship
No change in causal determination from the 2008 SOx ISA (U.S. EPA. 2008d): new evidence is consistent with previous determination.
Key evidence15
(Table 5-43)
Some epidemiologic studies report positive associations, but results are not entirely consistent, with
some studies reporting null associations. Additionally, there is no evidence for associations between
SO2 exposure and long-term respiratory or cardiovascular health effects to support an association with
mortality from these causes.
Overall epidemiologic study
ambient means:
1.6-24 ppb
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Table 1-1 (Continued): Key evidence contributing to causal determinations for sulfur dioxide exposure and health
effects evaluated in the current draft Integrated Science Assessment for Sulfur Oxides.
Health Effect Category3 and Causal Determination
SO2 Concentrations
Associated with Effects
Cancer and Long-Term Exposure (Section 5,6) Inadequate to infer a causal relationship
No change in causal determination from the 2008 SOx ISA (U.S. EPA, 2008d); new evidence is consistent with previous determination.
Key evidence15
(Table 5-44)
Among a small body of evidence, some epidemiologic studies report associations in lung cancer and
bladder cancer mortality. There is also some evidence identifying mutagenesis and genotoxicity as key
events in a proposed mode of action linking long-term SO2 exposure and cancer; however, toxicological
studies provide limited coherence with epidemiologic studies.
Overall epidemiologic study
ambient means: 1.5-28 ppb.
Toxicological studies: 5,000,
10,700, 21,400, 32,100 ppb
CVD = cardiovascular disease; ED = emergency department; IHD = ischemic heart disease; ISA = Integrated Science Assessment; Ml = myocardial infarction; PM = particulate
matter; SD = standard deviation; S02 = sulfur dioxide; SOx = sulfur oxides.
aA large spectrum of outcomes is evaluated as part of a broad health effect category including physiological measures (e.g., airway responsiveness, lung function), clinical outcomes
(e.g., respiratory symptoms, hospital admissions), and cause-specific mortality. Total mortality includes all nonaccidental causes of mortality and is informed by the nature of the
evidence for the spectrum of morbidity effects (e.g., respiratory, cardiovascular) that can lead to mortality. The sections and tables referenced include a detailed discussion of the
available evidence that informed the causal determinations.
bUncertainties remain for many of the studies included as key evidence. Uncertainty remains in some epidemiologic studies. Exposure assessments in epidemiologic studies using
central site monitors may not fully capture spatial variability of S02. Spatial and temporal heterogeneity may introduce exposure error in long-term effects. For studies of reproductive
and developmental outcomes, associations with exposure to S02 at particular windows during pregnancy are inconsistent between studies. Additionally, although S02 is generally
poorly to moderately correlated with other National Ambient Air Quality Standards pollutants at collocated monitors, copollutant confounding by these and other pollutants cannot be
ruled out.
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1.7
Policy-Relevant Considerations
As described in the Preamble to the ISAs (U.S. EPA. 2015b) and Section 1.1. this ISA
informs policy-relevant issues that are aimed at characterizing quantitative aspects of
relationships between ambient SO2 exposure and health effects and the impact of these
relationships on public health. To that end, this section integrates information from the
ISA to describe SO2 exposure durations and patterns related to health effects, the shape of
the concentration-response relationship, regional heterogeneity in relationships, the
adverse nature of health effects, and at-risk populations and lifestages. In addressing
these policy-relevant issues, this section focuses on respiratory effects associated with
short-term exposures, for which the evidence indicates there is a causal relationship.
1.7.1 Durations and Lag Structure of Sulfur Dioxide Exposure Associated
with Health Effects
Effects have been observed in controlled human exposure studies after SO2 exposures as
brief as 5-10 minutes. Consistent associations between SO2 concentrations and asthma
hospital admissions and ED visits that are generally unchanged in copollutant models
have been demonstrated in epidemiologic studies using daily exposure metrics (24-h avg
and 1-h daily max), although the observed effects could be related to very short duration
(5-10 minutes) peak exposures experienced during the day.
Regarding the lag in effects, the findings from controlled human exposure studies provide
evidence of a rapid onset of effects. The limited number of epidemiologic studies that
examined lag structures reported associations within the first few days of exposure.
1.7.2 Concentration-Response Relationships and Thresholds
Characterizing the shape of concentration-response relationships for health effects
associated with SO2 exposure aids in quantifying the public health impact of SO2
exposure. A key issue is often whether the relationship is linear across the full range of
ambient concentrations or whether there are deviations from linearity, and if so, at what
concentrations they occur. Another important issue is the evidence regarding potential
thresholds for key effects. Such thresholds may indicate exposures below which adverse
health outcomes are not elicited. Lack of a discernable threshold in the evidence for
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health effects of interest precludes the identification of an exposure level without risk of
those effects.
Results from controlled human exposure studies indicate wide interindividual variability
in response to SO2 exposures, with peak (5 to 10 minutes) exposures at concentrations as
low as 200-300 ppb eliciting lung function decrements in some individuals with asthma.
A clear increase in the magnitude of lung function decrements was observed with
increasing exposure concentrations between 200 and 1,000 ppb during 5-10 minute SO2
exposures. The limited epidemiologic research on concentration-response functions
relating SO2 concentrations to respiratory health morbidity does not provide evidence for
a deviation from linearity or a discernable population-level threshold.
1.7.3 Regional Heterogeneity in Effect Estimates
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) discussed spatial variability in SO2
concentrations and its impact on effect estimates from epidemiologic studies.
Correlations between monitors ranged from very low to very high, suggesting that SO2
concentrations at some monitoring sites may not be highly correlated with the community
average concentration. Of particular concern for SO2 is the predominance of point
sources, resulting in an uneven distribution of SO2 concentrations across an urban area.
Factors contributing to differences among monitoring sites include proximity to sources,
terrain features, and uncertainty regarding the measurement of low SO2 concentrations.
Spatial and temporal variability in SO2 concentrations can contribute to exposure error in
epidemiologic studies, whether such studies rely on central site monitor data or
concentration modeling for exposure assessment. SO2 has low to moderate spatial
correlations between ambient monitoring sites across urban geographic scales; thus, using
central site monitor data for epidemiologic exposure assessment introduces exposure
error into the resulting effect estimate. Spatial variability in the magnitude of
concentrations may affect cross-sectional and large-scale cohort studies by undermining
the assumption that intraurban concentration and exposure differences are less important
than interurban differences. This issue may be less important for time-series studies,
which rely on day-to-day temporal variability in concentrations to evaluate health effects.
Low correlations between monitors contribute to exposure error in time-series studies,
including bias toward the null and wider confidence intervals.
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1.7.4
Public Health Significance
The public health significance of air pollution-related health effects is informed by the
adverse nature of the health effects that are observed, the size of the population exposed
to the air pollutant or affected by the health outcome, and the presence of populations or
lifestages with higher exposure or increased risk of air pollution-related health effects.
1.7.4.1 Characterizing Adversity of Health Effects
Both the World Health Organization (WHO) and the American Thoracic Society (ATS)
have provided guidance in describing what health effects may be considered adverse.
WHO defines health as "the state of complete physical, mental, and social well-being and
not merely the absence of disease or infirmity" (WHO. 1948). By this definition, changes
in health outcomes that are not severe enough to result in a diagnosis of a clinical effect
or condition can be considered adverse if they affect the well-being of an individual. ATS
also has considered a wide range of health outcomes in defining adverse effects.
Distinguishing between individual and population risk, ATS described its view that small
air pollution-related changes in an outcome observed in individuals might be considered
adverse on a population level. This is because a shift in the distribution of population
responses resulting from higher air pollution exposure might increase the proportion of
the population with clinically important effects or at increased risk of a clinically
important effect that could be caused by another risk factor (ATS. 2000). Increases in
ambient SO2 concentrations are associated with a broad spectrum of health effects related
to asthma, including those characterized as adverse by ATS such as ED visits and
hospital admissions.
1.7.4.2 At-Risk Populations and Lifestages for Health Effects Related to Sulfur
Dioxide Exposure
The primary NAAQS are intended to protect public health with an adequate margin of
safety. In so doing, protection is provided for both the population as a whole and those
groups potentially at increased risk for health effects from exposure to the air pollutant
for which each NAAQS is set (Preface to this ISA). Hence, the public health significance
of health effects related to SO2 exposure also is informed by whether specific lifestages
or groups in the population are identified as being at increased risk of S02-related health
effects.
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At-risk populations or lifestages can be characterized by specific biological,
sociodemographic, or behavioral factors, among others. Since the 2008 ISA for Sulfur
Oxides (U.S. EPA. 2008d). the U.S. EPA has used a framework for drawing conclusions
about the role of such factors in modifying risk of health effects of air pollution exposure
[Table III of the Preamble to the ISAs (U.S. EPA. 2015b) I. Similar to the causal
framework, conclusions about at-risk populations are based on judgments of the
consistency and coherence of evidence within and across disciplines (Chapter 6). Briefly,
the evaluation is based on studies that compared exposure or health effect relationships
among groups that differ according to a particular factor (e.g., people with and without
asthma) and studies conducted in a population or animal model with a particular factor or
pathophysiological condition. Where available, information on exposure, dosimetry, and
modes of action is evaluated to assess coherence with health effect evidence and inform
how a particular factor may contribute to SCh-related risk of health effects (e.g., by
increasing exposure, increasing biological effect for a given dose).
There is adequate evidence that people with asthma are at increased risk for SCh-related
health effects (Section 6.3.1). which is consistent with the findings of the 2008 ISA for
Sulfur Oxides (U.S. EPA. 2008d). The conclusions are based on findings for short-term
SO2 exposure and respiratory effects (specifically lung function decrements), for which a
causal relationship has been determined (Section 5.2.1.9). There are a limited number of
epidemiologic studies evaluating S02-related respiratory effects that include stratification
by asthma status, but there is evidence for respiratory-related hospital admissions and
emergency department visits (Section 5.2.1.2). Further support for increased risk in
individuals with asthma is provided by biological plausibility drawn from modes of
action. Children with asthma may be particularly at increased risk relative to adults with
asthma due to their increased responsiveness to methacholine, increased ventilation rates
relative to body mass, and increased proportion of oral breathing, particularly among
boys. Among children in the U.S., asthma is the leading chronic illness (9.5% prevalence)
and largest reason for missed school days.
There is also evidence suggestive of increased risk for children and older adults relative
to other lifestages (Section 6.5.1). Although the 2008 ISA for Sulfur Oxides (U.S. EPA.
2008d) discussed several studies indicating stronger associations between SO2 and
respiratory outcomes for these lifestages, the recent evidence is not entirely consistent
with previous studies. For children, studies comparing S02-associated respiratory
outcomes reported mixed results. For adults, recent evidence generally found similar
associations for S02-related respiratory outcomes or mortality across age groups,
although those over 75 years of age were more consistently at increased risk. In addition,
there was insufficient toxicological evidence regarding the effect of lifestage on
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respiratory responses to SO2 to support observations made across epidemiologic studies
that evaluated lifestage.
1.7.4.3 Summary of Public Health Significance of Health Effects Related to Sulfur
Dioxide Exposure
Several aspects of the current evidence are important for considering the public health
significance of SCh-related health effects. One aspect is adversity of the health effects,
which may include health effects that are clearly adverse such as ED visits and hospital
admissions for asthma and asthma exacerbation. Magnitude of the affected population is
also important. As noted above, in the case of SC>2-related health effects, the potentially
affected population is large, given the number of people with asthma in the U.S.
The roles of co-occurring risk factors or combined higher SO2 exposure and health risk in
influencing the risk of SCh-related health effects is not well understood. The large
proportions of children and older adults in the U.S. population and the high prevalence of
asthma in children may translate into a large number of people affected by SO2, and thus,
magnify the public health impact of ambient SO2 exposure.
1.8 Summary and Health Effects Conclusions
This ISA is a comprehensive evaluation and synthesis of the policy-relevant science
regarding the potential health effects of ambient sulfur oxides, focusing on SO2. The ISA
development process involves review of the scientific literature, selecting and evaluating
relevant studies, and evaluating the weight of evidence to reach causal determinations
regarding the likelihood of independent health effects of SO2. Information is included in
the ISA on sources of SO2, atmospheric chemistry of SO2 and other sulfur-containing
compounds, ambient concentrations of SO2 nationwide and in urban areas, and modeling
approaches for estimating SO2 concentrations. Approaches for characterizing exposure to
ambient SO2, including monitoring and modeling, together with factors affecting ambient
exposure, are described in terms of their potential impact on epidemiologic study results.
Dosimetry of SO2 and potential modes of action are discussed to provide context for the
consideration of potential health effects of SO2, including respiratory effects,
cardiovascular effects, reproductive and developmental effects, cancer, and mortality.
Consistent with the findings of the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d).
studies continue to support the conclusion that there is a causal relationship between
short-term SO2 exposure and respiratory effects. This causal determination is based on
consistency of findings within disciplines, coherence among multiple lines of evidence,
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and biological plausibility indicating that there is a causal relationship between
short-term SO2 exposure and respiratory effects in individuals with asthma. The primary
evidence for this conclusion comes from controlled human exposure studies that showed
lung function decrements and respiratory symptoms in adult individuals with asthma
exposed to SO2 for 5-10 minutes under increased ventilation conditions. Supporting
evidence was provided by epidemiologic studies that reported positive associations
between short-term SO2 exposures and asthma hospital admissions and ED visits that
were generally unchanged in copollutant models involving PM and other criteria
pollutants.
For both long-term exposure and respiratory effects, as well as short-term exposure and
total mortality, the evidence is suggestive of, but not sufficient to infer, a causal
relationship. In both cases, there is some evidence of an association between SO2
exposure and health outcomes, but the evidence is inconsistent and uncertainties remain,
including exposure error and copollutant confounding. The evidence was considered to
be inadequate to infer the presence or absence of a causal relationship for other health
effects, including cardiovascular morbidity (short- and long-term exposure), reproductive
and developmental effects, total mortality (long-term exposure), and cancer. For these
outcome categories, the evidence generally was not consistent across specific outcomes,
showed a potential for copollutant confounding, and was lacking in biological
plausibility.
In considering the effects of SO2 on various populations and lifestages, there is adequate
evidence that people with asthma are at increased risk for SC>2-related health effects, as
well as suggestive evidence for increased risk among children and older adults. The large
proportions of children and older adults in the U.S. population and the high prevalence of
asthma in children may translate into a large number of people affected by SO2, and thus,
magnify the public health impact of ambient SO2 exposure.
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Chapter 2 Atmospheric Chemistry and Ambient
Concentrations of Sulfur Dioxide and Other
Sulfur Oxides
2.1 Introduction
The Clean Air Act requires the U.S. Environmental Protection Agency (U.S. EPA) to
periodically review the air quality criteria and the national ambient air quality standards
(NAAQS) for sulfur oxides (SOx), which is one of the six criteria air pollutants, and
revise the standards as may be appropriate. Sulfur oxides are a group of closely related
sulfur-containing gaseous compounds [e.g., sulfur dioxide (SO2), sulfur monoxide (SO),
disulfur monoxide (S2O), and sulfur trioxide (SO3)], and the NAAQS are currently set
using SO2 as the indicator species. Of the sulfur oxides, SO2 is the most abundant in the
atmosphere, the most important in atmospheric chemistry, and the one most clearly
linked to human health effects (U.S. EPA. 2008d). As in previous reviews, the presence
of sulfur oxides other than SO2 in the atmosphere has not been demonstrated (U.S. EPA.
1996b; HEW. 1969). Therefore, the emphasis in this chapter is on SO2. Note that the
mechanism of particle-phase SO42 formation is briefly described in Section 23_ [for more
detail, see Seinfeld and Pandis (2006). Finlavson-Pitts and Pitts (2000). and other
atmospheric chemistry texts]. The health effects of sulfate aerosol and other
particle-phase sulfur compounds are discussed in the ISA for Particulate Matter (U.S.
EPA. 2009a).
Sulfur dioxide is both a primary gas-phase pollutant (when formed during fuel
combustion) and a secondary pollutant [the product of atmospheric gas- or droplet-phase
oxidation of reduced sulfur compounds (sulfides)]. Fossil fuel combustion is the main
anthropogenic source of primary SO2, while volcanoes and landscape fires (wildfires as
well as controlled burns) are the main natural sources of primary SO2. Industrial chemical
and pulp and paper production, natural biological activity (plants, fungi, and
prokaryotes), and volcanoes are among many sources of reduced sulfur compounds that
ultimately lead, through various oxidation reactions in the atmosphere, to the formation
of secondary SO2.
This chapter provides concepts and findings relating to common sulfur oxides found in
the atmosphere (Section 2.1). source emissions (Section 2.2). atmospheric chemistry and
fate (Section 2.3). measurement methods (Section 2.4). environmental concentrations
(Section 2.5). and atmospheric modeling of sulfur oxides (Section 2.6). It is intended as a
prologue for detailed discussions on exposure and health effects evidence in the
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subsequent chapters, and as a source of information to help interpret that evidence in the
context of relevant ambient concentrations.
2.2 Anthropogenic and Natural Sources of Sulfur Dioxide
This section briefly describes the main U.S. anthropogenic and natural sources of SO2
emissions. Emissions estimates for natural and anthropogenic sulfide emissions for the
U.S. alone are not available in the literature. Therefore, a brief discussion of the sulfur
cycle and estimates of the contribution of sulfides at the global scale, all of which can be
found in the literature, are provided. Section 2.2.1 describes the main categories of
anthropogenic SO2 emissions, while Section 2.2.2 presents the geographic distribution of
SO2 sources across the U.S. The declining trend in anthropogenic SO2 emissions is
discussed in Section 2.2.3. Natural sources of SO2 are discussed in Section 2.2.4. Indirect
production of SO2 through oxidation of reduced sulfur compounds emitted from geologic
and biological sources is discussed in Section 2.2.5.
Sulfur is present to some degree in all fossil fuels, especially coal, and occurs as reduced
organosulfur compounds. Coal also contains sulfur in mineral form (pyrite or other
metallo-sulfur minerals) and in elemental form (Calkins. 1994V Of the most common
types of coal (anthracite, bituminous, subbituminous, and lignite), sulfur content varies
between 0.4 and 4% by mass. Fuel sulfur is almost entirely converted to SO2 (or SO3)
during combustion, making accurate estimates of SO2 combustion emissions possible
based on fuel composition and combustion rates.
The mass of sulfur released into the environment by anthropogenic sources is comparable
to natural sources (Brimblecombe. 2003). In addition to volcanic and other geologic SO2
emissions, naturally occurring SO2 is derived from the oxidation of sulfides emitted by
low flux "area" sources, such as the oceans and moist soils. Anthropogenic emissions of
sulfur are primarily in the form of SO2, emerging from point sources in quantities that
may substantially affect local and regional air quality.
2.2.1 U.S. Anthropogenic Sources
The largest S02-emitting sector within the U.S. is electricity generation based on coal
combustion (4,625,295 tons). The mass of emissions produced by the Fuel Combustion in
Electrical Utilities sector [i.e., coal-fired electric generating units (EGUs)] exceeds those
produced by the next largest sector [the Fuel Combustion—Industrial sector
(i.e., coal-fired boilers)] by nearly a factor of 7, and EGUs emit approximately 2.5 times
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3
as much SO2 as all other sources combined. Figure 2-1 provides a sector comparison of
annual emissions [in tons] found in the U.S. EPA 2011 National Emissions Inventory
(NEI) (U.S. EPA. 2013a).
EMISSIONS SECTOR
COMB = combustion; ELEC = electric; MFG = manufacturing; UTIL = utilities.
Note: "Fuel combustion—Other" includes commercial, institutional, and residential sources.
Source: https://www.epa.aov/air-emissions-inventories/air-pollutant-emissions-trends-data.
Figure 2-1 Sulfur dioxide emissions by sector in tons, 2011.
4 Because EGUs comprise the largest NEI source category, the spatial distribution of
5 SCh-emitting EGUs is presented here (U.S. EPA. 2013a). Most EGU sources are located
6 in the eastern half of the continental U.S., as indicated in Figure 2-2. There is a
7 particularly high concentration of EGUs in the Ohio River valley, upper Midwest, and
8 along the Atlantic coast. Many of the monitoring sites with elevated SO2 concentrations
9 are located in these same areas (Figure 2-11).
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SO2 Tons Emitted from
EGU Facilities
© 100 to 2,000
© > 2,000 to 10,000
Q > 10,000 to 100,000
^ > 100,000 to 150,000
Kilometers
Note: EGU = electric power generating unit; S02 = sulfur dioxide.
Source: https://www.epa.gov/air-emissions-inventories: U.S. EPA (2013a).
Figure 2-2 Distribution of electric power generating unit-derived sulfur
dioxide emissions across the U.S., based on the 2011 National
Emissions Inventory.
1 Industrial fuel combustion is the second largest source nationwide, emitting 675,927 tons
2 per year (tpy), followed by other fuel combustion (218,682 tpy). Miscellaneous (197,555
3 tpy) is the fourth-largest source and includes SO2 emissions by fire used in landscape
4 management and agriculture as well as wildfires (U.S. EPA. 2013a). Wildfires, as a
5 natural source of SO2emissions, are discussed in Section 2.2.4.3.
6 The commercial marine sector falls within the off-highway category (127,134 tpy), after
7 EGUs and Industrial Fuel Combustion U.S. EPA (2013a). Wang et al. (2007 modeled
8 SO2 emissions from commercial marine activity based on a combination of historical
9 shipping data and marine traffic predictions based on port sizes and probable routes using
10 data from 2002. A 200 nautical mile boundary was imposed around the marine, lake, and
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river international borders of the U.S. Thirty-eight percent of emissions were estimated
for the East Coast of the U.S. related to commercial marine shipping. Twenty percent
were estimated for the West Coast, and 26% of emissions were estimated for the Gulf
Coast. Smaller quantities were estimated elsewhere (10% for Alaska, 3% for Hawaii, and
2% for the Great Lakes). Interior waterway activity was not included in the Wang et al.
(2007) paper. In 2010, the International Maritime Organization introduced Emissions
Control Areas (ECA) around U.S., Canadian, and French waters under the International
Convention for the Prevention of Pollution from Ships (Office of Transportation and Air
Quality. 2010). The ECA is a 200 nautical mile buffer around the maritime borders, in
which fuels cannot contain more than 1,000 ppm sulfur as of 2015. The fuel sulfur
regulation was first lowered from 15,000 to 10,000 ppm in 2010. These reductions are
expected to be accomplished by maritime vessels switching fuel sources when they cross
the 200 nautical mile buffer to approach their port. Office of Transportation and Air
Quality (2010) estimates that this reduction in the amount of sulfur in marine fuels used
within the 200 nautical mile buffer results in an 85% reduction in SO2 emissions from the
commercial marine sector.
Monitoring data that can indicate the effects of the ECA on air quality near ports is very
limited. The SLAMS monitoring network used to implement the SO2 NAAQS (discussed
in Section 2.4.1.1) does not include any monitors located at ports. However, as part of its
Clean Air Action effort, the San Pedro Bay Ports in California, operate a network of
ambient monitors at the ports of Los Angeles and Long Beach (the two busiest ports in
the U.S.). The network includes six monitors, four sites in located at the Port of Los
Angeles and two sites located at the Port of Long Beach. A map of the network is
available at http://caap.airsis.com/MapView.aspx. The latest reports from these two ports
show SO2 concentration well below the NAAQS. At the Port of Los Angeles, the 3 year
average of the 99th percentile 1-h daily max for the latest reported period (May
2013-April 2016) ranged from 17 ppb to 23 ppb at the four Port of Los Angeles sites
(Leidos Inc. 2016). At the Port of Long Beach, the 3 year average of the 99th percentile
1-h daily max for the latest reported period (January 2013-December 2015) ranged from
13 ppb to 20 ppb at the two Port of Long Beach sites (Leidos Inc. 2016).
National SO2 emissions sector summaries cannot offer insight concerning the local
influence of individual S02-emitting facilities. In addition to fossil fuel-fired steam
electricity plants, other types of large emissions facilities that may be few in number
include copper smelters, coal cleaning plants, kraft pulp mills, Portland Cement plants,
iron and steel mill plants, sulfuric acid plants, petroleum refineries, and chemical
processing plants. For example, the Metals Processing sector represents less than 2.2% of
total emissions from the 2011 NEI (U.S. EPA. 2013a). but monitoring sites that have
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recorded some of the highest 1-h daily max SO2 concentrations in the U.S. are located
near copper smelters in Arizona (Section 2.5.2 and Section 2.5.4; Figure 2-11).
2.2.2 National Geographic Distribution of Large Sources
Figure 2-3 shows the geographic distribution of continental U.S. facilities emitting more
than 1,000 tpy SO2, with an enlargement of the Midwest states including the Ohio River
Valley, where a large number of these SCh-emitting sources are located.
U.S. EPA Sulfur Dioxide Data Requirements Rule
Another source of information of large sources of SO2 emissions is air agency
submissions in response to a regulatory requirement concerning characterization of
ambient SO2 concentrations in areas with large sources of SO2 emissions to help
implement the 1-hour S02 NAAQS (CFR, 51.1202-51.1203; 80 FR50152, August 21,
2015). This regulation requires that, at a minimum, air agencies must characterize air
quality around sources that emit 2,000 tons per year or more of SO2. An air agency may
avoid the requirement for air quality characterization near a source by adopting
enforceable emission limits that ensure that the source will not emit more than 2,000 tpy.
This final rule gives air agencies the flexibility to characterize air quality using either
modeling of actual source emissions or using appropriately sited ambient air quality
monitors. Under this requirement, air agencies submitted to the relevant EPA Regional
Administrator a final list identifying the sources in the state around which SO2 air quality
is to be characterized. The list included sources with emissions above 2,000 tpy SO2.
The EPA Regional Offices or air agencies included additional sources on this list that
they deemed necessary. The final list included 377 sources (https://www.epa.gov/so2-
polliition/so2-data-requirements-riile-soiirce-list). Figure 2-4 shows the locations of those
sources.
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A
Facilities in Midwest United States Emitting 1,000-150,000 Tons of S02
in NEI 2011
B
Note: NAAQS = national ambient air quality standards; NEI = National Emissions Inventory; S02 = sulfur dioxide.
Source: https://www.epa.aov/air-emissions-inventories: U.S. EPA (2013a).
Figure 2-3 Geographic distribution of (A) continental U.S. facilities emitting
more than 1,000 tpy sulfur dioxide, with (B) an enlargement of the
midwestern states, including the Ohio River Valley, where a large
number of these sources are concentrated.
Facilities Emitting 1,000-150,000 Tons of S02
in NEI 2011
Facilities SO: Emissions (tons)
• 1.000 to 5.000
• 5.000 to 50 000
• 50 000 W 150,000
U.S. Counties
2010 Population Density (persona per mi1)
0.0 to 1.0
1.0 to 20.0
20.0 to 85.0
H 85.0 to 500
|500 |o 2000
¦ 2,000 to 70.000
9 50,000 to 150X100
U.S. Counties
2010 Population Density {persons per nn*)
0.0 to t.O
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Sources Subject to EPA's Data Requirements Rule (DRR)
July 18, 2016
DRR = Data Requirements Rule; EPA = U.S. Environmental Protection Agency.
Source: U.S. EPA Office of Air Quality Planning and Standards.
Figure 2-4 Sulfur dioxide sources identified by state/local air agencies under
the U.S. Environmental Protection Agency's Data Requirements
Rule, as of July 18, 2016.
2.2.3 U.S. Anthropogenic Emission Trends
Anthropogenic emissions of SO2 in the U.S. have shown dramatic declines since the
1970s, and emissions reductions have accelerated since the 1990 amendments to the
Clean Air Act were enacted (USC Title 42 Chapter 85). Table 2-1 gives the annual SO2
emissions, percentage of the U.S. SO2 total emissions, and change in emissions rate from
2004 to 2011. Figure 2-5 illustrates the emissions trends by sector from 1970 to 2011 in
relation to the timeline over which the NAAQS for SO2 and the Clean Air Act control
programs [Acid Rain Program (ARP), NOx Budget Program (NBP), and Clean Air
Interstate Rule (CAIR)] have been implemented. Exceptions to the steep decline in SO2
emissions in the listed sectors are the marked increases in emissions from the commercial
storage and transport sectors and from miscellaneous, i.e. landscape fires. However,
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1 commercial storage and transport contributes only 0.1% of total 2011 SO2 emissions.
2 Landscape fires are a larger contributor to the NEI (3%) and are discussed further in
3 Section 2.2.4.3.
4 Hand etal. (2012) studied reductions in EGU-related annual SO2 emissions during the
5 period 2001-2010. They found that emissions decreased throughout the U.S. by 6.2% per
6 year, with the largest reductions in the western U.S. at 20.1% per year. The smallest
7 reduction (1.3% per year) occurred in the Great Plains states.
Table 2-1 Summary of 2011 U.S. Environmental Protection Agency sulfur
dioxide trends data by emissions sector. Values shown in bold
indicate increased emissions, 2001-2011.
Source Type
Tons (2011)
Percentage of Total
Percent Change Since 2001
Fuel combustion—electric utilities
4,625,295
71.4
-57
Fuel combustion—industrial
675,927
10.4
-70
Fuel combustion—other
218,682
3.4
-66
Miscellaneous (landscape fire)
197,555
3.0
+346
Other industrial processes
188,396
2.9
-56
Metal processing
144,410
2.2
-56
Off-highway vehicles
127,134
2.0
-71
Chemical and allied product manufacturing
126,510
2.0
-63
Petroleum and related industries
119,222
1.8
-63
Highway vehicles
29,465
0.5
-88
Waste disposal and recycling
16,829
0.3
-51
Storage and transport
9,277
0.1
+40
Note: "Fuel combustion—other" includes commercial, institutional and residential sources. "Petroleum and related industries"
include petroleum refineries, and oil and gas production. "Other industrial processes" include cement manufacturing, pulp and
paper production, and other industrial emissions that are NEC. "Off-highway" includes commercial marine. "Miscellaneous"
includes prescribed, agricultural and wild fires.
Source: https://www.epa.aov/air-emissions-inventories/air-pollutant-emissions-trends-data.
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S02 NAAQS (1971)
35,000
30,000
_ 25,000
>
Q.
O
® 20,000
V3
C
0
's 15,000
E
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2.2.4.1
The Global Sulfur Cycle
The total budget for sulfur, in all its forms, at Earth's surface is on the order of 1.1 x 1016
tons S (Schlesinger. 1997). The sulfur cycle comprises the many chemical and biological
processes that continuously interconvert the element between its four main oxidation
states (-2, 0, +4, +6). The reduced form of sulfur is present in the environment in
hydrogen sulfide, hydrogen disulfide, and a number of organic compounds. Oxidized
sulfur is present primarily as SO2 and sulfate (SO42 ).
Volcanoes and wildfires are nonbiological natural sources that directly emit SO2 to the
atmosphere. Biological natural sources, together with volcanoes, emit reduced sulfur
compounds that subsequently oxidize in the atmosphere to form SO2. Under anaerobic
conditions, various species of plants, fungi, and prokaryotes convert oxidized sulfur into
its reduced forms (Madigan et al.. 2006). Photosynthetic green and purple bacteria and
some chemolithotrophs oxidize sulfides to form elemental sulfur. Some species oxidize
elemental sulfur to form SO42 and SO2; others reduce elemental sulfur to sulfides
(dissimilative sulfur reduction), while others are capable of reducing SO42 all the way
down to sulfide (dissimilative SO4 reduction).
2.2.4.2 Volcanoes as a Natural Source of Sulfur Dioxide
Geologic activity, including fumaroles, geysers, and metamorphic degassing, emits a
number of gases, including SO2, carbon dioxide (CO2), hydrogen sulfide (H2S),
hydrochloric acid, chlorine, and others (Simpson etal.. 1999). Eruptive and noneruptive
volcanoes are the most important sources of geologic SO2 emissions. Noneruptive
volcanoes outgas at relatively constant rates and appear to be more important than
eruptive volcanoes as a source of SO2. The emissions of eruptive volcanoes are sporadic,
and therefore, vary from year to year (Simpson et al.. 1999).
The western U.S. borders the North American tectonic plate, which is subject to ongoing
volcanic activity due to subduction of the Pacific plate. The Aleutian volcanic arc, part of
the state of Alaska, comprises 75 volcanic centers. Volcanoes in this chain have erupted
once or twice per year on average over the past 100 years with impacts on local
communities (Power. 2013). Figure 2-6 shows an image derived from data collected by
the Atmospheric Infrared Sounder (AIRS) instrument aboard NASA's Aqua satellite
during the July 12-20, 2008 eruption of the Okmok Volcano in Alaska's Aleutian
Islands. The image shows sulfur dioxide at altitudes around 16 km (10 miles) released by
the volcano over that time span, with red indicating the highest concentrations, and pale
pink indicating the lowest (Prata et al. 2010). Sulfur dioxide has infrared absorption
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features at 4 and 7.3 (.an, which allowed Prata et al. (2010) to calculate the total mass of
SO2 emitted during the eruption as 319,670 ± 11,023 tons.
Okmok AIRS 7.3 (im Cumulative S02 12-20 July, 2008
5.0
70
170
4.5
4.0
-180
-50
60
~ 3.5
-60
50
2.5
O 2.0
-150
1.0
¦rto
0.5
0.0
AIRS = Atmospheric Infrared Sounder; S02 = sulfur dioxide.
Source: Image courtesy of Fred Prata of the Norwegian Institute for Air Research (NILU); NASA (2008a).
Figure 2-6 Sulfur dioxide released during the July 12-20, 2008 eruption of
the Okmok Volcano in Alaska's Aleutian Islands (image derived
from data collected by the Atmospheric Infrared Sounder
instrument aboard the National Aeronautics and Space
Administration Aqua satellite).
3 The line of volcanoes begins with the Aleutian Islands in Alaska and extends south and
4 east through the states of Washington, Oregon, California, Arizona, and New Mexico,
5 with outlying geologically active sites in Idaho (Craters of the Moon) and Wyoming
6 (Yellowstone). Figure 2-7 shows the geographic location and activity potential for these
7 sites within the continental U.S.
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Bellingham
A Glacier Peak
WASHINGTON •) sP°fcane
A Mount Rainier
Mount St Helens AA MountAdams'
•X
Seat
Great Falls
MONTANA
Billing
Portland
Mount Hood
A Mount Jefferson
Three Sisters .
, # At Bend
Eugene . ., „
A Newberry Crater
JAHC
A Yellowstone
A Craters of the Moon
A Crater Lake
OREGON
Boise
Casper
WYOMING
Mount Shasta A A Medicine Lake
en Peak A
Pocatello
Cheyenne
Salt Lake City
UTAH
Denver
C ear
NEVADA
Sacramento
San Francisc
A Long Valley Caldera
CALIFORNIA
Coso A
Las Vegas
Santa Fe
Albuquerque
A Bandera Field
A San Francisco Field
Volcano active during
past 2,000 years
Los Angeles
San Diego
ARIZONA
Phoenix
Other potentially active
volcanic aieas
EW v ex l.u
D 100 ZOO kilometers
Tucson
100 mies
Topinkd, USGS/CVO, 1999, Modified from: 8rzntiey, 1994, Vo/canoes of the United States: USGS Genera interest Publication
Source: USGS (1999). Map courtsey of Lyn Topinka (1999, USGS / CVO), Modified from Steve Brantley (USGS 1994), Volcanos of
the United States, USGS General Interest Pulication.
Figure 2-7 Geographic location of volcanoes and other potentially active
volcanic areas within the continental U.S.
1 The state of Hawaii, located over a "hot spot" in the north-central portion of the Pacific
2 tectonic plate, is a series of volcanic islands with one of the world's most active
3 volcanoes, Kilauea, located on the Big Island of Hawaii. Kilauea might typically be
4 described as a noneruptive volcano, emitting SO2 at a steady rate. In mid-March of 2008,
5 the volcano experienced a small explosion followed by a two- to fourfold increase in SO2
6 emissions. The Ozone Monitoring Instrument (OMI) aboard the NASA Aura satellite
7 detected this increase in SO2 emissions. Figure 2-8 shows the average concentration of
8 SO2 in the evolving plume for the March 20-27, 2008 period. Persistent easterly trade
9 winds moved the plume westward, away from populated areas.
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Aura/OMI - Average column for 20080320-20080327
-162 -160 -158 -156
S02 column [DU]
0.0
0.1
0.2
0.3
0.4
0.5
0.7
0.9
1.0
DU = Dobson units; OMI = Ozone Monitoring Instrument; S02 = sulfur dioxide.
Note: A DU is approximately equivalent to a total column concentration of 1 ppbv of S02. Horizontal axis is longitude with respect to
Greenwich, U.K. Vertical axis is latitude with respect to the equator.
Source: NASA (2008b).
Figure 2-8 National Aeronautics and Space Administration/Ozone Monitoring
Instrument image of the KTIauea sulfur dioxide plume during its
March 20-27, 2008 eruption.
Iii another study using SO2 column densities derived from GQME-2 satellite
measurements for the period 2007-2012, Beirle et al. (2013' determined KTlauea's
monthly mean SO2 emission rates and effective SO2 lifetimes. For the March through
November, 2008 period, the authors reported KTlauea's SO2 emission rates as
8,818-20,943 tons/day and the effective SO2 lifetime as 1-2 days. Several studies have
estimated the global SO2 emissions of sulfur by volcanoes to be in the range of 7.7 x
106-2.0 x 107 tpy (Chin et al.. 2000; Feichter et at.. 1996; Pham et al.. 1996; Langner and
Rodhe. 1991).
2.2.4.3
Wildfires as a Natural Source of Sulfur Dioxide
Sulfur is a component of amino acids in vegetation and is released during combustion,
mainly in the form of SO2. Using satellite data from various sources, including the
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Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies Product,
the Global Land Cover Characteristics 2000 data set, and the MODIS Vegetation
Continuous Fields Product in conjunction with the literature to determine fire location
and timing, fuel loadings, and emission factors, Wiedinmver et al. (2006) estimated SO2
emissions from fires for the U.S. at 176,370 tons in the year 2004. Canadian fires emitted
121,254 tons, and Mexican fires emitted 55,116 tons of SChforthe same period.
However, wildfire emissions do vary from year to year. Emissions estimates for SO2
derived from global modeling studies of wildfire range between 5.1 x 106—6.3 x 106 tpy
SO: (Chin et al. 2000; Feichter et al.. 1996; Pham et al.. 1996; Langner and Rodhe.
1991V
Projected increases in wildfire frequency and intensity under warming climate conditions
imply increasing wildfire-related SO2 emissions. However, these estimates are highly
uncertain due to the lack of data on the sensitivity of emissions composition with respect
to the effects of climate change on landscape species composition and burning
conditions. For comparison, the 2011 NEI also includes an estimate for agricultural and
prescribed burning emissions at 99,208 tpy, which is about half of the estimated SO2
emissions from wildfires (U.S. EPA. 2013a').
2.2.5 Reduced Sulfur Compounds as Indirect Sources of Sulfur Dioxide
Sulfides, including H2S, carbonyl sulfide (OCS), carbon disulfide (CS2),
methylmercaptan (CH3SH), dimethyl sulfide (DMS), and dimethyl disulfide (DMDS), are
emitted from energy production, industrial activities, agriculture, and various ecosystems,
especially coastal wetland systems, inland soils, and oceans. In addition to SO2,
volcanoes release sulfides, specifically H2S, OCS, and CS2. As described in Section 2.3.
all of these gases, with the exception of OCS, have short atmospheric lifetimes, given
their high rates of reaction with hydroxyl radicals and given the high rates of reaction of
nitrate radicals (NO3) with SO2 as a reaction product. Table 2-2 provides a list of the
natural and anthropogenic sources of the five main organosulfides. Dimethyl sulfide is
particularly important, both for the large role it plays as a source of atmospheric sulfur
and for its role in initiating the formation of marine clouds.
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Table 2-2 Global sulfide emissions in tpy sulfur.
Sources
OCS
cs2
CHsSH
DMS
DMDS
Seawater and marshes
3.4 x105
2.68 x 105
5.22 x10s
3.11 x 107
2.35 x105
Vegetation and soils
7.72 x104
1.91 x 10®
3.83 x 10®
9.57 x105
Volcanoes
1.21 x 104
1.87 x 104
Atmospheric oxidation
5.10 x 105
Biomass burning (all types)
5.07 x104
2.03 x 103
6.61 x 103
1.31 x 105
Pulp and paper industry
1.07 x 105
8.65 x 104
1.85 x10s
1.61 x 10®
3.01 x 105
Rayon/cellulosics manufacture
1.17x10s
1.52 x10s
1.05 x 105
Manure
3.64 x105
7.28 x 105
7.28 x105
Paddy fields
4.19 x 102
2.97 x 104
8.38 x102
2.76 x 104
6.28 x102
Pigment industry
8.16 x 104
2.26 x 105
Food processing and waste
6.94 x102
4.38 x 103
3.19 x 104
Gas industry
7.72 x 102
5.29 x 103
9.26 x 102
1.10 x 102
Wastewater
3.75 x 101
1.14 x 103
7.17 x 104
6.17 x 103
2.98 x104
Aluminum industry
9.70 x104
4.41 x 103
Coal combustion
1.80 x 104
3.64 x 102
Coke production
9.92 x 103
1.54 x 104
Biofuel combustion
5.16 x 104
2.09 x 103
Vehicles
6.61 x 103
3.31 x 102
Shipping
3.31 x 104
1.65 x 103
Tire wear
1.87 x 103
2.54 x 103
Tire combustion
3.31
6.61 x 10"2
Landfill
8.71 x 101
2.09 x 102
3.75 x102
2.87 x 102
8.82
Brick making
3.31 x 102
Total global sources
1.33 x 10s
1.90 x 10s
9.58x10®
3.74 x 107
2.41 x 10®
CH3SH = methylmercaptan; CS2 = carbon disulfide; DMDS = dimethyl disulfide; DMS = dimethylsulfide; OCS = carbonyl sulfide.
Adapted from (Lee and Brimblecombe. 2016).
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Dimethyl sulfide (DMS) is the most abundant reduced sulfur gas. It has appreciable
anthropogenic sources (pulp and paper production, agricultural operations), but these are
dwarfed by the quantity emitted by natural biological activity. Natural emissions of
dimethyl sulfide originate with the breakdown of dimethyl sulfoniopropionate, a
metabolite of the amino acid, methionine, produced by marine organisms living in
upwelling or coastal zones and by anaerobic bacteria in marshes and estuaries.
The oxidation of dimethyl sulfide contributes to low-level background SO2
concentrations in coastal environments. Lee and Brimblecombe (2016) provide a
literature-derived global estimate of DMS emissions from seawater and marshland of 3.1
x 107 tpy S. Earlier estimates for seawater DMS emissions range widely from 6.1 x 106 to
2.4 x 107 tpy (Liu et al.. 2005; Chin et al.. 2000; Feichter et al.. 1996; Pham et al.. 1996;
Langner and Rodhe. 1991). A warming climate may have a complex feedback effect on
DMS emissions, influencing both ocean surface temperatures and currents controlling
nutrient dispersion that impact the population and location of DMS producing
phytoplankton (kloster et al.. 2007).
Known sulfur oxides in the troposphere include SO2 and SO3 (U.S. EPA. 2008d). SO3
can be emitted by power plants and factories, but it reacts within seconds with water in
the stacks or immediately after release into the atmosphere to form H2SO4. Gas-phase
sulfuric acid quickly condenses onto existing particles or participates in new particle
formation (Finlavson-Pitts and Pitts. 2000). Of those species, only SO2 is present at
concentrations relevant for chemistry in the troposphere, boundary layer, and for human
exposures.
This section provides an overview of the primary atmospheric chemistry and removal
processes for SChof relevance to atmospheric concentrations at urban scales.
Section 2.3.1 describes the photochemical reactions that remove SO2 from the
atmosphere by converting it into compounds that condense into the particle or cloud
water phase. Section 2.3.2 describes the aqueous-phase oxidation of SO2, the major
oxidation mechanism in the atmosphere, as well as dry and wet deposition of SO2.
The atmospheric lifetime (r) of SO2 with respect to reactions with the OH radical in the
troposphere is 7.2 days. The rate constant for the reaction between SO2 and NO3 radical is
2.3
Atmospheric Chemistry and Fate
2.3.1
Photochemical Removal of Atmospheric SO2
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too small to be of any importance in the reduction of SO2 concentrations at urban or
regional scales. The same is true for the reaction between SO2 and the hydroperoxyl
(HO2) radical (Sander et al.. 2011).
In the stepwise oxidation of SO2 by OH, SO2 is oxidized to form SO3, taking the sulfur
atom from the S(IV) to S(VI) oxidation state, producing the bisulfite radical (HSO3):
SO2 + OH + M -> HSOs + M
Equation 2-1
where M is an unreactive gas molecule that absorbs excess, destabilizing energy from the
SO2-OH transition state. This reaction is followed by
HSOs + O2 ^ SOs + HO2
Equation 2-2
An alternative route involves a stabilized Criegee intermediate (sCI):
SO2 + sCI -> SO3 + products
Equation 2-3
The unspecified "products" of this reaction are other organic radicals derived from the
degradation of the Criegee intermediate (Bemdt et al.. 2012; Mauldin et al. 2012; Welz
et al.. 2012). Rate coefficients for the reaction of sCIs with SO2 have been reported as
4 x 10~15 cmVsec (Johnson et al.. 2001). approximately 3.5 x 1011 cmVsec (Liu et al..
2014b). and 3.9 x 1011 cmVsec (Welz et al.. 2012). Recent studies report rate
coefficients greater than 3 x 1011 cm Vsec (Friedman et al.. 2016; Lee. 2015; Bemdt et
al.. 2012). These reaction rate coefficients far exceed those of the reactions between these
intermediates and H2O. However, hydrolysis of SO2 could be limited if sCIs that are
potential SO2 oxidants are hydrolyzed via competing reactions (Kim et al.. 2015).
The efficiency of Criegee radical hydrolysis is sensitive to the molecular structure of the
alkene. Bimolecular hydrolysis rates constants vary by a factor of 1,000 between syn-
versus anti-substituted low molecular weight alkenes (Lin and Takahashi. 2016).
Criegee radicals are produced by the reaction of alkenes with O3 during both night and
day. The relative importance of the OH and sCI pathways depends in large measure on
the local concentration of alkenes, such as low molecular weight alkenes emitted by
motor vehicles and industrial processes as well terpenoids emitted by trees.
The importance of this mechanism as a sink for SO2 is supported by observations that
areas adjacent to SO2 sources, with high biogenic or industrial VOC concentrations, have
elevated organic PM concentrations (Friedman et al.. 2016). However, limited
information on the identity and concentrations of alkenes at urban scales prevents
estimates of the impact of this reaction pathway on urban SO2 concentrations.
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The SO3 that is generated by either oxidation mechanism (i.e., reaction with OH or via
the Criegee reaction mechanism) is a highly reactive species. Water vapor is sufficiently
abundant in the troposphere to ensure that SO3 is quickly converted to gas-phase sulfuric
acid, as shown in the equation below (Loerting and Liedl. 2000).
SOs + H2O + M -> H2SO4 + M
Equation 2-4
Because H2SO4 is extremely water soluble, gaseous H2SO4 will be removed rapidly by
dissolution into the aqueous phase of aerosol particles and cloud droplets. In a study of
SO2 plume transport in and out of foggy conditions, Eatough et al. (1984) observed that
roughly 30% of the SO2 converts to H2SO4 particulate each hour when inside a fog bank
and roughly 3.1% per hour outside a fog bank. Khoder (2002) observed that conversion
from SO2 to H2SO4 increases with increasing relative humidity and increasing O3, based
on a sampling campaign in an urban area of Egypt. Pearson correlation of S02-to-H2S04
conversion ratio with relative humidity was 0.81 in the winter and 0.89 in the summer.
Hung and Hoffmann (2015) recently conducted spray chamber experiments of SO2 to
H2SO4 conversion. They observed that SO2 deposited to the surfaces of water
microdroplets and then underwent rapid oxidation, first to HSO, and HSO4 , and then to
SO42 . Acidic conditions promoted more rapid oxidation of SO2.
2.3.2 Heterogeneous Oxidation of Sulfur Dioxide
The major sulfur-containing species in clouds are the HSO, and SO,2 (sulfite) ions that
form when SO2 dissolves in cloud droplets and subsequently undergoes acid dissociation.
Both exist in the S(IV) oxidation state, which readily oxidizes in the presence of
aqueous-phase oxidizing agents to form the S(VI) anions, HSO4 (bisulfate), and SO42 .
The major species capable of oxidizing S(IV) to S(VI) in cloud water are O3, peroxides
[either hydrogen peroxide (H2O2) or organic peroxides], OH radicals, and transition metal
ions such as Fe and Cu that catalyze the oxidation of S(IV) to S(VI) by O2.
The basic mechanism of the aqueous-phase oxidation of SO2 can be found in numerous
texts on atmospheric chemistry [e.g., (Seinfeld and Pandis. 2006; Jacobson. 2002;
Finlavson-Pitts and Pitts. 2000; Jacob. 1999)1. Similar initial steps occur in the fluids
lining the airways (Section 4.2.1). The steps involved in the aqueous phase oxidation of
SO2 are summarized below (Jacobson. 2002).
Dissolution of SO2 occurs first,
S02(g) <=> S02(aq)
Equation 2-5
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followed by the formation and dissociation of sulfiirous acid (H2SO3).
S02(aq) + H20(1) <=> H2SO3 <=> H+ + HSOs" <=> 2H+ + SOs2"
Equation 2-6
In the pH range commonly found in rainwater (2 to 6), H2O2 will oxidize HSO , to SO42 .
HSOs" + H2O2 + H+ -> S042" + H2O + 2H+
Equation 2-7
The rates of aqueous-phase oxidation of S(IV) to S(VI) as a function of pH are shown in
Figure 2-9. For pH values up to about 5.3, H2O2 is the predominant oxidant; above pH
5.3, O3, followed by Fe(III), becomes predominant.
Ambient ammonia (NH3) vapor readily dissolves in acidic cloud drops to form
ammonium (NH/). Because NH44" is very effective in controlling acidity, it amplifies the
rate of oxidation of S(IV) to S(VI) and the rate of dissolution of SO2 in particles and
cloud droplets. Therefore, in environments where NH3 is abundant, SO2 is subject to fast
removal by cloud and fog droplets and ultimately forms ammonium sulfate [(NFL^SO^.
Higher pH levels are expected to be found mainly in marine aerosols. In marine aerosols,
the chlorine radical-catalyzed oxidation of S(IV) may be more important (Hoppcl and
Caffrev. 2005; Zhang and Millero. 1991V
In the same way that it is removed from the gas phase by dissolution into cloud droplets,
SO2 can be removed by dry deposition onto wet surfaces (Shadwick and Sickles. 2004;
Clarke et al. 1997). For example, in the eastern U.S., SO2 is responsible for more than
85% of dry sulfur deposition (Sickles and Shadwick. 2007). However, aqueous SO42
may be removed through occult deposition of large fog or cloud droplets (Lillis et al.
1999; Pandis and Seinfeld. 1989; Pollard et al.. 1983). Scavenging by rain (wet
deposition) serves as another removal route. Modeling studies have shown that slightly
more than half of SO2 in both models is lost by gas- and aqueous-phase oxidation, with
the remainder of SO2 loss accounted for by wet and dry deposition (Long etal.. 2013; Liu
et al.. 2012a).
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*NO.
2
,12
>
to
"O
I
1-14
j-16
,18
0
2
3
6
1
4
5
PH
Fe = iron; H202 = hydrogen peroxide; Mn2+ = manganese ion; N02 = nitrogen dioxide; 03 = ozone; S = sulfur.
Note: The rate of conversion of aqueous (droplet)-phase S(IV) to S(VI) is shown as a function of pH. Conditions assumed are:
[S02(g)] = 5 ppb; [N02(g)] = 1 ppb; [H202(g)] = 1 ppb; [03(g)] = 50 ppb; [Fe(lll)(aq)] = 0.3 jiM; [Mn(ll)(aq)] = 0.3 jiM.
Source: Seinfeld and Pandis (2006).
Figure 2-9 The effect of pH on the rates of aqueous-phase sulfur (IV)
oxidation by various oxidants.
1
2 Sulfur dioxide is known to adhere to and then react on dust particles. Very recent
3 investigations have shown that, for some mineral compositions, SO2 uptake on dust
4 particles is sensitive to relative humidity, the mineral composition of the particle, and the
5 availability of H2O2, the relevant oxidant (Huang et al.. 2015b). Once SO2 is oxidized to
6 H2SO4 on the particle surface, glyoxyl, one of the most prevalent organic compounds in
7 the atmosphere, will adhere to the surface and react to form oligomers and organosulfate
8 compounds. This process is enhanced under high humidity conditions (Shen et al.. 2016).
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2.4
Measurement Methods
This section discusses the federal reference method (FRM) and federal equivalent method
(FEM) used for NAAQS compliance as well as the state, local, and tribal monitoring
networks across the U.S. used for NAAQS compliance monitoring. Detailed information
about monitoring methods, including accuracy, precision, limits of detection, and other
operational parameters was published in the 1982 Air Quality Criteria for Particulate
Matter and Sulfur Oxides Volume II (U.S. EPA. 1982a) and then updated in
Appendix B.6 of the 2008 ISA for Sulfur Oxides—Health Criteria (U.S. EPA. 2008d).
The List of Designated Reference and Equivalent Methods (U.S. EPA. 2016f) lists all
monitors approved as FRMs or FEMs and provides monitor specifications. A brief
summary of that information, along with a discussion of more recent studies evaluating
FRMs and FEMs for monitoring SO2 concentration (Section 2.4.1) or alternative SO2
monitoring methods (Section 2.4.2). is provided. Section 2.4.3 describes the sampling
network.
Currently, there are two FRMs for the measurement of SO2—the manual pararosaniline
wet-chemistry method and the automated pulsed ultraviolet fluorescence (UVF) method.
The manual method was approved as an FRM in the 1970s and was quickly replaced by
the flame photometric detection (FPD) method, an FEM because the manual method was
too complex and had a slow response even in automated form. The UVF method was
designated as an FEM in the late 1970s and ultimately replaced the FPD method.
The UVF method is inherently linear and relatively safe whereas the FPD method
requires highly flammable hydrogen gas. The UVF method has been the most commonly
used method by state and local monitoring agencies since the 1980s. It was added as an
FRM as a result of the new 1-hour SO2 primary NAAQS established in 2010 (75 FR
35520). The UVF method supports the need for a continuous monitoring method, as it
can easily provide 1-hour SO2 measurements. The existing pararosaniline manual method
was retained as a FRM, and although cumbersome, the method can provide hourly
measurements to support the 1-hour NAAQS.
In the UVF 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
electronic state. Once excited, the molecule loses a portion of its energy by collision with
another gas molecule and, then by emitting a photon of light at a longer wavelength
which returns to its electronic ground state. The intensity of the emitted light is, therefore,
proportional to the number of SO2 molecules in the sample gas. In commercial analyzers,
2.4.1
Federal Reference and Equivalent Methods
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light from a high-intensity UV lamp passes through a bandwidth filter that allows 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 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 detector is offset from and placed
perpendicular to the light path to detect the SO2 fluorescence. Because the SO2
fluorescence at about 330 nm is different from its excitation wavelength, an optical
bandwidth filter is placed in front of the detector to filter out any stray light from the UV
lamp. A lens is located between the filter and the detector to focus the fluorescence onto
the active area of the detector and optimize the fluorescence signal. A particulate filter is
also placed after the sample inlet to prevent damage, malfunction, and interference from
particles in the sampled air.
Studies have compared UVF to sampled SO2 from impregnated filters for quality
assurance. Comparison of 24-h avg concentration measurements obtained with the UVF
method and with impregnated filters showed annual-average differences within
±0.07 ppb, based on data obtained between 1993 and 2001 from four Finnish cities
(Leppanen et al.. 2005). Ferek et al. (1997) evaluated the Teco model UVF (developed at
the University of Washington) against carbonate-impregnated filters for measurement of
SO2 concentration in laboratory studies. The Teco UVF measured SO2 concentrations
down to 16 ppt and, on average, produced a positive difference of 7% compared with the
filter. The Teco UVF analyzed data at a frequency as high as 1 Hz, but noise was
curtailed by averaging up to 10 minutes. The Ferek et al. (1997) study highlighted the
Teco UVF but also included other SO2 measurement techniques in the SO2 monitor
comparison, including gas spectrometry/mass spectrometry, high performance liquid
chromatography, and a mist chamber, which produced a maximum of 30% differences
for filter-measured SO2 concentrations of 3-4 ppb averaged over 90 minutes.
2.4.1.1 Minimum Performance Specifications
Minimum performance specifications [in accordance with 40 Code of Federal
Regulations (CFR) Part 53] were made more stringent for any new FRM and FEM
automated method with the addition of the UVF method as an FRM. The new
specifications are provided in Table 2-3. The previous specifications were based on the
older, manual, wet-chemistry FRM and were updated to reflect current technology and
improved performance in SO2 instrumentation. The lower detection limit (LDL) for a
routine, automated SO2 analyzer is required to be 2 ppb. As part of the National Core
(NCore) monitoring network, new trace-level SO2 instruments have been developed and
added to State and Local Air Monitoring Sites (SLAMS). These new trace-level (i.e., low
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LDL) instruments have LDLs of 0.2 ppb or lower. Note that FRMs and FEMs may have
more stringent performance characteristics than the minimum performance specifications
presented in Table 2-3.
Table 2-3 Minimum performance specifications for sulfur dioxide based in
40 Code of Federal Regulations Part 53, Subpart B.
Performance Parameter
Specification
Range
0-0.5 ppm (500 ppb)
Noise
0.001 ppm (1 ppb)
Lower detectable limit (two times the noise)
0.002 ppm (2 ppb)
Interference equivalent
• Each interferent
±0.005 ppm (5 ppb)
• Total, all interferents
—
Zero drift (12 and 24 h)
±0.004 ppm (4 ppb)
Span drift (24 h)
• 20% of upper range limit
—
• 80% of upper range limit
±3.0%
Lag time
2 min
Rise time
2 min
Fall time
2 min
Precision
• 20% of upper range limit
2.0%
• 80% of upper range limit
2.0%
2.4.1.2 Positive and Negative Interferences
4 The UVF method has a number of positive and negative interferences. The most frequent
5 source of positive interference is other gases that fluoresce at the same wavelength as
6 SO2. The most common gases include volatile organic compounds (e.g., xylenes,
7 benzene, toluene) and polycyclic aromatic hydrocarbons (PAHs; e.g., naphthalene).
8 To reduce this source of positive interference, high-sensitivity SO2 analyzers are
9 equipped with scrubbers or "kickers" to remove these compounds from the air stream
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prior to entering the optical chamber. Luke (1997) evaluated a modified pulsed
fluorescence SO2 detector and found positive interference from nitric oxide (NO), CS2,
and several highly fluorescent aromatic hydrocarbons such as benzene, toluene, o-xylene,
ra-xylene, />-xylene, m-cthyltolucnc. ethylbenzene, and 1,2,4-trimethylbenzene.
The positive artifacts could be virtually eliminated by using a hydrocarbon "kicker"
membrane. At a flow rate of 300 standard cm3/minute and a pressure drop of 645 torr
across the membrane, the interference from ppm levels of many aromatic hydrocarbons
can be eliminated.
Another source of positive interference is NO, which fluoresces in a region close to that
of SO2. However, in high-sensitivity SO2 analyzers, the bandpass filter in front of the
detector is specifically designed to prevent detection of NO fluorescence at the detector.
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 in high-sensitivity SO2 analyzers is stray light
in the optical chamber. Because SO2 can be excited by a broad range of UV wavelengths,
any stray light entering the optical chamber with an appropriate wavelength can excite
SO2 in the air stream and increase the fluorescence signal. Additionally, stray light
entering the optical chamber with a similar wavelength of SO2 fluorescence may impinge
on the detector and increase the fluorescence signal. Stray light is also minimized with
changes in instrument design such as use of light filters, dark surfaces, and opaque
tubing.
H2O is a common source of negative interference in high-sensitivity SO2 monitors. When
excited SO2 molecules collide with water vapor as well as other common molecules in air
(e.g., nitrogen and oxygen), nonradiative deactivation (quenching) can occur. During
collisional quenching, the excited SO2 molecule transfers energy, kinetically allowing the
SO2 molecule to return to a lower energy state without emitting a photon. Collisional
quenching decreases the SO2 fluorescence and results in 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 approximately 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 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. Condensation is normally prevented by
heating sampling lines to a temperature above the expected dew point and to within a few
degrees of the controlled optical bench temperature. Some monitors are equipped with a
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dryer system to remove moisture from the sample gas before it reaches the particulate
filter.
2.4.2 Alternative Sulfur Dioxide Measurements
A number of optical methods for measuring SO2 are available. They include laser
induced fluorescence (LIF), cavity ring-down spectroscopy (CRDS), differential optical
absorption spectrometry (DOAS), and UV absorption. There are also methods based on
mass spectroscopy or mass spectrometry [e.g., chemical ionization mass spectroscopy
(CIMS) and atmospheric pressure ionization mass spectrometry (APIMS)]. These
methods are often too expensive and complex for routine monitoring applications and are
more suitable for source monitoring. However, approaches to reduce interferences and
increase SO2 selectivity could be extended to FRM and FEM instrumentation. The LIF,
CRDS, and DOAS methods will be discussed below as they have the potential to provide
trace-level SO2 measurements or have shown good agreement with UVF instrumentation.
LIF is a technique that can provide high sensitivity for ambient SO2 measurements and
reduces interferences with species that fluoresce at the same wavelength as SO2. Both
tunable and nontunable laser sources have been evaluated. Matsumi et al. (2005)
evaluated a LIF method using a tunable laser at an SO2 absorption peak at 220.6 nm and
trough at 220.2 nm. The difference between the signals at the two wavelengths is used to
estimate the SO2 concentration. This technique has a sensitivity of 5 ppt in 60 sec.
Simeonsson et al. (2012) evaluated a direct LIF technique using a nontunable laser source
at an absorption wavelength of 223 nm, which coincides with the SO2 absorption peak.
This technique has a high sensitivity with LDL of 0.5 ppb. Both the tunable and
nontunable instruments have low LDL (<0.5 ppb); therefore, they can provide trace-level
SO2 measurements.
CRDS is an optical absorption method based on measurement of the rate of light
absorption through a sample. CRDS has successfully been used to measure ambient NO2
and NO with high sensitivity. Medina et al. (2011) compared a CRDS-tunable laser
method to the routinely used pulsed ultraviolet fluorescence (UVF) method for measuring
SO2. At an absorption wavelength of 308 nm, the CRDS had an LDL of 3.5 ppb, which
was higher than those for routine and trace-level UVF SO2 monitors (e.g., Thermo
Scientific 43i and Thermo Scientific 43i-TLE). However, the response time was faster
compared to the UVF methods (a few seconds vs. 80 sec). To reduce interferences, a
ferrous sulfate scrubber was used to remove NO2 and O3, and a denuder was used to zero
SO2 levels. Improvements could be made to increase the sensitivity to about 1 ppb by
changing the placement of the mirrors to optimize laser light reaching the cavity or using
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a better detection system. Additionally, improving the mirror reflectivity could improve
the sensitivity to about 0.1 ppb, similar to the detection levels of trace-level SO2
monitors.
DOAS is an optical remote sensing method based on the absorption of light in the
UV-visible wavelength region to measure atmospheric pollutants. Kim and Kim (2001)
compared SO2 concentrations measured using a DOAS system with daily mean SO2
concentrations measured by an in situ monitor in Seoul, Korea during a 13-month period.
In this study, the DOAS typically reported SO2 concentrations around 10-40% above the
in situ technique, but SO2 concentrations measured by the DOAS were sometimes
100-200% below those measured with the in situ monitor. Across all measurements, the
daily mean SO2 concentration was 36% higher from the DOAS compared with the in situ
monitor. Discrepancies between the two methods were attributed to ability to respond to
meteorological factors. The DOAS was reported to have an LDL of 0.07 ppb, compared
with 1 ppb reported for the in situ method. A newer technique called multiaxis
differential optical absorption spectroscopy (MAX-DOAS) has been developed that
offers increased sensitivity in measuring SO2 (Honninger et al.. 2004). MAX-DOAS is
based on the measurement of scattered sunlight at multiple viewing directions and can
obtain both surface concentrations and vertical column density of SO2. Wang et al.
(2014b) compared MAX-DOAS SO2 column measurements in the 305 to 317.5 nm
absorption wavelength to surface SO2 measurements from a modified UVF SO2 monitor
(Thermo Environmental Instruments Model 43C) and found good agreement (r = 0.81,
slope = 0.90).
Remote sensing by satellites (e.g., OMI, infrared atmospheric sounding interferometer,
etc.) is an emerging technique for measuring SO2 as well as other pollutants. This
technique can be used for a variety of applications, including air quality management
(e.g., augmenting ground-based monitors, assessing emissions inventories), studying
pollutant transport, assessing emissions reductions, and evaluating air quality models.
Remote sensing methods employ a retrieval system using a combination of solar
backscatter or thermal infrared emission spectra and mathematical algorithms to estimate
pollutant concentrations. Remote sensing from space is particularly challenging for SO2
measurements for two reasons: (1) air scattering causes SO2 to have a low optical
thickness (three orders of magnitude lower than O3), so that only large SO2 sources can
be observed (Bogumil et al.. 2003) and (2) emissions reductions programs have led to
lower SO2 emissions from stationary sources, making it more difficult to see
anthropogenic SO2 emissions (Streets et al.. 2014). The majority of remote sensing
studies have focused on large natural sources (e.g., volcanoes), large anthropogenic
sources (e.g., coal-burning power plants, smelters), fuel extraction from oil sands, and
newly constructed coal-burning facilities with high, uncontrolled SO2 emissions
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(Bovnard et al.. 2014; McCormick et al.. 2014; Streets et al.. 2014; Clarisse et al.. 2012;
McLinden et al.. 2012; Fioletov et al.. 2011; Nowlan et al.. 2011; Bobrowski et al.. 2010;
Li et al.. 2010; Khokhar et al.. 2008; Cam et al.. 2007).
2.4.3 Ambient Sampling Network Design
Compliance with NAAQS is primarily carried out through the SLAMS network, although
modeling may also be used to characterize air quality for implementation purposes (75
FR 35520). There are 438 SLAMS sites reporting 1-hour SO2 concentrations to the Air
Quality System (AQS), U.S. EPA's repository for detailed air pollution data that is
subject to quality control and assurance procedures. In addition to their use in compliance
evaluations, some of these sites function as central monitoring sites for use in
epidemiological studies. The SLAMS network also reports either the maximum 5-minute
concentration in the hour (one of twelve 5-minute periods within an hour) or all twelve
5-minute average SO2 concentrations within the hour. Siting requirements for monitors in
the SLAMS network can be found in 40 CFR Part 58, Appendix E.
The SLAMS network includes the NCore monitoring network, which began January 1,
2011 and consists of 80 sites (63 urban and 17 rural). NCore is a multipollutant
measurement network and includes SO2 measurements as well as measurements for other
gaseous pollutants (O3, CO, NOx, oxides of nitrogen), PM2 5, PM10-2.5, and meteorology.
NCore is focused on characterizing trends in pollutants, understanding pollutant transport
in urban and rural areas, and evaluating data with respect to the NAAQS. Figure 2-10
shows the locations of these monitoring networks across the U.S. The Clean Air Status
and Trends Network (CASTNet) also measures ambient SO2 However, these data are not
used for NAAQS compliance purposes and are obtained predominantly in National Parks
or other ecologically sensitive sites. Because CASTNet monitors are not deployed in
populated areas, they are not useful in evaluating the health effects of SO2. This network
provides weekly averages of total sulfur (dry SO2, dry SO42 . and wet SO42 ) in about
90 sites located in or near rural locations to assess long-term trends in acidic deposition
due to emission reduction programs. CASTNet data are presented in the Integrated
Science Assessment for Oxides of Nitrogen and Sulfur—Ecological Criteria (U.S. EPA.
2008b).
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Puerto Rico and U.S. Virgin Islands
Routinely Operating S02
Monitor Networks
if NCORE, 5 min
O NCORE, 1 hr
SLAMS, 5 min
0 SLAMS, 1 hr
Kilometers
NCORE = National Core; SLAMS = State and Local Air Monitoring Sites; S02 = sulfur dioxide.
Figure 2-10 Routinely operating sulfur dioxide monitoring networks: National
Core and State and Local Air Monitoring Sites, reporting 1 hour
and 5 minute sulfur dioxide concentration data.
1 The minimum monitoring requirements for the SLAMS network are outlined in 40 CFR
2 Part 58, Appendix D. SO2 monitors at SLAMS sites represent four main spatial scales:
3 (1) microscale—areas in close proximity, up to 100 m from a SO2 point or area source,
4 (2) middle scale—areas up to several city blocks, with linear dimensions of about 100 to
5 500 in, (3) neighborhood scale—areas with linear dimensions of 0.5 to 4 km, and
6 (4) urban scale—urban areas with linear dimensions of 4 to 50 km. Microscale,
7 middle-scale, and neighborhood-scale sites are used to determine maximum hourly SOj
8 concentrations because these sites are close to stationary point and area sources, whereas
9 neighborhood- and urban-scale sites are used as central monitoring sites to characterize
10 population exposures and trends, such as in epidemiologic studies (Section 3.2.1).
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Urban-scale sites can also be used to determine background concentrations in areas where
monitors are located upwind of a local source. There are also a number of regional-scale
monitoring sites, representing length scales of tens to hundreds of kilometers, typically in
rural areas of uniform geography without large SO2 sources. These sites can be used to
determine the amount of regional pollution transport and to support secondary NAAQS.
Stationary sources are the primary emission sources of SO2. Prior to the revised SO2
primary NAAQS in 2010, U.S. EPA evaluated about 488 SO2 monitoring sites in
operation during 2008 and found that the network was not adequately focused to support
the revised NAAQS (U.S. EPA. 2009d). To address this deficiency, U.S. EPA
promulgated minimum monitoring requirements based on a near-source monitoring
approach. The Population Weighted Emissions Index (PWEI), which is based on
population and emissions inventory data at the core-based statistical area (CBSA) level,
was introduced to assign the appropriate number of monitoring sites in a given CBSA (75
FR 35520). The PWEI accounts for SO2 exposure by requiring monitor placement in
urban areas where population and emissions may lead to higher potential for population
exposure to maximum hourly SO2 concentrations. The PWEI value is calculated by
multiplying the population of each CBSA by the total amount of SO2 emissions (in tons
per year) in a given CBSA, using the most recent census data (or estimates) and
combining the most recent county-level emissions data (from the National Emissions
Inventory) for each county in each CBSA, respectively. This value is then divided by
1 million, resulting in a PWEI value with units of million person-tons per year.
A minimum of three SO2 monitoring sites is required for any CBSA with a PWEI value
greater than or equal to 1,000,000. For any CBSA with a PWEI value greater than or
equal to 100,000 but less than 1,000,000, a minimum of two SO2 monitoring sites is
required. Lastly, a minimum of one SO2 monitoring site is required for any CBSA with a
PWEI value greater than or equal to 5,000 but less than 100,000. The monitors sited
within a CBSA based on the PWEI criterion should also be, at minimum, one of the
following monitoring site types: population exposure, highest concentration, source
impacted, general background, or regional transport.
Another minimum monitoring requirement for the revised NAAQS involves the quantity
of monitoring sites in a given state, which is based on the state's contribution to the NEI
for SO2. This requirement was designed to offer some flexibility in monitoring site
placement, either inside or outside of a CBSA, independent of the PWEI criteria.
Additionally, all monitoring sites in the network must be placed at locations where
maximum peak hourly SO2 concentrations are expected. Monitoring sites in the NCore
network are not source oriented, and therefore, do not necessarily count towards the
minimum monitoring requirements for SO2. However, if an NCore SO2 monitoring site is
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located in a CBS A that meets the aforementioned requirements based on the PWEI
criteria, that monitoring site can count towards the minimum monitoring requirements.
2.5
Environmental Concentrations
This section provides an overview of SO2 ambient and background concentrations. SO2
data discussed in this section were obtained from the AQS. Section 2.5.1 introduces
different SO2 metrics used for NAAQS compliance and epidemiologic applications.
Ambient concentrations of SO2 are then discussed on various spatial and temporal scales.
Spatial variability is discussed in Section 2.5.2. which is divided into two sections
discussing large-scale variability (i.e., nationwide) and small-scale variability (i.e., urban
areas). Temporal variability is then discussed in Section 2.5.3. extending from multiyear
trends to subhourly variations. The relationships between 5-minute hourly max and
1-hour concentrations are described in Section 2.5.4. Background SO2 concentrations
from natural sources are subsequently discussed in Section 2.5.5.
Different metrics are used to represent ambient SO2 concentrations for epidemiologic
analysis and NAAQS compliance. As discussed in Section 2.5.4. hourly and 5-minute
concentration data are routinely reported to U.S. EPA's AQS data repository by state,
local, and tribal agencies. Metrics can be derived from these hourly and 5-minute data to
represent concentration and exposure levels on different time scales. Table 2-4 provides
information on how different SO2 metrics are derived. Daily metrics include the 24-h avg
SO2 concentration and the 1-h daily max SO2 concentration. Hourly metrics include the
5-minute hourly max concentration reported during a given hour and the 1-h avg
concentration. Metrics derived using maximum concentration statistics
(i.e., 1-h daily max or 5-minute hourly max) provide insight about peak ambient
concentrations occurring over a given hour or day.
The following sections include national and urban statistics on daily and hourly metrics.
When interpreting the statistics, it is important to consider the aggregation time when
comparing the magnitude and range of ambient concentrations related to different
metrics.
2.5.1
Sulfur Dioxide Metrics and Averaging Time
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Table 2-4 Summary of sulfur dioxide metrics and averaging times.
Metric
Aggregation Time
Averaging Time Description
24-h avg
Daily
Daily mean of 1-h avg SO2 concentrations
1-h daily max
Daily
Maximum 1-h SO2 concentration reported during the day
1-h avg
Hourly
Hourly mean SO2 concentrations reported during the day
5-min hourly max
Hourly
Maximum 5-min SO2 concentration reported during 1 h
avg = average; max = maximum; S02 = sulfur dioxide.
AQS SO2 data used to compute national statistics meet the data quality and completeness
criteria listed in Table 2-5. Three additional criteria were applied for the 5-minute data to
reduce the influence of outliers. The 5-minute data had to correspond to an hourly data
concentration, the mean of the 5-minute data could be no more than 120% of the hourly
mean, and the 5-minute hourly max concentration had to fall within 1 to 12 times the
1-h avg concentration. Although negative values may be entered into the AQS database,
they were excluded from this analysis. Concentrations below the monitor detection limit
were included as they likely represent true low values. Based on these criteria, statistics
were computed for data from a total of 380 sites across the U.S. for 5-minute hourly max
SO2 concentrations and for data from a total of 438 sites for the 1-h daily max, 24-h avg,
and 1-h avg SO2 metrics. 13% of sites did not have 5-minute data for comparison with
1-hour data.
2.5.2 Spatial Variability
This section provides a brief overview of national- and urban-scale SO2 spatial variability
and discusses how variations in ambient SO2 concentrations influence human exposure in
different geographical regions.
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Table 2-5 Summary of sulfur dioxide data sets originating from the Air Quality
System database.
AQS SO2 data used to compute national statistics (to meet the data quality and completeness criteria)
Years
2013-2015
Months
January-December
Completeness criteria
75% of 5-min periods in an hour (where 5-min data are available)
75% of hours in day
75% of days in calendar quarter
3 of 4 quarters of the year
Number of monitoring sites meeting
completeness criteria
380 sites reporting 5-minute data (2013-2015)
438 sites reporting 1-hour data (2013-2015)
2.5.2.1 Nationwide Spatial Variability
In the previous ISA for Sulfur Oxides (U.S. EPA. 2008(1). 24-h avg. 1-h daily max,
1-h avg, and 5-minute hourly max SO2 concentrations measured at AQS monitoring sites
during 2003-2005 were reported. Nationwide statistics of 5-minute hourly max SO2 data
were limited in the previous assessment due to a scarcity of monitoring sites reporting
such data. From 2003-2005 nationwide, central statistics (mean and median) of
1-h daily max and 24-h avg SO2 concentrations were generally low (less than 15 ppb),
while concentrations in the upper range of the distribution (e.g., 99th percentile) were
substantially higher (23-116 ppb), particularly for 1-h daily max concentrations (99th
percentile: 116 ppb). In addition, 1-h avg SO2 concentrations exhibited low mean
concentrations (4 ppb), with 99th percentile concentrations near 34 ppb. Relatively high
concentrations were typically observed at sites near stationary anthropogenic sources
(e.g., EGUs).
SO2 summary data provide a snapshot of recent concentrations and, compared with those
presented in the 2008 SOx ISA (U.S. EPA. 2008d). allow for ascertainment of trends. As
shown by Table 2-6. nationwide concentrations for 2013-2015 were slightly lower than
concentrations reported in the 2008 SOx ISA. For all 24-h avg, 1-h daily max, 1-h avg,
and 5-minute hourly max data pooled nationwide, mean statistics were below 6 ppb,
median statistics (50th percentile) were 2 ppb or below, and SO2 concentrations in the
upper range of the distribution (99th percentile) covered a wide range of concentrations
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but were never greater than the primary NAAQS level of 75 ppb. Across all metrics,
large differences were observed between mean and 99th percentile concentrations,
particularly for the SO2 1-h daily max and 5-minute hourly max data. Such large
differences between mean and 99th percentile concentrations are consistent with the
highly variable nature of SO2, which is characterized by periodic peak concentrations
superimposed on a relatively low background concentration. Higher concentrations in the
1-h daily max distribution compared with the 5-minute hourly max distribution were
likely attributable to the omission of high 5-minute concentrations from the
58 monitoring sites without 5-minute data.
The absolute highest 1-h daily max SO2 concentration in 2013-2015 was 2,071 ppb. 99th
percentile 1-h daily max concentrations over 200 ppb were reported at this site and other
sites near active volcanoes in Hawaii Table 2-6). which are discussed further in
Section 2.5.5. Other reports of 99th percentile, 1-h daily max concentrations greater than
200 ppb occurred at three monitoring sites near a copper smelter in Gila County, AZ, as
mentioned in Section 2.2.2. In addition, sites where the 99th percentile 1-h daily max
concentration was greater than 75 ppb were located in North Dakota, Illinois, Iowa,
Wisconsin, Arizona, Missouri, Indiana, Tennessee, Ohio, Kentucky, Louisiana, and
Pennsylvania, often near coal-fired EGUs. As shown in the nationwide map in
Figure 2-11. the majority of monitoring sites across the U.S. report 99th percentile,
1-h daily max concentrations below the primary NAAQS level of 75 ppb. The 99th
percentile of 24-h avg concentrations, which are often used as exposure metrics in
epidemiologic studies, followed a similar pattern, with most elevated values located in
the industrial Midwest (Figure 2-12).
2.5.2.2 Urban Spatial Variability
Air quality measurements from centrally located, urban monitoring sites are often used to
represent community-scale exposure in epidemiologic analyses. However, central site
exposure estimates may not fully capture variations in pollutant concentrations over
urban scales. SO2 spatial variability was characterized in six focus areas: Cleveland, OH;
Pittsburgh, PA; New York City, NY; St. Louis, MO; Houston, TX; and Gila County, AZ.
These focus areas were selected based on (1) their relevance to current health studies
(i.e., areas with peer-reviewed, epidemiologic analysis), (2) the existence of four or more
monitoring sites located within the area boundaries, and (3) the presence of several
diverse SO2 sources within a given focus area boundary.
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Table 2-6 National statistics of sulfur dioxide concentrations (parts per billion)
from Air Quality System monitoring sites, 2013-2015.a
Year
N of Obs
Mean
5%
10%
25%
50%
75%
90%
95%
98%
99%
Max
AQS
Max IDb
5-min hourly max
2013
3,105,078
2.3
0.0
0.0
0.2
1.0
2.0
4.2
7.0
15.0
26.0
1,441.4
160050004
2014
3,047,302
2.2
0.0
0.0
0.2
1.0
2.0
4.0
7.0
15.0
25.4
4,208.0
160050004
2015
2,997,344
1.8
0.0
0.0
0.2
0.8
1.6
3.0
5.4
12.0
20.3
1,678.0
160050004
2013-2015
9,149,724
2.1
0.0
0.0
0.2
1.0
2.0
4.0
6.7
14.0
24.0
4,208.0
160050004
1-h avg
2013
3,105,078
1.7
0.0
0.0
0.0
0.8
1.8
3.2
5.0
9.3
15.8
2,071.0
150010007
2014
3,047,302
1.6
0.0
0.0
0.0
0.8
1.5
3.0
5.0
9.6
16.0
1,830.0
150010007
2015
2,997,344
1.3
0.0
0.0
0.0
0.6
1.1
2.5
4.0
8.0
13.3
1,779.0
150010007
2013-2015
9,149,724
1.5
0.0
0.0
0.0
0.7
1.4
3.0
5.0
9.0
15.0
2,071.0
150010007
1-h daily max
2013
133,925
5.6
0.0
0.0
0.9
2.0
4.5
10.5
19.0
37.3
62.5
2,071.0
150010007
2014
131,553
5.7
0.0
0.0
0.8
2.0
4.4
11.0
19.8
41.0
68.0
1,830.0
150010007
2015
128,991
4.7
0.0
0.0
0.6
1.4
3.3
8.2
15.9
34.4
60.0
1,779.0
150010007
2013-2015
394,469
5.4
0.0
0.0
0.8
1.8
4.0
10.0
18.0
37.7
64.0
2,071.0
150010007
24-h avg
2013
133,925
1.6
0.0
0.0
0.3
0.9
1.8
3.5
5.2
8.6
13.1
366.5
150010007
2014
131,553
1.6
0.0
0.0
0.3
0.8
1.7
3.3
5.0
8.6
13.1
317.2
150010007
2015
128,991
1.3
0.0
0.0
0.2
0.7
1.4
2.7
4.0
7.4
12.1
393.0
150010007
2013-2015
394,469
1.5
0.0
0.0
0.2
0.8
1.7
3.2
4.8
8.3
12.8
393.0
150010007
AQS = Air Quality System; avg = average; ID = identification; mean = arithmetic average; max = maximum; N = population
number; Obs = observations.
aData below 0 ppb have been trimmed from the data set.
bAQS site ID number reporting the highest 3-yr concentration across the U.S.
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Puerto Rico and U.S. Virgin Islands
Alaska
0 50 100
*<5
Hawaii
ft*
0 130 260
o 400 aoo
Monitor 1-hr Daily Max SO2
2013 - 2015 99th Percentile
O <= 75 ppb
0 > 75 to 200 ppb
O > 200 to 400 ppb
0 > 400 ppb
400
Max = maximum; S02 = sulfur dioxide.
Figure 2-11 Map of 99th percentile of 1 -h daily max sulfur dioxide
concentration reported at Air Quality System monitoring sites,
2013-2015.
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Puerto Rico and U.S. Virgin Islands
Monitor 24-hr Daily Average
S02 2013 -2015 99th Percentile
o <=15ppb
Note: The 24-h avg concentration is a metric often used in epidemiologic studies.
S02 = sulfur dioxide.
Figure 2-12 Map of 99th percentile of 24-h avg sulfur dioxide concentration
reported at Air Quality System monitoring sites, 2013-2015.
1 Maps of individual focus areas indicating 99th percentile 5-minute hourly max
2 concentrations at monitoring sites and emissions from large point sources and their
3 locations are presented in Figure 2-13 through Figure 2-18. As shown by the maps, up to
4 12 SO2 monitoring sites are located in individual focus areas. Monitoring sites in each
5 focus area are located at various distances from SO2 sources. Due to the relatively short
6 atmospheric lifetime of SO2, monitoring sites within close proximity of large point
7 sources (e.g., electric generating units, industrial sources, copper smelting facilities,
8 shipping ports) are expected to detect higher SO2 concentrations than those further
9 downwind. However, other variables, particularly stack height and wind speed and
10 direction, influence concentrations observed near sources. For example, Sites C and E in
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Cleveland are both adjacent to large sources, but Site C has a much lower concentration
than Site E despite the source near Site C emitting much more SO2 than the source near
Site E.
20 Kilometers
_l
o
B - 61 ppb
0 0.5 1 Kilometers
o
D - 52 ppb
o
F-15 ppb
A
o
30 ppb
Esri, HERE, DeLorme.
Mapmylndia, © OpenStreetMap
contributors, and the GIS user
community
I*
S02 Emissions (tons/year)
A 2,000 - 5,000
A GT 5,000 - 25,0000
~ GT 25,000-50,000
~ GT 50,000- 150,000
S02 Concentration (ppb)
O 3 to 100
• GT 100 to 200
• GT 200 to 300
• GT 300 to 400
32041 tpy
2745 tpy
48300 tpy c " 29 PPb
E -85.7 ppb
Cleveland 2133 tpy
V A
JQ
G
Strongsville
Esri, HERE, DeLorme, Mapmylndia, © OpenStreetMap Contibutors, and the GIS user community
Note: Blue circles denote monitoring sites included in the U.S. Air Quality Monitoring System. Yellow triangles denote sources
emitting 2,000 tons/yr or more according to the 2011 U.S. National Emissions Inventory. The inset, upper right, displays a wind rose
of average wind speed and direction for data acquired at Cleveland Hopkins International Airport over the 3-yr period 2013-2015.
Figure 2-13 Map of the Cleveland, OH focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015.
December 2016
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DR AFT: Do Not Cite or Quote
-------
7510tpy
A
0 5 10 Kilometers
1— 1 25738 tpy
~
F -26 ppb
2015 tpy
A
~ 3170 tpy
21196tpy
46467 tpy
4153 tpy
D
G -17 ppb
9290 tpy
A
O
E-
Q
A -12 ppb
22 ppb Q
H-10.7 ppb
D -10 ppb
25122tpy
A
G
O
B - 56 ppb
S02 Emissions (tons/year)
NORTH——-*.
o
C -18 ppb
A 2,000 - 5,000
/' \ ' ;l_ I "• . 18*
A GT 5,000 - 25,0000
/ "S;. . '12*
~ GT 25,000 - 50,000
~ GT 50,000- 150,000
S02 Concentration (ppb)
\ V<^fi
O 3 to 100
~ -11.1
• GT 100 to 200
• GT 200 to 300
scutr--"
n 3964 tpy
n A >¦
• GT 300 to 400
, HERE, DeLorme, Mapmylndia, © OpenStreetMap contributors, and the GIS user community
Note: Blue circles denote monitoring sites included in the U.S. Air Quality Monitoring System. Yellow triangles denote sources
emitting 2,000 tons/yr or more according to the 2011 U.S. National Emissions Inventory. The inset, lower center, displays a wind
rose of average wind speed and direction for data acquired at Pittsburgh International Airport over the 3-yr period 2013-2015.
Figure 2-14 Map of the Pittsburgh, PA focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015.
December 2016
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DR AFT: Do Not Cite or Quote
-------
10 Kilometers
A -13 ppb
o
0 10 20 Kilometers
1 I
o
K -10.2 ppb
B - 5.7 ppb
o
D - 6 ppb
F - 5 ppb
o
q C-9 ppb
G -10 ppb
O
J - 9 ppb
A
4600 tpy
Q
I - 3.6 ppb
Esri, HERE, DeLorme, Mapmylndia,©
OpenStreetMap contributors, and the GIS user
community
15148tpy
0
E - 5 ppb
©
O
JCTV ifieW York ©
Eliz^SfethO
Mi^cks
tavit
^cksville
h/ittown West
H - 5.3 ppb Babylo.
"tw V'SK
p""n,'"""l-6.6 ppb
S02 Emissions (tons/year)
A 2,000 - 5,000
A GT 5,000-25,0000
A GT 25,000 - 50,000
~ GT 50,000 - 150,000
S02 Concentration (ppb)
Q 3 to 100
• GT 100 to 200
• GT 200 to 300
• GT 300 to 400
Esri, HERE, DeLorme, Mapmylndia
Note: Blue circles denote monitoring sites included in the U.S. Air Quality Monitoring System. Yellow triangles denote sources
emitting 2,000 tons/yr or more according to the 2011 U.S. National Emissions Inventory, inset, upper right, displays a wind rose of
average wind speed and direction for data acquired at Newark International Airport over the 3-yr period 2013-2015.
Figure 2-15 Map of the New York City, NY focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015.
December 2016
2-40
DR AFT: Do Not Cite or Quote
-------
5 10 Kilometers
I
A '
4899 tpy c . 23 ppb
AQ
8556 tpy
B - 14 ppb
F - 15.2 ppb
o
2998 tpy A
0 E - 27.1 ppb
O D - 15.2 ppb
Esri, HERE, DeLorme, Mapmylndia, ©
OpenStreetMap contributors, and the
GIS user community
57949 tpy
Washington
S02 Emissions (tons/year)
A 2,000 - 5,000
A GT 5,000-25,0000
A GT 25,000 - 50,000
~ GT 50,000 - 150,000
S02 Concentration (ppb)
Q 3 to 100
• GT 100 to 200
• GT 200 to 300
• GT 300 to 400
A - 6 ppb
A
Flonssan r
G - 80.4 ppb
°C)
St Louis
A
15282tpy
A
(A 15234tpy
28036 tpy
0 10 20 Kilometers
1 I
Cstus 10.62W
19066tpy
Pari:
_LM_
3536 tpy
Esri, HERE, DeLorme, Mapmylndia, © OpenStreetMap contributors, and the GIS user community
Note: Blue circles denote monitoring sites included in the U.S. Air Quality Monitoring System. Yellow triangles denote sources
emitting 2,000 tons/yr or more according to the 2011 U.S. National Emissions Inventory. The inset, upper left, displays a wind rose
of average wind speed and direction for data acquired at Lambert-St. Louis International Airport over the 3-yr period 2013-2015.
Figure 2-16 Map of the St Louis, MO-IL focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015.
December 2016
2-41
DR AFT: Do Not Cite or Quote
-------
0 10 20 Kilometers
1 I
o o
H ¦ 9.2 ppb 1. 5.6 ppb
Houston E-29.8 ppb
2775 tpy
3755 tpy ^ Baytown
Suga, B-11.8 ppb
Land ... . O
Missouri -
City
senberg
Q Pasadena
D-25.1 ppb O
F - 8.8 ppb
D
C -12.4 ppb
o
G - 5.7 ppb
Pearl and
49547 tpy
~
League
NORTH' •
• 20%
S02 Emissions (tons/year)
A 2,000 - 5,000
wm • , ' 1K Texas
¦ 12% \ Oty
1 « A-16.1 ppb
A GT 5,000 - 25,0000
•WEST: i
' EAST
A GT 25,000 - 50,000
IV "> Galveston
~ GT 50,000 - 150,000
S02 Concentration (ppb)
'v<$J
¦ WND SPEED
¦ *
Q 3 to 100
• GT 100 to 200
' "-i
¦ 6.8-11.1 ^
SOUTH-"" 5 57"6<1
¦ 3.8-67 3
ZD 2.1-3.6
• GT 200 to 300
B| 0.5- 2.1
Calms: 16.11*
• GT 300 to 400
can, nerve, uci-uiiiie, Mapmylndia, © OpenStreetMap contributors, and the GIS user community
Note: Blue circles denote monitoring sites included in the U.S. Air Quality Monitoring System. Yellow triangles denote sources
emitting 2,000 tons/yr or more according to the 2011 U.S. National Emissions Inventory. The inset, upper left, displays a wind rose
of average wind speed and direction for data acquired at George Bush Intercontinental Airport over the 3-yr period 2013-2015.
Figure 2-17 Map of the Houston, TX focus area showing emissions from large
sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015.
December 2016
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DR AFT: Do Not Cite or Quote
-------
12.5
0 0.5 1 Kilometers
1 |
Inspiration
Clay pool "J
Lower 10119 tpy
Miami
D -152 jijpb a
W
®A-116.1 ppb
Esri, HERE, DeLorme, Mapmylndia. ©
C - 162 ppb OpenStreetMap contributors, and the
GIS user community
S02
Emissions (tons/year)
A
2,000 - 5,000
A
GT 5,000 - 25,0000
~
GT 25,000 - 50,000
~
GT 50,000 - 150,000
S02
Concentration (ppb)
©
3 to 100
•
GT 100 to 200
•
GT 200 to 300
•
GT 300 to 400
25 Kilometers
_J
B - 282 ppb
A 21747tpy
tsri, i-ttKt, ueLorme, Mapmylndia, © OpenStreetMap contributors, and the GIS user community
Note: Blue circles denote monitoring sites included in the U.S. Air Quality Monitoring System. Yellow triangles denote sources
emitting 2,000 tons/yr or more according to the 2011 U.S. National Emissions Inventory. The inset, lower center, displays a wind
rose of average wind speed and direction for data acquired at the Phoenix Sky Harbor Intercontinental Airport over the 3-yr period
2013-2015.
Figure 2-18 Map of the Gila County, AZ focus area showing emissions from
large sources and the 99th percentile 5-minute hourly max
concentration at ambient monitors during 2013-2015.
Table 2-7 provides the distribution of I-h daily max SO2 concentrations and monitor type
(standard vs. trace level monitor) reported at individual AQS sites in the six focus areas.
Concentrations reported at these sites were similar to nationwide SO2 concentrations
discussed earlier in this section (Section 2.5.2.1). For all but one individual monitoring
site, median concentrations were below 15 ppb. The one exception was the monitoring
site in the Gila County, AZ focus area, for which the median concentration was 39 ppb.
This particular monitoring site (Site B) is located within 1 km of a copper smelting plant
December 2016
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DR AFT: Do Not Cite or Quote
-------
1 with markedly high annual SO2 emissions [greater than 20,000 tpy SO2 (U.S. EPA.
2 2013a)]-
Table 2-7 1-h daily max sulfur dioxide concentration distribution by Air Quality
System monitoring site in six focus areas, 2013-2015.a
AQS N of
Site Label Monitoring Site ID Obs Mean Min 10% 25% 50% 75% 90% 99% Max Monitor Type
Cleveland-Elyria-Mentor, OH
A 390350065 709 6.4 0.0 0.0 1.0 3.0 7.0 13.2 55.9 125.0 Standard
B 390350060 887 11.5 0.0 0.0 2.0 6.0 16.0 32.0 62.1 92.0 Standard
C 390850003 758 7.6 0.0 2.0 3.0 6.0 10.0 15.0 37.4 95.0 Standard
D 390350038 786 14.0 0.0 1.0 4.0 10.0 20.0 32.5 61.3 105.0 Standard
E 390850007 901 11.2 0.0 2.0 3.0 6.0 11.0 22.0 117.0 201.0 Standard
F 390350045 630 3.9 0.0 0.0 0.0 2.0 5.0 9.0 30.0 51.0 Standard
Pittsburgh, PA
A 421255001 1,020 3.6 0.0 0.0 0.0 3.0 5.0 9.0 17.0 53.0 Standard
B 420030064 1,076 16.6 0.0 2.0 4.0 11.0 21.0 39.5 90.8 244.0 Standard
C 421250005 1,044 6.1 0.0 2.0 3.0 4.0 7.0 11.0 33.6 61.0 Standard
D 420030067 1,069 3.4 0.0 0.0 1.0 2.0 4.0 7.0 19.0 55.0 Standard
E 420030002 1,090 5.9 0.0 1.0 2.0 4.0 7.0 12.0 41.0 75.0 Standard
F 420070005 1,014 7.0 0.0 0.0 1.0 4.0 10.0 17.0 40.0 80.0 Standard
G 420070002 1,028 5.6 0.0 1.0 2.0 4.0 8.0 12.0 24.7 45.0 Standard
H 420030008 706 4.0 0.0 0.9 1.7 2.8 4.5 7.7 20.2 100.3 Trace
New York-Northern New Jersey-Long Island, NY-NJ-PA
A 360050133 1,089 4.0 0.2 0.9 1.5 2.8 5.3 8.9 16.5 26.5 Standard
B 340130003 1,089 1.8 0.0 0.3 0.6 1.3 2.4 3.9 7.8 13.0 Trace
C 340170006 725 1.4 0.0 0.0 0.0 1.0 2.0 4.0 9.0 11.0 Standard
D 340171002 1,090 1.4 0.0 0.0 0.0 1.0 2.0 4.0 8.0 11.0 Standard
E 340273001 1,065 1.4 0.0 0.0 0.0 1.0 2.0 3.0 9.0 20.0 Standard
December 2016
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-------
Table 2-7 (Continued): 1 h daily max sulfur dioxide concentration distribution by
Air Quality System monitoring site in six focus areas,
2013-2015.a
AQS N of
Site Label Monitoring Site ID Obs Mean Min 10% 25% 50% 75% 90% 99% Max Monitor Type
F 340390003 1,089 1.3 0.0 0.0 0.0 1.0 2.0 3.0 6.0 12.0 Standard
G 340390004 1,081 2.3 0.0 0.0 1.0 1.0 3.0 5.0 13.2 109.0 Standard
H 360590005 1,001 2.0 0.2 0.8 1.1 1.5 2.3 3.6 8.3 14.6 Standard
360790005 1,083 1.2 0.1 0.4 0.6 0.8 1.3 2.2 5.8 10.3 Standard
J 360810124 1,086 2.5 0.0 0.5 0.9 1.7 3.3 5.4 11.0 18.5 Trace
K 360050110 1,075 3.1 0.0 0.8 1.2 2.2 4.1 6.8 14.3 32.1 Standard
L 361030009 898 1.7 0.0 0.2 0.4 1.0 2.3 4.1 8.7 15.8 Standard
St. Louis, MO-IL
A 171170002 646 2.2 0.0 0.8 1.0 2.0 3.0 4.0 8.5 21.0 Standard
B 171191010 1,023 4.1 0.0 0.9 1.3 3.0 5.0 9.0 18.0 40.0 Standard
C 171193007 1,041 5.6 0.0 1.0 2.0 4.0 7.0 11.6 24.4 42.0 Standard
D 171630010 1,018 4.7 0.0 1.0 2.0 3.6 6.0 10.0 20.8 30.0 Standard
E 295100085 921 7.2 0.0 1.3 2.4 4.2 9.1 16.5 40.2 51.4 Trace
F 295100086 1,077 4.5 0.5 1.2 1.8 3.3 5.6 9.5 19.6 31.8 Standard
G 290990027 1,089 11.6 0.3 1.1 2.2 4.2 8.8 36.3 94.5 252.7 Standard
Houston-Sugar Land-Baytown, TX
A 481670005 736 3.6 0.3 1.0 1.5 2.4 3.8 6.8 26.5 50.6 Standard
B 482010051 214 3.1 0.0 0.7 1.0 1.9 3.4 6.1 22.2 44.4 Standard
C 482010062 160 3.7 0.4 1.0 1.7 2.4 4.4 7.9 18.0 19.3 Standard
D 482010416 313 5.5 0.3 0.9 1.6 3.4 6.9 12.1 33.6 54.0 Standard
E 482011035 71 4.9 0.3 0.5 1.5 2.4 5.4 13.1 25.9 29.8 Standard
F 482011039 590 2.2 0.0 0.2 0.7 1.6 2.9 5.2 11.0 16.0 Trace
G 482011050 885 1.9 0.2 0.5 0.7 1.4 2.4 3.8 9.0 16.4 Standard
H 482010046 15 3.5 1.8 1.9 2.3 2.8 3.2 4.7 12.0 13.1 Standard
482011017 415 1.5 0.0 0.4 0.6 1.0 1.9 3.3 8.3 10.6 Standard
December 2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Table 2-7 (Continued): 1 h daily max sulfur dioxide concentration distribution by
Air Quality System monitoring site in six focus areas,
2013-2015.a
AQS
Site Label Monitoring Site ID
N of
Obs
Mean
Min
10%
25%
50%
75%
90%
99%
Max
Monitor Type
Gila County, AZ
A 40070009
1,080
24.9
0.0
2.0
3.0
12.0
34.3
64.0
153.2
259.0
Standard
B 40071001
889
50.8
0.0
1.0
13.0
39.0
71.0
114.2
247.2
368.0
Trace
C 40070011
739
28.5
0.0
1.0
2.0
9.0
36.0
84.0
204.9
380.0
Trace
D 40070012
630
31.3
0.0
1.0
2.0
8.0
39.8
95.0
230.7
324.0
Trace
AQS = Air Quality System; ID = identification; max = maximum; mean = arithmetic average; min = minimum.
aMonitor values below 0 ppb have been trimmed from the data set.
More substantial site-to-site differences were observed in the 99th percentile of SO2
concentrations. Across these monitoring sites, 99th percentile concentrations ranged from
5.8 to 247.2 ppb, with the majority of sites exhibiting 99th percentile concentrations at or
below 37.5 ppb. Relatively high 99th percentile concentrations were reported at
monitoring sites within 5 km of a large SO2 point source, particularly in Gila County, AZ.
Relatively high 99th percentile concentrations were also observed in the Cleveland, OH
and Pittsburgh, PA focus areas. These data were in agreement with previous studies,
which generally observed higher urban SO2 concentrations near local
industrial/combustion sources related to oil-burning units, diesel truck traffic, and EGUs
(Cloimhcrtv et al.. 2013; Wheeler et al.. 2008).
Over the past decade, the number of AQS monitoring sites reporting 5-minute SO2
concentrations has substantially increased. At the time of the 2008 SOx ISA (U.S. EPA.
2008d). a total of 98 monitoring sites periodically reported 5-minute hourly max
concentrations. To date, approximately 380 sites report 5-minute data, including urban
sites within focus areas, sites near city centers, and sites near SO2 sources (see
Figure 2-10 in Section 2.4.3).
Similar analyses of 5-minute hourly max concentrations were performed on more recent
data reported at individual monitoring sites in the six focus areas. Table 2-8 shows the
range in 5-minute hourly max SO2 concentrations reported at individual monitors, within
the six focus areas in the 2013-2015 time frame. Median 5-minute hourly max
concentrations are below 5 ppb, while maximum concentrations range from 15 to
1,241 ppb.
December 2016
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DRAFT: Do Not Cite or Quote
-------
Table 2-8 5-minute sulfur dioxide concentrations by Air Quality System
monitoring sites in select focus areas, 2013-2015.a
AQS N of
Site Label Monitoring Site ID Obs Mean Min 10% 25% 50% 75% 90% 99% Max Monitor Type
Cleveland-Elyria-Mentor, OH
A 390350065 16,201 3.7 0.0 0.0 0.0 2.0 5.0 8.0 27.0 397.0 Standard
B 390350060 18,585 4.9 0.0 0.0 0.0 1.0 4.0 13.0 53.0 159.0 Standard
C 390850003 15,966 3.6 0.0 0.0 1.0 2.0 5.0 8.0 26.0 241.0 Standard
D 390350038 17,321 6.0 0.0 0.0 0.0 2.0 7.0 16.0 49.0 180.0 Standard
E 390850007 19,297 5.6 0.0 0.0 1.0 3.0 5.0 9.0 69.0 428.0 Standard
F 390350045 13,720 1.5 0.0 0.0 0.0 0.0 2.0 4.0 15.0 131.0 Standard
Pittsburgh, PA
A 421255001 24,367 1.5 0.0 0.0 0.0 0.0 2.0 4.0 12.0 73.0 Standard
B 420030064 25,602 6.1 0.0 0.0 1.0 2.0 7.0 16.0 56.0 493.0 Standard
C 421250005 24,930 3.3 0.0 1.0 1.0 2.0 4.0 6.0 18.0 137.0 Standard
D 420030067 25,480 1.4 0.0 0.0 0.0 1.0 2.0 4.0 10.0 89.0 Standard
E 420030002 26,001 2.4 0.0 0.0 0.0 1.0 3.0 6.0 22.0 112.0 Standard
F 420070005 24,264 3.1 0.0 0.0 0.0 1.0 3.0 8.0 26.0 155.0 Standard
G 420070002 24,572 2.2 0.0 0.0 0.0 1.0 3.0 6.0 17.0 64.0 Standard
H 420030008 16,095 1.7 0.0 0.1 0.4 1.0 2.2 3.8 10.7 158.3 Trace
New York-Northern New Jersey-Long Island, NY-NJ-PA
A 360050133 25,699 2.5 0.0 0.4 0.8 1.5 3.2 5.8 13.0 32.3 Standard
B 340130003 25,928 0.9 0.0 0.1 0.2 0.5 1.2 2.3 5.7 23.1 Trace
C 340170006 17,200 0.8 0.0 0.0 0.0 0.0 1.0 3.0 9.0 29.0 Standard
D 340171002 25,826 1.0 0.0 0.0 0.0 1.0 1.0 2.0 6.0 34.0 Standard
E 340273001 24,451 1.2 0.0 0.0 1.0 1.0 1.0 2.0 5.0 58.0 Standard
F 340390003 25,887 1.2 0.0 0.0 0.0 1.0 2.0 3.0 5.0 47.0 Standard
G 340390004 25,748 1.4 0.0 0.0 0.0 1.0 2.0 3.0 10.0 317.0 Standard
H 360590005 23,683 1.4 0.1 0.6 0.8 1.1 1.6 2.3 5.3 21.5 Standard
December 2016
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DRAFT: Do Not Cite or Quote
-------
Table 2-8 (Continued): 5-minute sulfur dioxide concentrations by Air Quality
System monitoring sites in select focus areas, 2013-2015.a
AQS N of
Site Label Monitoring Site ID Obs Mean Min 10% 25% 50% 75% 90% 99% Max Monitor Type
360790005 25,630 0.9 0.0 0.4 0.5 0.7 1.0 1.4 3.6 16.1 Standard
J 360810124 25,557 1.5 0.0 0.1 0.3 0.8 1.9 3.8 9.0 26.8 Trace
K 360050110 25,333 2.1 0.0 0.4 0.8 1.5 2.7 4.5 10.2 46.6 Standard
L 361030009 22,128 1.4 0.0 0.3 0.5 1.0 1.8 3.0 6.6 30.5 Standard
St. Louis, MO-IL
A 171170002 14,260 1.5 0.0 0.5 1.0 1.2 2.0 2.7 6.0 56.0 Standard
B 171191010 22,801 1.7 0.0 0.0 0.0 0.9 2.0 4.0 15.0 240.0 Standard
C 171193007 23,684 2.7 0.0 0.0 0.8 1.3 3.0 6.0 24.0 94.0 Standard
D 171630010 22,691 1.9 0.0 0.0 0.0 1.0 2.0 4.2 15.0 87.4 Standard
E 295100085 20,653 3.3 0.0 0.6 1.2 2.0 3.3 6.3 26.5 93.7 Trace
F 295100086 25,720 2.4 0.2 0.8 1.1 1.5 2.5 4.5 15.2 53.0 Standard
G 290990027 26,002 5.7 0.2 0.5 0.9 2.1 3.6 8.0 80.4 657.1 Standard
Houston-Sugar Land-Baytown, TX
A 481670005 16,307 1.9 0.0 0.4 0.6 1.1 2.1 3.6 15.8 84.9 Standard
B 482010051 4,523 1.1 0.0 0.2 0.3 0.6 1.2 2.3 10.3 65.9 Standard
C 482010062 3,399 1.6 0.0 0.3 0.5 1.0 1.8 3.1 12.5 33.4 Standard
D 482010416 6,982 2.4 0.0 0.3 0.6 1.0 2.3 5.2 24.1 90.9 Standard
E 482011035 1,482 2.4 0.0 0.3 0.5 1.0 2.3 4.4 26.3 75.8 Standard
F 482011039 12,547 0.9 0.0 0.0 0.0 0.5 1.1 2.2 6.8 25.7 Trace
G 482011050 19,894 1.0 0.0 0.3 0.4 0.6 1.1 2.1 5.7 21.3 Standard
H 482010046 313 1.8 0.0 0.3 0.5 1.5 2.6 3.3 7.2 15.2 Standard
482011017 8,728 0.7 0.0 0.0 0.2 0.4 0.8 1.5 5.0 25.3 Standard
December 2016
2-48
DRAFT: Do Not Cite or Quote
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Table 2-8 (Continued): 5-minute sulfur dioxide concentrations by Air Quality
System monitoring sites in select focus areas, 2013-2015.a
AQS
Site Label Monitoring Site ID
N of
Obs
Mean
Min
10%
25%
50%
75%
90%
99%
Max
Monitor Type
Gila County, AZ
A 40070009
25,732
9.2
0.0
1.0
1.0
3.1
4.5
21.6
115.5
461.0
Standard
B 40071001
20,222
19.6
0.0
0.0
1.0
2.0
10.6
55.0
252.2
1,241.2
Trace
C 40070011
16,630
9.1
0.0
0.0
0.0
1.0
3.0
22.0
142.1
694.0
Trace
D 40070012
14,156
7.6
0.0
0.0
1.0
1.0
2.0
11.0
148.0
993.0
Trace
AQS = Air Quality System; ID = identification; max = maximum; mean = arithmetic average; min = minimum.
aMonitor values below 0 ppb have been trimmed from the data set.
To evaluate the extent of SO2 spatial variability over urban geographical scales,
concentration correlations between monitoring site pairs were calculated in each of the
six focus areas. To estimate the degree to which concentrations at two different
monitoring sites followed similar temporal trends, pairwise comparisons were evaluated
using Pearson correlations. Across the six focus areas, Pearson correlations ranged from 0
to 1.0 for 24-h avg data. Correlations close to 1 represent strong correspondence over
time between pairwise monitoring site concentrations, while values close to 0 represent
poor correspondence between concentrations. Figure 2-19 and Figure 2-20 respectively
show scatterplots of pairwise correlations of 24-h avg and 5-minute hourly max SO2
concentrations versus distance between monitoring site pairs. 24-h avg concentrations are
presented due to their frequent use in epidemiologic studies, while 5-minute hourly max
concentrations are a metric of interest for short-duration exposures. Given the
meandering nature of SO2 plumes and potential for plume touchdown several kilometers
from the stack (Turner. 1970). low correlation among monitoring sites would be expected
in most cases for the 5-minute hourly max data.
Inter-site pairwise comparisons in Figure 2-19 suggest high spatial variability of the
24-h avg SO2 concentration time series. In every focus area except for New York
(discussed below), low to moderate inter-site pairwise correlations of 24-h avg SO2
concentration data were observed, with the majority of Pearson correlations below 0.6.
Inter-site pairwise correlations tended to decrease with distance. Even within relatively
short distances (up to 15 km), most inter-site pairwise correlations were low, reflecting
the variable nature of ambient SO2 across urban spatial scales, possibly due to short
atmospheric residence time, variable meteorology, and the episodic nature of the
emissions as discussed in Section 2.2.
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Cleveland
50 100
Distance (km)
Pittsburgh
50 100
Distance (km)
New York
50 100
Distance (km)
St Louis
50 100
Distance (km)
Houston
50 100
Distance (km)
bila County
50 100
Distance (km)
Pairwise correlations of 24-h avg sulfur dioxide versus distance
between monitoring site pairs in six focus areas, 2013-2015.
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Cleveland
50 100
Distance (km)
• •
• • •
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Distance (Km)
Pittsburgh
WM «*K
50 100
Distance (km)
50 100
Distance (km)
9 • \ •
50 100
Distance (km)
Gita county
50 100
Distance (km)
Figure 2-20 Pairwise correlations of 5-minute hourly max data versus
distance between monitoring sites in six focus areas, 2013-2015.
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In comparison, 5-minute hourly max SO2 concentrations had somewhat higher spatial
variability across urban spatial scales (Figure 2-20). In most cases, inter-site pairwise
correlations of 5-minute hourly max concentrations are lower (less than 0.4) and decline
more dramatically with distance than inter-site pairwise correlations of 24-h avg
concentrations. Greater spatial variability in 5-minute hourly max concentrations may be
explained by the fact that maximum metrics tend to capture peak SO2 events that are
likely more variable across urban areas than 24-h avg concentrations.
While spatial variability is evident to some degree in all urban areas, the extent of this
variability is location dependent. For example, pairwise correlations in Cleveland, OH
and St Louis, MO indicate strong SO2 spatial heterogeneity. In comparison, pairwise
correlations in New York City, NY are generally high and uniform across more than
100 km despite sometimes large distances between monitoring sites. Stronger pairwise
correlations in New York City, NY may be related to similar temporal source patterns,
given that the focus area's smaller power plants (<2,000 tpy SO2 emissions), including
gas-coal cogeneration facilities in Brooklyn, NY and Sayreville, NJ; an oil-burning
facility in Queens, NY; a coal-fired power plant in Jersey City, NJ; and numerous homes
using oil-burning heat likely have similar periods of high operation across the
metropolitan area. This is analogous to observations about similarities in traffic patterns
across large distances that promote higher correlation despite distance between the
sources (Samat et al.. 2010). Conversely, high spatial variations in Cleveland, OH and St.
Louis, MO may be explained by the presence of a limited number of sources (>2,000 tpy)
located at unevenly distributed sites across the metropolitan area.
In summary, SO2 concentrations vary substantially across urban spatial scales as
evidenced by poor to moderate inter-site pairwise correlations observed in SO2 data in six
focus areas. Spatial heterogeneity in urban-scale SO2 concentrations and their temporal
patterns may be explained by the presence of multiple, unevenly distributed SO2 sources,
meteorological factors that lead to varying degrees of SO2 dilution, or removal through
cloud/fog chemistry and deposition. Additionally, in this analysis, metrics representing
maximum SO2 concentrations generally exhibited more spatial heterogeneity than
24-h avg metrics.
2.5.3 Temporal Variability
Temporal variations in outdoor SO2 concentrations affect the magnitude, duration, and
frequency in which humans are exposed to SO2. In this section, different types of
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1 temporal trends are discussed, spanning long-term temporal trends on an annual basis to
2 short-term trends on a subhourly basis.
2.5.3.1 Long-Term Trends
3 Trends in SO2 concentrations reported at AQS monitoring sites across the U.S. from 1980
4 to 2015 are shown in Figure 2-21 for the annual 99th percentile of the 1-h daily max SO2
5 concentration. Information on SO2 concentration trends at individual, local air monitoring
6 sites can be found at https://www.epa.gov/air-trends/sulfur-dioxide-trends (U.S. EPA.
7 2012b).
S02 = sulfur dioxide.
Note: The solid line shows the mean concentrations and the upper and lower dashed lines represent the 10th and 90th percentile
concentrations, respectively.
Source: https://www.epa.aov/air-trends/sulfur-dioxide-trends.
Figure 2-21 National sulfur dioxide air quality trend, based on the 99th
percentile of the 1-h daily max concentration for 163 sites,
1980-2015. A 76% decrease in the national average was observed
from 1990-2015.
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The steady decline in SO2 concentrations over the past 25 years is largely attributed to
emissions reductions at EGUs due to the Acid Rain and NOx Budget Programs, and the
Clean Air Interstate Rule (CAIR) implemented under the Clean Air Act Amendments of
1990 (USC Title 42 Chapter 85). The goal of the Acid Rain Program was to reduce
power plant SO2 emissions by 8.95 x 106 tons from 1980 levels. Reductions in SO2
emissions commenced in 1996 and continued into the 2000s, resulting in dramatic
decreases in total, nationwide SO2 emissions and concentrations (Figure 2-5). The NOx
Budget Program and CAIR led to further reductions in SO2 emissions. From 1990-2014,
the annual 99th percentile average of 1-h daily max SO2 concentration has decreased by
76% nationally.
Substantial declines in SO2 concentration over the past decades have also been observed
on regional scales. Blanchard et al. (2013) reported an average decline of 7.6% per year
(±1.6%) in SO2 emissions from 1999-2010 across four southeastern U.S. states
(Alabama, Florida, Georgia, Mississippi), primarily due to reductions in power plant
emissions, which account for approximately 75% of total SO2 emissions in the
southeastern U.S. region. This decline corresponded to large reductions in annual SO2
concentrations (between 5.1 and 9.7% per year) reported at monitoring sites across these
four states.
2.5.3.2 Seasonal Trends
In the 2008 SOx ISA (U.S. EPA. 2008d). month-to-month trends for SO2 were observed
across a number of metropolitan areas, and these seasonal profiles varied by location.
Some cities, such as Steubenville, OH and Phoenix, AZ showed clear wintertime
maxima, while other urban areas (Philadelphia, PA; Los Angeles, CA; Riverside, CA)
exhibited higher SO2 concentrations during summer months. Differences in seasonal
profiles were attributed to variations in source emissions, topography, and meteorological
conditions among different areas.
Month-to-month variability based on more recent 1-h daily max concentrations
(2013-2015) is shown for the six focus areas introduced earlier in this chapter
(Section 2.5.2.2). Figure 2-22 displays the range of SO2 concentrations reported at all
monitoring sites within each focus area.
The data indicate that 1-h daily max SO2 concentrations vary across seasons, especially in
the higher concentrations within monthly SO2 concentration distributions. Among the
five urban focus areas, median concentrations (50th percentile: black line) varied by no
more than 6 ppb throughout the year, while the median concentration in the Gila County,
AZ focus area varied by 30 ppb. Large variations across all focus areas are observed in
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the upper end (greater than 75th percentile) of SO2 concentrations. Notably, mean
monthly SO2 concentrations were higher and more variable than median values,
indicating that the distribution is skewed by high, infrequent observations.
Recent data further demonstrate that seasonal profiles vary by location. While each focus
area exhibits some degree of seasonal variation, no consistent seasonal profile was
observed across the focus areas. For example, springtime maxima in 1-h daily max SO2
are evident in Cleveland, OH and Gila County, AZ, corresponding to focus areas with the
highest SO2 concentrations. Alternatively, New York City, NY, Houston, TX, and
Pittsburgh, PA show clear wintertime maxima.
Month-to-month variations in SO2 concentrations are consistent with month-to-month
emissions patterns (Lee etal.. 201 la) and the atmospheric chemistry of SO2.
Summertime minima, observed in the New York City, NY, and Houston, TX, focus
areas, may correspond to enhanced oxidation of SO2 to SO42 by photochemically derived
atmospheric oxidants that are more prevalent during the humid summer (Khoder. 2002).
The difference in seasonality among these cities suggest that SO2 can be substantially
variable across local and regional scales.
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Pittsburgh
a
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Sulfur dioxide month-to-month variability based on 1-h daily max
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statistical area, 2013-2015.
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2.5.3.3
Diel Variability
The 2008 SOx ISA (U.S. EPA. 2008(1) explored nationwide patterns in diel variability of
SO2 concentrations (i.e., variability of SO2 concentrations across a 24-hour period), and
found clear daytime maxima and nighttime minima, with larger day-night differences
with increasing SO2 concentrations. Daytime maxima were attributed to entrainment of
SO2 from elevated point sources (e.g., power plants and industrial sources) into the mixed
boundary layer, which expands due to rising surface temperatures.
Diel patterns were investigated in the focus areas using 1-h avg and 5-minute hourly max
SO2 data for the 2013-2015 time frame. Figure 2-23 and Figure 2-24 show variations in
1-h avg and 5-minute hourly max SO2 concentrations in the six focus areas.
Consistent with the nationwide diel patterns reported in the 2008 SOx ISA (U.S. EPA.
2008d). SO2 concentrations in the six focus areas were generally low during nighttime
and approach maxima values during daytime hours (Figure 2-23 and Figure 2-24). In
Pittsburgh, PA; New York City, NY; St. Louis, MO; Houston, TX; and Gila County, AZ,
daytime maxima occurred during early morning hours (6:00 to 9:00 a.m. LST). In
Cleveland, OH, SO2 tended to peak later in the morning or in some cases early- to
mid-afternoon.
The timing and duration of daytime SO2 peaks in the six focus areas were likely a result
of a combination of source emissions and meteorological parameters. The 2008 SOx ISA
(U.S. EPA. 2008d) concluded that higher daytime SO2 likely reflected an increase in
power plant emissions coupled with an increase in entrainment of these elevated
emissions into the lower atmosphere as the mixed layer expands throughout the day.
Distinct morning peaks may have been related to stable atmospheric conditions, which
tend to trap atmospheric pollution near the ground, resulting in an overall increase in
ground-level pollution.
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the six focus areas, 2013-2015.
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Figure 2-24 Diel trend based on 5-minute hourly max data in the six focus
areas, 2013-2015.
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Notably, SO2 concentrations were all well below the primary NAAQS level during all
hours of the day in every focus area except Gila County, AZ. In all focus areas, median
5-minute hourly max and 1-h avg concentrations were less than 5 ppb. SO2
concentrations were for the most part below 15 ppb for all but Gila County, AZ, even
when examining the upper end of the distribution of 5-minute hourly max concentrations.
For Gila County, AZ, the 95th percentile of 5-minute hourly max and 1-h avg SO2
concentrations exceeded 65 ppb and 25 ppb, respectively.
Diel SO2 concentration patterns may be influenced by seasonal factors. Diel plots of
5-minute hourly max for winter and summer are presented for Cleveland, OH and Gila
County, AZ in Figure 2-25. A clear contrast can be seen between the two locations.
Cleveland, OH exhibited very little change in diel patterns between the cold and warm
seasons. In contrast, the mode of the diel pattern occurred earlier in summer compared
with winter for Gila County, AZ. Factors that may influence the mode of the diel pattern
include peak smelter operation times and atmospheric mixing. For example, seasonal
differences in solar radiation prolong nighttime inversion periods during the winter.
Transport to downwind monitoring sites may be impeded by stable conditions. Moreover,
increased solar radiation during the summer enhances mixing, increasing the probability
of plume touchdown (Slade. 1968b). The median and average 5-minute hourly max SO2
concentrations were also somewhat lower during the summer compared with winter in
Gila County, AZ. O3 production in the summer may have promoted oxidation of SO2
(Khoder. 2002) to produce the observed losses.
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Note: For every hour, median concentrations are displayed as black lines inside the box, and the mean concentrations are displayed
as diamond-shaped red markers. The interquartile concentration range (25th to 75th percentile range) is outlined by the box, and
5th and 95th percentile concentrations are shown by the top and bottom whiskers, respectively.
Figure 2-25 Diel trend based on 5-minute hourly max data in the Cleveland,
OH and Gila County, AZ focus areas during winter and summer,
2013-2015.
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2.5.4
Relationships between Hourly Mean and Peak Concentrations
Peak concentrations within an SO2 plume can greatly exceed the mean concentration at
the plume centerline, so that exposure to the peak may be much greater than an hourly or
daily SO2 measurement. Plume dispersion is a Gaussian process, but the plume meanders
so that the peak at any instant in time exceeds the mean of the plume centerline found by
averaging over some longer time period, such as 1 hour or 1 day (Slade. 1968a; Gifford.
I960). Several studies (Dourado et al.. 2012; Schauberger et al.. 2012; Venkatram. 2002;
Turner. 1970) have characterized the peak-to-mean ratio (PMR), showing that the ratio
increases with longer averaging time. Venkatram (2002) used dispersion modeling to
illustrate the stochasticity of the dispersion process, where the mean over a longer time
period is determined by an ensemble average across simulations. At a fixed location, the
results of Venkatram (2002) imply that exposure to the plume peak occurs with varying
probabilities based on the time scale used to represent the instantaneous plume, the time
scale over which the average is computed, the intermittency of atmospheric turbulence,
and atmospheric stability.
The PMR has been computed in the literature as a function of the ratio of the
mean-to-peak concentration integration times raised to some power in the range of 0.2 to
0.5 (Venkatram. 2002) or 0 to 0.68 (Schauberger et al.. 2012). with the increasing
exponent corresponding to increased atmospheric instability. When 5-minute hourly max
data are compared with 1-h avg data, the mean-to-peak integration time ratio is
60 minutes-to-5 minutes = 12. A peak-to-mean ratio of 1 to 5.4 would be expected using
the wider range of exponents (i.e., 12° to 12°6S).
Scatterplots of collocated 5-minute hourly max and 1-h avg measurements are displayed
for all monitors in Figure 2-26 and by focus area in Figure 2-27. Data for the PMR
analyses were subject to the same completeness criteria outlined in Table 2-5
(Section 2.5.1).
PMRs were used extensively in the previous SO2 NAAQS review to evaluate the
distribution of 5-minute hourly max concentrations corresponding to a given 1-h avg SO2
concentration (U.S. EPA. 2009b). PMRs are determined by dividing the 5-minute hourly
max concentration by the 1-h avg concentration. Using this approach, a PMR of 1
demonstrates that 5-minute hourly max and 1-h avg concentrations are equivalent. A high
PMR value (up to a maximum value of 12 in this case) indicates that the 5-minute hourly
max concentration is higher than the 1-h avg concentration. For example, a PMR of 2
(shown as 2:1 on Figure 2-26 and Figure 2-27) indicates that 5-minute hourly max
concentration is 2 times higher than the 1-h avg concentration. PMR values of 1 (1:1)
through (3:1) are displayed as lines in Figure 2-26 and Figure 2-27. Median PMRs
obtained from comparing the 5-minute hourly max with the 1-h avg AQS data at sites
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where both measures were available simultaneously, and neglecting concentrations below
0 ppb, had a range of 1 to 5.5 with a median of 1.3, in reasonable agreement with the
predicted range of 1 to 5.4 for the PMR.
1 i i i r~
0 200 400 600 000
1-hr Average S02 Concentration (ppb)
S02 = sulfur dioxide.
Note: Peak-to-mean ratios are displayed on each scatter plot as 1:1 (5-min hourly max = 1-h avg), 2:1 (5-min hourly max is 2times
higher than 1-h avg), and 3:1 (5-min hourly max is 3 times higher than 1-h avg).
Figure 2-26 Scatterplot of 5-minute hourly max versus 1-h avg sulfur dioxide
concentrations, 2013-2015.
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Pitts burgh
1-hr Average S02 Concentration (ppb)
1-hr Average S02 Concentration (ppb)
Cleveland
1-hr Average S02 Concentration (ppb)
1-hr Average S02 Concentration (ppb)
Houston
Gila County
1-hr Average S02 Concentration (ppb) 1-hr Average S02 Concentration (ppb)
S02 = sulfur dioxide.
Note: Peak-to-mean ratios are displayed on each scatter plot as 1:1 (5-rnin hourly max = 1-h avg), 2:1 (5-min hourly max is 2times
higher than 1-h avg), and 3:1 (5-min hourly max is 3 times higher than 1-h avg).
Figure 2-27 Scatterplot of 5-minute hourly max versus 1-h avg sulfur dioxide
concentrations by focus area, 2013-2015.
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Table 2-9 displays the range of temporal correlations between corresponding 5-minute
hourly max and 1-h avg concentrations and the range of PMRs computed from SO2
measurements reported at these monitoring sites within the six focus areas shown in
Figure 2-27. Similar to results in the 2008 SOx ISA (U.S. EPA. 2008d). 5-minute hourly
max concentrations tend to correlate well with 1-h avg metrics, suggesting that 1-h avg
metrics, in most cases, adequately represent changes in 5-minute hourly max data over
time. However, 5-minute hourly max concentrations tend to be higher than 1-h avg
concentrations. PMRs were skewed higher for the Gila County focus area and slightly
higher for the New York City focus area. However, overall 1-h daily max concentrations
in New York were relatively low (highest 99th percentile 1-h daily max was 16.5 ppb), so
a PMR of 2 or 3 would lead to a 5-minute hourly max of 33 or 49.5 ppb. In contrast, the
1-h daily max concentrations in Gila County were much higher (highest 99th percentile
1-h daily max was 247 ppb), which would lead to 5-minute hourly max concentrations of
494 ppb if the PMR were 2 and of 741 ppb if the PMR were 3.
2.5.5 Background Concentrations
With the exception of periodic volcanic eruptions in Hawaii, natural and international
transboundary sources of SO2 make only minor contributions to the total atmospheric
burden of SO2 in the U.S. Section 2.2.4 and Section 2.2.5 describe those sources
contributing to background SO2.
No new studies have appeared that attempt to estimate background SO2 concentrations
since the 2008 SOx ISA (U.S. EPA. 2008d). The 2008 SOx ISA discussed a global scale
three-dimensional modeling study that estimated annual mean SO2 concentrations in
surface air including both anthropogenic and natural sources, using the MOZART-2
(Model of Ozone and Related Chemical Tracers) Horow itz et al. (2003). Sources
included in the study included emissions from fossil and biofuel combustion, biomass
burning, biogenic and soil emissions, and oceanic emissions. Background SO2
concentration estimates were below 0.01 ppb over much of the U.S. Maximum
background concentrations of SO2 are 0.03 ppb. In the U.S. Northwest, geothermal
sources of SO2 are responsible for 70 to 80% of the background SO2 concentration; even
so, total SO2 concentrations are still on the order of ~2 ppb or less. In these simulations,
background contributed less than 1% to SO2 concentrations in surface air in 2001
throughout much of the contiguous U.S.
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Table 2-9 Pearson correlation coefficient and peak-to-mean ratio for maximum
sulfur dioxide concentrations in the six focus areas, 2013-2015.
Focus Area
N Monitoring Sites
Correlation Coefficient
Median PMRa
Cleveland, OH
7
0.89-0.93
1.00-1.85
Pittsburgh, PA
9
0.91-0.97
1.00-1.40
New York City, NY
12
0.66-0.98
1.28-2.33
St Louis, MO
7
0.88-0.94
1.17-1.38
Houston, TX
9
0.91-0.95
1.33-1.69
Gila County, AZ
4
0.84-0.93
3.24-6.15
N = population number; PMR = peak-to-mean ratio.
aMedian PMR = 5 min max/1-h avg. The range of data represents median PMR across each site within the focus area.
Satellite-borne instruments have mapped large SO2 sources globally and have obtained
data showing intercontinental transport. Fioletov et al. (2013) identified a number of
"hotspots" for continuous SO2 emissions, both anthropogenic and volcanic
(e.g., industrial sources in China, Russia, the U.S., the Gulf of Mexico and Saudi Arabia;
volcanic sources in Kilauea, HI and Anahatan in the Marianas). Clarisse et al. (2011)
showed evidence for transport of SO2 from Asia to Alaska and Canada. In one such
episode in November 2010, there was a clearly defined plume crossing the Pacific.
As described in Section 2.2.4.2. volcanic sources of SO2 in the U.S. are found in the
Pacific Northwest, Alaska, and Hawaii. The most important domestic effects from
volcanic SO2 occur on the Hawaiian Islands. Nearly continuous venting of SO2 from
Mauna Loa and Kilauea produces SO2 in high concentrations that can affect populated
areas on the Big Island of Hawaii (as well as others in the chain, depending on wind
conditions). Figure 2-28A shows the 2008-2013 time series for 1-h daily max SO2
concentrations at Hilo, HI, (population of approximately 40,000), which is located about
50 km northeast of Kilauea. Figure 2-28B shows the same time series at Pahala
(population -1,300) which is located about 30 km southeast of Kilauea (Longo et al..
2010). As demonstrated by these figures, 1-h daily max SO2 concentrations can reach
levels greater than 1,000 ppb. Figure 2-29 shows a 6-month concentration time series for
the Ka'u District, one of the other communities scattered throughout the southern half of
the island that are also exposed to high SO2 concentrations (Longo et al.. 2010).
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S02 = sulfur dioxide.
Note. The dashed line represents the World Health Organization 24-h avg S02 guideline = 7.5 ppbv (WHO, 2006).
Data source: S02 measured continuously by a TECO pulsed-fluorescence monitor, State of Hawaii Air Quality Division.
Source Lonao et al. (2010).
Figure 2-29 Average 24-hour ambient sulfur dioxide concentrations during
low and high (volcanic gas) concentration study periods
(November 26, 2007 to June 6, 2008) for Ka'u District, located
downwind of KTIauea Volcano.
2.6 Atmospheric Modeling
1 This section discusses various modeling techniques to estimate ambient concentrations of
2 SO2. Different types of models are discussed in terms of their capabilities, strengths, and
3 limitations. Section 2.6.1 focuses on dispersion models, which are the most widely used
4 and the most relevant for modeling the influence of large point sources on local-scale SO2
5 concentrations in the urban and other near-field environments. Section 2.6.2 briefly
6 discusses chemical transport models (CTMs) that can be used to model SO2
7 concentrations at regional and national scales.
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2.6.1
Dispersion Modeling
Atmospheric transport and dispersion (ATD) models are important mathematical tools for
simulating the fate of air pollutants in support of a wide variety of environmental
assessments. ATD models can be used to estimate SO2 concentration for regulatory
purposes if monitoring data are not available or sufficient (75 CFR 35520). Using
equations that represent the physical and chemical atmospheric processes that govern
dispersal and fate, ATD models provide an estimate of the concentration distribution,
both temporally and spatially, of pollutants emitted from sources such as industrial
facilities, roadways, and urban areas. The processes that are most important vary
depending on the particular model application. The models must specifically account for
the characteristics of the source or sources of the pollutant (e.g., buoyant releases), the
meteorological conditions, the surrounding surfaces and complexities (e.g., buildings,
terrain, and trees), the background concentrations from sources not considered directly in
the modeling and the chemical transformations of the pollutant in the atmosphere.
Dispersion models are particularly important to pollutant studies where monitoring is not
practical or sufficient. For pollutants such as SO2 where spatial distributions of 1-h avg
concentrations associated with large sources often contain extreme gradients, the siting of
individual monitors to capture high ground-level concentrations over a wide variety of
sources and meteorological conditions would be challenging at best. Extensive arrays of
monitors are impractical. Thus, the implementation program for the 2010 primary SO2
NAAQS allows for air quality modeling to be used in place of monitoring to characterize
air quality, and for such air quality information to be used in the process for informing
final designation decisions (75 FR 35520). The SO2 NAAQS is currently the only criteria
pollutant standard for which modeling may be used to characterize air quality for the
purpose of the area designation process. In addition, modeling is critical to the
assessment of the impact of future sources or proposed modifications where monitoring
cannot inform. Also, modeling is helpful in the design and implementation of mitigation
techniques for addressing existing pollution problems and for compliance evaluations.
ATD models take many forms. They include steady-state (emissions and meteorology),
Gaussian-based formulations [e.g., AERMOD, (Cimorelli et al.. 2005)1; Lagrangian
models [e.g., SCIPUFF, (SvkesetaL 2007): HYSPLIT, (Draxler. 1999): (NOAA.
2014)1. which are particularly useful when emissions and meteorological conditions are
variable over the modeling increment, and Eulerian photochemical grid-based models
[e.g., Community Multiscale Air Quality (CMAQ), (Bvun and Schere. 2006)1. which
explicitly model chemical processes and have modeling resolution ranges from about one
to tens of kilometers. Additionally, there are stochastic or statistical approaches using, for
example, Monte Carlo techniques (Hanna et al.. 1982) or those using simple regression
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approaches (Baneriee etal.. 2011). For very complex flows such as a release within an
urban canopy of a city, computational fluid dynamics models are considered. Hanna et al.
(2006) demonstrated that these models are capable of reproducing the general flow and
measured tracer dispersion patterns when very detailed source and three-dimensional
building information are available.
In the U.S., steady-state Gaussian models are the most common dispersion models used
for primary pollutants like SO2 (U.S. EPA. 2010a). These models may be used to
determine compliance with standards and primary pollutant impacts from new or
proposed sources. The same is true for these types of analyses in other countries. For
example, AD MS (Carruthers et al.. 1995). HPDM (Hanna and Chang. 1993). OML
(Olesen et al.. 1992). and several other steady-state Gaussian-based models have been
recommended by the European Environment Agency (van Aalst et al.. 1998) for
applications involving SO2 from smoke stacks. Other examples in which Gaussian-type
models are found to be applicable for near-field applications are by the U.K. Department
of Environment, Food, and Rural Affairs (Williams et al.. 2011) and by the New Zealand
Ministry of the Environment (Bluett et al.. 2004). The primary concerns for many of
these compliance-type applications are the magnitude, location, and frequency of high
concentrations and the strong gradients of concentrations found near sources. Often the
highest concentrations are found within a few kilometers and sometimes within tens of
meters of the source. Near-field or near-to-the-source dispersion is the real strength of
steady-state modeling.
AERMOD is the preferred model for the vast majority of near-field applications with
OCD being used for offshore emissions and alternative models used for unique situations
(e.g. CALPUFF for Class I area screening application) where justified. AERMOD
represents a modernization of applied Gaussian models with advances in areas such as:
boundary layer scaling formulations; dispersion rates for both surface and elevated
releases; plume interactions with buildings and complex terrain; and characteristics of
point, area, and volume source types. In convective conditions, where dispersion
produces a distinctly non-Gaussian vertical pollutant distribution, AERMOD provides a
three-part formulation (each Gaussian) that when combined yield distributions
representative of those observed (Weil et al.. 1997; Brings. 1993). The challenges faced
by Gaussian models in very light wind conditions are addressed in AERMOD by
simulating a meandering plume, and providing turbulence-based lower limits on the
transport wind speed and an empirically based correction for the surface friction velocity.
In recent years, U.S. EPA has been working to improve AERMOD predictions under
light wind conditions, including an adjustment of surface friction velocity under stable
light wind conditions (80 FR 45340). For modeling applications where light and variable
winds are dominant and reliable wind field estimates are available, models such as
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SCIPUFF or HYSPLIT provide estimates of plume trajectories and more temporally
resolved concentration distributions [e.g., Wan n be re etal. (2010)1.
AERMOD and models like it are designed to simulate concentrations on an hourly
increment, and model evaluations are focused on averaging times of 1 hour or greater
(Perry et al.. 2005). Longer term concentrations are obtained by averaging the 1-hour
concentrations. Spatial resolution is simply determined by the density of receptors
included in the analysis (i.e., very high resolution possible). For each hour, emissions and
other source characteristics, land surface characteristics, and meteorological conditions
are provided to the model. Additionally, the model requires a description of buildings and
complex terrain within the modeling domain that are expected to influence pollutant
dispersion. The model can simulate hundreds of sources and receptors, providing for
analyses in urbanized and industrialized areas.
One limitation of the Gaussian approach is the assumption of steady conditions over a
1-hour modeling period and over the plume transport distance to the receptors. The model
is recommended for receptors up to 50 km from a source when steady conditions are
appropriate (U.S. EPA. 2005b). However, this can be challenging, especially for light
winds. Under low wind conditions, there are concerns that AERMOD can overestimate
measured SO2 concentrations without adjustment for empirical relationships between
wind and concentration (Paine et al.. 2015). Recent updates to AERMOD have been
made by the U.S. EPA to address those concerns (80 FR 45340). AERMOD is also
limited in its treatment of SO2 chemistry, using a method much simpler than the more
rigorous simulation of atmospheric transformation of SO2 found in models such as
CMAQ or SCICHEM (Chowdhurv et al.. 2012). AERMOD uses a simple 4-hour half-life
assumption for reducing SO2 concentration in the plume with travel time (Turner. 1964).
This approach yields results consistent with the SO2 residence time estimates by Hidv
(1994) and Seinfeld and Pandis (2006). Therefore, for conditions and sources where the
highest hourly concentrations are expected to be relatively close to the source, chemistry
is not expected to play a major role in determining compliance with primary standards.
Lagrangian puff dispersion models, such as CALPUFF, have been developed as an
alternative to Gaussian dispersion models, such as AERMOD. CALPUFF models SO2 as
particles and then uses a Lagrangian step algorithm to model nonsteady-state dynamics,
using time-varying winds specified by meteorological models, such as MM5 [e.g., Atabi
et al. (2016). Abdul-Wahab et al. (2011). Souto et al. (2014). Lee et al. (2014). Zhang et
al. (2015a) I. The nonsteady-state approach offered by Lagrangian puff dispersion models
may be considered an alternative to Gaussian dispersion models that do not account for
time dependence. Comparisons have been conducted between Lagrangian models such as
CALPFUFF and Gaussian plume models such as AERMOD. CALPUFF predictions of
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24-hour SO2 concentrations at an oil refinery in Sohar, Oman compared within 36% of
measurements (Abdul-Wahab et al.. 20I I). Comparison of CALPUFF and AERMOD to
SO2 measurements at a gas refinery in South Pars, Qatar showed that, while CALPUFF
and AERMOD both typically underestimated SO2 measurements, CALPUFF predictions
were usually closer to measured SO2 concentrations compared with AERMOD (Atabi et
al.. 2016). However, Rood (2014) observed that Lagrangian puff models and Gaussian
dispersion models both underpredicted 1-h and 9-h avg concentrations, but the magnitude
of bias was larger in the Lagrangian puff models applied at a field site in Colorado with
variable winds and natural topography. Holnicki etal. (2016) noted that the model
performance improved with longer averaging times and that the 1-h avg concentration
predicted by CALPUFF was less accurate than predictions for annual average
concentrations, when compared to SO2 measurements. However, recent dispersion
modeling results were compared between CALPUFF and AERMOD for the Section 126
Petition from New Jersey for the Portland Generating Station (76 FR 69052) where
CALPUFF overestimated 1-h daily max SO2 observations taken in Columbia, NJ by
226%, while AERMOD overestimated the same observations by 14%.
Uncertainty in the model predictions is influenced by the uncertainty in model input data
(in particular emission or source characterization and meteorological conditions) as well
as by inadequacies in model formulations. Uncertainty related to model input variables is
generally estimated by propagating the expected errors in the individual input variables
(e.g., wind speed, emission rate) through the model using Monte Carlo techniques
(Dabberdt and Miller. 2000). In addition, there is uncertainty related to the fundamental
difference between modeled and measured concentrations. Monitored data (within
sampling error) represents actual realizations of events, while modeling estimates
represent ensemble mean concentrations (Rao. 2005). Based on a study comparing a
variety of models (including Gaussian) to a number of tracer field study results, Hanna et
al. (1993) found that for continuous point releases and receptors within a kilometer of the
source, uncertainty in model inputs in combination with the stochastic nature of the
atmosphere result in typical mean biases on the order of 20 to 40% and normalized mean
square errors up to 70%. The author points out that these levels of difference between
model and monitor results would likely exist even for more sophisticated models. Hanna
(2007) provided a comprehensive review of methods for determining sensitivity and
uncertainty in ATD models.
Focusing on the uncertainties in model inputs, it is easy to see that an individual model
estimate paired in time and space with a monitored concentration will likely differ,
sometimes substantially, due to the propagation of errors through the model. Weil (1992)
pointed out that wind direction uncertainties alone can cause disappointing results in
space and time pairings from otherwise well-performing dispersion models. With wind
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direction errors, the plume footprints from the model and that from the observations may
not overlap. However, a model that is based on appropriate characterizations of the
important physical processes should be able to reproduce the distribution of observed
concentrations assuming that the distributions of model inputs is similar to that of the
observed conditions (Venkatram et al.. 2001). Meteorological inputs coupled with
AERMOD can impact the results, and the output may depend on the use of recorded
meteorological observations or meteorological models (e.g., Weather Research and
Forecasting (WRF) model). Meteorological models may add error to the dispersion
simulation, and that error is impacted by model selection and resolution (Isakov et al..
2007). Therefore, in evaluating a model's ability to predict concentrations within the
modeling domain, it is important to include an analysis of modeled and monitored
concentration distributions for any location studied. As part of the proposed update to the
Guideline on Air Quality Models, U.S. EPA proposed to allow the use of prognostic
meteorological data for regulatory applications of AERMOD (80 FR 45340). U.S. EPA
conducted several assessments comparing observed meteorological data to prognostic
meteorological data and found that the prognostic data performed adequately (U.S. EPA.
2015a).
Chang and Hanna (2004) provided a comprehensive discussion of methods for evaluating
the performance of air quality models. They discuss a series of performance measures
that included statistical metrics such as fractional bias (FB), geometric mean bias,
normalized mean squared error and the fraction of estimates within a factor of two
observations. These and other measures are included in the commonly used BOOT
software (Chang and Hanna. 2005). which also allows for estimation of confidence limits
on the concentrations computed and provides insight about the sources of bias in the
model (Irwin. 2014). Chang and Hanna (2004) also discussed exploratory analysis
methods of plotting and analyzing the modeled and measured concentrations. They
pointed out that the most useful model evaluation studies are those that examine a
number of models and compare them with a number of field studies.
For models intended for application to compliance assessments (e.g., related to the
1-h daily max SO2 standard), the model's ability to capture the high end of the
concentration distribution is important. Measures such as robust highest concentration
(RHC) (Cox and Tikvart. 1990). and exploratory examinations of quantile-quantile plots
(Chambers et al.. 1983) are useful. The RHC represents a smoothed estimate of the top
values in the distribution of hourly concentrations. In contrast, for dispersion modeling in
support of health studies where the model must capture concentrations at specified
locations and time periods, additional measures of bias and scatter are important.
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The intended use of a model and the objective of a model evaluation guide the selection
of evaluation criteria. Frost (2014) considered model performance for AERMOD, applied
to the study of 1 year of SO2 emissions from three coal-fired EGUs. The authors found
good agreement (judged to be within a factor of two of the 99th percentile SO2 design
value) for the majority of the data but noted performance outside a factor of two for the
top 5% of measured 1-h avg concentrations. However, Rehbein et al. (2014) found that
the model fell within a factor of two of the monitoring data even at high concentrations
for a model validation outside a nickel smelting facility in Sudbury, Ontario, Canada.
U.S. EPA also conducted evaluations of prognostic meteorological data in AERMOD
(U.S. EPA. 2015a'). including the facility modeled by Frost (2014). These evaluations
included data analysis adhering to the U.S. EPA Protocol for Best Performing Models,
which includes a scientific and operational component of model performance (U.S. EPA.
1992). SO2 concentrations modeled by AERMOD were within a factor of two of
observations in all but one simulation when using the metrics of the protocol.
Meteorological parameters were modeled with FB within 20% of observations (U.S.
EPA. 2015a).
At the time of its inclusion into the U.S. EPA Guideline on Air Quality Models (U.S.
EPA. 2005b). the performance of AERMOD was evaluated against seventeen field-study
databases over averaging times from 1 hour to 1 year (Perry et al.. 2005). In each case,
the emissions characteristics and background concentrations were well known;
meteorological data were available on site; and tracer concentrations were measured at
multiple locations where high plume impacts were expected. Four of the studies involved
very dense sampler arrays. For the four intensive studies, Perry et al. (2005) found the
ratio of modeled 1-h avg RHC to monitored RHC ranged from 0.77 to 1.18
[i.e., relatively unbiased in estimating extreme (high) values]. For studies involving tall
buoyant stacks with more limited monitoring locations, 1-hour ratios were not reported,
but the 3-h avg ratios ranged from 1.0 to 1.35 (i.e., a slight tendency to overpredict the
high concentrations). Examination of quantile-quantile plots supported the findings that
the model was capturing the upper end of the 1- and 3-h avg concentration distribution.
Hanna et al. (2001) evaluated the AERMOD and ADMS Gaussian dispersion models
with five field study databases including area sources, low releases and tall power plant
stacks in rural, flat, and complex terrain. Among the median performance measures they
reported, the ratio of maximum modeled to maximum observed concentrations was 0.77
for AERMOD and 0.80 for ADMS, each a small underprediction. The median value over
the five databases of the geometric mean (MG, a measure of the ratio of averaged
modeled to monitored concentration) was 1.7 for AERMOD and 1.22 for ADMS. With
1.0 as the ideal value, both models were found to overpredict (with ADMS less biased).
Unlike the ratio of maximum values, MG is a measure of performance over the entire
distribution of concentrations. Hurlev (2006) also evaluated AERMOD and two
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Australian models against seven field studies and found no database against which
AERMOD performed poorly.
With the adoption of the 2010 1-h daily max SO2 standard, there is renewed interest in
AERMOD's abilities to simulate near-field maximum short-term concentrations.
A number of specific areas for model improvement were discussed at the 10th and 11th
Modeling Conference on Air Quality in 2012 (U.S. EPA. 2012a) and 2015 (U.S. EPA.
2016a). Among them were concerns about simulations in stable conditions with light and
meandering winds, use of prognostic meteorological data, modeling of emissions from
haul roads, plume chemistry, and building downwash. Proposed improvements include an
adjusted friction velocity model for stable/low wind conditions in AERMET, a new
model for dispersion options in AERMOD, and an option for buoyant line sources in
AERMOD (U.S. EPA. 2016a). Research in many of these areas is underway, and
improvements to AERMOD have been made based on the outcomes of those
conferences, largely as part of EPA rulemaking to revise the Guideline. While the
stochastic nature of the atmosphere will always preclude the development of a perfect
model, improvements to the model formulations will continue with the goal of estimating
hourly average concentrations while reducing model uncertainty and expanding
applicability.
2.6.2 Chemical Transport Models
Chemical transport models are an important tool for characterizing regional- and
national-scale air quality. The scales at which they typically operate are too large to
satisfactorily capture meteorological and chemical processes involving SO2 at the local or
near-source scale. The dispersion models discussed previously are thus preferable for
characterizing SO2 concentrations at these scales.
Chemical transport models such as the Community Multiscale Air Quality (CMAQ)
model, are deterministic models of chemical transport that account for physical and
chemical processes, including advection, turbulence, diffusion, deposition, gas-phase and
heterogeneous chemistry, and convective cloud transport, while following the constraint
of mass conservation (Bvun and Schere. 2006). CTMs provide regional concentration
estimates and are typically run with horizontal grid resolutions of 4, 12, or 36 km.
Temporal resolutions are typically 1 hour, although larger temporal aggregation often
occurs for the purpose of maintaining reasonable data file size. CTMs are used to
compute interactions among primary atmospheric pollutants and their transformation
products, the production of secondary aerosols, the evolution of particle size distribution,
and transport and deposition of pollutants. CTMs are driven by emissions inventories for
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primary species such as SO2, NO2, NH3, VOCs, and primary PM, and by meteorological
fields produced by other numerical weather prediction models. Values for meteorological
variables such as winds and temperatures are taken from a meteorological model that is
nudged by operational analyses, re-analyses, or general circulation models. In most cases,
these are off-line meteorological predictions, thus they are not modified by radiatively
active species generated by the air quality model. Work to integrate meteorology and
chemistry was initiated in the mid-1990s [by Lu et al. (1997a) and Lu et al. (1997b) and
references therein], although limits to computing power prevented widespread
application. More recently, new integrated models of meteorology and chemistry are
available; see, for example, the Weather Research and Forecast model with chemistry
(WRF-Chem: http://rue.noaa. gov/wrf/wrf-chem/) and WRF-CMAQ (Wong et al.. 2012).
Biases in SO2 concentrations predicted by CTMs can occur as a result of error in model
representation of atmospheric processes converting SO2 to H2SO4 and in removal
processes. For example, overestimates of cloud-based reactions converting SO2 to H2SO4
have been shown to negatively bias SO2 concentration estimates in CMAQ v4.6 (Mueller
et al.. 2011). Improvements to modeling these processes, such as capturing metal
catalysis of the SO2 H2SO4 conversion process, have been included in CMAQ v5.0.2
to improve model estimates of SO2 and SO42 (Alexander et al.. 2009). Therefore, when
using CMAQ to estimate exposure to SO2, attention must be given to the version of the
model so that any inherent biases are understood.
The Air Quality Model Evaluation International Initiative (AQMEII) was developed by
scientists in Europe and North America to evaluate several CTMs against each other
using common input data sets (Rao etal.. 2011). Pouliot et al. (2015) assembled
emissions input data for European and North American simulations performed over two
phases of the AQMEII study and found a 12% reduction in SO2 emission estimates for
2006 in both Europe and North America. These differences were attributed to differences
in methodologies used to estimate emissions and to differences in input data that
influence the CTM output. In a comparison of CTM models of SO2 with surface
measurements in Europe, the Modeling Atmospheric Composition and Climate (MACC)
model reanalysis overestimated surface SO2 concentrations by 40% in winter and
underestimated surface SO2 levels by 63% in summer (Giordano et al.. 2015). In North
America, MACC underestimated SO2 in summer by 81%. MACC results were higher
than regional CTMs in the winter for North America, and seasonal variability was not
well captured (r = 0.16 in summer and r = 0.19 in winter). These errors were thought to
relate to the differences in the lifetime of SO2 transported from the domain borders to the
domain center being shorter than the time scale of the model bias.
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2.7
Summary
Of the sulfur oxides, SO2 is the most abundant in the atmosphere, the most important in
atmospheric chemistry, and the one most clearly linked to human health effects. Thus, the
NAAQS are currently set using SO2 as the indicator species. As a consequence of several
U.S. air quality regulatory programs, emissions of SO2 have declined by approximately
72% for all NEI source categories during the time period 1990-2011 (Section 22).
Coal-fired EGUs remain the dominant anthropogenic source by nearly an order of
magnitude above the next highest source (coal-fired boilers), emitting 4.6 x 106 tons SO2
annually, according to the 2011 NEI. Natural sources include volcanoes, wildfires, and
biogenic sulfides that are intermittent and of limited spatial extent.
Beyond the size of the emissions source, the important variables that determine the
concentration of SO2 downwind of a source are the photochemical removal processes
occurring in the emissions plume (Section 2.3) and local meteorology. The gas-phase
oxidation of SO2 by hydroxyl radical is slow in comparison to aqueous-phase oxidation
in cloud and fog droplets. Clouds and fog can reduce local SO2 concentrations by
converting it to H2SO4 in the droplet phase. Another gas-phase oxidation mechanism
involves a Criegee intermediate biradical that participates in converting SO2 to SO3.
The Criegee-based SO2 oxidation mechanism may amplify the rate of SO2 removal in
areas with high concentrations of Criegee precursors (i.e., low molecular weight organic
gases, such as biogenic compounds, and unsaturated hydrocarbons) present downwind of
industrial sites and refineries. The atmospheric SO2 oxidation processes, coupled with
variable meteorological conditions, including wind, atmospheric stability, humidity, and
cloud/fog cover, influence the observed SO2 concentrations at urban monitoring sites.
Changes were undertaken to the existing U.S. EPA monitoring network as a result of the
new 1-h daily max primary NAAQS standard promulgated in 2010 (Section 2.4). First,
the automated pulsed ultraviolet fluorescence (UVF) method, the method most
commonly used by state and local monitoring agencies for NAAQS compliance, was
designated as a FRM. Second, new SO2 monitoring guidelines require states to report
5-minute data in light of health effects evidence on lung function decrements among
exercising individuals with asthma following a 5-10 minute exposure of SO2 above
200 ppb (Section 5.2.1.2). There are 380 monitoring sites across the U.S. reporting
5-minute data. Analysis of environmental concentrations of SO2 data reported in this
chapter reflect the monitoring network changes, particularly the analysis of the recent
5-minute data.
On a nationwide basis, the average 1-h daily max SO2 concentration reported during
2013-2015 is 5.4 ppb (Section 2.5.2.1). However, peak concentrations (99th percentile)
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of the 1-h daily max SO2 concentrations can be greater than 75 ppb at some monitoring
sites located near large anthropogenic or natural sources (e.g., volcanoes). SO2
concentration is highly variable across urban spatial scales (Section 2.5.2.2), exhibiting
moderate to poor correlations between SO2 concentrations measured at different
monitoring sites across a metropolitan area. This high degree of urban spatial variability
may not be fully captured by central site monitoring estimates.
Long-term concentration trends show a steady decline in the mean, 10th, and 90th
percentile of the site-specific 99th percentile of the 1-h daily max SO2 concentrations
(Section 2.5.3). The data show a 76% decline in 99th percentile 1-h daily max SO2
concentration over the period 1990-2015. Seasonal trends were examined for six focus
areas, and only New York and, to a lesser extent, Houston, exhibited strong intra-annual
trend in which cool season 1-h daily max SO2 concentrations were higher than warm
season 1-h daily max SO2 concentrations. Diel patterns in 1-h avg SO2 concentration
mostly shows daytime concentrations peak in the morning or midday, and the time of the
peak can vary by location and may be influenced by seasonal conditions.
Peak concentrations within an SO2 plume can greatly exceed the mean concentration at
the plume centerline, so that exposure to the peak may greatly exceed an hourly or daily
SO2 measurement (Section 2.5.4). PMRs obtained from comparing the 5-minute hourly
max with the 1-h avg AQS data at sites where both measures were available
simultaneously had a range of 1 to 5.5 with a median of 1.3. In a city with low SO2
concentrations, a high PMR may still be related to elevated 5-minute hourly max SO2
concentration. For example, overall 1-h daily max concentrations in the New York focus
area were relatively low (highest 99th percentile 1-h daily max was 16.5 ppb), so a PMR
of 2 or 3 would lead to a 5-minute hourly max of 33 or 49.5 ppb. In contrast, the
1-h daily max concentrations in Gila County were much higher (highest 99th percentile
1-h daily max was 247 ppb), which would suggest 5-minute hourly max concentrations of
504 ppb if the PMR were 2 and of 741 ppb if the PMR were 3.
Contributions to background concentrations include natural emissions of SO2 and
photochemical reactions involving reduced sulfur compounds of natural origin, as well as
the transport of sulfur compounds from outside of the U.S. (Section 2.5.5). In the U.S.
Northwest, geothermal sources of SO2 are responsible for 70 to 80% of the background
SO2 concentration; even so, total SO2 concentrations are still on the order of ~2 ppb or
less. In model simulations, background contributed less than l%to SO2 concentrations in
surface air in 2001 throughout much of the contiguous U.S. Even with ambient
concentrations for 2013-2015 that were roughly half the magnitude of those measured
around 2001, the estimated background SO2 would contribute only 2% to ambient SO2
concentrations in most of the contiguous U.S.
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1 Atmospheric modeling includes dispersion and chemical transport models to estimate
2 SO2 concentrations in locations where monitoring is not practical or sufficient
3 (Section 2.6). Because existing ambient SO2 monitors may not be sited in locations to
4 capture peak 1-h daily max concentrations, the implementation program for the 2010
5 primary SO2 NAAQS allows for air quality modeling to be used to characterize air
6 quality for informing designation decisions (75 FR 35520). Modeling is critical to
7 assessing the impact of future sources or proposed modifications when monitoring cannot
8 be informative, and for designing and implementing mitigation techniques.
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Chapter 3 Exposure to Ambient Sulfur Dioxide
3.1
Introduction
The 2008 SOx ISA (U.S. EPA. 2008(1) evaluated ambient SO2 concentrations and
exposure assessment in multiple microenvironments, presented methods for estimating
personal and population exposure via monitoring and modeling, analyzed relationships
between personal SO2 exposure and ambient SO2 concentrations, and discussed the
implications of using ambient SO2 concentrations to estimate exposure in epidemiologic
studies. This chapter summarizes that information and presents new information
regarding exposure to ambient SO2. The chapter will focus on the inhalation exposure
route for SO2 from the key sources described in Chapter 2 because the presence of other
SOx species in the atmosphere has not been demonstrated, as discussed previously.
Exposure to particulate sulfate formed by oxidation of SO2 is considered in the PM ISA
(U.S. EPA. 2009a). Sections within the chapter are organized to first present broad
exposure concepts applicable to air pollution in general, followed by SCh-specific
material. Topics addressed in the chapter include methodological considerations for use
of exposure data, and exposure assessment and epidemiologic inference. Many new
studies are included in this chapter to better characterize exposure and understand
exposure error. This material provides context for interpreting the epidemiologic studies
described in Chapter 5.
A variety of metrics and terms are used to characterize air pollution exposure. They are
described here at the beginning of the chapter to provide clarity for the subsequent
discussion.
The concentration of an air pollutant is defined as the mass or volume of the pollutant in
a given volume of air (e.g., |ig/m3 or ppb). Concentrations observed in outdoor locations
are referred to as ambient concentrations. The term exposure refers to contact with a
specific pollutant concentration over a certain period of time (Zartarian et al.. 2005). in
single or multiple locations. For example, contact with a concentration of 10 ppb SO2 for
1 hour would be referred to as a 1-hour exposure to 10 ppb SO2, and 10 ppb is referred to
3.2
Conceptual Overview of Human Exposure
3.2.1
Exposure Metrics
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as the exposure concentration. As discussed in Chapter 4. dose incorporates the concept
of intake into the body (via inhalation). Exposure concentrations are particularly relevant
for interpreting controlled human exposure studies, where participants are exposed to a
well-defined pollutant concentration, or panel epidemiologic studies that use personal
exposure monitors. Ambient concentrations are more relevant to epidemiologic studies
using measured or modeled concentrations.
A location where exposure occurs is referred to as a microenvironment, and an
individual's daily exposure consists of the time-integrated concentrations in each of the
microenvironments visited during the day. Ambient air pollution may penetrate indoors
(see Section 3.4.1.1 on infiltration), where it combines with air pollution from indoor
sources (nonambient air pollution) to produce the total measured indoor concentration.
Exposure to the ambient fraction of this concentration, together with exposure to ambient
concentrations in outdoor microenvironments, is referred to as ambient exposure (Wilson
et al.. 2000).
Because personal exposures are not routinely measured, the term surrogate is used in this
chapter to describe a quantity meant to estimate or represent exposure, such as an SO2
concentration measured at a central site monitor (Sarnat et al.. 2000). When surrogates
are used for exposure assignment in epidemiologic studies, exposure misclassification or
exposure error can result. Exposure misclassification refers to exposure error for
categorical variables, such as diseased and nondiseased individuals. Exposure
misclassification due to exposure assignment methods and spatial and temporal
variability in pollutant concentrations may be either differential (i.e., systematic), or
nondifferential (i.e., random). An example of differential misclassification is the use of
geocoding to estimate air pollution exposure by proximity to roadways, because
concentrations are different upwind and downwind of a major roadway (Lane et al..
2013; Singer et al.. 2004). Nondifferential misclassification refers to the situation where
exposure characterization is similarly accurate across all groups.
Exposure misclassification and exposure error can result in bias and reduced precision of
the effect estimate. Bias refers to the difference between the population-average
measured and true exposure, while precision is a measure of the variation of
measurement error in the population (Armstrong et al.. 1992). Bias toward the null, or
attenuation of the effect estimate, indicates an underestimate of the magnitude of the
effect, and is characteristic of nondifferential measurement error. Bias away from the null
can occur through differential exposure measurement error or under certain exposure
scenarios (Armstrong et al.. 1992).
Exposure error refers to the bias and uncertainty associated with using concentration
metrics to represent the actual exposure of an individual or population (Lipfcrt and
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Wvzga. 1996). Exposure error has two components: (1) exposure measurement error
derived from uncertainty in the metric being used to represent exposure, and (2) use of a
surrogate target parameter of interest in the epidemiologic study in lieu of the true
exposure, which may be unobservable. Classical error is defined as error scattered around
the true personal exposure and independent of the true exposure. Berkson error is defined
as error scattered around the measured exposure surrogate (in most cases, the central site
monitor measurement) and independent of the measured value (Goldman et al.. 2011;
Reeves et al.. 1998). Section 3.4.4 provides additional definitions for specific types of
exposure error and discusses the potential impact of such errors on epidemiologic study
results.
3.2.2 Conceptual Model of Personal Exposure
A theoretical model of personal exposure is presented in this section to highlight
measurable quantities and uncertainties. This model has been developed and presented in
previous ISAs, most recently in the 2016 ISA for Oxides of Nitrogen (U.S. EPA. 20166*).
and it is reproduced here to provide context for the current document.
An individual's time-integrated total exposure to SO2 can be described based on a
compartmentalization of the person's activities throughout a given time period:
ET = J Cj dt
Equation 3-1
where AY = total exposure over a time period of interest, Q = airborne SO2 concentration
at microenvironment j, and dt = portion of the time period spent in microenvironment j.
Total exposure can be decomposed into a model that accounts for exposure to SO2 of
ambient (A'„) and nonambient (A',1;i) origin of the form:
Et — Ea + Ena
Equation 3-2
Although indoor combustion of sulfur-containing fuels, particularly kerosene, is a
nonambient source of SO2 (see Section 3.4.1). these sources are specific to individuals
and may not be important sources of population exposure. This ISA focuses on the
ambient component of exposure because this is more relevant to the NAAQS review.
Assuming steady-state outdoor conditions, A';i can be expressed in terms of the fraction of
time spent in various outdoor and indoor (including enclosed microenvironments such as
vehicles) microenvironments (U.S. EPA. 2006; Wilson et al.. 2000):
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Ea ~ Zf0C0 + ZfiFinf,iC0,i
Equation 3-3
where/= fraction of the relevant time period (equivalent to dt in Equation 3-1); subscript
o denotes outdoor microenvironments; subscript /' denotes indoor microenvironments;
subscript o,i denotes outdoor microenvironments adjacent to a given indoor
microenvironment; and Fmj = infiltration factor for indoor microenvironment i.
Equation 3-3 is subject to the constraint ~Lf0 + "Lfi = 1 to reflect the total exposure over a
specified time period, and each term on the right-hand side of the equation has a
summation because it reflects various microenvironmental exposures. Here, "indoors"
refers to being inside any aspect of the built environment, [e.g., home, office buildings,
enclosed vehicles (automobiles, trains, buses), and/or recreational facilities (movie
theaters, restaurants, bars)]. "Outdoor" exposure can occur in parks or yards, on
sidewalks, and on bicycles or motorcycles. Assuming steady-state ventilation conditions,
the infiltration factor (I'm) is a function of the penetration (/') of SO2 into the
microenvironment, the air exchange rate (a) of the microenvironment, and the rate of SO2
loss (k) in the microenvironment:
In epidemiologic studies, the central site ambient SO2 concentration, Ca, is often used in
lieu of outdoor microenvironmental data to represent these exposures based on the
availability of data. Thus, it is often assumed that the local outdoor concentration C0 = Ca
and that the fraction of time spent outdoors can be expressed cumulatively as the
indoor terms still retain a summation because infiltration differs for different
microenvironments. If an epidemiologic study employs only Ca, then the assumed model
of an individual's exposure to ambient SO2, given in Equation 3-3. is re-expressed solely
as a function of Ca:
The spatial variability of outdoor SO2 concentrations due to meteorology, topography,
and oxidation rates; the design of the epidemiologic study; and other factors determine
whether Equation 3-5 is a reasonable approximation for Equation 3-3. These equations
also assume steady-state microenvironmental concentrations. Errors and uncertainties
inherent in using Equation 3-5 in lieu of Equation 3-3 are described in Section 3.4.4 with
respect to implications for interpreting epidemiologic studies. Epidemiologic studies may
Pa
Finf ~ (a + k)
Equation 3-4
Equation 3-5
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use concentration measured at a central site monitor to represent ambient concentration;
thus a, the ratio between personal exposure to ambient SO2 and the ambient concentration
of SO2, is defined as:
a
Equation 3-6
Combining Equation 3-5 and Equation 3-6 yields:
a — fo ^fi Finf,i
Equation 3-7
where a varies between 0 and 1. Estimates of a for SO2 are provided in Section 3.4.1.3. If
a person's exposure occurs in a single microenvironment, the ambient component of a
microenvironmental SO2 concentration can be represented as the product of the ambient
concentration and I'm. Time-activity data and corresponding estimates of I'm for each
microenvironmental exposure are needed to compute an individual's a with accuracy
(U.S. EPA. 2006). In epidemiologic studies, a is assumed to be constant in lieu of
time-activity data and estimates of Fjnf, which varies with building- and
meteorology-related air exchange characteristics (Section 3.4.1.IV If important local
outdoor sources and sinks exist that are not captured by central site monitors, then the
ambient component of the local outdoor concentration may be estimated using dispersion
models, land use regression (LUR) models, receptor models, fine-scale chemical
transport models (CTMs), or some combination of these techniques. These techniques are
described in Section 3.3.2.
The inhalation exposure pathway relevant for SO2 is influenced by sources, chemistry,
meteorology, and ambient concentrations, described in detail in Chapter 2 and
summarized briefly here. The vast majority of SO2 is emitted by coal-fired EGUs
(Section 22); the point source nature of these emissions contributes to the relatively high
spatial variability of SO2 concentrations (both ambient and exposure) compared with
pollutants such as PM and O3 (Section 2.5; Section 3.4.2.2V Another contributing factor
to spatial variability is the dispersion and oxidation of SO2 in the atmosphere
(Section 23), resulting in decreasing ambient SO2 concentrations with increasing
distance from the source. SO2 travels as a plume, which may or may not impact portions
of an urban area depending on meteorological conditions. Ambient SO2 concentrations do
not exhibit consistently strong temporal variability over daily or seasonal time scales
3.2.3
Exposure Considerations Specific to Sulfur Dioxide
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(Section 2.5); however, in some areas, concentrations are low during nighttime and show
a daytime maximum, affecting temporal exposure patterns. Due to the relative lack of
indoor SO2 sources, personal SO2 exposure is expected to be dominated by ambient
exposure (Section 3.4.1.3).
3.3 Methodological Considerations for Use of Exposure Data
This section describes techniques that have been used to measure microenvironmental
concentrations of SO2 that serve as surrogates for personal SO2 exposures in
epidemiologic studies. Previous studies from the 2008 SOx ISA (U.S. EPA. 2008d) are
described along with newer studies.
3.3.1 Measurements
3.3.1.1 Central Site Monitoring
Central site monitors are sited for the purpose of determining whether attainment goals
are met under the Clean Air Act. However, central site monitoring ambient SO2
concentration data are also often used in epidemiologic studies as a surrogate for
exposure to SO2, as discussed in Section 3.4.4. Methods, errors, and uncertainties
regarding measurements made by central site monitors are described in Section 2A.
The effect of errors and uncertainties due to instrumentation issues depends on
epidemiologic study design, as described further in Section 3.4.4. Various uses of these
data are possible depending on the design of the epidemiologic study. Short-term
(e.g., daily, hourly) data can be used for time-series studies and long-term (e.g., annual
average) data for longer term studies. For a given CBSA, central site monitors are sited at
a fixed location based on the number of people living in the CBSA and the sources of
SO2 emissions (40 CFR 58, Appendix D). Even in CBSAs with multiple monitors, the
monitors do not fully capture spatial variability in SO2 concentration across the study
area.
3.3.1.2 Personal Monitoring Techniques
Personal SO2 monitors have been used in studies characterizing relationships between
indoor and outdoor SO2 concentrations and relationships between personal exposure to
SO2 and ambient SO2 concentrations (Section 3.4.1.3). Additionally, personal monitoring
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is used infrequently in the epidemiologic studies described in Chapter 5. As described in
the 2008 SOx ISA (U.S. EPA. 2008d). both active and passive samplers have been used
to measure personal SO2 exposures. The Harvard-EPA annular denuder system is an
active sampler initially developed to measure particles and acidic gases simultaneously
(Braueret al.. 1989; Koutrakis et al.. 1988). The system draws air at 4 L/minute past an
impactor to remove particles and then through an annular denuder coated with sodium
carbonate to trap SO2 and other acidic gases. Gases collected within the denuder are
extracted with ultrapure water and analyzed by ion chromatography. The detection limit
depends on the sensitivity of the ion chromatography analysis as well as the volume of air
sampled, and is typically below 1 ppb (Braueret al.. 1989). with a collection efficiency of
99.3% (Koutrakis et al.. 1988). Another active sampler, developed for a study in
Baltimore, MD, used a hollow glass denuder coated with triethanolamine, with SO2
detection by ion chromatography (Chang et al.. 2000). At a sampling rate of
100 mL/minute for 1 hour, the detection limit was 62 ppb, resulting in many of the 1-hour
SO2 samples being below the detection limit; see Section 25_ for a summary of typical
ambient SO2 concentrations.
Passive badge-type samplers have also been developed to eliminate the need for a
powered sampling pump. A common version is manufactured by Ogawa USA, Inc. and
consists of a cellulose fiber filter coated with triethanolamine (Ogawa & Co. 2007). SO2
is detected via ion chromatography with a reported detection limit for a 24-hour sample
of 2-6 ppb (Sarnat et al.. 2006; Sarnat et al.. 2005; Sarnat et al.. 2000). Passive badge
samplers can also be combined with active particle samplers to create a multipollutant
sampler [e.g., Demokritou et al. (2001)1. Passive badges for measuring SO2
concentrations are not very sensitive to ambient concentration level, temperature, relative
humidity, or exposure duration, unlike passive badges for measuring NO2 (Swaans et al..
2007). The cumulative sampling approach and the relatively high detection limit of the
passive badges makes them mainly suitable for monitoring periods of 24 hours or greater,
which limits their ability to measure short-term daily fluctuations in personal SO2
exposures.
3.3.2 Modeling
Models can be used to predict the outdoor concentration of SO2 across geographic
regions or at specific locations of interest where people spend time (e.g., outdoors at
homes, schools, workplaces, roadways). The modeled concentration can be used as a
surrogate for human exposure to SO2. Models do not estimate exposures to ambient SO2
directly, because time-activity patterns and indoor concentrations of ambient SO2 in
various microenvironments are not considered. Approaches described below include
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source proximity models (SPM), LUR, inverse distance weighting (IDW) models,
dispersion models, CTM, and microenvironmental models. These models can be
employed at urban, regional, or national scales to estimate daily, or longer, average
ambient SO2 concentrations as an exposure surrogate. Short-term (e.g., daily) ambient
SO2 concentration estimates are needed for ambient SO2 exposure surrogates in acute
exposure assessments, whereas long-term (e.g., annual) ambient SO2 concentration
estimates can be used for ambient SO2 exposure surrogates in chronic exposure
assessments.
3.3.2.1 Source Proximity Models
SPMs provide a simple method to estimate ambient SO2 concentration as a surrogate for
ambient SO2 exposure. These models calculate the distance from receptors (e.g., homes,
schools) to a source of SO2 emissions (e.g., industrial facilities). It is assumed that
ambient SO2 concentration is some function of distance from the source. SO2 emitted
from a point source is thought to disperse as a meandering plume, such that average
ambient SO2 concentration decreases with distance from the source (Section 2.6.1). These
models do not necessarily account for the effect of stack height to limit ambient SO2
concentrations in the immediate vicinity of the point source. Burstvn et al. (2008)
avoided the stack height issue by modeling ambient SO2 concentration as a function of
the inverse distance within 2- and 50-km buffers of each gas plant and oil well. In another
study, proximity to source was treated as a Boolean variable as a surrogate for high and
moderate ambient SO2 exposure (Cam bra et al.. 2011). Likewise, Liu et al. (2012b)
computed relative risk of respiratory disease using ZIP codes with fuel-fired power plants
compared with the reference of ZIP codes without fuel-fired power plants. One study
specifically examined near-road proximity and ambient SO2 concentration and found no
statistically significant decrease in ambient SO2 concentration near a highway (McAdam
et al.. 2011).
SPMs are widely applied for exposure assessments because few input data are required.
The main limitation of an SPM is the potential for exposure error because none of the
factors affecting emission rates, dispersion, and photochemical activity of pollutants
(e.g., emission rates, atmospheric physics, chemistry, meteorology) are considered [e.g.,
Zou et al. (2009a)l.
To improve the accuracy of SPMs in providing a surrogate for exposure, an
emission-weighted proximity model (EWPM) was developed that considers the emission
rate and duration of each ambient SO2 point source, in addition to the distance from
source. Zou etal. (2009b) evaluated the SPM and EWPM to estimate ambient SO2
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concentrations in Dallas and Ellis counties, TX. Normalized ambient SO2 concentration
estimates based on SPM and EWPM were compared to normalized ambient SO2
concentration measurements at three monitoring sites and found that EWPM-based
ambient SO2 concentration estimates agreed more closely to the observed ambient SO2
concentrations than SPM-based ambient SO2 concentration estimates. Epidemiologic
estimates of risk also were in closer agreement between EWPM and AERMOD compared
with the comparison of results using SPM and AERMOD (Zou etal.. 2011). In addition,
surface maps of EWPM- and SPM-predicted ambient SO2 concentrations across two
counties showed that with SPM risk of exposure is usually overestimated in the region of
dense emission sources and underestimated where emission sources were sparse (Zou et
al.. 2009b). As compared to SPM, EWPM more accurately predicted ambient SO2
concentrations that individuals were exposed to across these regions.
3.3.2.2 Land Use Regression Models
LUR models are used to estimate ambient SO2 concentration as a surrogate for exposure
in some large health studies, because they provide spatial variability in estimates of
ambient SO2 concentration across the geographic area of the study population. A detailed
description of LUR models is provided in Chapter 3 of the 2016 ISA for Oxides of
Nitrogen (U.S. EPA. 2016e). Briefly, LUR fits a multiple linear regression model of
concentration based on local data (e.g., proximity to SO2 emissions sources, road length,
land use, population density) and then applies that model to locations without monitors as
an attempt to increase heterogeneity in the spatial resolution of the ambient SO2
concentration field compared with other methods, such as central site monitoring
(Marshall et al.. 2008). A structured framework for comparing modeling approaches
could occur with reporting of metrics such as spatial scale, averaging time, out-of-sample
coefficient of variation (i.e., goodness of fit of the model with data not used to fit it to
cross-validate the model), in-sample coefficient of variation (i.e., goodness of fit of the
model with data used to fit it), and root mean squared error (RMSE). However, studies in
the literature of LUR model results do not consistently report all of these parameters.
The discussion of LUR models below includes the metrics provided in specific papers.
Models are typically calibrated using ambient SO2 concentration data from passive
sampler measurements and several local predictor variables. Given that most passive
ambient SO2 concentration measurement methods are not designed for short-term
sampling, LUR models are typically based on several days, weeks, or years of data and
thus do not account well for short-term temporal variability in the ambient SO2
concentration estimates. Hence, LUR is commonly used to estimate air pollution
exposure in long-term epidemiologic studies. Although LUR is usually employed for
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NO2, it has also been used to study spatial variability in ambient SO2 concentration in a
small number of studies [e.g., Atari et al. (2008)1. Several methodological issues must be
considered when interpreting LUR model results. These issues include number of
measurement sites used to fit the statistical model, predictor variable selection, and
comparison of LUR performance among LUR model formulations and with other
models. These issues affect how well the spatial variability of ambient SO2 concentration
in a city is represented by the LUR. For example, in a study incorporating aerosol optical
density from satellite measurements and three-dimensional building data with land use
variables in predicting variation in SO2 concentration across space, the LUR model fit
improved from adjusted R2 = 0.52 to 0.71 (Gong etal.. 2016).
LUR models have been applied to estimate ambient SO2 concentrations in close
proximity to industrial SO2 sources. Atari et al. (2008) developed an LUR model to
predict ambient SO2 concentrations in Sarnia, Ontario, Canada, an area known as
"Chemical Valley" for its high density of chemical industries. Ambient SO2
concentrations measured by passive badge monitors were used to "train" the model, and
the explanatory variables for the LUR model were distance to an industrial zone, location
within 1,200 m of industrial areas, and location within 100 m of major roads.
Measurements of ambient SO2 concentration for model training were collected with
passive samplers at 37 locations across the city for 2 weeks in the fall of 2005, with an
average concentration of 3.4 ppb. The in-sample coefficient of determination was
R2 = 0.66. An out-of-sample coefficient of determination was calculated to cross-validate
the model. The out-of-sample coefficient ranged from R2 = 0.62 to If = 0.73, and the
RMSE of the out-of-sample predictions were 0.3 to 1 ppb. The ambient SO2
concentration validation produced a wider range of errors and lower out-of-sample R2
compared with LUR simulations for ambient NO2 concentration; Atari et al. (2008)
attributed this moderate validation to a skewed ambient SO2 concentration distribution
compared with the concentration distribution of ambient NO2, although skewness metrics
were not provided.
Spatial variability in ambient SO2 concentrations offered by LUR has been used to
estimate inter-individual variability in exposure by assuming the ambient SO2
concentration modeled at the study participants' homes matched their exposure. Ambient
SO2 concentrations computed using LUR by Atari et al. (2008) were used by Atari et al.
(2009) to correlate modeled ambient SO2 concentrations with individual and community
perceptions of odor, by Oiamo and Luginaah (2013) to study whether males and females
are affected differently by ambient SO2 exposure, and by Oiamo et al. (2011) to
investigate the relationship between estimated ambient SO2 exposure and access to a
general practitioner. Kanaroglou et al. (2013) used a spatial autocorrelation LUR model
to estimate ambient SO2 concentrations, in which the spatial autocorrelation component
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of the model's residuals was removed. Kanaroglou et al. (2013) applied the spatial
autocorrelation LUR model in the vicinity of an industrial area in Hamilton, Ontario,
Canada and observed that location and difference between wind direction and direction of
the industrial area to the receptor were each statistically significant predictors of ambient
SO2 concentration (p < 0.001, RMSE = 1.24).
LUR has also been applied to predict ambient SO2 concentrations in the vicinity of urban
sources. Cloughertv et al. (2013) modeled concentrations of ambient SO2, NO2, PM2 5,
and black carbon (BC) across New York City, NY. Ambient SO2 concentration was
predicted by the reference site mean (partial R2 = 0.35), number of oil-burning units
(partial R2 = 0.36), and nighttime population within 1 km (partial If = 0.06) to give an
overall out-of-sample model fit of R2 = 0.77, where R2 was based on the comparison
between raw ambient SO2 concentrations and model predictions. Traffic covariates were
not included in the model. The study authors thought these findings reflected the presence
of large combustion boilers in Manhattan and western Bronx, where ambient SO2
concentrations were predicted to be highest because sulfur content in residential heating
fuel is high. Ambient SO2 concentration was not influenced by vehicle traffic, unlike the
other air pollutants studied. Beelen et al. (2007) modeled ambient SO2, NO2, NO, and
black smoke (BS) concentrations as the sum of regional, urban, and local components.
LUR was applied at the urban level to indicate land use (as location in a nonrural, urban,
or industrial area) and at the local level to indicate traffic intensity with the combined
spatial scale model in-sample R2 = 0.56. The analysis used data from 1999-2000, when
diesel fuel contained higher concentrations of sulfur, prior to 2006 and 2007 when the
fuel standards promulgated in 2001 (66 FR 5002) reducing sulfur concentrations in diesel
fuel took effect for highway vehicles and heavy-duty vehicles, respectively.
The out-of-sample RMSE was 1.6 ppb for the background model and 1.2 ppb for the
urban model; RMSE was not reported for the local model. Ambient SO2 concentrations
modeled in the Beelen et al. (2007) study were used as exposure estimates in a
longitudinal cohort study of vascular damage among young adults [see Section 5.3.2.5
and Lenters et al. (2010)1. Wheeler et al. (2008) applied LUR for a study of ambient SO2
concentration to estimate exposure in Windsor, Ontario and found that distance to the
Ambassador Bridge, housing density, and SO2 emission sources from Detroit within 3 km
were all significant predictors of ambient SO2 concentration with in-sample R2 = 0.69 and
out-of-sample R2 = 0.65. Wheeler et al. (2008) also evaluated LUR performance for
predicting ambient SO2 concentration across seasons by comparing the LUR results with
measurements to estimate air pollutant exposure in Windsor, Ontario. They found that
correlation of summer predictions of ambient SO2 concentrations with those from other
seasons was lower, suggesting that photochemistry might not be well represented in the
LUR model.
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3.3.2.3
Inverse Distance Weighting
IDW, in which ambient SO2 concentration at a receptor point is calculated as the
weighted average of ambient SO2 concentration measured at monitoring locations, has
been used to estimate exposure based on ambient SO2 concentration surfaces. Several
recent studies using IDW have been published. The weighting factor is an inverse
function of distance between the receptor and the monitor. For example, Braueretal.
(2008) and Maclntyre et al. (2011) estimated exposure to ambient SO2 and other
industrial pollutants within 10 km of point sources using an IDW sum of ambient SO2
concentration and the three closest monitors within 50 km for application in
epidemiologic models (Clark et al.. 2010). Often, the weighting factor is the inverse
distance raised to some power, and a higher power is applied to increase the weight on
monitors that are closer to the receptor. Rivera-Gonzalez et al. (2015) applied an
inverse-distance-squared weighting and compared the results with a citywide average,
use of the nearest monitor, or kriging to develop an ambient SO2 concentration surface.
The results from IDW were correlated with the other three methods (r = 0.88-0.97), and
the mean ambient SO2 concentration estimated with IDW was within 10% of the mean
computed with the other methods. However, Neupane et al. (2010) estimated the ambient
SO2 concentration surface using both bicubic spline interpolation and IDW for a study of
long-term exposure to air pollutants and risk of hospitalization for pneumonia in
Hamilton, Ontario, Canada in a case-control study design. Bicubic spline interpolation
produced a lower mean ambient SO2 concentration and larger IQR compared with IDW;
the odds ratio (OR) was higher for the cubic splines model [OR: 0.23, 95% confidence
interval (CI): 0.02-0.45] compared with the IDW model (OR: 0.06, 95% CI:
-0.06-0.18), probably due to greater variability in the ambient SO2 concentration data.
3.3.2.4 Dispersion Models
Gaussian dispersion models have been applied to estimate ambient SO2 concentration as
a surrogate for human exposure to SO2. A detailed description of Gaussian dispersion
modeling, along with its strengths and limitations for modeling ambient SO2
concentrations, can be found in Section 2.6. This section highlights examples of using
dispersion models to estimate ambient SO2 concentration as a surrogate for exposure.
Zou et al. (2009c) developed a hybrid modeling system to estimate source-specific
ambient SO2 concentration across space as a surrogate for population exposure to
ambient SO2 in Dallas County, TX. First, an AERMOD dispersion model was run for
three source scenarios (vehicle only, industrial only, and combined vehicle and
industrial), and kriging interpolation was applied to the modeling results to produce a
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monthly average ambient SO2 concentration grid map (100 m x 100 m). The population
exposure was next estimated by multiplying the ambient SO2 concentration value and the
corresponding population density value for each grid cell (100 m x 100 m) and for the
three source classifications. The results showed that monthly population SO2 exposure
concentrations were moderately correlated with simulated ambient SO2 concentrations
from vehicle sources (r = 0.440) and weakly correlated with ambient SO2 concentrations
from industrial sources (r = 0.069); this study used emissions data from the year 2000,
before the ultra-low sulfur diesel fuel regulations were enacted.
Lagrangian particle modeling has also been used to estimate ambient SOx concentration
as a surrogate for ambient SOx exposure from specific sources (Ancona et al. 2015) to
study the relationship of long-term exposure to SOx with mortality for all-causes
(Section 5.5.2.2). cardiovascular disease (Section 5.3.2.2). and cancer (Section 5.6.1).
The Lagrangian particle model tracks the movement of SOx as nonreactive parcels
(i.e., massless particles), considering SOx to be a marker of the emission source
representing some combination of directly emitted SO2 and sulfate formed in the
atmosphere (Section 2.3). Gariazzo et al. (2004) compared this type of Lagrangian
particle model against ambient SO2 concentration measurements and observed reasonable
agreement, although the observations seemed to lag the modeled ambient SO2
concentration at times. The results suggest that the model would have provided a
reasonable estimate of exposure in the Ancona et al. (2015) study, especially given the
long-term nature of the study.
3.3.2.5 Chemical Transport Models
Ambient SO2 concentrations calculated with CTMs, such as the CMAQ model, are
sometimes used to estimate human exposure to ambient SO2 (Section 2.6). For example,
Lipfert et al. (2009) estimated ambient SO2 concentration based on the CMAQ model for
use as an exposure surrogate. Annual average ambient SO2 concentrations were estimated
with a 36-km by 36-km grid across the contiguous U.S. The modeled ambient SO2
concentrations were used as exposure surrogates to determine their association with
county-level mortality data for the Washington University-Electric Power Research
Institute Veterans Cohort Mortality Study. To assign exposures at the county level, the
CMAQ grid that included the largest city within each county was determined, and the
associated CMAQ ambient SO2 concentration was used as the exposure metric for the
entire county.
CTMs can be applied in epidemiologic studies of either short- or long-term exposure to
ambient SO2 but are more commonly used in long-term ambient SO2 exposure studies.
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Given observed biases in the CTMs [e.g., U.S. EPA (2008c)l. much attention has been
given to bias correction of these models for application in exposure assessment. Chen et
al. (2014a) evaluated CMAQ v4.7.1 results for several pollutants and found that ambient
SO2 concentration was underpredicted by roughly a factor of two, but this problem was
largely ameliorated through bias correction techniques. Improvements to modeling
ambient S02-related reactions have been corrected in CMAQ v5.0.2, so that ambient SO2
concentrations used for exposure surrogates from this or later versions would have
smaller exposure errors.
One major limitation of CTMs for estimating ambient SO2 concentrations as exposure
surrogates is that the grid resolution, typically between 4 and 36 km, can be much larger
than the length scale of the meandering plume upon touch-down. This limitation presents
the possibility that ambient SO2 concentrations can be underestimated along the plume
path when localized peaks are averaged over space. Baldasano et al. (2014) recognized
this limitation and merged HYSPLIT with a CTM simulation of ambient SO2 and PM10
transport in the vicinity of a refinery. HYSPLIT models dispersion of pollutants, such as
ambient SO2, as particle trajectories; the WRF meteorological model is coupled with the
particle trajectory model to account for wind speed, wind direction, and atmospheric
turbulence. China et al. (2006) nested smaller grids (1,4, 12 km) within larger grids
(36 km) to improve spatial variability of the simulation. Similarly, Karamchandani et al.
(2010) coupled a plume-in-grid model with CTM that treats dispersion as a Gaussian
process with parameters that are set using micrometeorological conditions. Inclusion of
subgrid-scale modeling enables calculation of the ambient SO2 plume at finer spatial
scales so that maximum ambient SO2 concentration, and potentially maximum exposures,
can be estimated by the model suite (Baldasano et al.. 2014).
3.3.2.6 Microenvironmental Exposure Models
Microenvironmental exposure models are designed to account for variations in the
amount of time people spend in different locations by using time-weighted SO2
concentrations in each microenvironment (e.g., outdoors; indoors at home, school,
workplace; in-vehicle) for the exposure surrogate. Models such as SHEDS and APEX are
used occasionally for exposure assessment in epidemiologic studies (Dionisio et al..
2014; Mannshardt et al.. 2013; Chang et al.. 2012a). and they are also used for the risk
assessment performed as part of the NAAQS review process, as was done for the risk and
exposure assessment during the last review of the SO2 NAAQS (U.S. EPA. 2009b).
The fundamental principles of stochastic population exposure models are described in
detail in the 2008 NOx ISA Annex 3.6 (U.S. EPA. 2008a). Briefly, the models combine
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ambient concentration data with information on infiltration into enclosed
microenvironments, such as buildings and vehicles (see Section 3.4.LP. to estimate
microenvironmental concentrations. The models then use demographic variables such as
age and sex to select appropriate activity patterns from a database. For the risk
assessment done during the last review of the SO2 NAAQS, the U.S. EPA used CHAD,
which is described in Section 3.4.2.1 and in the 2016 NOx ISA (U.S. EPA. 2016eV
Inhalation rates are determined from the level of effort associated with each activity
(e.g., sitting, walking, or running). Inhalation rates and microenvironmental
concentrations are combined to estimate dose. Depending on the availability of controlled
human exposure data, response functions based either on microenvironmental exposure
concentrations or inhaled dose are used to characterize expected health effects. For
population-level exposure assessments, exposure models such as SHEDS and APEX
estimate the distribution of exposures across the population of interest (U.S. EPA. 2012c;
Burke et al.. 2001).
To improve the characterization of activity patterns, mobile electronic devices, such as
smartphones with embedded GPS receivers and dedicated GPS data loggers, are
increasingly used to collect time-location information. However, manual processing of
GPS data to determine time spent in different microenvironments is limited due to large
(potentially thousands of samples per person per day) and multidimensional (location,
speed, time, signal quality) data sets, missing data due to loss of GPS signal reception
while inside certain buildings, and difficulty discriminating among certain
microenvironments (e.g., wooden structures have no substantial indoor/outdoor
differences in satellite signal strength). To address these limitations, automated
microenvironmental classification models have been developed (Breen et al.. 2014a; Kim
et al.. 2012; Wu et al.. 2011a; Adams et al.. 2009; Elgethun et al.. 2007). For example,
Breen et al. (2014a) recently developed a classification model called MicroTrac to
estimate time of day and duration spent in eight microenvironments (indoors and
outdoors at home, work, school; inside vehicles; other locations) from GPS data and
geocoded building boundaries. MicroTrac estimates were compared with diary data and
correctly classified the microenvironment for 99.5% of the daily time spent by the
participants. In conjunction with accelerometers, air pollutant monitors, and health
monitors, GPS-based time-activity data and related monitors have the potential to reduce
error in exposure assessment (NRC. 2012). Although these techniques are promising,
researchers to date have not applied them to estimate exposures to SO2 or to large field
studies that could provide activity patterns suitable for inclusion in CHAD.
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3.3.3 Choice of Exposure Metrics in Epidemiologic Studies
Epidemiologic studies use a variety of methods to assign a surrogate for ambient SO2
exposure. Study design, data availability, and research objectives are all important factors
when selecting an exposure assessment method. Common methods for assigning an
exposure surrogate from monitoring data include using ambient SO2 concentration
measured at a single monitor to represent population exposure and averaging ambient
SO2 concentrations from multiple monitors. Investigators may also use statistical
adjustment methods, such as trimming extreme values, to prepare the ambient SO2
exposure concentration data. Epidemiologic study design influences the relevance and
utility of exposure metrics. Table 3-1 summarizes various metrics used in epidemiologic
studies of ambient SO2 exposure, appropriate applications for the metrics, and errors and
uncertainties that may be associated with the metrics.
3.4 Exposure Assessment, Error, and Epidemiologic Inference
This section describes exposure assessment issues related to the use of surrogates for
ambient SO2 exposure in epidemiologic studies that may influence or introduce error into
the observed health effect estimate.
3.4.1 Relationships between Personal Exposure and Ambient
Concentration
Several factors influence the relationship between personal SO2 exposure and ambient
SO2 concentration. Indoor SO2 concentrations are highly dependent on air exchange rate
(AER) due to the lack of indoor SO2 sources and the rapid deposition of ambient SO2
after it penetrates into enclosed microenvironments (Section 3.4.1.1). Generally, indoor
SO2 concentrations are lower than ambient SO2 concentrations measured outdoors.
Because people spend the bulk of their time indoors (Section 3.4.2.1). personal SO2
exposures are often much lower than ambient SO2 concentrations. For example, Brown et
al. (2009) reported the mean winter personal SO2 exposure concentrations in Boston to be
1.8 ppb, while the ambient SO2 concentration was 11.3 ppb. Both personal SO2 exposure
concentration and ambient SO2 concentration were even lower in summer, with mean
values of near zero and 3.6 ppb, respectively. The following sections describe studies
evaluating AER, relationships between indoor and outdoor SO2 concentrations, and
personal-ambient relationships for SO2.
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Table 3-1 Summary of exposure assignment methods, their typical use in
sulfur dioxide epidemiologic studies, strengths, limitations, and
related errors and uncertainties.
Exposure
Assignment
Epidemiologic
Errors and
Method
Description
Application
Strengths
Limitations
Uncertainties
Central site
A FRM or FEM
Short-term
Ambient SO2
Measurements of
Correlation
monitors
monitor located
community
concentration
ambient SO2
between outdoor
(Section 3.3.1.1)
at a fixed
time-series
measurements
concentration
SO2
location to
studies:
undergo rigorous
made at a fixed
concentrations
measure
surrogate for
quality assurance
location may differ
proximal to the
ambient SO2
ambient SO2
from an exposed
receptors and
concentration
exposure of a
individual's true
ambient SO2
population
exposure, and no
concentration
within a city
spatial variation is
measurements
assumed
typically
decreases with
increasing
distance from the
monitor,
potentially
leading to
decreased
precision and
bias towards the
null
Long-term
Potential for bias
epidemiologic
and reduced
studies:
precision if the
surrogate for
monitor site does
ambient SO2
not correspond to
exposure to
the location of
compare
the exposed
populations
population
among multiple
cities
Active personal
Air is pulled
Short-term
SO2 concentrations
High detection limit High detection
exposure monitors
through a pump
panel
are obtained at the
limit and potential
(Section 3.3.1.2)
and sampled for
epidemiologic
site of the exposed
for nonambient
ambient SO2
studies: SO2
person
SO2 exposure
concentration
exposure
sampling may
using ion
(e.g., personal
lead to reduced
chromatography
or residential
precision
to measure
samples)
personal SO2
within a
exposure
geographic
area
Long-term
Potential for
epidemiologic
nonambient SO2
studies: SO2
exposure
exposure
sampling may
within a city or
lead to bias and
among multiple
reduced
cities
precision
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Table 3-1 (Continued): Summary of exposure assignment methods, their typical
use in sulfur dioxide epidemiologic studies, strengths,
limitations, and related errors and uncertainties.
Exposure
Assignment
Epidemiologic
Errors and
Method
Description
Application
Strengths
Limitations
Uncertainties
Passive personal
SO2 is captured
Long-term
SO2 concentrations
Integrated sample
High detection
exposure monitors
on a coated
epidemiologic
are obtained at the
does not allow for
limit and potential
(Section 3.3.1.2)
filter via passive
studies:
site of the exposed
time-series
for nonambient
exposure for a
ambient SO2
person
analysis; high
SO2 exposure
time period to
exposure
detection limit
sampling may
measure a
within a city or
lead to bias and
personal or
among multiple
reduced
area sample
cities
precision
Source proximity
Ambient SO2
Long-term
Few input data
Does not consider
Potential for bias
model
concentrations
epidemiologic
required
emission rate and
and reduced
(Section 3.3.2.1)
are estimated
studies:
duration,
precision if
from distance of
surrogate for
atmospheric
ambient SO2
receptor from
ambient SO2
chemistry, or
concentration at
source
exposure
within a city or
among multiple
cities or
regions
physics
a receptor
location is higher
or lower than the
average ambient
SO2
concentration
over the area of
the circle formed
around the
source with
radius equal to
the distance
between the
source and
receptor
Emission weighted
proximity model
(Section 3.3.2.1)
Ambient SO2
concentrations
are estimated
from distance of
receptor to
pollution
source,
emission rate,
and duration
Long-term
epidemiologic
studies:
surrogate for
ambient SO2
exposure
within a city or
among multiple
cities or
regions
Considers emission
rate and duration
Does not consider
atmospheric
chemistry or
physics
Potential for bias
and reduced
precision if
ambient SO2
concentration at
a receptor
location is higher
or lower than the
average ambient
SO2
concentration
over the area of
the circle formed
around the
source with
radius equal to
the distance
between the
source and
receptor
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Table 3-1 (Continued): Summary of exposure assignment methods, their typical
use in sulfur dioxide epidemiologic studies, strengths,
limitations, and related errors and uncertainties.
Exposure
Assignment
Epidemiologic
Errors and
Method
Description
Application
Strengths
Limitations
Uncertainties
Land use
Measured
Long-term
High spatial
Does not account
Potential for bias
regression model
ambient SO2
epidemiologic
resolution
for atmospheric
and reduced
(Section 3.3.2.2)
concentrations
studies:
chemistry and
precision if grid is
are regressed
surrogate for
physics, has
not finely
on local
ambient SO2
limited
resolved
variables
exposure,
generalizability,
Potential for bias
(e.g., land use
usually across
and moderate
and reduced
factors), and the
a city but
resources are
precision if the
resulting model
sometimes
needed
model is
is used to
among multiple
misspecified or
estimate
cities
applied to a
ambient SO2
location different
concentrations
from where the
at specific
model was fit
locations
Inverse distance
Measured
Long-term
High spatial
Does not fully
Potential for
weighting and
ambient SO2
epidemiologic
resolution, few
capture spatial
negative bias
kriging
concentrations
studies:
input data needed
variability of
and reduced
(Section 3.3.2.3)
are interpolated
surrogate for
ambient SO2
precision if
to estimate
ambient SO2
concentration
ambient SO2
ambient SO2
exposure,
among monitors
sources are not
concentration
usually within a
captured or
surfaces across
city or
overly smoothed
regions. IDW
geographic
uses an inverse
region
function of
distance to
monitors, and
kriging uses a
statistical
algorithm for
interpolation
Dispersion
Ambient SO2
Long-term
High spatial and
Resource
Potential for bias
modeling
concentrations
epidemiologic
temporal
intensive, very
where the
(Section 3.3.2.4)
at specific
studies:
resolution,
limited
dispersion model
locations are
surrogate for
accounts for
representation of
does not capture
estimated from
ambient SO2
atmospheric
atmospheric
boundary
emissions,
exposure
physics from local
chemistry or
conditions and
meteorology,
within a city or
emission sources
background SO2
resulting fluid
and
geographic
concentrations
dynamics well
atmospheric
region
(e.g., in large
physics
cities with urban
topography
affecting
dispersion)
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Table 3-1 (Continued): Summary of exposure assignment methods, their typical
use in sulfur dioxide epidemiologic studies, strengths,
limitations, and related errors and uncertainties.
Exposure
Assignment
Epidemiologic
Errors and
Method
Description
Application
Strengths
Limitations
Uncertainties
Chemical transport
Grid-based
Long-term
Accounts for
Limited grid cell
Potential for bias
model
ambient SO2
epidemiologic
atmospheric
resolution
and reduced
(Section 3.3.2.5)
concentrations
studies:
chemistry and
(i.e., grid cell
precision when
are estimated
surrogate for
physics
length scale is
grid cells are too
from emissions,
ambient SO2
typically 4-36 km
large to capture
meteorology,
exposure,
and much larger
spatial variability
and
sometimes
than plume width),
of ambient SO2
atmospheric
within a city but
resource-intensive,
exposures
chemistry and
more typically
does not account
physics
across a larger
region
for local SO2
emissions sources
Microenvironmental
Estimates
Panel
Accounts for
Input data from
Potential for bias
model (e.g., APEX,
distributions of
epidemiologic
variability of SO2
ambient SO2
and reduced
SHEDS)
micro-
studies; no
exposures across
concentrations are
precision when
(Section 3.3.2.6)
environmental
epidemiologic
large populations,
required, does not
the modeled
SO2
studies cited
accounts for
estimate
distributions of
concentrations,
here use
different
exposures for
ambient SO2
exposures, and
micro-
concentrations in
individuals
concentration,
doses for
environmental
different
indooroutdoor
populations
models
microenvironments,
pollutant ratios,
(e.g., census
accounts for
and time-activity
tracts) based on
location-activity
patterns differ
air quality data,
information
from the true
demographic
distributions
variables, and
activity patterns
APEX = air pollutants exposure model; FEM = federal equivalent method; FRM = federal reference method; IDW = inverse
distance weighting; SHEDS = stochastic human exposure and dose simulation; S02 = sulfur dioxide.
3.4.1.1 Air Exchange Rate
1 AER, which is the airflow into and out of a building and is represented by a in the
2 conceptual model presented in Section 3.2.2. influences the rate of entry of ambient SO2
3 and hence personal exposure to SO2, because people spend an average of 87% of their
4 time indoors (klepeis et al.. 2001). Several factors affect the AER, including the physical
5 driving forces of the airflows (e.g., pressure differences across the building envelope
6 from wind, indoor-outdoor temperature differences, and mechanical ventilation), building
7 characteristics (e.g., local wind sheltering, tightness of the building envelope), and
8 occupant behavior (e.g., opening windows, operating outdoor-vented fans, thermostat
9 temperature setting during heating and cooling seasons). Therefore, substantial spatial
10 and temporal AER variations can occur due to temporal and geographical differences in
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weather conditions, building characteristics, and occupant behavior. The resulting
spatial-temporal variations in ambient SO2 exposure may help explain possible
differences in epidemiologic associations between ambient SO2 concentrations and health
effects in different U.S. communities (Baxter and Sacks. 2014).
Field studies indicate that the AER of U.S. residences varies by season and region, with
substantial variability among different residences. Yamamoto et al. (2010) reported AER
measured at residences in Los Angeles, CA, Elizabeth, NJ, and Houston, TX as part of
the Relationship Among Indoor, Outdoor, and Personal Air (RIOPA) Study conducted
between 1999 and 2001. Among the three cities and across seasons, AER was 0.71/hour.
Regional differences can be seen when breaking the data down by season and location.
Median AERs in Los Angeles, Elizabeth, and Houston were 0.87/hour, 0.88/hour, and
0.47/hour. Differences between AER for Houston and AER for Los Angeles and
Elizabeth may in part be related to larger home sizes (average home volume was 304 m3
for Houston, compared with 163 m3 in Los Angeles and 252 m3 in Elizabeth). Seasonally,
median AER was higher in summer compared to winter in Los Angeles (summer:
1.14/hour; winter: 0.61/hour). However, the opposite pattern occurred in Elizabeth
(summer: 0.88/hour; winter: 1.07/hour) and Houston (summer: 0.37/hour; winter:
0.63/hour). More prevalent use of open windows in Los Angeles, where summertime
tends to be less humid than in Elizabeth or Houston, may promote greater air exchange.
This difference may grow smaller with the increased prevalence of air conditioning,
because air conditioning usage is an important factor in infiltration (Allen et al.. 2012).
Low AER values in autumn may be due to a diminished "stack effect" resulting from
indoor-outdoor temperature differential (Breen et al.. 2014b).
Intra- and inter-home variability in AER was also tested in the RIOPA Study Yamamoto
et al. (2010). Intra-home variability in AER indicated that individual homes' AER
changed considerably between seasons (32, 37, and 37% for Los Angeles, Elizabeth, and
Houston, respectively). Inter-home variability also differed substantially for all three
cities, with the interquartile range of AER exceeding the median AER consistently across
seasons and cities.
AER is a critical parameter for estimating indoor SO2 concentrations, because indoor
sources of SO2 are relatively scarce and SO2 rapidly reacts with indoor surfaces [see
Grontoft and Ravchaudhiiri (2004) and references cited therein] or oxidizes rapidly via
indoor Criegee intermediates [see Section 23_ for a description of Criegee chemistry or
Shallcross et al. (2014) for the role of indoor Criegee intermediates in SO2 losses].
The main indoor source of SO2 is combustion of sulfur-containing fuels, such as
kerosene, which is generally considered an emergency or supplemental source of heat in
the U.S. Kerosene heaters, but not fireplaces, woodstoves, or gas space heaters, caused
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elevated SO2 concentrations indoors in a study conducted in Connecticut and Virginia
(Triche et al.. 2005). The median indoor SO2 concentration measured by passive sampler
over two weeks in homes using kerosene heat was 6.4 ppb, compared with 0.22 ppb for
homes that did not use kerosene heat in the two-week period. This relatively low
concentration when the kerosene heater was not in use is consistent with the rapid
removal rate of infiltrated ambient SO2. As discussed in Section 2.3. SO2 is removed
from the atmosphere by both dry and wet deposition to surfaces, represented by k in the
conceptual model presented in Section 3.2.2. The deposition rate of SO2 in apartments in
Athens, Greece was found to range from 0.76-4.3 /hour, similar to the rate observed for
O3, but an order of magnitude higher than the deposition rate measured for NO2 (Halios
et al.. 2009).
Limited information was identified regarding the penetration factor P (Equation 3-4).
Lopez-Aparicio et al. (2011) measured SO2 concentrations indoors and outdoors at the
National Library in Prague, Czech Republic from July 2009 to March 2010 and observed
SO2 penetration values ranging from/' = 0.25 to 0.74. Measured outdoor SO2
concentrations were higher for the cold months of January, February, and March
compared with the remainder of the sampling campaign, and penetration was lower
during that period (P = 0.25 to 0.48). The literature search only produced this one recent
study of SO2 infiltration.
Vehicle AERs can be substantially higher than residential AERs, leading to rapid
infiltration of on-road pollutants. While on-road SO2 emissions have declined due to
reductions in fuel sulfur content (Section 2.2.3). high vehicle AER would increase
exposure in areas with high ambient SO2 concentrations. Many factors affect vehicle
AER, including vehicle make and model, vehicle age, driving speed, and
fan/recirculation setting on the vehicle ventilation system. The combined effect of these
factors result in AERs that vary by more than two orders of magnitude, from less than
1/hour (approximately equivalent to atypical residential AER) to more than 100/hour
(Hudda et al.. 2011). In a model fit to AER measurements on 59 vehicles driven at three
different speeds under recirculation conditions, the most important variables were vehicle
age, mileage, and speed, plus an adjustment for manufacturer (Fruin etal.. 2011). Fan
speed and vehicle shape were not influential variables.
3.4.1.2 Indoor-Outdoor Relationships
A number of studies from the U.S., Canada, Europe, and Asia summarized in the 2008
SOx ISA (U.S. EPA. 2008d). as well as a few new studies conducted outside the U.S.,
have characterized the relationship between outdoor and indoor SO2 concentrations.
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Ratios and slopes of the indoor SO2 concentration versus the SO2 concentration
immediately outside the indoor microenvironment had an extremely wide range in the
studies described in the 2008 SOx ISA, from near zero to near unity. One of the most
detailed older studies of SO2 in a school was able to detect an indoor-outdoor slope of
0.02-0.03, with near-zero intercept and a correlation of 0.79-0.91, while measuring
indoor concentrations < 1 ppb, obtained over 10-hour periods when school was in session
and 14-hour periods when the school was vacant (Patterson and Eatoimh. 2000). Studies
conducted since the 2008 SOx ISA have focused on public buildings and show generally
similar results to older studies. A historic library in Prague without heating or air
conditioning had indoor:outdoor ratios of 0.25-0.74 (mean = 0.49) for monthly average
outdoor SO2 concentrations of 1-7 ppb obtained with passive samplers (Lopez-Aparicio
et al.. 2011). In Brazil, ratios of average indoor and outdoor SO2 concentrations from
2-week passive samples were 0.7 and 1.0 for urban and suburban schools, respectively
(Godoi et al.. 2013).
Several factors could contribute to the differences observed among studies, including
building characteristics (e.g., forced ventilation, building age, and building type such as
residences or public buildings), behaviors affecting air exchange rates such as opening
windows, indoor deposition of SO2, and analytical capabilities. When reported,
correlations between indoor and outdoor ambient SO2 concentrations were relatively high
(>0.75), suggesting that variations in outdoor ambient SO2 concentration are driving
indoor SO2 concentrations. These high correlations were observed across seasons and
geographic locations. This is consistent with the relative lack of indoor sources of SO2
(Section 3.4.1.1). For other criteria pollutants, nonambient sources can be an important
contributor to total personal exposure, but personal SO2 exposure is expected to be
dominated by ambient SO2 in outdoor microenvironments and in enclosed
microenvironments with high air exchange rates (e.g., buildings with open windows and
vehicles).
3.4.1.3 Personal-Ambient Relationships
As discussed in the 2008 SOx ISA (U.S. EPA. 2008d). personal monitoring studies for
SO2 exposure assessment have frequently found that most SO2 exposure concentrations
are below the detection limit of the personal samplers used in the study. Several studies
using passive samplers (Section 3.3.1.2) found that 95% or more of the personal SO2
exposure concentrations were less than the field detection limit of 2-6 ppb for 24-h avg
samples (Sarnat et al.. 2006; Sarnat et al.. 2005; Sarnat et al.. 2001; Sarnat et al.. 2000).
Thus, these data are not suitable for evaluating the relationship between personal
exposure and ambient concentration for SO2.
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A study in Boston using a different type of sampler, a personal annular denuder
(Section 3.3.1.2) with a detection limit of 0.19 ppb, found that the slope between 24-hour
personal SO2 exposure concentration and ambient SO2 concentration was 0.13, with a
standard error of 0.02 and zero intercept (Braueret al.. 1989). The 2008 SOx ISA
reported slopes of 0.03-0.13. Assuming that there are no nonambient sources of SO2, the
slope can be considered an estimate of a. The R2 value was 0.43 (r = 0.66) in this
analysis, which excluded values below the detection limit, indicating that personal SO2
exposure concentration was moderately correlated with ambient SO2 concentration.
3.4.2 Factors Contributing to Error in Estimating Exposure to Ambient
Sulfur Dioxide
Ambient SO2 concentrations measured at central monitoring sites are commonly used for
exposure surrogates in epidemiologic studies. As noted in Section 3.3.1.1. use of a central
site SO2 monitor to capture a surrogate for true, likely unobserved ambient SO2 exposure
may lead to exposure error. Factors that may influence this type of error include human
activity patterns, spatial and temporal variation in ambient SO2 concentration, and indoor
exposure to ambient SO2 (Brown et al.. 2009; Zeger et al.. 2000). Additionally,
uncertainty in the metric used to represent exposure is a source of exposure error. This
type of error may be influenced by method detection limit, accuracy, and precision of the
instrument. These factors are discussed in the following section.
3.4.2.1 Activity Patterns
The activity pattern of individuals is an important determinant of their exposure.
Variation in SO2 exposure concentrations among microenvironments means that the
amount of time spent in each location will influence an individual's exposure to ambient
SO2. The effect of activity pattern on exposure is explicitly accounted for in Equation 3-3
by the fraction of time spent in different microenvironments. As discussed in the 2008
SOx ISA (U.S. EPA. 2008d). although activity patterns vary both among and within
individuals, resulting in corresponding variations in exposure across a population and
over time, people generally spend more than 80% of their time indoors (Spalt et al.. 2015;
klepeis et al.. 2001).
Time spent in different locations has been found to vary by age. Table 3-2 summarizes
National Human Activity Pattern Survey (NHAPS) data reported for four age groups,
termed very young (0-4 years), school age (5-17 years), working (18-64 years), and
retired (65+ years) (Klepeis et al.. 1996). The working population spent the least time
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outdoors, while the school age population spent the most time outdoors. NHAPS
respondents aged 65 years and over spent somewhat more time outdoors than adults aged
18-64 years, with a greater fraction of time spent outdoors at a residence. Children aged
0-4 years also spent most of their outdoor time in a residential outdoor location. On
average, the fraction of time spent outdoors by school age respondents was 2.62
percentage points higher than working respondents, corresponding to approximately
38 minutes more time outdoors per day. Moreover, in a survey comparing children
(mostly less than age 8 years), their parents who were mostly under age 55 years, and
adults older than age 55 years, a larger proportion of children reported spending over
30 minutes performing vigorous outdoor physical activity (Wu etal. 201 lb).
Table 3-2 Mean fraction of time spent in outdoor locations by various age
groups in the National Human Activity Pattern Survey study.
Age Group (yr)
Residential-Outdoor (%)
Other Outdoor (%)
Total Outdoors (%)
0-4
5.38
0.96
6.34
5-17
5.05
2.83
7.88
18-64
2.93
2.33
5.26
65+
4.48
1.27
5.75
Source: Data from Klepeis et al. (1996)
Longitudinal activity pattern information is also an important determinant of exposure, as
different people may exhibit different patterns of time spent outdoors over time due to
race/ethnicity, age, sex, employment, and lifestyle-dependent factors. Spalt et al. (2015)
analyzed the relationship between time-activity patterns and demographic patterns for the
MESA Air cohort. They found that time spent indoors was best predicted by employment
status, and participants of Chinese ethnicity were more likely to spend time indoors
compared with white, black, or Hispanic study participants. These differences may
manifest as higher mean SO2 exposures or more frequent high-exposure episodes for
some individuals. The extent to which longitudinal variability in individuals contributes
to the population variability in activity and location can be quantified by the ratio of
between-person variance to total variance in time spent in different locations and
activities [the intraclass correlation coefficient (ICC)]. Xue et al. (2004) quantified ICC
values in time-activity data collected by Harvard University for 160 children aged
7-12 years in Southern California (Gevh et al.. 2000). For time spent outdoors, the ICC
was approximately 0.15, indicating that 15% of the variance in outdoor time was due to
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between-person differences. The ICC value might be different for other population
groups.
Several methods are available for sampling diary information, and the method chosen can
affect estimated personal SO2 exposures and related exposure errors. Che et al. (2014)
evaluated how diary sampling methods influenced estimates of children's exposure (in
this case, to ambient PM2 5). Random resampling, diversity and autocorrelation, and
Markov-chain cluster methods of diary sampling were tested. The three sampling
methods provided similar results for total ambient exposure, outdoor ambient exposure,
and ambient exposure at homes and indoor locations not including home, school, or
vehicles.
The U.S. EPA's National Exposure Research Laboratory has consolidated many of the
most important human activity databases into one comprehensive database called the
Consolidated Human Activity Database (CHAD). The current version of CHAD contains
data from 22 human activity pattern studies (including NHAPS), which were conducted
between 1982 and 2010 and evaluated to obtain over 54,000 person-days of 24-hour
human activities in CHAD (Isaacs. 2014; McCurdv et al.. 2000). Five studies conducted
between 1997 and 2010 comprising over 30,000 person-days have been added to CHAD
since the previous SOx ISA (University of Michigan. 2016; Isaacs et al.. 2013; Wu et al..
2012; Hertz-Picciotto et al.. 2010; Knowledge Networks. 2009; Williams et al.. 2009).
The surveys include probability-based recall studies conducted by U.S. EPA and the
California Air Resources Board, as well as real-time diary studies, telephone interviews,
and internet-based surveys conducted nationally and in individual U.S. metropolitan areas
using both probability-based and volunteer subject panels. All ages of both sexes are
represented in CHAD. The data for each subject consist of 1 or more days of sequential
activities, in which each activity is defined by start time, duration, activity type, and
microenvironmental classification (i.e., location). Activities vary from 1 minute to 1 hour
in duration, with longer activities being subdivided into clock-hour durations to facilitate
exposure modeling. CHAD also provides information on the level of exertion associated
with each activity, which can be used by exposure models, including the APEX model, to
estimate ventilation rate and pollutant dose (Section 3.3.2.6).
Recent studies have focused on the use of global positioning system (GPS) technologies,
such as in smartphones, to develop detailed time-activity pattern data. GPS technology
has the potential to provide increased resolution in recording activity patterns. For
example, Glasgow et al. (2014) analyzed the frequency of Android-based smartphones in
recording positional data among a panel of study participants and found that on average
74% of the data were collected over intervals shorter than 5 minutes, which is a marked
improvement over many time-activity studies using diaries.
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Positional errors are a concern for GIS and GPS-based technologies. Lane et al. (2013)
compared three geocoding techniques with aerial photography and observed median
positional errors of 7-23 m. Glasgow et al. (2014) also compared smartphone positions
with geocoded diary-based locations to test the positional accuracy of the phones. For all
data combined, the smartphones had a median positional accuracy of 342.3 m. When
broken down by network, the median positional accuracy varied from 98.0 to 1,168.8 m.
Wu et al. (2010) compared several portable GPS devices to aerial photography. Median
positional errors were 7.3-20.8 m for indoor measurements taken 3 m from a door or
window. For outdoor measurements taken 6.1m from a window or door, median
positional errors were 4.1-16.3 m, and for on-road measurements, median positional
errors were 3.5-5.5 m. Ganguly et al. (2015) compared two automated (GIS-based)
geocoding techniques with GPS positional data in Detroit, MI. Median positional errors
for two GIS methods were 26 m for both methods in comparison with GPS.
3.4.2.2 Spatial Variability
Spatial variability in ambient SO2 concentrations can contribute to exposure error in
epidemiologic studies, whether the studies rely on central site monitor data or model
output as a surrogate for exposure concentration. Low correlations between the monitor
used to measure concentration as an exposure surrogate and the true exposure
concentrations at the locations of the study population contribute to exposure error in
time-series studies Goldman et al. (2010).
The 2008 SOx ISA (U.S. EPA. 2008d) discussed spatial variability in ambient SO2
concentrations and the impact of this variability on effect estimates from epidemiologic
studies. Inter-monitor correlations within urban areas ranged from very low to very high
values, suggesting that ambient SO2 concentrations at some monitors may not be highly
correlated with the community average SO2 exposure concentration. Of particular
concern for SO2 is the predominance of point sources, resulting in an uneven distribution
of ambient SO2 concentrations across an urban area. Factors contributing to differences
among monitors include the presence of point sources, proximity to point sources, terrain
features, and uncertainty regarding the measurement of low ambient SO2 concentrations.
The 2008 SOx ISA (U.S. EPA. 2008d) concluded that low correlation between a specific
monitor and the community average ambient SO2 exposure concentration will tend to
bias an effect estimate toward the null.
Because ambient SO2 concentrations can have high spatial variability, average SO2
exposure concentration estimates may have less error for populations living close to a
monitor. Figure 3-1 and Figure 3-2 illustrate proximity of populations and SO2 monitors
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to multiple ambient SO2 sources in the Cleveland and Pittsburgh CBSAs, respectively
(discussed in Chapter 2). These CBSAs were chosen for further discussion here, because
they have both high population density and numerous sources above 2,000 tpy.
Figure 3-1 shows the location of central site SO2 monitors and sources with respect to
population density for the Cleveland, OH CBSA. Four of the monitors are centrally
located in the urban area, and are also within 10 km of SO2 sources, while two other
monitors are located much closer to point sources (<5 km). While some densely
populated areas are near central site SO2 monitors, some of the highest density census
block groups are located more than 10-15 km from central site monitors despite
proximity to the sources. Table 3-3 indicates that approximately one-third of the
population in various age groups lives more than 15 km from a central site SO2 monitor.
For the Pittsburgh CBSA (Figure 3-2). only two of the monitors are located near sources,
with the other monitors distributed among population centers and less densely populated
areas. Here, approximately 40% of the population lives more than 15 km from a central
site SO2 monitor (Table 3-4). Such variability in the proximity of populations to central
site monitors suggests that some portions of an urban area may be subject to increased
exposure error. While only minor differences were noted among age groups in the portion
of the population living at specific distances from monitors, the potential exists for
exposure error to differ among other potentially at-risk groups due to monitor proximity.
Several recent studies have evaluated the impact of spatial variability in ambient SO2
concentration on epidemiologic effect estimates. Strickland et al. (2011) reported a
relatively low chi-squared statistic for ambient 1-hour SO2 exposure concentration (from
a central site monitor, unweighted average across monitors, and population-weighted
average) compared with other primary and secondary criteria pollutants in Atlanta, GA.
The authors attributed this poor fit to spatial heterogeneity in ambient SO2 exposure
concentrations used as exposure surrogates and the inability of a central site monitor to
capture ambient SO2 plume touch-downs in other parts of the city. The chi-squared
statistic moderately increased when average ambient SO2 exposure concentrations (both
population-weighted and unweighted) from monitors across the city were used. Effect
estimates were higher for the monitor average metrics than for the central site monitor,
and this difference was magnified when effect estimates were based on a standardized
increment rather than the IQR. Because the IQR of the data covered the range of values
observed across the monitors in Atlanta for the Strickland et al. (2011) study, spatial
variability was partially accounted for in the IQR. The different exposure assignment
approaches only altered the magnitude, not direction, of observed associations.
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Distance to NEI Facilities
O NEI Facility
| 5 km
| 10 km
| 15 km
# Urban S02 Monitor
2011 ACS Population Per Sq Km Based
On Cleveland CBSA Block Groups
0-507
508 - 914
| 915-1387
| 1388-1977
1978-2895
2896-5194
50 Kilometers
ACS = American Cancer Society; CBSA = core-based statistical area; NEI = National Emissions Inventory.
Note that the current map projection (GCS-WGS-1984) creates buffers that take on an elliptical shape instead of a circle. The map
projection was chosen to preserve the projection integrity across the data files and reduce error associated with merging data
projections.
Figure 3-1 Map of the Cleveland, OH core-based statistical area including
National Emissions Inventory facility locations, urban sulfur
dioxide monitor locations, and distance to each facility with
respect to core-based statistical area block group population
density estimates for 2011. National Emissions Inventory facility
emissions ranged from 1,942 tons/year to 48,300 tons/year.
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Table 3-3 2011 American Community Survey population estimates of people
living within a specified distance of an urban sulfur dioxide monitor
in the Cleveland, OH core-based statistical area. Population
estimates are based on census block group estimates.
Age Group
Total Population
Within 1 km
Within 5 km
Within 10 km
Within 15 km
Total
2,080,318
11,816
266,777
759,078
1,310,309
<4 yr
121,820
781
17,608
46,551
75,947
5-17 yr
364,740
1,872
44,719
129,432
222,401
18-64 yr
1,280,478
7,793
178,439
482,808
822,787
>65 yr
313,280
1,370
26,011
100,287
189,174
Source: Data from the 2011 American Community Survey (U.S. Census Bureau. 2011).
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Distance to NEI Facilities
© NEI Facility
1 | 5 km
[ 10 km
[ | 15 km
• U rban SO:- M on itor
2011 ACS Population Per Sq Km
Based on Pittsburgh CBSA Block Groups
0-391
m 393 - 958
959 -1634
1685-2665
2666
4463 -8146
50 Kilometers
TV
M i i I i " ' I
0 5 10 20 Kilontelere
ACS = American Cancer Society; CBSA = core-based statistical area; NEI = National Emissions Inventory.
Note that the current map projection (GCS-WGS-1984) creates buffers that take on an elliptical shape instead of a circle. The map
projection was chosen to preserve the projection integrity across the data files and reduce error associated with merging data
projections.
The inset map shows National Emissions Inventory facilities located to the southeast of the highly urbanized areas.
Figure 3-2 Map of the Pittsburgh, PA core-based statistical area including
National Emissions Inventory facility locations, urban sulfur
dioxide monitor locations, and distance to each facility with
respect to core-based statistical area block group population
density estimates for 2011. National Emissions Inventory facility
emissions ranged from 1,279 tons/year to 46,467 tons/year.
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Table 3-4 2011 American Community Survey population estimates of people
living within a specified distance of an urban sulfur dioxide monitor
in the Pittsburgh, PA core-based statistical area. Population
estimates are based on census block group estimates.
Total Population
Within 1 km
Within 5 km
Within 10 km
Within 15 km
Population
2,357,769
64,224
494,382
1,076,465
1,428,871
<4 yr
121,101
2,646
24,748
56,178
73,853
5-17 yr
358,500
8,641
65,882
152,858
211,204
18-64 yr
1,471,310
41,989
325,041
683,445
897,459
>65 yr
406,858
10,948
78,711
183,984
246,355
Source: Data from the 2011 American Community Survey (U.S. Census Bureau. 2011).
High spatial and temporal variability in ambient SO2 concentration leading to a
null-biased effect estimate was also observed in Atlanta by Goldman et al. (2010) when
using 1-h daily max SO2 concentration as an exposure surrogate. In this study, the authors
used a semivariance analysis incorporating both spatial and temporal variability to show
that secondary pollutants such as PM2 5 and O3 have lower exposure error (where ambient
concentration is a surrogate for exposure) than primary pollutants such as CO and SO2,
for which concentrations tend to have higher spatial variability than those of secondary
pollutants. Goldman et al. (2010) simulated exposure error as the difference between
concentration measured at the central site monitor and the concentration estimated at a
receptor's location. The study authors computed a semivariance term over distance to the
central site monitor to concentration at a distance from the monitor. The estimated error
for SO2 was then added to a base case scenario, in which the authors assumed that the
central site monitor would produce an accurate exposure. Both the central site monitor
estimate and the estimate at the receptor location were used in epidemiologic models to
estimate the risk ratio for cardiovascular emergency department visits. The authors
estimated that the risk ratio was biased towards the null by approximately 60% when
estimating exposure using the central site monitor in lieu of estimating exposure at the
receptors' locations. In a related study, Goldman et al. (2012) used different methods to
obtain the surrogate for exposure: central site monitor, unweighted average across
monitors, population-weighted average across monitors, and area-weighted average
across monitors. The bias decreased for 1-h daily max SO2 when using unweighted,
population-weighted, and area-weighted averages of concentrations from multiple
monitors for the exposure estimate compared with using concentration from a central site
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monitor for the exposure estimate. Similarly, epidemiologic studies in the U.S. (Kumar.
2012; Morello-Frosch et al.. 2010) and Australia Jalaludin et al. (2007) found higher
associations between ambient SO2 concentrations (used as exposure surrogates) and birth
outcomes when the analysis was restricted to mothers matched with an ambient SO2
monitor within 3-5 km of their residence, suggesting bias towards the null remained in
the spatial averages used in the base case (Section 5.4).
3.4.2.3 Temporal Variability
The influence of plume dynamics on human exposures is important for considering
results of time-series studies of ambient SO2 exposure. As described in Section 2.5.4.
peak concentrations within the ambient SO2 plume can exceed concentrations averaged
over an hour by up to a factor of five; for the observations made in this assessment, the
peak was observed to exceed the mean by up to a factor of 5.5. Hence, SO2 central site
monitoring with averaging times of 1 hour or 1 day, commonly used in time-series
epidemiologic studies as an exposure metric (Chapter 5). may fail to characterize the
variability and peak SO2 exposure concentrations associated with a meandering plume,
resulting in exposure error. Moreover, controlled human exposure studies have
demonstrated health effects at 5-minute time scales (Chapter 5). The longer averaging
times used in epidemiologic studies may be misaligned with the critical time window of
the health effect corresponding to peak SO2 exposure.
Most of the community time-series epidemiologic studies on the health effects of ambient
SO2 exposure described in Chapter 5 use 24-h avg concentration as a surrogate for
exposure. Correlations among different temporal aggregations (1-h avg vs. 5-minute
hourly max, 24-h avg vs. 1-h daily max, and 24-h avg vs. 5-minute daily max) were
computed from the AQS data presented in Section 2.5.4 to glean an indication of how
well the 24-h avg represents the 1-h daily max and 5-minute daily max measures that
correspond to peak SO2 plume exposure (Figure 3-3). Approximately 75% of correlations
between 1-h avg and 5-minute hourly max were above 0.9. Correlations between
24-h avg and 1-h daily max were slightly lower, with roughly 75% of the data having
correlations above 0.75. A larger range of data was observed for the correlations between
24-h avg and 5-minute daily max, with 75% of the data having correlations above 0.60
and more than 50% of the data having correlations above 0.70. These moderate-high
correlations suggest that 24-h avg data used in many time-series epidemiologic studies
capture the peak exposure reasonably well, but exceptions may be found for specific
sites, as suggested by the lower outliers (r < 0.35) and lower whisker (r < 0.6) of the
correlation between 24-h avg and 5-minute daily max data.
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CO
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1-h avg:5-min max 24-h avg:l-h max 24-h avg:5-min max
Data below 0 ppb trimmed from the data set.
Figure 3-3 Pearson correlations between 1-h avg and 5-minute hourly max,
24-h avg and 1-h daily max, and 24-h avg and 5-minute daily max
sulfur dioxide concentrations.
1 A study in Canada suggests that ambient SO2 concentration measured over a single year
2 can represent ambient SO2 exposure concentration over a multidecade period.
3 The authors compared measurement methods used to represent long-term SO2 exposure
4 concentration and found that the annual average ambient SO2 exposure concentration in
5 the census tract of a subject's residence during 1980 and 1994 was well correlated
6 (Pearson R = 0.83 and 0.85 for all subjects, respectively) with an ambient SO2 exposure
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concentration metric accounting for movement among census subdivisions during
1980-2002 (Guav et al.. 2011). This result may have been due in part to a relatively low
rate of movement, with subjects residing on average for 71% of the 22-year period in the
same census subdivision they were in during 1980. Guav et al. (2011) also found that
coverage of the study population reduced from 40% for the fixed-time exposure
assignments, to 31% when averaging fixed-time exposure assignments with exposure
assignments based on census subdivision, to 29% when assigning exposures based only
on census subdivision, suggesting that improved spatial and temporal resolution in
long-term studies may come at the expense of data completeness.
3.4.2.4 Method Detection Limit, Instrument Accuracy, and Instrument Precision
Personal SO2 exposure measurements with ambient SO2 concentration typically have
correlations of 0.4 < r < 0.9 when personal SO2 exposure measurements are above the
MDL. However, although the magnitude of personal SO2 exposure measurements is often
much lower than the magnitude of ambient SO2 concentrations | Section 3.4.1.3; U.S.
EPA (2008d)l. Moderate to high correlation indicates that using ambient concentration as
a surrogate for personal exposure captures the variability needed for epidemiologic
studies, particularly for time-series and panel studies. Low personal-ambient correlations
reported in the literature are strongly influenced by low personal exposures relative to the
detection limits of personal samplers. When this happens, personal samplers are unable to
provide a signal to correlate with variations in ambient concentration. Low correlations
(r < 0.4) in situations with a high proportion of samples below the detection limit should
not be interpreted as evidence for the lack of a relationship between personal exposure
and ambient SO2 concentrations. Instead, a low personal sample value likely represents a
true low exposure and thus appropriately leads to a low personal:ambient ratio. Low
personakambient ratios may be due to low penetration and high deposition of SO2 in
indoor microenvironments where people spend most of their time. In a study of
personal:ambient exposure ratios by Brown et al. (2009). the authors cited personal SO2
samples below MDL and extremely low SO2 levels to rationalize not pursuing further
analysis.
Instrument error occurs when the measured SO2 concentrations are subject to
interferences that cause biases or noise leading to error in estimating exposure. Ambient
SO2 concentrations measured by FRM or FEM are subject to positive bias from the
detection of interfering compounds. See Section 2.4.1.2 for details on errors that affect
FRMs and FEMs used for central site monitoring. Inter-monitor comparison is often used
to estimate instrument precision. Goldman et al. (2010) used a simulation to investigate
the influence of instrument precision error at locations where ambient SO2 central site
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monitors were collocated. Instrument precision error increased with increasing ambient
concentration for the central site monitors. When instrument error and ambient SO2
concentration were correlated, error was larger in locations with more prevalent or
stronger sources or at times when SO2 emissions were higher for a given location. For
example, the magnitude of the instrument error was expected to be largest at times of day
when SO2 emissions were highest, such as during peak energy usage times. Instrument
error was also observed to exhibit some autocorrelation at 1- and 2-day lags in the
Goldman et al. (2010) simulation. Hence, the diurnal variability in relative SO2
instrument error does not change substantially from day to day. For epidemiologic studies
of short-term SO2 exposure that use central site-monitored ambient SO2 concentration as
a surrogate for exposure, instrument error would not be expected to influence the
exposure surrogate on a daily basis. When comparing health effect estimates among cities
for an epidemiologic study of long-term SO2 exposure, differences in instrument error
among cities could lead to biased exposure surrogates, given the reliance on differences
in magnitude of the exposure surrogate to study spatial contrasts. Section 3.4.4 describes
the influence of instrument error and high MDL on exposure error and health effect
estimates for community time-series (Section 3.4.4.1). long-term average
(Section 3.4.4.2). and panel (Section 3.4.4.3) epidemiologic studies.
3.4.3 Copollutant Relationships
Simulations by Zeger et al. (2000) indicate that unaccounted correlation among exposure
concentrations or exposure errors for copollutants may lead to bias and uncertainty in the
health effect estimates in epidemiologic studies. Correlation among copollutant exposure
concentrations may amplify the health effect estimates. In some cases, this could promote
a false conclusion of an association between a health effect and the copollutant exposure
concentration even if no relationship between the health effect and copollutant exposure
actually exists. Correlation of the errors in measuring copollutant concentrations may
cause bias in the health effect estimate, especially when one is measured with more error
than the other (Zeger et al.. 2000). Confounding is described in the Preamble to the ISAs
(U.S. EPA. 2015b). Briefly, confounding occurs when the copollutant exposure
concentrations are correlated with those of the pollutant of interest and the health effect.
Confounding can cause misleading results for estimating the health effect of SO2 if the
copollutant is not accounted for (Rothman and Greenland. 1998). This differs from effect
modification, where the health effect estimate for SO2 is conditional upon the copollutant
exposure concentration via interaction of the SO2 and copollutant exposures.
To assess the independent health effects of ambient SO2 exposure in an epidemiologic
study, it is necessary to identify (Bateson et al.. 2007) (1) measurement error for all
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copollutants; (2) which copollutants (e.g., NO2, PM2.5, UFP, BC) are potential
confounders of the health effect-SCh relationship so that their correlation and collinearity
with SO2 can be tested and, if needed, accounted for in the epidemiologic model; (3) the
time period over which correlations might exist so that potential confounders are
considered appropriately for the time period relevant for the epidemiologic study design
(e.g., pollutants or other factors that are correlated over the long term might not be
important for a short-term exposure epidemiologic study); and (4) the spatial correlation
structure across multiple pollutants, if the epidemiologic study design is for long-term
exposure Paciorek (2010). Additionally, confounding can also vary by the health
endpoint studied.
When SO2 and a copollutant are correlated, copollutant epidemiologic models may be
used to adjust the SO2 effect estimate for potential confounding by the copollutant
(Tolbert et al.. 2007). Two-pollutant models can help identify which is the better
predictor of the effect, particularly if the etiologically linked pollutant is measured with
more error than the other pollutant (Zeger et al.. 2000). However, collinearity potentially
affects the epidemiologic model's effect estimate when highly correlated pollutants are
modeled simultaneously, and differences in the spatial distribution of ambient SO2
concentration and the copollutants' ambient concentrations may also complicate model
interpretation [Section 5.1.2.1 and Gryparis et al. (2007)1. Because ambient SO2 exhibits
a relatively high degree of exposure error compared with other criteria pollutants
[e.g., Section 3.4.4. kGoldman et al. (2010)1. two-pollutant models in which the SO2
effect estimate remains robust may provide additional support for a health effect to be
associated with SO2 exposure [e.g., Ito et al. (2007)1.
This section considers temporal copollutant correlations and how relationships among
copollutants may change in space using AQS data and data reported in the epidemiologic
literature (Chapter 5). Temporal copollutant correlations are computed from the time
series of ambient concentrations for two copollutants measured with collocated AQS
monitors. Spatial relationships are evaluated by comparing within-pollutant variation
across space for different pollutants. The following sections review coexposures that can
potentially confound the relationship between a health effect and ambient SO2 exposure
over different temporal and spatial resolutions.
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3.4.3.1 Temporal Relationships among Ambient Sulfur Dioxide and Copollutant
Exposures
Short-Term Temporal Correlations
Short-term copollutant correlations were studied using collocated air quality data reported
within the U.S. EPA AQS repository system during 2013-2015. 438 sites met the 75%
data completeness criteria presented in Section 2.5.1. Daily air quality metrics
representing either 1-h daily max or 24-h avg ambient SO2 concentration values were
used. Pearson correlations were used to evaluate temporal correlations among ambient
SO2 concentrations and NAAQS copollutant concentrations. In addition, correlations
between ambient SO2 and PM2 5-sulfur were examined because PM2 5-sulfur serves as a
surrogate for SO2 oxidation products (i.e., sulfate) and may have confounding effects on
health outcomes associated with ambient SO2 exposure. Figure 3-4 and Figure 3-5
display the distribution of correlations between NAAQS copollutants and SO2 daily
metrics (24-h avg, 1 -h daily max) for all data combined, and Figure 3-6 and Figure 3-7
display those copollutant correlations broken down by season. Because epidemiologic
studies may use either daily average or daily maximum metrics, correlations are
presented for both metrics, when available. For CO and NO2, 1-h daily max
concentrations are used, while for O3, 8-h daily max concentrations are considered.
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CO Max
CO Avg
N02 Max
N02 Avg
03 Max
PM25 S Avg
PM25 Avg
PM10 Avg
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
CO = carbon monoxide; N02 = nitrogen dioxide; 03 = ozone; PM25 = particulate matter with a nominal aerodynamic diameter less
than or equal to 2.5 |jm; PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm; S = sulfur.
Note: Shown are the median (red line), mean (green star), and inner-quartile range (box), 5th and 95th percentile (whiskers) and
extremes (black circles)
Figure 3-4 Distribution of Pearson correlation coefficients for comparison of
24-h avg sulfur dioxide concentration from the year-round data
set with collocated National Ambient Air Quality Standards
pollutants (and sulfur in PM2.5) from Air Quality System during
2013-2015.
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CO Max
CO Avg -
N02 Max -
N02 Avg
03 Max
PM25 S Avg -
PM25 Avg
PM10 Avg
-1.0
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8
1.0
CO = carbon monoxide; N02 = nitrogen dioxide; 03 = ozone; PM25 = particulate matter with a nominal aerodynamic diameter less
than or equal to 2.5 |jm; PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm; S = sulfur.
Note: Shown are the median (red line), mean (green star), and inner-quartile range (box), 5th and 95th percentile (whiskers) and
extremes (black circles)
Figure 3-5 Distribution of Pearson correlation coefficients for comparison of
daily 1-h max sulfur dioxide concentration from the year-round
data set with collocated National Ambient Air Quality Standards
pollutants (and sulfur in PM2.5) from Air Quality System during
2013-2015.
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Winter
Spring
CO Max -
CO Avg
N02 Max-
N02 Avg
03 Max -
PM25 S Avg
PM25 Avg
PM10 Avg
f
• — ¦¦¦¦¦
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- -4
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• • » a* •
—h
b
—h
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Summer
CO Max
CO Avg
N02 Max
N02 Avg
03 Max
PM25 S Avg
PM25 Avg
PM10 Avg
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
correlation with S02 hourly daily average
•-H
-•-[—
- ^ —h-
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- + h+ -
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-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Fall
-4-
• M —|
+
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
correlation with S02 hourly daily average
CO = carbon monoxide; N02 = nitrogen dioxide; 03 = ozone; PM25 = particulate matter with a nominal aerodynamic diameter less
than or equal to 2.5 pm; PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 pm; S = sulfur;
S02 = sulfur dioxide.
Note: Shown are the median (red line), mean (green star), and inner-quartile range (box), 5th and 95th percentile (whiskers) and
extremes (black circles).
Figure 3-6 Distribution of Pearson correlation coefficients for comparison of
daily 24-h avg sulfur dioxide ambient concentration stratified by
season with collocated National Ambient Air Quality Standards
pollutants (and PM2.5) from Air Quality System during 2013-2015.
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Winter
CO Max -
CO Avg
N02 Max-
N02 Avg
03 Max -
PM25 S Avg
PM25 Avg
PM10 Avg
•• *-f
]—
*
f—
• mmm—
b
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• • M
—
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+
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mm •
• wm|
-
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• •
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IH •
m"H
*
—h
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
CO Max -
CO Avg
N02 Max -
N02 Avg
03 Max -
PM25 S Avg
PM25 Avg
PM10 Avg
Summer
!*• M
—KID—h — •
::
—t—~—f •
—H f i—f *
Z
-•¦KXld:
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
correlation with S02 hourly daily max
Spring
¦K
+-•
I
H-H
-HZ
I
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Fall
1 |» f**# ^ *
- +
•4
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
correlation with S02 hourly daily max
CO = carbon monoxide; N02 = nitrogen dioxide; 03 = ozone; PM25 = particulate matter with a nominal aerodynamic diameter less
than or equal to 2.5 pm; PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 pm; S = sulfur;
S02 = sulfur dioxide.
Note: Shown are the median (red line), mean (green star), and inner-quartile range (box), 5th and 95th percentile (whiskers) and
extremes (black circles).
Figure 3-7 Distribution of Pearson correlation coefficients for comparison of
daily 1-h max sulfur dioxide ambient concentration stratified by
season with collocated National Ambient Air Quality Standards
pollutants (and PM2.5) from Air Quality System during 2013-2015.
1 While 24-h avg ambient SO2 concentration exhibits a wide range of correlations with
2 NAAQS copollutants, median correlations are all below 0.4 (Figure 3-4). The lowest
3 correlations are observed between ambient SO2 concentration and ambient O3
4 concentration, with median correlations below 0.1. Slightly higher correlations are
5 observed between ambient SO2 concentration and other primary NAAQS pollutant
6 concentrations (NO2 and CO), with median correlations between 0.3 and 0.4. Common
7 fuel combustion sources may be responsible for these correlations (Section 2.2). Lower
8 correlations with PM2.5 sulfur than PM2.5 mass may reflect the secondary formation of
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sulfate by oxidation of SO2, while PM2 5 mass also has a primary component.
Correlations close to 1 or below 0 are sometimes observed but only occur at a few outlier
monitoring sites. Comparatively, copollutant correlations of daily 1-h max ambient SO2
in Figure 3-5 are also slightly lower than the copollutant correlations based on ambient
SO2 24-h avg values in Figure 3-4. The medians of correlations between daily 1-h max
ambient SO2 concentrations and other NAAQS pollutants are below 0.3, with the
exception of NO2, which exhibits median correlations slightly above 0.3. These results
indicate that for short-term epidemiologic studies, the minority of sites with stronger
correlations may introduce a greater degree of confounding into those epidemiologic
results. It is notable that the nature of correlations between SO2 and copollutants is
changing given rulemaking on use of ultra-low sulfur diesel fuel that went into effect in
2006 (66 FR 5002). Some of the epidemiologic studies cited in Chapter 5 included data
obtained prior to 2006 and 2007, when the new sulfur standards took effect for highway
vehicles and heavy-duty vehicles, respectively. This change may have contributed to the
wider variation observed in correlation between ambient SO2 and copollutant
concentrations. Note that potential for confounding also varies by health endpoint.
Correlations between ambient SO2 and NAAQS copollutant concentrations demonstrate
very little variability across seasons (Figure 3-6 and Figure 3-7). All median and average
copollutant correlations are below 0.4 across every season. The only substantial seasonal
difference in correlations between ambient SO2 and copollutant concentrations occurs
during the winter, when ambient SO2 concentration exhibits lower negative correlations
with ambient O3 concentration (median winter correlations = -0.1). SO2-O3 correlations
are generally low year-round, potentially because the regional nature of O3 formation
contrasts with the local nature of SO2 plumes from point sources. In winter, the low
correlations could be directly linked to relatively low ambient O3 concentrations during
this time of year due to less photochemical O3 production and SO2 oxidation.
Overall, daily and hourly ambient SO2 concentrations generally exhibit median
correlations around 0.2-0.4 with respect to other collocated NAAQS copollutants at AQS
monitoring sites. However, given that a small subset of sites report relatively higher
copollutant correlations, confounding may need to be considered on a study-by-study
basis, preferably with correlations reported in the individual studies. High copollutant
correlations in the national distribution could be due either to consistently low
concentrations for both SO2 and the copollutant or to consistent fluctuations in
concentrations of both pollutants due to source behavior and meteorology.
Exposure studies have also examined correlations between ambient SO2 concentration
and ambient or personal copollutant exposure concentrations, generally reporting low or
moderate correlations. For SO2, within-hourly concentrations have median correlations
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around 0.2 for most PM of different cut-points and species. For gases, median
correlations of within-hourly data were lower for O3 than for CO and NO2, respectively,
but median correlations did not surpass 0.4. Correlations were mostly positive for all but
O3, which exhibits both negative and positive correlations. See Figure 3-8 and references
cited therein for copollutant correlation data reported in the literature (Liu et al. (2016);
Mendolaetal. (2016a); Michikawa et al. (2016); Neophvtou et al. (2016); Smith et al.
(2016); Wallace et al. (2016); Ancona et al. (2015); Assibev-Mensah et al. (2015);
Bentaveb et al. (2015); Bvers et al. (2015); Deng et al. (2015a); Dibben and Clemens
(2015); Huang et al. (2015a); Hwang et al. (2015b); lerodiakonou et al. (2015);
Michikawa et al. (2015); Oian et al. (2015); Radwan et al. (2015); Ware et al. (2015);
Yorifiiji etal. (2015b); Zhu et al. (2015); Chen et al. (2014b); Gorai et al. (2014); Lin et
al. (2014); Liu et al. (2014a); Winquist et al. (2014); Xu et al. (2014); Altug et al. (2013);
C'arev et al. (2013); Cloughertv et al. (2013); Dong et al. (2013a); Faiz et al. (2013);
Greenwald et al. (2013); Mehta et al. (2013); Oiu et al. (2013b); Slama et al. (2013); Son
et al. (2013); Zheng et al. (2013); Costa Nascimento et al. (2012); Ebisu and Bell (2012);
Faiz et al. (2012); HEI (2012); Le et al. (2012); Lee et al. (2012); Portnov et al. (2012);
Tsai et al. (2012); Turin et al. (2012); Bhaskaran et al. (2011); Darrow et al. (2011);
Hwang etal. (2011); Ito etal. (2011); Lee et al. (2011b); Li etal. (2011); Liao et al.
(2011); Peel et al. (2011); Samoli et al. (2011); Zhao et al. (2011); Akinbnmi etal
(2010); Chen et al. (2010b); Hsieh et al. (2010); Pan et al. (2010); Penard-Morand et al.
(2010); Arbex et al. (2009); Amedo-Pena et al. (2009); Cheng et al. (2009); Darrow et al.
(2009); Forbes et al. (2009c); Guo et al. (2009); Lipfert et al. (2009); Rich et al. (2009);
Sahsuvaroglu et al. (2009); Stieb et al. (2009); Strickland et al. (2009); Dales et al.
(2008); Hwang and Jaakkola (2008); Jalaludin et al. (2008); Segalaetal. (2008);
Woodruff et al. (2008); Ko et al. (2007a); Liu et al. (2007); Tolbert et al. (2007); ATS PR
(2006); Ballesteret al. (2006); Condon et al. (2006); Fung et al. (2006); Jalaludin et al.
(2006); Leem et al. (2006); Lipfert et al. (2006a); Filleul et al. (2005); Llorca et al.
(2005); Peel et al. (2005); Sagiv et al. (2005); Wilson et al. (2005); Metzger et al. (2004);
Jaffe et al. (2003); Lee et al. (2003); Liu et al. (2003); Sheppard (2003); Yang et al.
(2003b); Yang et al. (2003a); Anderson et al. (2001); Ballester et al. (2001); Ha et al.
(2001); Krewski et al. (2000); Lipfert et al. (2000b); Abbey et al. (1999); Sheppard et al.
(1999); Pereira et al. (1998); Burnett et al. (1997); Schwartz (1997)).
More data were available for within-daily correlations of SO2 and copollutant exposure
concentrations. Median correlation around 0.5 were observed for SO42 , NO3 . black
smoke (BS), and organic carbon (OC) PM2 5 species, PM10, and NO2 for that time scale.
Median correlation was around 0.3 for PM10-2.5, around 0.4 for CO and PM2 5, and around
-0.2 for O3. Both data availability and inter-site variability were much greater for the
gases, PM2 5, and PM10 compared with the individual PM2 5 species or PM10-2.5. Where
data were available, a large degree of scatter was evident in the data. In studies where
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within-daily correlations of SO2 exposure concentrations with NO2 and CO exposure
concentrations were observed to be high, it is possible the data were collected before the
rulemaking to reduce sulfur content in diesel fuel went into effect in 2006 (66 FR 5002)
or when coal was in greater use in energy generation (Section 2.2). The minority of sites
with stronger correlations may introduce a greater degree of confounding into the
epidemiologic results. For this reason, copollutant correlations need to be reported in
individual epidemiological studies to assess if confounding is a possibility.
Data for correlations between ambient SO2 concentrations and personal copollutant
exposures were reported in the 2008 SOx ISA (U.S. EPA. 2008d). and no studies have
been produced to substantiate or revise the observations reported at that time.
Between-subject correlations of daily ambient SO2 concentration with personal PM2 5
exposures were found to vary widely with positive and negative correlations in the Sarnat
et al. (2005) and Sarnat et al. (2001) studies. In the (Sarnat et al.. 2005) study, 95-97% of
the SO2 data were below the MDL, indicating high uncertainty. This evidence suggests
that correlations between personal copollutant exposures and ambient SO2 concentration
vary among individuals, and thus the potential for copollutant confounding cannot be
ruled out.
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With in-Hourly Correlations
Within-Daily Correlations
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Data from the studies cited in Figure 3-8 suggest that the correlations between exposure
concentrations of SO2 and copollutants have changed over time for some cases
(Figure 3-9). On average, copollutant correlations using 1-hour data have declined in
magnitude over the last two decades for CO, NO2, PM10, and PM2 5, albeit with a lot of
scatter in these relationships reflected in the mostly low correlation values. These trends
may be related to the adoption of alternatives to coal in energy generation (Section 2.2).
Most of the studies presented were performed during periods that precede 2006, when the
ultra-low sulfur diesel rule went into effect (66 FR 5002). The amount of SO2 co-emitted
with CO and NOx during combustion processes has since been greatly reduced. Hence,
copollutant confounding is less probable for newer studies of the health effects of SO2
exposure compared with older studies. At the same time, scatter in the copollutant
correlation trends suggests that copollutant correlations need to be checked for individual
epidemiological studies to assess if confounding is a possibility.
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0.8
0.7 x
0.6
A
0.5 x
0.4 ~ ____ ° °
. "o— oco
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1992 1994 1996 1993 2000 2002 2004 2006 2008 2010 2012
CO = carbon monoxide; N02 = nitrogen dioxide; 03 = ozone; PM2.5 = particulate matter with a nominal aerodynamic diameter less
than or equal to 2.5 [jm; PM1Q = particulate matter with a nominal aerodynamic diameter less than or equal to 10 pm;
p = correlation; x = year.
Figure 3-9 Trends in copollutant correlations computed using hourly
(1-h avg or 1-h daily max) concentration data.
Long-Term Correlations
1 Long-term epidemiologic studies that have reported copollutant correlations are also
2 displayed in Figure 3-8 and references cited therein for within-monthly and longer term
3 correlations. Data were limited for many of the PM2 5 components. For exposure
4 concentrations of PM25, PM10, O3, CO, and NO2, a wide range of correlations has been
5 reported. Median correlation was lower for PM2 5 exposure concentration (r = 0.2)
6 compared with that of PM'10 (r = 0.4), CO (r = 0.3), and NO2 (r = 0.3). Median correlation
7 was negative (r = -0.3) for O3 exposure concentration. For correlations between exposure
8 concentrations of SO2 and PM2 5, most of the data were clustered around the median,
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while variability in the correlations was larger for the other copollutants. As for
short-term copollutant relationships, no clear conclusion can be drawn regarding the
potential for confounding of long-term SO2 epidemiologic estimates by copollutants.
Wide variability in copollutant correlations with the highest correlations around 0.7-0.8
for PM2 5, PM10, CO, and NO2 suggests that confounding may need to be considered on a
study-by-study basis.
3.4.3.2 Spatial Relationships among Ambient Sulfur Dioxide and Copollutants
Spatial confounding can potentially influence health effect estimates in epidemiologic
studies of long-term SO2 exposure. Paciorek (2010) performed simulations to test the
effect of spatial confounding on health effect estimates in long-term exposure
epidemiologic studies. He identified unmeasured spatial confounding as a key driver in
biasing health effect estimates in a spatial regression. The study author maintained that
bias can be reduced when variation in the exposure metric occurs at a smaller spatial
scale than that of the unmeasured confounder.
3.4.4 Implications for Epidemiologic Studies of Different Designs
Exposure error is defined in Section 3.2.1. To summarize, exposure error refers to the
bias and uncertainty associated with using concentration metrics to represent the actual
exposure of an individual or population. Exposure error has two components:
(1) uncertainty in the metric used to represent exposure concentration and (2) the
difference between the surrogate parameter of interest in the epidemiologic study and the
true exposure (which may not be observable) (Zeger etal.. 2000). Classical error can be
considered the component of exposure measurement error derived from uncertainty in the
metric being used to represent exposure. Classical error is defined as error scattered
around the true personal exposure and independent of the measured exposure
concentration. Classical error results in bias of the epidemiologic health effect estimate
that is typically towards the null (no effect of the exposure). Classical error can also cause
inflation or reduction of the standard error of the health effect estimate. Berkson error can
be considered the component of exposure error related to the use of a surrogate target
parameter of interest in the epidemiologic study in lieu of the true exposure. Berkson
error is defined as error scattered around the exposure surrogate (in most cases, the
central site monitor measurement) and independent of the true value (Goldman et al..
2011; Reeves et al.. 1998). Pure Berkson error is not expected to bias the health effect
estimate.
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When investigators use statistical models to predict exposure concentrations, the
exposure error is no longer purely classical or purely Berkson but may have
characteristics of each error type. Measurement error for modeled exposure
concentrations has been decomposed into Berkson-like and classical-like components,
sharing some characteristics with Berkson and classical errors, respectively, but with key
differences (Szpiro et al.. 2011V Berkson-like errors occur when the modeled exposure
concentration does not capture all of the variability in the true exposure. Under ideal
conditions, Berkson-like errors increase the variability around the health effect estimate
in a manner similar to pure Berkson error and does not induce bias, but Berkson-like
error is spatially correlated and not independent of predicted exposure concentrations, so
it results in underestimation of standard errors. Szpiro and Paciorek (2013) analyzed the
impact of Berkson-like error under more general conditions and found that it can bias
health effect estimates either toward the null or away from the null. For example, in one
simulation study in which the spatial distributions of monitor and subject locations were
dramatically different, the health effect estimates were biased away from the null. In
another example, where spatially structured covariates were included in the health model
but not in the exposure model, the health effect estimates were biased toward the null.
Hence, Berkson-like error can lead to bias of the health effect estimate in either direction
and should not be ignored. Classical-like errors result from uncertainty in estimating
exposure model parameters. It can add variability to predicted exposure concentrations
and can bias health effect estimates in a manner similar to pure classical error, but it
differs from pure classical error in that the additional variability in estimated exposure
concentrations is also not independent across space. Exposure error can bias
epidemiologic associations between ambient pollutant concentrations and health
outcomes, compared with the effect estimate obtained using the true exposure, and it
tends to widen confidence intervals around those estimates beyond nominal coverage of
the confidence intervals (Sheppard et al.. 2005; Zeger et al.. 2000).
Exposure error can be an important contributor to uncertainty and variability in
epidemiologic study results. Time-series studies assess the daily health status of a
population of thousands or millions of people over the course of multiple years
(i.e., thousands of days) across an urban area by estimating people's exposure
concentrations using a short monitoring interval (hours to days). In these studies, the
community-averaged concentration of an air pollutant measured at central site monitors is
typically used as a surrogate for individual or population ambient exposure. In addition,
panel studies, which consist of a relatively small sample (typically tens) of study
participants followed over a period of days to months, have been used to examine the
health effects associated with short-term exposure to ambient concentrations of air
pollutants [e.g., Dclfino et al. (1996)1. Panel studies may also apply a
microenvironmental model to represent exposure concentrations for an air pollutant.
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A longitudinal cohort epidemiologic study, such as the American Cancer Society (ACS)
cohort study, typically involves hundreds or thousands of subjects followed over several
years or decades [e.g., Jerrett et al. (2009)1. Concentrations are generally aggregated over
time and by community to estimate exposures. The importance of exposure error varies
with study design and is dependent on the spatial and temporal aspects of the design.
Factors that could influence exposure estimates include topography of the natural and
built environment, meteorology, instrument errors, use of ambient SO2 concentration as a
surrogate for exposure to ambient SO2, and the presence of SO2 in a mixture of
pollutants. The following sections will consider various sources of error and how they
affect the interpretation of results from epidemiologic studies of different designs.
3.4.4.1 Community Time-Series Studies
In most short-term exposure epidemiologic studies of the health effects of SO2, the health
effect endpoint is modeled as a function of ambient exposure, A',. which is defined as the
product of Ca, and a, a term encompassing time-weighted averaging and infiltration of
SO2 (Section 3.2.2). Community time-series epidemiologic studies capturing the
exposures and health outcomes of a large cohort frequently use the ambient concentration
at a central site monitor (Ca,Csm) as a surrogate for i?a in an epidemiologic model (Wilson
et al.. 2000). At times, an average of central site-monitored concentrations is used for the
E3 surrogate. For studies involving thousands of participants, it is not feasible to measure
personal exposure concentrations or time-activity patterns. Moreover, for community
time-series epidemiology studies of short-term exposure, the temporal variability in
ambient SO2 concentration is of primary importance to relate to variability in the health
effect estimate (Zegeret al.. 2000). Ca,CSm can be an acceptable surrogate if the central site
monitor captures the temporal variability of the true air pollutant exposure. Spatial
variability in ambient SO2 concentrations across the study area could attenuate an
epidemiologic health effect estimate if the exposures are not correlated in time with Ca,Csm
when central site monitoring is used to represent exposure in the epidemiologic model. If
exposure assessment methods that more accurately capture spatial variability in the
concentration distribution over a study area are employed, then the confidence intervals
around the health effect estimate may decrease. Ca,CSm may be an acceptable surrogate for
A'a if the concentration time series at the central site monitor is correlated in time with the
exposures.
In a time-series study of ED visits for cardiovascular disease, Goldman et al. (2011)
simulated the effect of classical and Berkson errors due to spatiotemporal variability
among ambient or outdoor (i.e., a noncentral site monitor situated outside the home) air
pollutant concentrations over a large urban area. For 1-h daily max SO2, the relative risk
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(RR) per ppm was negatively biased in the case of classical error (-1.3%) and negligibly
positively biased in the case of Berkson error (0.0042%). The 95% confidence interval
range for RR per ppm was wider for Berkson error (0.028) compared with classical error
(0.0025).
Recent studies have explored the effect of spatial exposure error on health effect
estimates to test the appropriateness of using central site monitoring for time-series
studies. Goldman et al. (2010) simulated spatial exposure error based on a semivariogram
function across monitor sites with and without temporal autocorrelation at 1- and 2-day
lags to analyze the influence of spatiotemporal variability among ambient concentrations
over a large urban area on a time-series study of ED visits for cardiovascular disease.
A random term was calculated through Monte Carlo simulations based on the data
distribution from the semivariogram, which estimated the change in spatial variability in
exposure concentration with distance from the monitoring site. The average of the
calculated random term was added to an ambient central site monitoring SO2
concentration time series (considered in this study to be the base case) to estimate SO2
population exposure concentration subject to spatial error. For the analysis with temporal
autocorrelation considered, RR per ppm for 1-h daily max SO2 dropped slightly to 1.0045
(95% CI: 1.0023, 1.0065) when it was compared with the central site monitor RR per
ppm = 1.0139 (for all air pollutants).1 When temporal autocorrelation was not considered,
RR per ppm dropped very slightly to 1.0042 for 1-h daily max SO2. The results of
Goldman et al. (2010) suggest that spatial exposure error from the use of ambient central
site SO2 concentration monitoring data results in biasing the health effect estimate
towards the null, but the magnitude of the change in effect was small.
In another simulation study analyzing the influence of spatiotemporal variability among
ambient concentrations over a large urban area on health effect estimates, Goldman et al.
(2012) evaluated the effect of different types of spatial averaging on bias in the health
effect risk ratio and the effect of correlation between measured and reference ambient
concentrations of SO2 and other air pollutants. Ambient concentrations were simulated at
alternate monitoring locations using the geostatistical approach described above
(Goldman et al.. 2010) for the 20-county Atlanta metropolitan area for comparison with
ambient concentration measurements obtained directly from monitors at those sites.
Geostatistical-simulated ambient exposure concentrations were designated as the
reference in this study, and other exposure assessment methods were assumed to have
some error. Five different exposure assessment approaches were tested: (1) using a single
central site monitor, (2) averaging the simulated exposures across all monitoring sites,
1 Note that 95% CIs were not reported for the central site monitor RR or for the cases where temporal
autocorrelation was not considered.
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(3) performing a population-weighted average across all monitoring sites, (4) performing
an area-weighted average across all monitoring sites, and (5) performing
population-weighted averaging of the geostatistical simulation. Goldman et al. (2012)
observed that the exposure error was somewhat correlated with both the measured
exposure concentration and the reference ambient concentrations, reflecting both Berkson
and classical error components. For the central site monitor, the exposure errors were
somewhat inversely correlated with the exposure concentration reference value but had
relatively higher positive correlation with the measured ambient concentration. For the
other exposure estimation methods, the exposure errors were inversely correlated with the
reference exposure concentration, while having positive but lower magnitude correlation
with the measured ambient concentration. Additionally, the exposure bias, given by the
ratio of the exposure error to the measured value, was much higher in magnitude at the
central site monitor than for the spatial averaging techniques for SO2. Hence, compared
with other exposure assessment methods, the health effect estimate would likely have
greater bias towards the null with reduced precision when a central site monitor is used to
measure ambient SO2 concentration as a surrogate for exposure. However, exposure error
is likely to cause some bias and imprecision for other exposure surrogate methods as
well.
In addition to the effect of the correlations and ratios themselves, spatial variation across
urban areas also impacts time-series epidemiologic results. The Goldman et al. (2010)
and Goldman et al. (2012) findings suggest more Berkson error in the spatially resolved
exposure concentration metrics compared with the central site monitor ambient
concentration and more classical error for the central site monitor ambient concentration
estimate compared with the other exposure concentration measurement techniques.
Hence, more bias would be expected for the health effect estimate calculated from the
central site monitor ambient concentration, and more variability would be expected for
the health effect estimate calculated from exposure concentrations estimated by the more
spatially resolved methods. Differences in the magnitude of exposure concentration
estimates are not likely to cause substantial bias, but they tend more to widen confidence
intervals and thus reduce the precision of the effect estimate beyond the nominal
coverage of the confidence intervals that would be obtained if using the true exposure
(Zeger et al.. 2000). The more spatially variable air pollutants studied in Goldman et al.
(2012) also had more bias in the health effect estimates. This occurred across exposure
assignment methods but was more pronounced for the central site measurement ambient
concentration data. Note that the Goldman et al. (2010). Goldman et al. (2011). and
Goldman et al. (2012) studies were performed only in Atlanta, GA. These simulation
studies are informative, but similar simulation studies in additional cities would aid
generalization of these study results.
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Section 3.4.2.4 describes the influence of high MDL on the relationship between
measured ambient SO2 concentrations and personal SO2 exposures. When measurements
are above MDL, then the amount of correlation between personal SO2 exposure and
ambient SO2 concentrations determines the extent of bias in a time-series study. If the
reported values of personal exposure measurements are below MDL, correlation between
personal SO2 exposure measurements and ambient SO2 concentrations will likely be low
due to random noise in the signal. To the extent that true correlations are less than one,
epidemiologic effect estimates based on ambient concentration will be biased toward the
null, based on simulations by Zeger et al. (2000). Time-series epidemiologic studies
employing data below MDL may demonstrate attenuated effect, but this scenario cannot
be used to reject the hypothesis of a health effect.
Section 3.4.2.4 also describes the influence of instrument accuracy and precision on the
relationship between ambient SO2 concentrations and personal SO2 exposures. Exposure
measurement error related to instrument precision has a smaller influence on health effect
estimates in time-series studies compared with error related to spatial gradients in the
ambient SO2 concentration because instrument precision would not be expected to
modify the ability of the instruments to respond to changes in ambient concentration over
time. Goldman et al. (2010) investigated the influence of instrument error on health effect
estimates in a time-series epidemiology study by studying differences in exposure
concentration estimates and health effect estimates obtained using collocated monitors. In
this study, a random error term based on observations from collocated monitors was
added to a central site monitor's ambient concentration time series to simulate population
estimates for ambient air concentrations subject to instrument precision error in
1,000 Monte Carlo simulations. Very small changes in the risk ratios were observed for
1-h daily max SO2 ambient concentrations. For 1-h daily max SO2 ambient concentration,
the RR per ppm of SO2 ambient concentration with simulated instrument precision error
was 1.0132 compared with RR per ppm = 1.0139 for the central site monitor. The amount
of bias in the health effect estimate related to instrument precision was very small.
As described in Section 3.4.1 nonambient sources of SO2 are rare. Even in
microenvironments where nonambient SO2 exposure is substantial, such as in a room
with a kerosene heater, such nonambient exposure concentrations are unlikely to be
temporally correlated with ambient SO2 exposure concentrations (Wilson and Suh. 1997).
and therefore would not affect epidemiologic associations between ambient SO2 exposure
concentrations and a health effect in a time-series study. Sheppard et al. (2005) concluded
that nonambient exposure does not influence the health outcome effect estimate if
ambient and nonambient exposure concentrations are independent. Personal exposure to
ambient SO2 is some fraction of the ambient concentration. Therefore, effect estimates
based on personal SO2 exposure rather than ambient SO2 concentration will be positively
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biased in proportion to the ratio of ambient SO2 concentration to ambient SO2 exposure
concentration. Daily fluctuations in this ratio can widen the confidence intervals in the
ambient SO2 concentration effect estimate beyond the nominal coverage of the
confidence intervals obtained using the true exposure. Uncorrelated nonambient exposure
concentration will not bias the effect estimate but may also widen the confidence
intervals (Sheppard et al. 2005; Wilson and Suh. 1997).
3.4.4.2 Long-Term Cohort Studies
For cohort epidemiologic studies of long-term human exposure to SO2, where the spatial
difference in the magnitude of the ambient SO2 exposure is often of most interest and if
Ca,csm is used as a surrogate for E&, then a can be considered to encompass the exposure
measurement error related to uncertainties in the time-activity data and air exchange rate.
Spatial variability in ambient SO2 exposure concentrations across the study area could
lead to bias in the health effect estimate if Ca,Csm is not representative of A',. This could
occur, for example, if the study participants were clustered in a location where their SO2
exposure concentration is higher or lower than the exposure concentration estimated at a
modeled or measurement site. (";LCsm may be an acceptable surrogate for A'„ if the central
site monitor is located close to the study participants and the ambient SO2 source
(e.g., near the plume touch-down of a power plant) and spatial variability of the ambient
SO2 concentration across the study area where the study participants are located is
minimal in the vicinity of each sample group.
For long-term epidemiologic studies, the lack of personal exposure data means that
investigators must rely on central site monitoring data or model estimates. Concentration
data may be used directly, averaged across counties or other geographic areas, or used to
construct geospatial or regression models to assign exposure concentrations to
unmonitored locations. The number of long-term studies of SO2 exposure that permit
evaluation of the relationship between long-term average SO2 concentrations and
personal or population exposures is limited, and the value of short-term exposure
concentration data for evaluating long-term exposure concentration relationships is
uncertain. If the longer averaging time (annual vs. daily or hourly) smoothes out
short-term fluctuations, long-term concentrations may be well correlated with long-term
exposure concentrations that can be employed in long-term epidemiologic studies. For
example, Guav et al. (2011) observed high correlation between
single-year/single-location SO2 concentrations used for an exposure surrogate with
concentrations averaged over a 22-year period when the annual SO2 concentrations were
assigned based on the study participants' census subdivision. However, lower correlation
between long-term exposure and ambient concentration could occur if important
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exposure determinants change over a period of several years, including activity pattern
and residential air exchange rate.
Minimization of error in the exposure concentration estimate does not always minimize
error in the health effect estimate. Szpiro etal. (2011) used simulation studies to evaluate
the bias and uncertainty of the health effect estimate obtained when using correctly
specified and misspecified long-term exposure concentration models. The correct
exposure concentration model was considered to be an LUR with three covariates while
the misspecified model included only two of these three covariates. The study authors
estimated the exposure concentration model parameters using monitor data and predicted
exposure concentrations at subject locations. They studied two conditions: where the
variation in the third covariate was identical in the monitor and subject data versus where
it was much smaller in the monitor data than in the subject data. Szpiro etal. (2011)
showed that prediction accuracy of the exposure concentration estimate was always
higher for the correctly specified model compared with the misspecified model.
The health effect estimate had lower RMSE for the correct model when the third
covariate had identical variability in the monitor and subject data. However, when the
third covariate was much less variable in the monitor data, then the health effect estimate
had lower RMSE for the misspecified model. The results of the Szpiro etal. (2011)
simulations demonstrate one situation where use of a more accurately defined exposure
concentration metric does not improve the health effect estimate.
Error correction is a relatively new approach to estimate the correct standard error and to
potentially correct for bias in air pollution cohort studies. Szpiro and Paciorek (2013)
established that two conditions must hold for the health effect estimate to be predicted
correctly: (1) the exposure concentration estimates from monitors must come from the
same underlying distribution as the true exposure concentrations and (2) the health effect
model includes all covariates relevant to the population. Szpiro and Paciorek (2013) and
Bergen and Szpiro (2015) developed methods to correct for bias from classical-like
measurement error by exploiting asymptotic properties of the variability in exposure
concentration model parameter estimates and propagating these variances through the
health model by means of the delta method. Valid standard error estimates are obtained
by means of the nonparametric bootstrap. Methods have also been proposed to correct for
bias from Berkson-like error, but these require stronger conditions, including
compatibility between subject and monitor locations and inclusion of spatially structured
health model covariates in the exposure concentration model.
In the Szpiro and Paciorek (2013) study, when the assigned exposure concentration
measurements were set to be uniform across space, the health effect estimate was biased
away from the null with different standard error compared with the case when the
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exposure subjects were collocated with the study participants. When an additional spatial
covariate was omitted, the health effect estimate was biased towards the null with
different standard errors compared with the correctly specified model. Bias correction
and bootstrap calculation of the standard errors reduced bias in the model prediction,
even when the true model contained several degrees of freedom (df). Furthermore, bias
correction in conjunction with bootstrapped simulation of standard error improved the
confidence interval coverage of the simulation. With no correction, nominal coverage of
the 95% confidence interval was 80% with 5 df and decreased to 50% for 25 df. With
bias correction and bootstrapping, nominal coverage of the confidence interval was
maintained around 95% with an increase in the expected value of the standard error,
regardless of the number of df constraining the model. These findings imply that without
bias correction, effect estimates would be biased with standard errors that underestimate
the true standard error. None of the epidemiologic studies cited in Chapter 5 applied bias
correction. Spiegelman (2013) noted that the new measurement error correction methods
developed by Szpiro and Paciorek (2013) are a version of regression calibration. This
study illustrated the influence of classical-like and Berkson-like errors on long-term
exposure cohort study health effect estimates through these simulations.
Instrumentation bias could be expected to influence health effect estimates from
epidemiologic studies of long-term SO2 exposures in some situations. Section 2.4.1
describes how the presence of copollutants can cause ambient SO2 concentrations
measured using central site monitors to be overestimated and how high relative humidity
can cause ambient SO2 concentration measurements to be underestimated. Relative
humidity would not be expected to vary greatly within a city. However, local ambient
copollutant concentrations may be spatially variable such that failure to account for
differences in measurement errors could lead to some differential bias in health effect
estimates across a city related to instrument error. Because climate and ambient sources
are more likely to differ among cities, instrumentation error could have a larger influence
on the comparison of health effect estimates among cities when central site monitors are
used to estimate exposure concentrations.
3.4.4.3 Panel Studies
Panel or small-scale cohort studies involving dozens of individuals (including some
studies cited in Section 5.2.2.2 and Section 5.2.2.3) may use more individualized
exposure concentration measurements, including personal exposures, residential indoor
or outdoor concentration measurements, or concentration data from local study-specific
monitors. Modeled concentrations are typically not used as exposure surrogates in panel
epidemiologic studies. A main disadvantage of the modeling approach is that the results
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of modeling exposure concentration must be compared to an independent set of measured
exposure concentration levels (Klepeis. 1999). In addition, a modeling approach requires
resource-intensive development of validated and representative model inputs, such as
human activity patterns, distributions of AER, and deposition rate. Therefore, modeled
exposure concentrations are used much less frequently in panel epidemiologic studies.
Section 3.4.2.4 describes the influence of high MDL on the relationship between
measured ambient SO2 concentrations and personal exposures for ambient SO2. Personal
exposure measurements below MDL will likely cause the correlation between personal
exposure measurements and ambient SO2 concentrations to be low due to random noise
in the signal. Noise in the exposure signal would add noise to the health effect estimate in
a panel epidemiologic study as well. Below MDL measurements would be unlikely to
bias the effect estimate, however, because the magnitude of exposure would be low
whether measured with a high-precision or low-precision device.
It is also possible that the ratio of personal SO2 exposure to ambient SO2 concentration in
panel studies is low due to the compound's low penetration and high reactivity. This
results in attenuation of the magnitude of the exposure concentration-based effect
estimate relative to the ambient concentration-based effect estimate (see Equation 3-6).
However, if the ratio is approximately constant over time, the strength of the statistical
association would be similar for ambient concentration- and exposure
concentration-based effect estimates (Sheppard. 2005; Sheppard et al.. 2005).
3.5 Summary and Conclusions
The 2008 SOx ISA (U.S. EPA. 2008d) evaluated studies of ambient SO2 concentrations
and exposures in multiple microenvironments, discussed methods for estimating personal
and population exposure concentrations via monitoring and modeling, analyzed
relationships between personal exposure and ambient concentrations, and discussed the
implications of using ambient SO2 concentrations as estimates of exposure concentration
in epidemiologic studies. Key findings were that indoor SO2 concentrations and personal
SO2 exposure concentrations tended to be below the detection limit of personal SO2
samplers for averaging times of 24 hours or less, making it difficult to evaluate the
relationship between ambient SO2 concentrations and indoor or personal SO2 exposure
concentrations. However, in studies with the bulk of personal samples above the
detection limit, personal measurements of SO2 exposure were moderately correlated with
ambient SO2 concentrations. Regarding the influence of exposure concentration estimates
on epidemiologic study results, high spatial variability of ambient SO2 concentrations
across an urban area results in highly variable correlations among urban SO2 monitors.
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Low correlations between individual monitored ambient SO2 concentrations and the
community average ambient SO2 concentration tend to bias effect estimates toward the
null, while variations in individual personal-ambient relationships across a community
will tend to widen confidence intervals around the effect estimates compared with the
nominal coverage that would be obtained if the true exposure were used in the
epidemiologic model. All of these findings are supported by the recent evidence available
since the previous ISA.
In the current ISA, increased focus has been placed on the use of exposure surrogates in
epidemiologic studies. Multiple techniques can be used to assign SO2 exposure
concentrations for epidemiologic studies, including the use of central site monitor
ambient SO2 concentrations, personal SO2 monitors, and various types of models. Each
has strengths and limitations, as summarized in Table 3-1. Central site monitors provide a
continuous record of ambient SO2 concentrations over many years, but they do not fully
capture the relatively high spatial variability in ambient SO2 concentration across an
urban area, which tends to attenuate health effect estimates in time-series epidemiologic
studies. For long-term studies, bias may occur in either direction depending on whether
the monitor is over- or underestimating ambient SO2 exposure concentration for the
population of interest. In all study types, use of central site monitor ambient SO2
concentrations in lieu of the true SO2 exposures is expected to widen confidence intervals
beyond the nominal coverage of the confidence intervals that would be obtained if the
true exposure were used. Personal SO2 monitors directly measure exposure, but low
ambient SO2 concentrations often result in a substantial fraction of the samples falling
below the MDL for averaging times of 24 hours or less. Personal monitors also provide a
relatively limited data set, making them more suitable for panel epidemiologic studies.
Computational models can be used to develop exposure concentration surrogates for
individuals and large populations when personal exposure measurements are unavailable.
Modeling approaches may include SPMs, LUR models, IDW, dispersion models, and
CTMs. Strengths and limitations of each method are discussed in Table 3-1. Briefly,
SPMs, LUR, and IDW do not take into account atmospheric chemistry and physics.
SPMs require only distances between SO2 sources and receptors for input. EWPM also
require emission rates. IDW is a weighted average of ambient SO2 concentrations
measured at several monitors. Other spatial interpolation techniques, such as kriging, also
require ambient SO2 concentrations from several monitors and apply more complex
mathematical functions to interpolate among monitors. LUR regresses measured ambient
SO2 concentrations on local variables and then uses the resulting model to predict
ambient SO2 concentrations across a study area or at the locations of specific receptors.
As such, LUR enables higher spatial resolution of predicted ambient SO2 concentrations
and requires more detailed input data compared with IDW and LUR. Mechanistic
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models, such as dispersion models and CTMs, simulate the transport and dispersion of
ambient SO2, and in the case of CTMs, the atmospheric chemistry. The strength of
mechanistic models is increased accuracy of the ambient SO2 concentration field over
time and space. However, they are much more computationally intensive.
Microenvironmental models require personal sensor data for input and are resource
intensive. The strength of these models is that they account for time the exposed
population spend in different microenvironments. With the exception of
microenvironmental models, these methods tend to be used in epidemiologic studies of
long-term ambient SO2 exposure. Depending on the modeling approach, there is the
potential for bias and reduced precision due to model misspecification, missing sources,
smoothing of concentration gradients, and complex topography. Evaluation of model
results helps demonstrate the suitability of that approach for particular applications.
The current ISA also reviews the newly available literature regarding indoor and personal
exposures to SO2. New studies of the relationship between indoor and outdoor SO2
concentrations have focused on public buildings and are consistent with previous studies
showing that indoor:outdoor ratios and slopes cover an extremely wide range, from near
zero to near one. Differences in results among studies are due to the lack of indoor
sources of SO2, indoor deposition of ambient SO2, building characteristics (e.g., forced
ventilation, building age, and building type such as residences or public buildings),
personal activities, and analytical approaches. When reported, correlations between
indoor and outdoor SO2 concentrations were relatively high (>0.75), suggesting that
variations in outdoor SO2 concentrations are driving indoor SO2 concentrations. Several
studies of personal-ambient SO2 relationships available at the time of the previous ISA
showed a large fraction of samples below the MDL, making them unsuitable for
determining personal-ambient correlations. In a study with all personal samples above the
MDL, personal exposure was moderately correlated with ambient concentration.
Additional factors that could contribute to error in estimating exposure to ambient SO2
include time-location-activity patterns, spatial and temporal variability in SO2
concentrations, and proximity of populations to central site monitors and sources.
Activity patterns vary both among and within individuals, resulting in corresponding
variations in exposure across a population and overtime. Ambient SO2 concentrations
among different microenvironments and the amount of time spent in each location will
jointly influence an individual's exposure to ambient SO2 (see Equation 3-3). Time spent
in different locations has also been found to vary by age, with younger and older age
groups spending a greater percentage of time outdoors than adults of typical working age
(18-64 years). These variations in activity pattern contribute to differences in exposure
and introduce error into population-averaged SO2 exposure estimates.
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Spatial and temporal variability in ambient SO2 concentrations can contribute to exposure
error in epidemiologic studies, whether the study relies on central site monitor data or
concentration modeling for exposure assessment. Ambient SO2 concentrations have low
to moderate spatial correlations between ambient monitors across urban geographic
scales; thus, using ambient SO2 concentration data measured at central site monitors as
exposure surrogates in epidemiologic studies introduces exposure error into the resulting
health effect estimate. Spatial variability in the magnitude of ambient SO2 concentrations
can affect cross-sectional and large-scale cohort studies by undermining the assumption
that intra-urban ambient SO2 exposure differences across space are less important than
inter-urban differences. This issue may be less important for time-series studies, which
rely on day-to-day temporal variability in ambient SO2 exposure concentrations to
evaluate health effects.
Proximity of populations to ambient SO2 monitors may influence how well human
exposure to ambient SO2 is represented by measurements at the monitors, although
factors other than distance also play an important role. While many ambient SO2
monitors are located near dense population centers, other monitors are located near
sources and may not fully represent ambient SO2 concentrations experienced by
populations in epidemiologic studies. Use of these near-source monitors introduces
exposure error into health effect estimates, and this error may be mitigated by using
average ambient SO2 concentrations across multiple monitors in an urban area.
Exposure to copollutants may result in confounding of health effect estimates. For
ambient SO2, daily concentrations generally exhibit low to moderate correlations with
daily NAAQS copollutant concentrations at collocated monitors (Figure 3-4). However, a
wide range of copollutant correlations is observed at different monitoring sites, from
moderately negative to moderately positive. In studies where daily correlations of
ambient SO2 concentrations with ambient NO2 and CO concentrations were observed to
be high, it is possible the data were collected before rulemaking to reduce sulfur content
in diesel fuel went into effect in 2006 (66 FR 5002). Sites with stronger correlations may
introduce a greater degree of confounding into epidemiologic results, depending on the
relationship between the copollutants and the health effect of interest. A similar impact is
expected for epidemiologic studies of long-term ambient SO2 exposure, because a wide
range of copollutant correlations have also been reported over time periods of months to
years.
Exposure error can contribute to variability in epidemiologic study results by biasing
effect estimates toward or away from the null and widening confidence intervals beyond
the nominal coverage of the confidence intervals that would be produced if the true
exposure had been used. The importance of exposure error varies according to the study
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design, especially regarding the study's spatial and temporal aspects. For example, in
time-series and panel studies, low personal-ambient correlations tend to bias the effect
estimate toward the null, while spatial variation in personal-ambient correlations across
an urban area contributes to widening of the confidence interval around the effect
estimate compared with the nominal confidence interval. For long-term studies, bias of
the health effect estimate may occur in either direction depending on whether the monitor
is over- or underestimating true ambient SO2 exposure for the population of interest. In
all study types, use of central site monitors in lieu of the true ambient SO2 exposure is
expected to decrease precision of the health effect estimate because spatial variation in
personal-ambient correlations across an urban area contributes to widening of the
confidence interval around the effect estimate compared with the nominal coverage of the
confidence intervals obtained if the true ambient SO2 exposure were used. Choice of
exposure estimation method also influences the impact of exposure error on
epidemiologic study results. Central site monitors offer a convenient source of time-series
data. However, because they are in a fixed location, ambient SO2 concentration
measurements obtained from a central site monitor do not account for the effects of
spatial variation in ambient SO2 concentration, ambient and nonambient concentration
differences, and varying activity patterns on personal exposure to ambient SO2. Personal
exposure measurements, such as those made in panel epidemiologic studies, provide
specific exposure estimates that may more accurately reflect spatial and temporal
variability, but sample size is often small and only a limited set of health outcomes can be
studied. Modeled ambient SO2 concentration or exposure concentration estimates offer
alternatives or supplementation to measurements, with the advantage of estimating
ambient SO2 exposure concentrations over a wide range of scales, populations, and
scenarios, particularly for locations lacking monitoring data. Model estimates are most
useful when compared to an independent set of measured ambient SO2 concentrations or
exposure concentrations. The various sources of exposure error and their potential impact
are considered in the evaluation of epidemiologic study results in this ISA.
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Chapter 4 Dosimetry and Mode of Action
4.1
Introduction
Chapter 4 begins by providing background information on the structure and function of
the respiratory tract (Section 4.1.1') and breathing rates and habits (Section 4.1.2V
The subsequent discussion of dosimetry of inhaled SO2 (Section 4.2) considers the
chemical properties of SO2 and the processes of absorption, distribution, metabolism, and
elimination, as well as sources and levels of exogenous and endogenous sulfite.
The biological pathways that potentially underlie health effects are described in "Modes
of Action of Inhaled Sulfur Dioxide" (Section 4.3). This section includes a description of
processes by which inhaled SO2 initiates a cascade of molecular and cellular responses
and the organ-level responses that follow. Together, these sections provide the foundation
for understanding how exposure to inhaled SO2 may lead to health effects. This
understanding may provide biological plausibility for effects observed in the
epidemiologic studies.
The basic structure of the human respiratory tract is illustrated in Figure 4-1. In the
literature, the terms extrathoracic (ET) region and upper airways or upper respiratory
tract are used synonymously. The terms lower airways and lower respiratory tract are
used to refer to the intrathoracic airways [i.e., the combination of the tracheobronchial
(TB) region, which includes the conducting airways and the alveolar region, the
functional part (parenchyma) of the lung where gas exchange occurs].
4.1.1
Structure and Function of the Respiratory Tract
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Posterior
Nasal Passage
Nasal Part
Oral Part
Extrathoracic
Region
Pharynx
Larynx
Trachea
Main Bronchi
Tracheobronchial
Region
Bronchi
Bronchioles
Bronchioles
— Terminal Bronchioles
Respiratory Bronchioles
Alveolar
Region
Alveolar Duct +
Alveoli
Source: Based on iCRP (1994)
Figure 4-1 Diagrammatic representation of respiratory tract regions in
humans.
4.1.2 Breathing Rates and Breathing Habit
4.1.2.1 Breathing Rates
1 Breathing rates vary across the day and are generally a function of an individual's age,
2 sex, and activity level. Table 4 1 provides median ventilation rates extracted from
3 Tables 6 17 and 6 19 of the Exposure Factors Handbook (U.S. EPA. 2011). Additional
4 information for other ages and percentiles of the ventilation rate distribution are available
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from those tables. Except for the oldest age range, ventilation rates (volume/time)
increase with activity level and age and are greater in men than women.
Table 4-1 Ventilation rates in humans as a function of activity.
Median Ventilation Rate (L/min)
Sex
Age (Years)
Sleep
Light Activity
Moderate Activity
Strenuous Activity
Male
3 to <6
4.29
11.1
20.6
37.8
6 to <11
4.46
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41.9
21 to <61
5.71
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52.9
>81
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50.9
Female
3 to <6
4.1
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19.8
33.3
6 to <11
4.24
10.8
20.4
38.0
21 to <61
4.06
11.1
23.0
44.2
>81
4.39
10.7a
20.6
41.4
aNo value for >81 provided, substituted 71 to <81 value.
Ventilation rates are also increased in overweight individuals compared to those of
normal weight (Brochu et al.. 2014). For example, median daily ventilation rates (m3/day)
are about 1.2 times greater in overweight [>85th percentile body mass index (BMI)] than
normal-weight children (5-10 years of age). In 35-45-year-old adult males and females,
ventilation rates are 1.4 times greater in overweight (BMI > 25 kg/m2) than
normal-weight (18.5 to <25 kg/m2 BMI) individuals. Across all ages, overweight/obese
individuals respire greater amounts of air and associated pollutants than age-matched
normal-weight individuals.
Another way to consider differences in ventilation rates between adults and children is to
normalize to body weight. This metric is relevant especially for SO2 absorbed in the nasal
airways and the fraction of absorbed SO2 that distributes systemically (see Section 4.2.3V
Normalized to body mass, median daily ventilation rates (m3/kg-day) decrease over the
course of life (Brochu et al.. 2011). This decrease in ventilation relative to body mass is
rapid and nearly linear from infancy through early adulthood. Relative to normal-weight
male and female adults (25-45 years of age; 0.271 m3/kg-day), ventilation rates
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normalized to body mass are increased 1.5 times in normal-weight children (7-10 years
of age; 0.402 m3/kg-day) and doubled in normal-weight infants (0.22-0.5 years of age;
0.538 m3/kg-day). Although adults have greater absolute ventilation rates than children in
terms of inhaled volume per unit time, normalized to body size children intake greater
volumes of air and associated pollutants than adults.
The metric for effects on the bronchi and differences between children and adults in
bronchial effects of SO2 is likely to be SO2 absorbed dose per bronchial surface area (see
Section 4.2.2). Ventilation per tracheobronchial surface area is also used to approximate
absorbed dose per bronchial surface area for interspecies extrapolation [see Appendix
A of U.S. EPA (2009c)I.
4.1.2.2 Breathing Habit
As humans, we breathe oronasally (i.e., through both our nose and mouth). In general, we
breathe through our nose when at rest and increasingly through the mouth with increasing
activity level. Few people breathe purely through their mouth. In contrast to the oronasal
breathing of humans, rodents are obligate nasal breathers. Described in Section 4.2.2. the
nasal passages more efficiently remove SO2 from inhaled air than the oral passage. As the
fraction of inhaled air passing through the mouth increases so too does the amount of
inhaled SO2 reaching the tracheobronchial airways where SO2 may cause
bronchoconstriction. Thus, route of breathing (namely, the fraction of inhaled air passing
through the mouth) is a critical determinate of dose to the lower airways and the potential
respiratory effects of SO2. This section describes how route of breathing, also referred to
as "respiratory mode" or "breathing habit" in the literature, is affected by age, sex,
obesity, activity level, and upper respiratory tract anomalies.
One of the more commonly referenced studies in dosimetric papers is Niinimaa et al.
(1981). These investigators found that most people, 87% (26 of 30) in the study, breathed
through their nose until an activity level was reached where they switched to oronasal
breathing. Thirteen percent (4 of 30) of the subjects, however, were oronasal breathers
even at rest. These two subject groups are commonly referred to in the literature (e.g..
ICR P. 1994) as "normal augmenters" and "mouth breathers," respectively. Bennett et al.
(2003) reported a more gradual increase in oronasal breathing with males (n = 11;
22 ± 4 years) tending to have a greater oral contribution than females (n = 11; 22 ± 2
years) at rest (87 vs. 100% nasal, respectively) and during exercise (45 vs. 63% nasal at
60% max workload, respectively).
Consistent with this trend for women to have a greater nasal contribution (Bennett et al..
2003). in a large study of children (63 M, 57 F; 4-19 years), Leiberman et al. (1990)
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reported a statistically greater nasal fraction during inspiration in girls relative to boys (77
and 62%, respectively; p = 0.03) and a marginally significant difference during expiration
(78 and 66%, respectively;p = 0.052). Another large study (88 M, 109 F; 5-73 years),
also reported a significant sex effect of route of breathing with females as having a
greater nasal fraction than males (Via and Zaiac. 1993). This effect was largest in
children (5-12 years) with an inspiratory nasal fraction of 66% in males and 86% in
females. This study also reported that the partitioning between the nose and mouth was
almost identical between inspiration and expiration. In children and adults, sex explains
some inter-individual variability in route of breathing with females breathing more
through the nose than males.
A few studies have attempted to measure oronasal breathing in children as compared to
adults (Bennett et al.. 2008; Becquemin et al. 1999; James et al.. 1997; Vig and Zaiac.
1993). James et al. (1997) found that children (n = 10; 7-16 years) displayed more
variability than older age groups (n = 27; 17-72 years) with respect to their oronasal
pattern of breathing with exercise. Becquemin et al. (1999) found that children (n = 10;
8-16 years) tended to display more oral breathing both at rest and during exercise than
adults. The highest oral fractions were also found in the youngest children. Similarly,
Bennett et al. (2008) reported children (n = 12; 6-10 years) tended to have a greater oral
contribution than adults (n = 11; 18-27 years) at rest (68 vs. 88% nasal, respectively) and
during exercise (47 vs. 59% nasal at 40% max workload, respectively). Vig and Zaiac
(1993) reported a statistically significant effect of age on route of breathing which was
most apparent in males with the fraction of nasal breathing increasing from 67% in
children (5-12 year olds) to 82% in teens (13-19 year olds), and 86% in adults
(>20 years). Females had a nasal fraction of 86% in children and teens and 93% in adults.
Based on these studies, the nasal fraction increases with age until adulthood.
Several large studies have reported an inverse correlation (r of 0.3 to 0.6) between nasal
resistance and nasal breathing fraction (Vig and Zaiac. 1993; Leiberman et al.. 1990;
Leiter and Baker. 1989). However, neither pharmaceutical constriction nor dilation of the
nasal passages affected the nasal fraction (Leiberman et al. 1990; Leiter and Baker.
1989). Nasal resistance decreases with age and is lower in females and may account for
larger nasal fractions in adults and females (Vig and Zaiac. 1993). Smaller studies
(n = 37) have not found a significant correlation between nasal resistance and nasal
fraction, but have noted that those having high resistance breathe less through the nose
(James et al.. 1997). Bennett et al. (2003) reported a tendency of lower nasal resistance in
African-American blacks (5 M, 6 F; 22 ± 4 years) relative to Caucasians (6 M, 5 F;
22 ± 3 years). The nasal fraction in blacks tended to be greater at rest and 40% max
workload and achieved statistical significance relative to Caucasians at 20 and 60% max
workload. (Leiter and Baker. 1989) reported that of the 15 mouth-breathing children as
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identified by a dentist, pediatrician, or otolaryngologist in their study, the 3 having
greatest nasal resistance breathed 100% through the mouth. These investigators also
reported that the nasal fraction was negatively correlated (p < 0.004) with nasal resistance
during both inspiration and expiration; however, the correlation appears driven by the
three individuals with 100% mouth breathing. Overall, breathing habit is related to nasal
resistance and may explain some of the age and sex effect on breathing habit.
Diseases affecting nasal resistance may also affect breathing route. Chadha et al. (1987)
found that the majority (11 of 12) of patients with asthma or allergic rhinitis breathe
oronasally (i.e., they breathe partially through the mouth) even at rest. James et al. (1997)
also reported the subjects (n = 37; 7-72 years) having hay fever, sinus disease, or recent
upper respiratory tract symptoms tended to the have a greater oral contribution relative to
those absent upper respiratory tract symptoms. James et al. (1997) additionally observed
that two subjects (5.4%) breathed purely through the mouth, but provided no other
characteristics of these individuals. Greater oral breathing may occur due to upper
respiratory tract infection and inflammation.
Some studies of children suggest obesity also affects breathing habit. Using MRI,
Schwab et al. (2015) examined anatomic risk factors of obstructive sleep apnea in
children (n = 49 obese with sleep apnea, 38 obese control, 50 lean controls; 11-16 years
of age). In obese children with sleep apnea, adenoid size was increased relative to both
obese and lean controls not having sleep apnea. The size the adenoid was also increased
in male obese controls (n = 24) relative to male lean controls (n = 35), whereas adenoid
size was similar between female obese controls (n = 14) and female lean controls
(n = 15). Both nasopharyngeal cross-sectional area and minimum area were similar
between lean and obese controls, but decreased in obese children with obstructive sleep
apnea. In a longitudinal study of children (n = 47 F, 35 M) assessed annually from 9 to
13 years of age, Crouse and Laine-Alava (1999) found nasal cross-section was minimal at
10 years of age. The authors speculated this may be due to prepubertal enlargement of the
adenoids. In a 5-year longitudinal study of children (n = 17 M, 9 F) following
adenoidectomy, Kerretal. (1989) reported a change in mode of breathing from oral to
nasal. These studies suggest the obese children, especially boys, also have increased oral
breathing relative to normal weight children.
In summary, breathing habit is affected by age, sex, nasal resistance, and perhaps by
obesity. Numerous studies show children to inhale a larger fraction of air through their
mouth than adults. Across all ages, males also inhale a larger fraction of air through their
mouth than females. Other factors that increase nasal resistance such as allergies or acute
upper respiratory infections can also increase the fraction of oral breathing. Obesity,
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especially in boys, may also contribute to increased nasal resistance and an increased oral
fraction of breathing relative to normal weight children.
4.2 Dosimetry of Inhaled Sulfur Dioxide
This section provides a brief overview of SO2 dosimetry and updates information
provided in the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d). Dosimetry of SO2 refers
to the measurement or estimation of the amount of SO2 and its reaction products reaching
and/or persisting at specific sites within the respiratory tract or systemically after
exposure. One principal effect of inhaled SO2 is to stimulate bronchial epithelial irritant
receptors and initiate a reflexive contraction of smooth muscles in the bronchial airways.
Health effects may be due to the inhaled SO2 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. 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 epithelial lining fluid (ELF);
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. Few studies have investigated SO2 dosimetry since the 1982 AQCD for
Particulate Matter and Sulfur Oxides (U.S. EPA. 1982a) and the 1986 Second Addendum
(U.S. EPA. 1986b).
The following sections will address the chemistry, and the processes of absorption,
distribution, metabolism, and elimination that pertain to the dosimetry of inhaled SO2.
Studies investigating the dosimetry of SO2 generally are for concentrations of SO2 that
are higher than those present in ambient air. However, these studies are included here
because they provide the foundation for understanding SO2 toxicokinetics and
toxicodynamics. The discussion of dosimetry will conclude with a consideration of other
sources of S02-derived products in the body.
4.2.1 Chemistry
Physicochemical properties of SO2 most relevant to respiratory tract uptake include its
solubility in the ELF and its chemical transformations and reactions that occur there.
Henry's law relates the gas-phase and liquid-phase interfacial concentrations at
equilibrium and is a function of temperature and pressure. The Henry's law constant,
defined as the ratio of partial pressure or concentration of SO2 in the gas phase to SO2
dissolved in the liquid phase, is an inverse measure of solubility. Although the solubility
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of most gases in the ELF is not known, the Henry's law constant is known for many
gases in water, and for SO2, it is 0.047 (mol/L)air per (mol/L)„ater at 37°C and
1 atmosphere (Hales and Sutter. 1973). For comparison, Henry's law constant for O3 is
6.4 (mol/L)air per (mol/L)„ater under the same conditions (kimbell and Miller. 1999).
Thus, SO2 is nearly 140-times more soluble than O3 in water. In general, the more soluble
a gas is in biological fluids, the more rapid, and proximal its absorption will be in the
respiratory tract. In addition to the Henry's law constant, it is also necessary to consider
the transport of SO2 from the lumen to the ELF of the tracheobronchial airways (see
Section 4.2.2). 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 (see Section 4.2.5).
Once SO2 contacts the fluids lining the airways, it dissolves into the aqueous
compartment and rapidly hydrates to form H2SO3, which forms hydrogen (H+) ions,
bisulfite HSO? anions, and sulfite (SOr ) anions (Gunnison et al.. 1987a; Gunnison.
1981).
-H+ -H+
S02 + H20^H2S03 ^ hso3 ^ SOf"
+H+ +H+
Equation 4-1
The prevalence of these sulfur species in solution is determined primarily by pH and, to a
lesser extent, by temperature and ionic strength. In the human respiratory tract (pH of 7.4
and 37°C), dissolved SO2 exists as a mixture exclusively of bisulfite and sulfite with the
latter predominating (Gunnison. 1981). Subsequent reactions of bisulfite and sulfite such
as sulfitolysis, enzymatic detoxification, and auto-oxidation are described below.
4.2.2 Absorption
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 5 of U.S. EPA (2013c) 1. The mass transfer (cm/s) of SO;
from the air-phase to the ELF is proportion to the Sherwood number (dimensionless) and
diffusion coefficient of SO2 in air (0.23 cm2/s) and inversely proportion to the diameter
(cm) of an airway [see Equation 10 of Asgharian et al. (2011)1. The Sherwood number
for various breathing patters from infants to young adults may be calculated using
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Equation 13 of Asgharian et al. (2011) in combination with age specific airway
morphology from Phalen et al. (1985). For 50th-percentile ventilation rates from Brochu
et al. (2011). the mass transfer rates of SO2 in the trachea and bronchi of infants
(4-months) are about 1.8-times greater than in young adults (18 years). By 8.5 years of
age, the mass transfer rate is only about 1.2-times greater than in young adults.
Melville (1970) measured the absorption of SO2 [1.5 to 3.4 parts per million (ppm)]
during nasal and oral breathing in 12 healthy volunteers. Total respiratory tract
absorption of SO2 (expressed as a percentage of the amount inhaled) was significantly
greater (p < 0.01) during nasal than oral breathing (85 vs. 70%, respectively) and was
independent of the inspired concentration. Respired flows were not reported. Andersen et
al. (1974) measured the nasal absorption of SO2 (25.5 ppm) in seven volunteers at an
average inspired flow of 23 L/minute [i.e., eucapnic hyperpnea (presumably to simulate
light 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 seven healthy subjects at an average
ventilation of 8.5 L/minute (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.
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
(35S02) at concentrations of 1, 10, 25, or 50 ppm was passed separately through the nose
and mouth at steady flows of 3.5 and 35 L/minute for 5 minutes by Brain (1970).
The nasal absorption of SO2 (1 ppm) was effectively 100% at 3.5 L/minute and 96.8% at
35 L/minute. A negligible effect of SO2 concentration was observed with nasal
absorption increasing from 99.9% at 1 ppm to 99.99% at 10 ppm and 99.999% at 50 ppm.
The oral absorption of SO2 (1 ppm) was 99.56% at 3.5 L/minute, but only 34% at
35 L/minute. There was a slight decrease in oral SO2 absorption from 99.56 to 96.3%
when the concentration was increased from 1 to 10 ppm at 3.5 L/minute, whereas nasal
absorption was unaffected by changes in concentration (1-50 ppm). In an earlier
experiment, Frank et al. (1967) showed that nasal absorption of 2.2 ppm 35SC>2 at
3.5 L/minute was 100% throughout the first 20 minutes of exposure. On average, there
was a small reduction in 35SC>2 absorption to 94% approaching 30 minutes of exposure.
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 there was a minor effect of
inhaled concentration on SO2 absorption, the route of breathing and rate of flow were the
main factors affecting the magnitude of SO2 absorption in the upper airways of dogs.
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Modeling shows that virtually all SO2 reaching the lower airways in young adults, as well
as in dogs and rats, is absorbed in the bronchi and does not penetrate into the bronchioles
or alveolar region (Tsuiino et al.. 2005). Considering the effect of age on SO2 dose to the
airways of humans, dose as ventilation per bronchial surface area can be estimated using
bronchial morphology from Phalen et al. (1985) and 50th-percentile ventilation rates
from Brochu et al. (2011). This approximation shows a gradual reduction in bronchial
surface dose with decreasing age from young adults to infants. Using this approximation,
an infant (4-months) would have approximately 80% of the bronchial surface dose of a
young adult (18-years). However, as described in Section 4.1.2.2. children breathe more
through the mouth than adults, which is associated with greater SO2 penetration to the
lower respiratory tract. In addition, as described above, mass transfer rates of SO2 from
the lumen to the ELF in the trachea and bronchi increase with decreasing age. Based on
these observations, it is expected that SO2 penetrating through the upper airways is
rapidly removed in the trachea and first several generations of bronchi and this may result
in somewhat greater airway surface doses of SO2 of children than adults in proximal
bronchi.
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/minute, nasal absorption is relatively unaffected, whereas oral absorption is reduced
from 100 to 34%. Inhaled SO2 concentration has a negligible effect of nasal absorption,
where oral absorption may decrease slightly with increasing concentration from 1 ppm to
10 ppm SO2. Thus, the rate and route of breathing have a great effect on the magnitude of
SO2 absorption in the upper airways and on the penetration of SO2 to the lower airways.
Overall, the available data clearly show a 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. Due
to their increased amount of oral breathing, children (particularly boys and the obese) and
individuals with allergies or upper airway infections may be expected to have greater SO2
penetration into the lower respiratory tract than healthy adults (see Section 4.1.2).
Children may also be expected to have a greater intake dose of SO2 per body mass than
adults due to their ventilation rates (see Section 4.1.2).
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4.2.3
Distribution
Once inhaled, SO2 is absorbed in the respiratory tract and SC>2-derived products are
widely distributed throughout the body, as was demonstrated in early studies using
radiolabeled 35SC>2. Although rapid extrapulmonary distribution of SCh-derived products
occurs, the highest tissue concentrations of the 35S retained in the body at any given time
are found primarily in the respiratory tract (upper and lower) and may be detected there
for up to a week following inhalation (Balchum et al.. 1960. 1959). Frank et al. (1967)
observed 35S in the blood and urine of dogs within 5 minutes, the first time point, after
starting 22 ppm 35SC>2 exposures of the surgically isolated nasal airways. At the end of
30-60-minute exposures, the authors estimated that 5-18% of the administered 35S was
in the blood. Balchum et al. (1959) investigated the tissue distribution of 35S in dogs
exposed for 20-40 minutes to 35SC>2 ranging in concentration from 1.1 to 141 ppm via
tracheostomy or by nose/mouth breathing. At approximately 1-hour post-exposure,
regardless of the exposure route or the 35SC>2 exposure concentration, about 6% of the
retained 35 S was found in the liver, with lesser amounts found in the heart, spleen, kidney,
brain, and other tissues. However, the percent of retained 35S was, on average, 13-times
greater in the trachea and lungs of the tracheostomized group than in the nose/mouth
breathing group, demonstrating the protection of the lower respiratory tract provided by
SO2 removal in the upper airways. Comparison of dogs retaining similar total amounts of
35S (i.e., controlling for retained dose), showed that the blood concentrations of 35S were
higher in the tracheostomized dogs than in the nose/mouth breathing dogs. Given very
high 35S concentrations in the tongues of the nose/mouth breathing dogs and that blood
concentrations had not decreased in two-thirds of these dogs by 1-hour post-exposure, the
authors postulated that a substantial portion of the 35SC>2 products may have been retained
within the upper airways with only slow absorption into the blood. Studies in rabbits and
rats also show that there can be an accumulation and retention of SC>2-derived products
within proximal regions of the respiratory tract (discussed below).
The distribution and clearance of inhaled SO2 from the respiratory tract may involve
several intermediate chemical reactions and transformations. In particular, hydrated SO2
transforms to sulfite/bisulfite at physiologic pH. Sulfite can diffuse across cell
membranes, and bisulfite can react with disulfide bonds (R1-S-S-R2) to form thiols
(Ri-SH) and S-sulfonates (R2-S-SO3 ) by a process termed sulfitolysis (Gunnison and
Benton. 1971). Because disulfide bonds are important determinants of protein structure
and function in biological systems, breaking such bonds may have important biologic
effects. Secreted airway mucins contain many disulfide bonds, and breaking these bonds
might alter their function and thereby alter mucociliary clearance.
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Studies in rabbits and rats found measurable levels of sulfite and S-sulfonates in the
upper respiratory tract following inhalation of 10-30 ppm SO2. Levels of sulfite and
S-sulfonates were increased in tracheal washings of rabbits exposed to 10 ppm SO2 for up
to 72 hours (Gunnison et al.. 1981V This implies reaction of sulfite with disulfide groups
in mucus proteins in the ELF. In addition, tracheal tissue contained elevated levels of
S-sulfonates, implicating reaction of sulfite with disulfide groups in tissue proteins.
Bronchial tissue from rats had increased levels of sulfites and S-sulfonates when higher
concentrations (30 ppm) of SO2 were employed (Gunnison et al. 1987b). Under these
conditions, no S-sulfonates were found in lung parenchyma, and neither sulfites nor
S-sulfonates were found in the plasma. The lack of sulfites and S-sulfonates in the plasma
of rats may have been due to their high levels of sulfite oxidase and rapid metabolism of
sulfite (see Section 4.2.4). Consistent with 35S rapidly appearing in the blood of
35SC>2-exposed dogs, S-sulfonates were found in plasma of rabbits following 10 ppm SO2
exposure, providing evidence for absorption of sulfite into the blood of rabbits (Gunnison
et al.. 1981; Gunnison and Palmes. 1973). Studies with ex vivo plasma suggested that
disulfide bonds in albumin and fibronectin are reactive with sulfite (Gregory and
Gunnison. 1984).
Exposure of humans to SO2 also resulted in measurable S-sulfonates in plasma (Gunnison
and Palmes. 1974). In this study, humans were exposed continuously to 0.3-6 ppm SO2
for up to 120 hours and plasma levels of S-sulfonates were positively correlated with
concentrations of SO2 inhaled. The regression line for this relationship had a correlation
coefficient of 0.61 and the slope was 1.1 nmol/mL of plasma S-sulfonate for each 1-ppm
increment in SO2 concentration. Recently, a subacute study measured sulfite plus
S-sulfonate content of the lung, liver, and brain of mice exposed to 5, 10, or 20 ppm SO2,
4 hours/day for 7 days (Meng et al.. 2005a). A concentration-dependent increase in sulfite
and S-sulfonate levels was observed. Thus, in humans and mice, the amount of
SCh-derived species in blood and other tissues increases with the concentration in inhaled
air. It should also be noted that measurable amounts of sulfite/S-sulfonate were found in
tissues of humans and mice inhaling filtered air instead of SO2 (Meng et al.. 2005a;
Gunnison and Palmes. 1974). Besides inhaled SO2, sulfite is derived from other
exogenous, as well as endogenous sources (see Section 4.2.6).
Inhaled SO2 need not reach the lower airways for SC>2-derived species to be found in the
blood. During the 5 full day of SO2 exposure in the Gunnison and Palmes (1974) study,
volunteers were likely at rest or sleeping for much of their exposures. Given that
ventilation rates would be relatively low and breathing would be largely nasal (see
Section 4.1.2). most inhaled SO2 would likely be absorbed in the extrathoracic airways
(see Section 4.2.2). A number of studies also exposed the surgically isolated upper
airways of dogs to 35SC>2 and observed 35S to rapidly appear in the blood and for the
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concentration in blood to continually increase during exposure (e.g., Yokovama et al..
1971; Frank et al.. 1967). Frank et al. (1969) proposed the majority of SC>2-derived
products found in the blood originated from SO2 absorbed in the upper airways.
In summary, inhaled SO2 is readily dissolved in the ELF where it exists as a mixture of
bisulfite and sulfite with the latter predominating. Bisulfite reacts with disulfide groups
forming S-sulfonates; sulfite can diffuse across cell membranes and reach the circulation.
Following absorption in the respiratory tract, SC>2-derived products (e.g., sulfite and/or
S-sulfonates) are widely distributed throughout the body and have been observed in the
blood and urine within 5 minutes of starting an SO2 exposure of surgically isolated nasal
airways. Measurable levels of S-sulfonates have been observed in plasma following
inhalation of SO2 in humans, dogs, mice, and rabbits. Perhaps due to higher levels of
hepatic sulfite oxidase relative to other species, sulfites, and S-sulfonates are not found in
the plasma of rats. Although the majority of SC>2-derived products remain in the
respiratory tract following exposure, extrapulmonary SC>2-derived products are found in
the liver, with lesser amounts found in the heart, spleen, kidney, brain, and other tissues.
The amount of SCh-derived species in blood and other tissues increases with the
concentration of SO2 in inhaled air, while the distribution within the body is generally
unaffected. A substantial portion of SC>2-derived products appear to be retained within the
upper airways, particularly during nasal breathing, with only slow absorption into the
blood.
4.2.4 Metabolism
The primary route of sulfite metabolism is by sulfite oxidase-catalyzed enzymatic
oxidation to sulfate (Gunnison. 1981). Because of this pathway, intra-cellular steady-state
concentrations of sulfite are low in normal individuals (Gunnison etal.. 1987a). Sulfite
oxidase is a molybdenum-containing enzyme that is found in mitochondria. Its
distribution varies widely across tissues. While lung tissue has very low sulfite oxidase
activity, liver has high sulfite oxidase activity and plays a major role in detoxification of
circulating sulfite. Maieretal. (1999) examined the distribution of sulfite oxidase activity
in the respiratory tract and liver of four beagle dogs. Sulfite oxidase activity was highest
in the liver. The median sulfite oxidase activity in the nose was about 30% of the liver.
Median activity levels in the trachea and bronchi were about 20% of the liver and the
median activity levels in the lung parenchyma were only 10% of those in the liver.
The 1982 AQCD (U.S. EPA. 1982a) noted that depleting the activity of sulfite oxidase in
an animal model through a low-molybdenum diet supplemented with the competitive
inhibitor tungsten resulted in a substantial lowering of the lethal dose for intraperitoneally
injected bisulfite. A deficiency in sulfite oxidase activity may lead to toxicity even in the
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absence of exogenous sulfite or bisulfite exposures. For example, humans and mice with
homozygous genetic defects in the sulfite oxidase protein or in the enzymes required for
synthesis of the essential molybdenum cofactor develop ultimately lethal neurologic
disease attributable to accumulation of endogenous sulfite post-natally (i.e., following
loss of maternal protection in utero) (Johnson-Winters et al.. 2010; Reiss et al.. 2005).
Sulfite oxidase activity is highly variable among species. Liver sulfite oxidase activity in
the rat is 10-20 times that in humans. Rapid metabolism of circulating sulfite to sulfate
may explain the lack of sulfite/S-sulfonates found in blood of rats exposed by inhalation
to 30 ppm SO2, whereas these products were found in other species (Gunnison et al.
1987a). In sulfite oxidase-deficient rats, plasma sulfite levels increase with the severity of
the deficiency (Gunnison et al.. 1987b).
Gunnison and Benton (1971) also identified S-sulfonate in blood as a reaction product of
inhaled SO2. S-sulfonates, which are produced by the reaction of bisulfite with disulfide
bonds, may be metabolized back to disulfides. Although the enzymatic pathways and
cofactors are not clearly established for this repair process, it requires reducing
equivalents, and thus, has a metabolic cost.
In summary, the primary route of sulfite metabolism is by sulfite oxidase-catalyzed
oxidation into sulfate. The sulfite oxidase levels vary widely among tissues with very low
levels found in the lung and high levels found in the liver, which plays a major role in the
detoxification of circulating sulfite. Sulfite oxidase activity is also highly variable among
species with liver sulfite oxidase activity in rats being 10-20 times greater than in
humans.
4.2.5 Elimination
Mechanisms involved in elimination include both desorption of SO2 from the respiratory
tract and the clearance of reaction products from the body.
When the partial pressure of SO2 on mucosal surfaces exceeds that of the gas phase, such
as during expiration, some desorption of SO2 from respiratory tract lining fluids may be
expected. 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 minutes after the
25- to 30-minute SO2 exposure, another 3% was desorbed. In total, 15% of the amount of
originally inspired and absorbed SO2 was desorbed from the nasal mucosa. Frank et al.
(1969) reported that up to 18% of the SO2 was desorbed within -10 minutes after
exposure.
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SO2 that does not desorb is transformed to bisulfite/sulfite (Section 4.2.1). Because the
lung tissue has a low activity of sulfite oxidase, diffusion into the circulation may be a
more important route of sulfite clearance from the lung than enzyme-catalyzed
transformation to sulfates. Within a period of minutes after starting 35SC>2 inhalation
exposures,35 S was observed in the blood and urine of dogs and distributed about the
body (Frank et al.. 1967; Baldwin et al.. 1959V At the end of 30-60-minute exposures,
5-18% of the administered 35S was in the blood, and 1-6% had been excreted in the
urine by 3 hours post-exposure (Yokovama et al.. 1971; Frank et al.. 1967). The rate of
urinary excretion was proportional to the blood concentration, and 92% of the urinary 35 S
was in the form of sulfate (Yokovama et al.. 1971). In contrast, S-sulfonates formed in
the circulation were reported to have a clearance half-time of 3.2 days (Gunnison and
Palmes. 1973).
In summary, when the partial pressure of SO2 on mucosal surfaces exceeds that of the gas
phase, such as during expiration or following exposure, some desorption of SO2 from the
respiratory tract lining fluids may be expected. SO2 that does not desorb is transformed to
bisulfite/sulfite. Given the low activity of sulfite oxidase in the respiratory tract, sulfite is
more likely to diffuse into the circulation or react with tissue constituents than be
metabolized to sulfate. Circulating sulfite may subsequently react with constituents of the
blood to form S-sulfonates or other species. It may appear in other organs, particularly
the liver (Section 4.2.3). where it is efficiently metabolized to sulfate (Section 4.2.4).
Urinary excretion of sulfate is rapid and proportional to the concentration of SO2
products in the blood. S-sulfonates are cleared more slowly from the circulation with a
clearance half-time of days. The portion of SCh-derived products that are retained within
the respiratory tract are only slowly absorbed into the blood (Section 4.2.3).
4.2.6 Sources and Levels of Exogenous and Endogenous Sulfite
The primary endogenous contribution of sulfite is from the catabolism of
sulfur-containing amino acids (namely, cysteine and methionine). Sulfite may
subsequently be metabolized to sulfate in a reaction catalyzed by sulfite oxidase in most
tissues, but especially in the liver (Section 4.2.4). Mean daily sulfate produced following
ingestion of cysteine and methionine in the U.S. increases from 70 mg/kg-day in infants
(2-6 months) to 100 mg/kg-day in young children (1-3 years) and then decreases to
30 and 40 mg/kg-day in adult (19-50 years) females and males, respectively (IQM.
2005). To facilitate comparison with exogenous sources, a mole of SO2 can produce a
mole of sulfate, but the SO2 mass is only two-thirds of the sulfate mass.
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Sulfite is also added to foods because it has antioxidant and antimicrobial properties
(Vandeviivere et al.. 2010; Gunnison. 1981). In a study considering actual food
consumption of Belgian adults and measured sulfite levels in food, Vandeviivere et al.
(2010) observed a wide distribution in exogenous sulfite from ingestion. Expressed in
terms of SO2 equivalents, rates of exogenous sulfite ingestion may be described by a
log-normal distribution with a median intake of 0.14 SO2 mg/kg-day and a geometric
standard deviation of 2.15. Individuals at the 5th and 95th percentiles of this distribution
are estimated to consume 0.04 and 0.49 SO2 mg/kg-day. In a comparison of theoretical
food-consumption data with maximum permissible SCh/sulfites to foods, the Belgian
adults in the Vandeviivere et al. (2010) study had a similar potential sulfite intake to U.S.
adults. The estimated intake for children could be in the range of that for adults or less
due to the likely minimal consumption of sulfite sources such as wine. Endogenous
sulfite from catabolism of ingested sulfur-containing amino acids far exceeds exogenous
sulfite from ingestion of food additives [by 140 and 180 times in adult (19-50 years)
females and males, respectively, and by 500 times or more in young children
(1-3 years)].
Exogenous sulfite may also be derived from SO2 inhalation. For the purposes of
comparisons herein, all inhaled SO2 is assumed to contribute to systemic sulfite levels. In
reality, as discussed in Section 4.2.3. the majority of S02-derived products from SO2
inhalation are retained in the respiratory tract and may be detected there for up to a week
following inhalation. The potential contribution of inhaled SO2 to systemic sulfite levels
varies with age, activity level, and SO2 concentration. Using median and 97.5th percentile
daily ventilation rates from Brochu et al. (2011). adults (25-45 years of age) are
estimated to receive 0.004 and 0.006 mg SO2 per kg body mass, respectively, from a full
day exposure to 5 parts per billion (ppb) SO2. As an upper-bound estimate for ambient
exposure in most locations, a full-day exposure to 75 ppb SO2 (the level of the current
National Ambient Air Quality Standard for SO2) would result in 0.053 SO2 mg/kg-day
and 0.085 SO2 mg/kg-day for adults having median and 97.5th percentile ventilation
rates, respectively. The estimated daily SO2 intake (mg/kg-day) would be roughly
1.5 times greater in children (7-10 years of age) and doubled in infants (0.22-0.5 years
of age) due to the greater ventilation rate per body mass of children compared to adults
(25-45 years of age). Even upper-bound sulfite levels from inhalation (75 ppb SO2,
24 hours, 97.5th percentile ventilation) are far less than those derived from catabolism of
sulfur-containing amino acids, by 230 to 300 times in adults (25-45 years) and nearly
500 times in young children (1-3 years).
Comparison of sulfite derived from SO2 inhalation with that from ingestion of food
additives is more complicated. In adults (25-45 years), sulfite intake (mg/kg-day) from
inhalation (75 ppb SO2, 24 hours, 97.5th percentile ventilation) is 1.6 times lower than
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median sulfite intake from ingestion of food additives. In children (<10 years), assuming
similar levels of sulfite intake as adults, sulfite intake from inhalation (75 ppb SO2,
24 hours, 97.5th percentile ventilation) is approximately the same as median sulfite intake
from ingestion of food additives. However, ingested sulfite absorbed into the blood goes
directly to the liver where much of it will be metabolized into sulfate. The majority of
sulfite derived from inhalation that enters the blood is rapidly distributed [as either sulfite
or S-sulfonate (Yokovama et al.. 1971; Baldwin et al.. 1959)1 about the body with
around a quarter of total blood flow going to the liver (ICRP. 2002) where there is a high
activity of sulfite oxidase compared to other tissues. For lower exposure concentrations
and durations than considered above, sulfite (and/or S-sulfonate) levels in the blood
following SO2 inhalation could exceed those from ingestion of food additives,
particularly in children.
In summary, exogenous sources contribute hundreds of times lower amounts of sulfite
than the catabolism of sulfur-containing amino acids, when averaged across the entire
body. Sulfite and sulfate derived from the catabolism of sulfur-containing amino acids
are distributed broadly and do not accumulate in respiratory tract tissues. Following
ingestion of sulfite-containing food additives, sulfite enters the circulation and is subject
to first pass clearance in the liver where it is metabolized to sulfate. Following inhalation,
a substantial portion of SC>2-derived products accumulate and are retained within the
respiratory tract; SCh-derived products that enter the circulation are rapidly distributed
throughout the body, appear primarily in the liver, and are excreted via the urine
(Section 4.2.5).
4.3 Mode of Action of Inhaled Sulfur Dioxide
This section describes the biological pathways that potentially underlie health effects
resulting from short-term and long-term exposure to SO2. Extensive research carried out
over several decades in humans and in laboratory animals has yielded much information
about these pathways. This section is not intended to be a comprehensive overview, but
rather, it updates the basic concepts derived from the SO2 literature presented in the
AQCD (U.S. EPA. 1982a) and the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) and
introduces the recent relevant literature. While this section highlights findings of studies
published since the 2008 SOx ISA (U.S. EPA. 2008d). earlier studies that represent the
current state of the science are also discussed. Studies conducted at more environmentally
relevant concentrations of SO2 (i.e., <2 ppm, see Section 1.2) are of greater interest
because biological pathways responsible for effects at higher concentrations may not be
identical to those occurring at lower concentrations. Some studies at higher
concentrations are included if they were early demonstrations of key biological pathways
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or if they are recent demonstrations of potentially important new pathways. This
information will be used to develop a mode of action framework for inhaled SO2 that
serves as a guide to interpreting health effects evidence presented in Chapter 5.
Mode of action refers to a sequence of key events, endpoints, and outcomes that result in
a given toxic effect (U.S. EPA. 2005a'). Elucidation of mechanism of action provides a
more detailed understanding of key events, usually at the molecular level (U.S. EPA.
2005a'). The framework developed in this chapter will include some mechanistic
information on initiating events at the molecular level, but will mainly focus on the
effects of SO2 at the cellular, tissue, and organism level.
SO2 is a highly reactive antioxidant gas. At physiologic pH, its hydrated forms include
sulfurous acid, bisulfite, and sulfite, with the latter species predominating. Sulfite is a
strong nucleophilic anion that readily reacts with nucleic acids, proteins, lipids, and other
classes of biomolecules. It participates in many important types of reactions including
sulfonation (sulfitolysis) and autoxidation with the generation of free radicals. This latter
reaction may be responsible for the induction of oxidative stress that occurs as a result of
exposure to SO2.
As described in Section 42, SO2 is a water-soluble gas that is absorbed almost entirely in
the upper respiratory tract. However, under conditions of mouth breathing and exercise,
some SO2 may penetrate to the tracheobronchial region. The main effects of SO2
inhalation are seen at the sites of absorption (i.e., the respiratory tract) and include
(1) activation of sensory nerves in the respiratory tract resulting in neural reflex
responses, (2) injury to airway mucosa, and (3) increased airway hyperreactivity and
allergic inflammation. Effects outside the respiratory tract may occur at very high
concentrations of inhaled SO2. Biologic pathways involved in mediating these responses
to inhaled SO2 will be discussed below. In addition, a brief synopsis of pathways involved
in mediating the effects of endogenous SCh/sulfite will be presented. This section will
conclude with the development of a mode of action framework.
4.3.1 Activation of Sensory Nerves in the Respiratory Tract
SO2 is classified as a sensory (or nasal) irritant in mice, guinea pigs, rats, and humans
(Alarie. 1973). As such, it may stimulate trigeminal nerve endings when inhaled by the
nose, which results in an inhibition of respiration. It may also stimulate trigeminal nerves
in the larynx, which results in coughing, and in the cornea, which induces tearing. Other
reflexes stimulated by trigeminal nerve endings include decreased heart rate, peripheral
vasoconstriction, closure of the glottis, closure of the nares, and increased nasal flow
resistance. These responses are variable among species. Increased nasal flow resistance
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has been demonstrated in humans breathing SO2 gas through the nose. Furthermore,
de sensitization of the respiratory rate response occurs with repeated exposure. Most
sensory (or nasal) irritants, including SO2, also cause bronchoconstriction, but at
concentrations higher than those stimulating nerve endings in the nose.
SO2 is also classified as a pulmonary (or bronchial) irritant that evokes reflex reactions
through effects on pulmonary nerve endings (Alarie. 1973). These reactions usually
include an increase in respiratory rate accompanied by a decrease in tidal volume,
sometimes preceded by coughing and brief apnea, and sometimes accompanied by
bronchoconstriction. These responses have been observed in guinea pigs and cats
breathing via a tracheal cannula, which bypasses the nose. In the cat, SO2 exposure
increased the activity of vagal afferent fibers by either stimulating or sensitizing
tracheobronchial receptors on the nerve endings. SO2 also increased airway resistance in
guinea pigs and humans breathing through the nose, mouth, and/or tracheal cannula.
Increased airway resistance may occur via a variety of mechanisms including
accumulation of secretions, inflammatory changes of the airway walls, collapsing
airways, and constrictions of the central and peripheral airways. Constriction may be due
to direct action on the smooth muscle, axonal reflexes, vagal nerve stimulation, and
release of mediators such as histamine.
Continuous or repeated exposure to inhaled SO2 has a different pattern of responses in
different species (Alarie. 1973). In guinea pigs, the increase in airway resistance rose to a
plateau upon exposure and decreased to baseline with cessation of exposure. In humans
and dogs, resistance increased with exposure but decreased after 10 minutes (humans) or
3 minutes (dogs) despite the continuous presence of the gas. Studies in adults with
asthma demonstrated a different pattern. When exposure to SO2 occurred during a
30-minute period with continuous exercise, the response to SO2 developed rapidly and
was maintained throughout the 30-minute exposure (Kehrl et al.. 1987; Linn et al.. 1987;
Linn et al.. 1984c). Sequential exposures in nonasthmatic human subjects and in cats
resulted in a decreased response to SO2 in the second exposure compared with the first,
indicative of desensitization.
Early experiments demonstrated that SC>2-induced reflexes were mediated by cholinergic
parasympathetic pathways involving the vagus nerve and inhibited by atropine (Grunstein
et al.. 1977; Nadel et al.. 1965a. b). Bronchoconstriction was found to involve smooth
muscle contraction because (3-adrenergic agonists such as isoproterenol reversed the
effects. Rapid shallow breathing was observed in SC>2-exposed tracheotomized cats
(bypassing the nose). Histamine was proposed to play a role in SCh-induced
bronchoconstriction (U.S. EPA. 1982a). but this hypothesis remains unconfirmed.
Hydrogen ions, sulfurous acid, sulfite, and bisulfite are all putative mediators of the
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reflex responses (Gunnison et al.. 1987a). In particular, sulfite-mediated sulfitolysis of
disulfides present in receptor proteins on sensory nerve fibers has been postulated
because S-sulfonate formation may potentially disrupt protein structure or function
(Alarie. 1973).
More recent experiments in animal models conducted since 1982 have demonstrated that
both cholinergic and noncholinergic mechanisms may be involved in SC>2-induced
effects. In two studies using bilateral vagotomy, vagal afferents were found to mediate
the immediate ventilatory responses to SO2 (Wang et al.. 1996). but not the prolonged
bronchoconstrictor response (Barthelemv et al.. 1988). Other studies showed that atropine
failed to block SC>2-induced bronchoconstriction, and that a local axon reflex resulting in
C-fiber secretion of neuropeptides (i.e., neurogenic inflammation) was responsible for the
effect (Haii et al.. 1996; Atzori et al.. 1992). Neurogenic inflammation has been shown to
play a key role in animal models of airway inflammatory disease (Groneberg et al..
2004). Furthermore, in isolated perfused and ventilated guinea pig lungs,
bronchoconstriction to SO2 was biphasic. The initial phase was mediated by a local axon
reflex involving the release of the neuropeptide calcitonin gene-related peptide from
sensory nerves, while the later phase involved other mechanisms (Bannenberg et al..
1994).
In humans, the mechanisms responsible for SC>2-induced bronchoconstriction are not
entirely understood. In nonasthmatic subjects, near complete attenuation of
bronchoconstriction has been demonstrated using the anticholinergic agents atropine and
ipratropium bromide (Yildirim et al.. 2005; Snashall and Baldwin. 1982; Tan et al..
1982). However, in asthmatic subjects, these same anticholinergic agents (Field et al..
1996; Myers et al.. 1986a). as well as short- and long-acting (32-adrenergic agonists
(Gong et al.. 1996; I inn et al.. 1988). theophylline (Koenig et al.. 1992). cromolyn
sodium (Myers et al.. 1986a). neodocromil sodium (Bigbv and Boushev. 1993). and
leukotriene receptor antagonists (Gong et al.. 2001; Lazarus et al.. 1997) only partially
blocked SC>2-induced bronchoconstriction. 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 asthmatic individuals. Strong
evidence of this was borne out in a study by Myers et al. (1986a) 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. Besides mast cell
stabilization, cromolyn sodium may also reduce the activity of lung irritant receptors
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(Harries et al.. 1981). providing an alternative mechanism for the reduction in
SCh-induced bronchoconstriction observed.
It has been proposed that inflammation contributes to the enhanced sensitivity to SO2
seen in asthmatic human subjects by altering autonomic responses (Tunnicliffe et al..
2001). enhancing mediator release (Tan et al. 1982). and/or sensitizing C-fibers and
rapidly adapting receptors (Lee and Widdicombe. 2001). Whether local axon reflexes
also play a role in SC>2-induced bronchoconstriction in asthmatic individuals is not known
(Groneberg et al.. 2004; Widdicombe. 2003; Lee and Widdicombe. 2001). 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. 2003; Widdicombe and Lee. 2001). Furthermore, enhanced
sensitivity to SO2 in asthmatic individuals may be related to genetic polymorphisms of
inflammatory mediators, such as TNF-a (Winterton et al.. 2001).
Studies in vitro provide support for SO2 exposure-mediated effects that involve
inflammatory cells. It is known that sulfite exposure of cultured rat basophil leukemia
cells, a mast cell analog, causes immunoglobulin E (IgE)-independent degranulation,
release of histamine, serotonin and other mediators, and intracellular production of
reactive oxygen species (Collaco et al.. 2006). In addition, peroxidases, such as
neutrophil myeloperoxidase, oxidize bisulfite anion to several radical species that in turn
attack proteins (Ranguelova et al.. 2013; Ranguelova et al. 2012). This represents a
potentially important new toxicological pathway for sulfite, especially in the presence of
neutrophilic and/or eosinophilic inflammation.
Irritant responses are indicative of a chemical's ability to damage the respiratory tract
(Alarie and Luo. 1986; Alarie. 1981). In the case of sensory irritation, there is a
characteristic decrease in respiratory rate, which is often used to set health-protective
standards for occupational exposures. Chemicals that are pulmonary irritants often lead to
rapid shallow breathing. They typically induce pulmonary edema or congestion if inhaled
for a long enough period of time. Some chemicals are both sensory and pulmonary
irritants and pulmonary irritation may occur at concentrations below which sensory
irritation occurs. In the case of SO2, a concentration-dependent hierarchy of effects has
been noted in humans (Kane etal.. 1979). Lethal or extremely severe injury to the
respiratory tract has been reported at and above 190 ppm. Intolerable sensory irritation
and respiratory tract injury that may occur with extended exposure has been associated
with 10-15-minute exposures to 30-100 ppm SO2, and tolerable sensory irritation has
been associated with 10-minute exposures to 5-11.5 ppm SO2. Minimal sensory irritation
has been associated with exposures at and below 1 ppm. Increased airway resistance,
likely due to pulmonary irritation and reflex bronchoconstriction, has been observed at
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5 ppm in adults without asthma at rest and at 1 ppm SO2 in adults without asthma while
exercising (Arts et al.. 2006). However, lung function changes have been observed at
concentrations of SO2 lower than 1 ppm in exercising adults with asthma. Thus,
pulmonary irritation may occur at levels of SO2 below those that cause sensory irritation,
especially in exercising adults with asthma.
In summary, SO2 acts as both a sensory and a pulmonary irritant through activation of
sensory nerves in the respiratory tract resulting in neural reflex responses. This occurs in
a variety of species, including humans. Pulmonary irritant responses due to SO2 exposure
result in reflex bronchoconstriction, especially in adults with asthma. Both cholinergic
parasympathetic pathways involving the vagus nerve and inflammation contribute to
reflex bronchoconstriction in asthmatic individuals.
4.3.2 Injury to Airway Mucosa
A common feature of irritant gases, including SO2, is the capacity to injure airway
mucosa, resulting in decreased epithelial barrier function, inflammation, and
compromised ciliary function (Carson et al.. 2013). Despite being the initial site of SO2
absorption and having low activity of sulfite oxidase, the respiratory tract of healthy
humans is thought to be capable of detoxifying 5 ppm inhaled SO2 (Gunnison et al..
1987a). In fact, exposure to 0.5-2 ppm SO2 for 4 hours did not result in any measurable
changes in biomarkers of oxidative stress or inflammation in exhaled breath condensate
(EBC) or nasal lavage fluid (NALF) from healthy adults subjected to two periods of
moderate exercise (Raulf-Heimsoth et al.. 2010). In addition, no changes in nasal lining
fluid ascorbic acid or uric acid levels were observed following 1-hour exposure of adults
with asthma to 0.2 ppm SO2 (Tunnicliffe et al.. 2003).
However, respiratory tract injury has been observed in humans exposed for extended
periods to SO2 concentrations of 30 ppm and greater. In animal models, airway injury and
histopathological changes, such as mucous cell metaplasia and intramural fibrosis, have
generally been observed following chronic exposure to SO2 concentrations of 10 ppm and
higher (U.S. EPA. 2008d). Rats exposed to 20 ppm SO2 for several weeks exhibit fibrotic
remodeling of airway epithelium and mucus hypersecretion, key features of COPD and
chronic asthma in humans (Wagner et al.. 2006). Inflammatory changes have been noted
in some animal models following subacute exposure to 5-100 ppm SO2 (U.S. EPA.
2008d). However, adults with asthma and animal models of allergic airway disease
exhibit greater sensitivity to SO2 (see below). Impaired mucociliary clearance has also
been demonstrated at high concentrations of SO2. In humans, nasal mucus flow was
decreased during a 5-hour exposure to 5 and 25 ppm SO2 (Gunnison et al.. 1981).
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Impaired mucus flow in the trachea has been observed in rats exposed subacutely to
11.4 ppm SO2 and in dogs exposed chronically to 1 ppm SO2 (Gunnison et al.. 1981;
Hirsch et al. 1975). Whether these effects were due to compromised ciliary function or
altered properties of the mucus due to sulfite-mediated sulfitolysis of disulfide bonds in
mucus was not investigated.
Recent studies provide additional insight. An ultrastructural examination of nasal biopsy
tissue by freeze fracture microscopy was conducted in humans exposed to 0.75 ppm SO2
for 2 h (Carson et al.. 2013). Evidence of fragmentation of the tight junctional complex
and polymorphonuclear infiltrate was reported although no effects on ciliary membranes
were observed. These subtle responses suggest a slight decrease in barrier function due to
acute SO2 exposure at this level. Furthermore, a subacute exposure of rats to 2.67 ppm
SO2 (6 hours/day, 7 days) resulted in altered lung mRNA levels for inducible nitric oxide
synthase (involved in inflammation) and for bax (or B-cell lymphoma 2-like protein 4;
involved in regulating apoptosis) (Sang et al.. 2010). In this study, gene expression
changes were also found in the heart and they were more pronounced than in the lung.
These results suggest that, despite low sulfite oxidase activity, the respiratory tract may
be more resistant than the heart to the effects of inhaled SO2.
In summary, exposure to SO2 results in injury to airway mucosa, especially at higher
concentrations and following extended periods of exposure. There is little evidence of
injury or inflammation in response to acute exposures to concentrations of 2 ppm SO2 or
less in human subjects. However, one new study found subtle histopathological changes
at the ultrastructural level following a 2-hour exposure to 0.75 ppm SO2. New evidence
also suggests subtle changes in the lung related to inflammation and apoptosis in rats
exposed over several days to 2.67 ppm SO2.
4.3.3 Modulation of Airway Responsiveness and Allergic Inflammation
Asthma is a chronic inflammatory disease of the airways that is characterized by
increased airway responsiveness [i.e., airway hyperresponsiveness (AHR)] and variable
airflow obstruction. Respiratory irritants, including SO2, are thought to be a major cause
of occupational asthma (Baur et al.. 2012; Andersson et al.. 2006). Both peak high-level
exposures and low-level persistent exposures have been associated with the development
of irritant-induced asthma.
Studies in several different animal species have shown that a single exposure to SO2 at a
concentration of 10 ppm or less failed to induce AHR following a challenge agent (U.S.
EPA. 2008d). However, in an animal model of allergic airway disease, SO2 exposure
enhanced airway responsiveness. In this study, sheep previously sensitized and
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challenged with Ascaris suum extract were exposed to 5 ppm SO2 for 4 hours (Abraham
et al.. 1981). Airway responsiveness to carbachol was increased at 24 hours, but not
immediately, after SO2 exposure. This response was not observed in sheep that had not
been sensitized and challenged with Ascaris suum extract. The mechanism underlying the
SCh-induced AHR was not investigated in this study. However, the AHR response could
have resulted from sensitization of vagal irritant receptors, greater sensitivity of smooth
muscle to bronchoconstriction agents, or enhanced concentrations of bronchoconstriction
agents reaching the receptors or bronchial smooth muscle. The delayed nature of the
response points to a possible role of inflammation in mediating AHR.
Two controlled human exposure studies in adults with asthma provide further evidence of
AHR to an allergen when exposure to SO2 was in combination with NO2. In one of these
studies, exposure to 0.2 ppm SO2 or 0.4 ppm NO2 alone did not affect airway
responsiveness to house dust mite allergen immediately after a 6-hour exposure at rest
(Dcvalia et al.. 1994). However, following exposure to the two pollutants in combination,
subjects demonstrated an increase response to the inhaled allergen. Rusznak et al. (1996)
confirmed these results in a similar study and found that AHR to dust mites persisted up
to 48 post-exposure. These results provide further evidence that SO2 may elicit effects
beyond the short time period typically associated with this pollutant.
Several other studies have examined the effects of SO2 exposure on allergic
inflammation. One of these was a controlled human exposure study of adults with
asthma. Subjects were exposed for 10 minutes to 0.75 ppm SO2 while exercising at a
moderate level (Gong et al.. 2001). In addition to changes in lung function and
symptoms, there was a statistically significant increase in eosinophil count in induced
sputum 2 hours post-exposure. Pretreatment with a leukotriene receptor antagonist
dampened these responses, implicating a role for leukotrienes in mediating SO2
exposure-induced effects.
The other studies investigated the effects of repeated exposure to SO2 on inflammatory
and immune responses in an animal model of allergic airways disease. Li et al. (2007)
demonstrated that in ovalbumin-sensitized rats, exposure to 2 ppm SO2 for 1 hour
followed by challenge with ovalbumin each day for 7 days resulted in an increased
number of inflammatory cells in bronchoalveolar lavage fluid (BALF) and an enhanced
histopathological response compared with rats treated with SO2 or ovalbumin alone.
Similarly, intercellular adhesion molecule 1 (ICAM-1), a protein involved in regulating
inflammation, and mucin 5AC glycoprotein (MUC5AC), a mucin protein, were
upregulated in lungs and trachea to a greater extent in rats treated both with SO2 and
ovalbumin. A follow up study involving the same exposure regimen (2 ppm SO2 for
1 hour) in the same allergic animal model (rats sensitized and challenged with
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ovalbumin) also found that repeated SO2 exposure enhanced inflammatory and allergic
responses to ovalbumin (Li et al.. 2014). Numbers of eosinophils, lymphocytes, and
macrophages were greater in the BALF of SC>2-exposed and ovalbumin-treated animals
than in animals treated only with ovalbumin. In addition, SO2 exposure enhanced
upregulation and activation of nuclear factor kappa-light-chain-enhancer of activated B
cells (NFkB), a transcription factor involved in inflammation, and upregulation of the
cytokines interleukin-6 (IL-6) and interleukin-4 (IL-4) in lung tissue. Furthermore, BALF
levels of IL-6 and IL-4 were increased to a greater extent in S02-exposed and
ovalbumin-treated animals compared with ovalbumin treatment alone. These results
indicate that repeated SO2 exposure enhanced activation of the NFkB inflammatory
pathway and upregulation of inflammatory cytokines in ovalbumin-treated animals.
Furthermore, SO2 exposure enhanced the effects of ovalbumin on levels of interferon
gamma (IFN-7) (decreased) and IL-4 (increased) in BALF and on IgE levels in serum
(increased). Because levels of IL-4 are often indicative of T helper 2 (Th2) status and
levels of IFN-y are indicative of a T helper 1 (Thl) status, these results suggest a shift in
Thl/Th2 balance away from Th2 in rats made allergic to ovalbumin, an effect that was
exacerbated by SO2 exposure. These Th2-related changes are consistent with the
observed increases in serum IgE and BALF eosinophils in ovalbumin-treated animals,
effects that were also enhanced by SO2 exposure. Taken together, these results indicate
that repeated exposure to SO2 exacerbated inflammatory and allergic responses in this
animal model. It should be noted, however, that group 2 innate lymphoid cells can
mediate Type 2 immunity, as has been described for 03-mediated responses in mice (Ong
et al.. 2016). Whether group 2 innate lymphoid cells mediate effects of inhalation of SO2,
which like O3 is an irritant gas, is unexplored.
Two other follow-up studies by the same laboratory examined the effects of inhaled SO2
on the asthma-related genes encoding epidermal growth factor (EGF), epidermal growth
factor receptor (EGFR), and cyclooxygenase-2 (COX-2), and on apoptosis-related genes
and proteins in this same model based on sensitization with ovalbumin (Xic et al.. 2009;
Li et al.. 2008). While EGF and EGFR are related to mucus production and airway
remodeling, COX-2 is related to apoptosis and may play a role in regulating airway
inflammation. SO2 exposure enhanced the effects of ovalbumin in this model, resulting in
greater increases in mRNA and protein levels of EGF, EGFR and COX-2 in the trachea
compared with ovalbumin treatment alone. SO2 exposure enhanced other effects of
ovalbumin in this model, resulting in a greater decline in mRNA and protein levels of
tumor protein p53 (p53) and bax and a greater increase in mRNA and protein levels of
B-cell lymphoma 2 (bcl-2) in the lungs compared with ovalbumin challenge alone.
The increased ratio of bcl-2:bax, an indicator of susceptibility to apoptosis, observed
following ovalbumin challenge, was similarly enhanced by SO2. Thus, repeated exposure
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to SO2 may impact numerous processes involved in inflammation and/or airway
remodeling in allergic airways disease.
The effects of repeated SO2 exposure on the development of an allergic phenotype and
altered physiologic responses in naive animals was examined in two studies in which SO2
exposure preceded allergen sensitization. Repeated exposure of guinea pigs to SO2
promoted allergic sensitization and subsequently enhanced allergen-induced bronchial
obstruction, as reported by U.S. EPA (2008d). Riedel et al. (1988) examined 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 hours/day for 5 days. During
the last 3 days, SO2 exposure was followed by exposure to nebulized ovalbumin for
45 minutes. Following bronchial provocation with inhaled ovalbumin (0.1%) 1 week
later, bronchial obstruction was measured by examining the respiratory loop obtained by
whole-body plethysmography. In addition, specific antibodies against ovalbumin were
measured in serum and BALF. Results showed significantly higher bronchial obstruction
in animals exposed to SO2 (at all concentration levels) and ovalbumin, compared with
animals exposed only to ovalbumin. In addition, significant increases in anti-ovalbumin
immunoglobulin G (IgG) antibodies were detected in BALF 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 enhanced 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 hours/day for 5 days and sensitized with 0.1% ovalbumin aerosols for
45 minutes on Days 4 to 5 (Park et al.. 2001). One week later, animals were subjected to
bronchial challenge with 0.1% ovalbumin and lung function was evaluated 24 hours later
by whole-body plethysmography. Results demonstrated a significant increase in
enhanced pause, a measure of airway obstruction, in animals exposed to SO2 and
ovalbumin but not in animals treated with ovalbumin or SO2 alone. Results also
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. These experiments
indicate that repeated exposure to near ambient levels of SO2 plays a role in allergic
sensitization and also exacerbates allergic inflammatory responses in the guinea pig.
Furthermore, increases in bronchial obstruction observed in both studies suggest that
repeated SO2 exposure increased airway responsiveness.
Longer term exposure of naive newborn rats to SO2 (2 ppm, 4 hours/day for 28 days)
resulted in altered cytokine levels that suggest a shift in Thl/Th2 balance away from Th2
(Song et al.. 2012). Th2 polarization is one of the steps involved in allergic sensitization.
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It should be noted, however, that group 2 innate lymphoid cells can mediate Type 2
immunity, as has been described for Ch-mediated responses in mice (Ong et al.. 2016).
Whether group 2 innate lymphoid cells mediate effects of inhalation of SO2, which like
O3 is an irritant gas, is unexplored. In naive animals exposed to SO2, levels of IL-4,
which is indicative of a Th2 response, were increased and levels of IFN-y, indicative of a
Thl response, were decreased in BALF. In ovalbumin-sensitized newborn rats, SO2
exposure resulted in a greater enhancement of lavage fluid IL-4 and an increase in serum
IL-4 levels compared with ovalbumin-sensitization alone. In addition, SO2 exposure led
to AHR and airway remodeling, as indicated by increased content of airway smooth
muscle, in the ovalbumin-sensitized animals. Stiffness and contractility of airway smooth
muscle was assessed in vitro using cells from experimentally treated animals. In allergic
rats, both stiffness and contractility were increased as a result of SO2 exposure,
suggesting an effect on the biomechanics of airway smooth muscle. This study provides
evidence for allergic sensitization by SO2 in naive newborn rats and for enhanced allergic
inflammation, AHR, and airway remodeling in SC>2-exposed allergic newborn rats.
Supportive evidence that SO2 may promote allergic sensitization is provided by a study in
mice that were first treated with sodium sulfite and then sensitized and challenged with
house dust mite allergen (Lin etal.. 2011a). Sulfite is formed in ELF following inhalation
of SO2 (Section 4.2.1). Repeated intranasal treatment with 10 (iL of a 5-mM solution of
sodium sulfite aggravated inflammation (measured by histopathology) and allergic
sensitization in this model. Specific IgE levels were higher in sulfite-treated and
allergen-challenged animals compared with either sulfite treatment or allergen challenge
alone. Specific IgG2a levels, indicative of a Thl response, were decreased as a result of
sulfite treatment in house dust mite-challenged mice. In addition, interleukin-5 (IL-5)
levels, indicative of a Th2 response, and the ratio of Il-5:IFN-y, a marker of Th2
polarization, were higher in lung tissue from sulfite-treated and allergen-challenged mice
compared with either sulfite treatment or allergen challenge alone.
Mixtures of SO2 and other criteria pollutants have also been shown to modulate airway
responsiveness and/or allergic inflammation. As discussed above, AHR to house dust
mite allergen occurred in human subjects with mild allergy and asthma immediately
following 6 hours of concurrent exposure to 0.2 ppm SO2 and 0.4 ppm NO2, but not to
either pollutant alone (Rusznak et al.. 1996; Devalia et al.. 1994). This effect persisted for
48 hours. Recently, the effects of simulated downwind coal combustion emissions
(SDCCE) on allergic airway responses was investigated in mice (Barrett et al.. 2011).
Mice were sensitized and challenged with ovalbumin and exposed for 6 hours/day for
3 days to several concentrations of SDCCE with and without a particle filter. SDCCE
exposure was followed by another challenge with ovalbumin in some animals. Results
demonstrated that both the particulate and the gaseous phases of SDCCE exacerbated
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allergic airways responses. Airway responsiveness (measured by the forced oscillation
technique) was enhanced by the gaseous phase of SDCCE in mice that were challenged
with ovalbumin after SDCCE exposure. Concentration of SO2 in the highest exposure
was 0.2 ppm. Other gases present in this exposure were NO2 (0.29 ppm), NO (0.59 ppm),
and carbon monoxide (0.02 ppm). Results of this study are consistent with SO2 playing a
role in exacerbating AHR and allergic responses, although the other mixture components
may have contributed to the observed effects.
In summary, a growing body of evidence supports a role for SO2 in exacerbating AHR
and/or allergic inflammation in animal models of allergic airway disease, as well as in
asthmatic individuals. In addition, repeated or prolonged exposure to SO2 promotes
allergic sensitization in naive newborn animals. Furthermore, one study in newborn
allergic rats suggests that airway remodeling may contribute to AHR following prolonged
exposure to SO2.
4.3.4 Induction of Systemic Effects
As described in the 2008 SOx ISA (U.S. EPA. 2008d). two controlled human exposure
studies reported that acute exposure to 0.2 ppm SO2 resulted in changes in heart rate
variability in healthy adults and in asthmatic adults (Routledge et al.. 2006; Tunnicliffe et
al.. 2001). More recently, altered parasympathetic regulation of heart rate was reported in
rats exposed to 5 ppm SO2 during the peri-natal and post-natal period (Wocrman and
Mendelowitz. 2013a. b). Whether these responses were due to activation of sensory
nerves in the respiratory tract resulting in a neural reflex response and altered autonomic
function or some other mechanism is not known.
Numerous studies over several decades have reported other extrapulmonary effects of
inhaled SO2 (U.S. EPA. 2008d). Most of these occur at concentrations far higher than
those measured in ambient air. As discussed in_Section 4.2.3. studies in mice and humans
demonstrating the presence of sulfite and S-sulfonates in blood and tissues outside of the
respiratory tract point to the likely role of circulating sulfite in mediating these responses.
A subacute study measured sulfite plus S-sulfonate content of the lung, liver, and brain of
mice exposed to 5, 10, or 20 ppm SO2 for 4 hours/day for 7 days (Meng et al.. 2005a') and
found a concentration-dependent increase. Similarly, exposure of human subjects to
0.3-6 ppm SO2 for up to 120 hours resulted in the appearance in the plasma of sulfite
plus S-sulfonates (Gunnison and Palmes. 1974). The relationship between
sulfite/sulfonate concentration and chamber SO2 concentration was linear (regression
coefficient of 0.61) with a slope of 1.1 nmol/mL of plasma S-sulfonate for each 1-ppm
increment in SO2 concentration. These results indicate that prolonged (i.e., hours to days)
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exposure to as low as 0.3 ppm SO2 results in measurable amounts of circulating sulfite in
humans. The relationship between circulating sulfite/S-sulfonate and extrapulmonary
effects of inhaled SO2 has not yet been explored in human subjects.
Because the activity of sulfite oxidase is variable among species, the degree of sensitivity
to SCh-mediated effects is likely to be variable among species. For example, sulfite
oxidase in rats is 10-20 times greater than in humans and 3-5 times greater than in
rabbits or rhesus monkeys (Gunnison et al.. 1987a; Gunnison. 1981). Thus, the toxicity of
SO2 may be less in rats due to more rapid metabolism of sulfite to sulfate.
Systemic effects are likely due to oxidative stress, possibly from sulfite autoxidation.
Alternatively, sulfite-mediated S-sulfonate formation may disrupt protein function, and
metabolic reduction of S-sulfonates may alter reduction-oxidation (redox) status.
Moreover, sulfite may serve as a substrate for peroxidases, such as myeloperoxidase and
eosinophil peroxidase, to produce free radicals, as has been demonstrated in neutrophils
and eosinophils (Ranguelova et al.. 2013; Ranguelova et al.. 2012; Ranguelova et al..
2010). These sulfur-based free radical species may then initiate protein or lipid oxidation.
Baskurt (1988) found that exposure of rats to 0.87 ppm SO2 for 24 hours resulted in
increased hematocrit, sulfhemoglobin, and osmotic fragility, as well as decreased whole
blood and packed cell viscosities. These results indicate a systemic effect of inhaled SO2
and are consistent with an oxidative injury to red blood cells. Other studies have reported
lipid peroxidation in erythrocytes and tissues of animals exposed to SO2 (Oin et al. 2012;
Ziemann et al.. 2010; Haider et al.. 1982). Supplementation with ascorbate and
a-tocopherol decreased SCh-induced lipid peroxidation in erythrocytes (Etlik et al..
1995). Additionally, recent studies report mitochondrial changes in the hearts and brains
of rats exposed to 1.34 ppm (4 hours/day) SO2 for several weeks (Oin et al.. 2016; Oin et
al.. 2012). Demonstration of mitochondrial biogenesis in rat brain suggests that SO2
exposure induces an adaptive response to oxidative stress (Oin et al.. 2012). Changes in
cardiac function were observed at higher concentrations (2.7 ppm SO2); however
pretreatment with antioxidants blocked this effect (Oin et al.. 2016).Other recent studies
report altered markers of brain inflammation and synaptic plasticity following several
weeks to months of exposure to 1.34 ppm (4 hours/day) SO2 (Yao et al.. 2015; Yao et al..
2014). Further studies are required to confirm that inhalation exposures of SO2 at or near
ambient levels increase blood sulfite levels sufficiently for oxidative injury to occur in
blood cells or other tissues.
In summary, exposure to SO2 may result in effects outside the respiratory tract via
activation of sensory nerves in the respiratory tract resulting in a neural reflex response or
mediated by circulating sulfite. A few studies employing concentrations of 2 ppm SO2 or
less have demonstrated effects that are consistent with sulfite-mediated redox stress, such
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as increased sulfhemoglobin in red blood cells and lipid peroxidation in the brain. Recent
studies also suggest possible inflammation and other effects in tissues distal to the
absorption site following several weeks to months of exposure to 1.34 ppm SO2.
Endogenous SCh/sulfite is a product of normal metabolism of sulfur-containing amino
acids (e.g., cysteine and methionine) (Liu etal.. 2010). While SO2 gas is measured in the
head space gas of preparations of various tissues or bodily fluids (Balazv et al.. 2003).
sulfite/bisulfite is measured in soluble fractions. The distribution of SO2 and enzymes
responsible for SO2 generation has been reported in tissues of the rat (Luo etal.. 2011).
Chemical transformations between bisulfite/sulfite/S02 and the gasotransmitter H2S also
occur. H2S is similarly derived from sulfur-containing amino acids. Evidence has
accumulated that endogenous H2S acts as a biological signaling molecule (Filipovic et al..
2012) and plays important roles in the cardiovascular (Coletta et al.. 2012) and other
systems. Recent studies suggest that endogenous SChmay also be a gasotransmitter (Liu
et al.. 2010). Like the other gasotransmitters NO and CO, SO2 at physiologic levels may
activate guanylyl cyclase to generate cyclic guanosine monophosphate (cGMP), which
mediates effects through cGMP-dependent kinases (Li et al.. 2009). However, SO2 may
also act through non-cGMP-dependent pathways. Experimental studies in animal models
and in vitro systems demonstrate a myriad of effects of exogenous SO2 on the
cardiovascular system, including vasorelaxation, negative inotropic effects on cardiac
function, anti-inflammatory and antioxidant effects in pulmonary hypertension, and
decreased blood pressure (BP) and vascular remodeling in hypertensive animals, and
cvtoprotective(Liu et al.. 2010). Effects were in many cases concentration dependent.
In vivo studies generally were conducted using 5 ppm and higher concentrations of SO2
(or sulfite/bisulfite) (Liu et al.. 2010). In summary, endogenous SO2 is a newly
recognized gasotransmitter that may play important roles in cardiovascular and other
systems.
This section describes the key events, endpoints, and outcomes that comprise the modes
of action of inhaled SO2. Here, key events are subclinical effects, endpoints are effects
that are generally measured in the clinic, and outcomes are health effects at the organism
level. Biological pathways discussed above that may contribute to health effects resulting
from short-term and long-term exposures to SO2 (Chapter 5) are summarized as a part of
4.3.5
Role of Endogenous Sulfur Dioxide/Sulfite
4.3.6
Mode of Action Framework
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this analysis. These proposed modes of action are based on the available evidence and
may not reflect all of the pathophysiology underlying health effects.
Figure 4-2 depicts the mode of action for respiratory effects due to short-term exposure
to SO2.
so,
Legend
E Pollutant
I Key Events
¦ Endpoints
¦ Outcomes
trigger
Bronchoconstriction
Asthma
exacerbation
T" Inflammatory
mediators
Airway
hyperresponsiveness
Activation/
Sensitization of
neural reflexes
•T1 Allergic
inflammation/
Allergic
sensitization
Formation of
sulfite In ELF/
Redox reactions
and formation of
sulfitolysis and/or
other products
ELF = epithelial lining fluid; redox = reduction-oxidation; S02 = sulfur dioxide.
Note: Pathways indicated by a dotted line are those for which evidence is limited to findings from experimental animal studies, while
evidence from controlled human exposure studies is available for pathways indicated by a solid line. Dashed lines indicate proposed
links to the outcomes of asthma exacerbation. Key events are subclinical effects, endpoints are effects that are generally measured
in the clinic, and outcomes are health effects at the organism level.
Source: National Center for Environmental Assessment.
Figure 4-2 Summary of evidence for the mode of action linking short-term
exposure to sulfur dioxide and respiratory effects.
A characteristic feature of individuals with asthma is an increased propensity of their
airways to narrow in response to bronchoconstrictive stimuli relative to nonatopic
individuals without asthma. This characteristic is termed airway hyperresponsiveness
(AHR). Different kinds of stimuli can elicit bronchoconstriction, but in general they act
on airway smooth muscle receptors (direct stimuli, e.g., methacholine) or act via the
release of inflammatory mediators (indirect stimuli, e.g., allergens) (O'Bvrne et al..
2009). SO2 is a nonspecific bronchoconstrictive stimuli that is not easily classified as a
direct or indirect stimuli, as was discussed in Section 4.3.1.
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Because inhalation of SO2 results in chemical reactions in the ELF, the initiating event in
the development of respiratory effects is the formation of sulfite, sulfitolysis products,
and/or other products. Both sulfite and S-sulfonates have been measured in tracheal and
bronchial tissue as well as in tracheal washings of experimental animals exposed to SO2.
Reactive products formed as a result of SO2 inhalation are responsible for a variety of
downstream key events, which may include activation or sensitization of sensory nerves
in the respiratory tract resulting in neural reflex responses, release of inflammatory
mediators, and modulation of allergic inflammation or sensitization. These key events
may collectively lead to several endpoints, including bronchoconstriction and AHR.
Bronchoconstriction is characteristic of an asthma attack. However, individuals who are
not asthmatic may also experience bronchoconstriction in response to SO2 inhalation;
generally, this occurs at higher concentrations than in an individual who is asthmatic
(>1 ppm). Additionally, SO2 exposure may increase airway responsiveness to subsequent
exposures of other stimuli such as allergens or methacholine. These pathways may be
linked to the epidemiologic outcome of asthma exacerbation.
The strongest evidence for this mode of action comes from controlled human exposure
studies. SO2 exposure resulted in increased airway resistance due to bronchoconstriction
in healthy adults and in adults with asthma. In adults without asthma, this response
occurred primarily as a result of activation of sensory nerves in the respiratory tract
resulting in neural reflex responses mediated by cholinergic parasympathetic pathways
involving the vagus nerve. However, in adults with asthma, evidence indicates that the
response is only partially due to vagal pathways and that inflammatory mediators such as
histamine and leukotrienes also play an important role. Activation of sensory nerves in
the respiratory tract, which result in neural reflex responses, has been studied in humans
exposed to occupationally relevant concentrations of SO2 (up to 2 ppm). Responses
measured in these studies include increased respiratory rate and decreased tidal volume,
which involve the vagus nerve, and increased nasal air-flow resistance, which involves
the trigeminal nerve. These responses are not a part of the mode of action described here,
but are mentioned because they are known irritant effects of SO2. Studies in experimental
animals demonstrate that SO2 exposure activates reflexes that are mediated by cholinergic
parasympathetic pathways involving the vagus nerve. However, noncholinergic
mechanisms may also play a role because some studies demonstrate that a local axon
reflex resulting in C-flber secretion of neuropeptides (i.e., neurogenic inflammation) is
responsible for the effects of SO2.
Evidence demonstrates that SO2 exposure modulates allergic inflammatory responses.
Enhancement of allergic inflammation was observed in adults with asthma who were
exposed for 10 minutes to 0.75 ppm SO2 (i.e., leukotriene-mediated increases in numbers
of sputum eosinophils). In an animal model of allergic airway disease, repeated exposure
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to 2 ppm SO2 led to an enhanced inflammatory response, as measured by numbers of
BALF inflammatory cells, levels of BALF cytokines, histopathology, activation of the
NFkB pathway, and upregulation of intracellular adhesion molecules, mucin, and
cytokines, in lung tissue. Furthermore, repeated exposure to SO2 enhanced Th2
polarization (or group 2 innate lymphoid cell-mediated Type 2 immunity), numbers of
BALF eosinophils, and serum IgE levels in this same model. Other studies demonstrated
that repeated exposure of naive animals to SO2 (as low as 0.1 ppm) over several days
promoted allergic sensitization (allergen-specific IgG levels) and enhanced
allergen-induced bronchial obstruction (an indicator of AHR) and inflammation (airway
fluid eosinophils and histopathology) when animals were subsequently sensitized and
challenged with an allergen. Similarly, intranasal treatment with sulfite both aggravated
allergic sensitization (Th2 cytokines and allergen specific IgE levels) and exacerbated
allergic inflammatory responses (histopathology) in animals subsequently sensitized and
challenged with allergen. These changes in allergic inflammation may enhance AHR and
promote bronchoconstriction in response to a trigger. Thus, allergic inflammation and
AHR may also link short-term SO2 exposure to asthma exacerbation.
Figure 4-3 depicts the mode of action for respiratory effects due to long-term exposure to
S02.
SO,
Legend
Pollutant
¦ Key Events
¦ Endpoints
~ Outcomes
»
Allergic
sensitization
X*
%
Recurrent
Airway
mHIH Airwav
redox stress
~
~
inflammation
HHH hyperresponsiveness
\
\
*
Airway
remodeling
trigger
New onset asthma/
Asthma exacerbation
redox = reduction-oxidation; S02 = sulfur dioxide.
Note: Pathways indicated by a dotted line are those for which evidence is limited to findings from experimental animal studies, while
evidence from controlled human exposure studies is available for pathways indicated by a solid line. Dashed lines indicate proposed
links to the outcomes of new onset asthma/asthma exacerbation. Key events are subclinical effects, endpoints are effects that are
generally measured in the clinic, and outcomes are health effects at the organism level.
Source: National Center for Environmental Assessment.
Figure 4-3 Summary of evidence for the mode of action linking long-term
exposure to sulfur dioxide and respiratory effects.
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The initiating event in the development of respiratory effects due to long-term SO2
exposure is the recurrent or prolonged redox stress due to the formation of reactive
products in the ELF. This is the driving factor for the potential downstream key events,
airway inflammation, allergic sensitization, and airway remodeling that may lead to the
endpoint AHR. Airway inflammation, airway remodeling, and AHR are characteristic of
asthma. The resulting outcome may be new asthma onset, which presents as an asthma
exacerbation that leads to physician-diagnosed asthma.
Evidence for this mode of action comes from studies in both naive and allergic
experimental animals. Exposure of naive newborn animals to SO2 (2 ppm) for several
weeks resulted in hyperemia in lung parenchyma, inflammation in the airways, and Th2
polarization (or group 2 innate lymphoid cell-mediated Type 2 immunity), the latter of
which is a key step involved in allergic sensitization. Support is also provided by
short-term studies in naive animals in which repeated exposure to SO2 (2 ppm) over
several days led to pathologic changes, including inflammatory cell influx. Th2
polarization (or other Type 2 immune responses) and airway inflammation may set the
stage for AHR. In addition, short-term SO2 exposure (0.1 ppm) promoted allergic
sensitization and enhanced other allergic inflammatory responses and AHR when animals
were subsequently sensitized with an allergen. Further, repeated exposure of allergic
newborn animals to SO2 (2 ppm) over several weeks enhanced allergic responses and
resulted in morphologic responses indicative of airway remodeling and in AHR. Thus,
repeated exposure to SO2 in naive animals may lead to the development of allergic
airway disease, which shares many features with asthma. Furthermore, repeated exposure
of allergic animals to SC^may promote airway remodeling and AHR. The development
of AHR may link long-term exposure to SO2 to the epidemiologic outcome of new onset
asthma.
Figure 4-4 depicts the mode of action for extrapulmonary effects due to short-term or
long-term exposure to SO2.
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SO,
Legend
Pollutant
¦ Key Events
¦ Endpoints
Formation of
sulfite in ELF/
redox reactions
and formation of
sulfitolysis products
Transport of
sulfite into
circulation
Activation/
sensitization of
neural reflexes
Systemic
redox stress
Othe
1
orga
effec
9
Altered heart rate
and/or heart rate
variability
ELF = epithelial lining fluid; redox = reduction-oxidation; S02 = sulfur dioxide.
Note: Pathways indicated by a dotted line are those for which evidence is limited to findings from experimental animal studies, while
evidence from controlled human exposure studies is available for pathways indicated by a solid line. No links to outcomes are
proposed. Key events are subclinical effects and endpoints are effects that are generally measured in the clinic.
Source: National Center for Environmental Assessment.
Figure 4-4 Summary of evidence for the mode of action linking exposure to
sulfur dioxide and extrapulmonary effects.
Although SO2 inhalation results in extrapulmonary effects, there is uncertainty regarding
the mode of action underlying these responses. Evidence from controlled human
exposure studies (0.2 ppm, 1 hour) points to SO2 exposure-induced
activation/sensitization of neural reflex responses as a key event leading to the endpoint
of altered heart rate or heart rate variability. Evidence also points to transport of sulfite
into the circulation. Controlled human exposure and experimental animal studies have
demonstrated the presence of sulfite and S-sulfonates in plasma, liver, or brain following
SO2 exposure. This occurred at a concentration as low as 0.3 ppm SO2 in humans
exposed for up to 120 hours. Sulfite is highly reactive and may be responsible for redox
stress (possibly through auto-oxidation or peroxidase-mediated reactions to produce free
radicals) in the circulation and extrapulmonary tissues. However, this is likely to occur
only at very high concentrations or during prolonged exposures because circulating
sulfite is efficiently metabolized to sulfate in a reaction catalyzed by hepatic sulfite
oxidase.
Besides inhalation of SO2, the ingestion of food additives and the catabolism of
sulfur-containing amino acids also contribute to levels of sulfite in the body
(Section 4.3.5). In humans, the amount of sulfite derived from inhaled SO2 (assuming
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100% absorption, 75 ppb and 24-hour exposure) is comparable to that derived from the
expected daily consumption of food additives. The amount of sulfite derived from the
breakdown of endogenous sulfur-containing amino acids is far greater. Sulfite derived
from inhaled SO2, unlike that derived from food additives, enters the circulation without
first passing through the liver, which efficiently metabolizes sulfite to sulfate. Thus, the
potential exists for inhaled SO2 to have a greater impact on circulating sulfite levels than
sulfite derived from food additives. While the amount of sulfite derived from the
breakdown of endogenous sulfur-containing amino acids is far greater, its metabolic
pathways and impact on circulating sulfite levels are not clear. Thus, the potential exists
for prolonged exposure to high concentrations of inhaled ambient SO2 to result in
extrapulmonary effects due to circulating sulfite.
In summary, this section provides a foundation for understanding how exposure to the
gaseous air pollutant SO2 may lead to health effects. This encompasses the many steps
between uptake into the respiratory tract and biological responses that ensue.
The reaction of inhaled SO2 with components of the ELF initiates a cascade of events
occurring at the cellular, organ, and organism level. Biological responses discussed in
this section were organized in a mode of action framework that serves as a guide to
interpreting health effects evidence presented in Chapter 5.
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Chapter 5 Integrated Health Effects of Exposure to
Sulfur Oxides
5.1
Introduction
5.1.1
Scope of the Chapter
While the term "sulfur oxides" refers to multiple gaseous oxidized sulfur compounds
(e.g., SO2, SO3), this chapter focuses on evaluating the health effects associated with
exposure to SO2. As discussed in Section 2J_, the presence of sulfur oxide species other
than SO2 in the atmosphere has not been demonstrated, and the available health evidence
examines SO2. The health effects of particulate sulfur-containing compounds
(e.g., sulfate) are considered in the current review of the NAAQS for PM and were
evaluated in the 2009 ISA for PM (U.S. EPA. 2009a') (see Section 1.1).
This chapter evaluates the epidemiologic, controlled human exposure, and animal
toxicological evidence of S02-related respiratory (Section 5.2). cardiovascular
(Section 5.3). reproductive and developmental (Section 54, total mortality (Section 5.5).
and cancer (Section 5.6) effects. Evidence from epidemiologic and animal toxicological
studies of other SC>2-related effects are included in Supplemental Tables 5S-1 (U.S. EPA.
20161) and 5S-2 (U.S. EPA. 2015e). Sections for respiratory, cardiovascular, and
mortality effects are divided into subsections describing the evidence for short-
(i.e., 1 month or less) and long-term (i.e., more than 1 month) exposures. The evidence
for reproductive and developmental and cancer effects is considered within one long-term
exposure section, with time-windows of exposure addressed as appropriate. Causal
conclusions are determined for both short- and long-term exposures by evaluating the
evidence for each health effect and exposure category independently, using the causal
framework [described in the Preamble to the ISAs (U.S. EPA. 2015b)1.
Each chapter section begins with a summary of the conclusions from the 2008 ISA for
Sulfur Oxides, followed by an evaluation of recent studies (i.e., those published since the
completion of the 2008 ISA for Sulfur Oxides) that build upon evidence from previous
reviews. Within each of the sections focusing on morbidity outcomes (e.g., respiratory
morbidity, cardiovascular morbidity), the evidence is organized into more refined
outcome groupings (e.g., asthma exacerbation, myocardial infarction) that comprise a
continuum of subclinical to clinical effects. The discussion of specific health outcomes is
then organized by scientific discipline (i.e., epidemiology, controlled human exposure,
toxicology). This structure helps in evaluating coherence and biological plausibility of the
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effects observed in association with exposure to SO2 and promotes the transparent
characterization of the weight of evidence in drawing the causal conclusions found at the
end of each section (e.g., see Section 5.2.1.9). Causal determinations for total mortality
are based on the evidence for nonaccidental causes of mortality and informed by the
extent to which evidence for the spectrum of cardiovascular and respiratory effects
provides biological plausibility for S02-related total mortality. Findings for
cause-specific mortality inform multiple causal determinations. For example, studies of
respiratory and cardiovascular mortality are used to assess the continuum of effects and
inform the causal determinations for respiratory and cardiovascular morbidity. As
described in Section 1.2. judgments regarding causality are made by evaluating the
evidence over the full range of exposures in animal toxicological, controlled human
exposure, and epidemiologic studies defined in this ISA to be relevant to ambient
exposure (i.e., <2,000 ppb).
5.1.2 Evidence Evaluation and Integration to Form Causal Determinations
5.1.2.1 Evaluation of Individual Studies
As described in the Preamble to the ISAs (U.S. EPA. 2015b) (Section 5.a), causal
determinations were informed by integrating evidence across scientific disciplines
(e.g., exposure, animal toxicology, epidemiology) and related outcomes, as well as by
judgments on the strength of inference from individual studies. These judgments were
based on evaluating strengths, as well as various sources of bias and uncertainty related
to study design, study population characterization, exposure assessment, outcome
assessment, consideration of confounding, statistical methodology, and other factors.
This evaluation was applied to controlled human exposure, animal toxicological, and
epidemiologic studies included in this ISA, comprising studies from previous
assessments as well as those studies published since the 2008 ISA for Sulfur Oxides.
Aspects comprising the major considerations in the individual study evaluation are
described in the Annex for Chapter 5 of this ISA and are consistent with current best
practices employed in other approaches for reporting or evaluating health science data.1
Additionally, these aspects are compatible with published U.S. EPA guidelines related to
1 For example, National Toxicology Program Office of Health Assessment and Translation approach (Roonev et al..
2014). Integrated Risk Information System Preamble (U.S. EPA. 2013e). ToxRTool (Klimischetal.. 1997).
STROBE guidelines (von Elm et al.. 2007). Animals in Research: Reporting In Vivo Experiments guidelines
(Kilkenny et al.. 2010).
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cancer, neurotoxicity, reproductive toxicity, and developmental toxicity (U.S. EPA.
2005a. 1998. 1996a. 1991).
The aspects described in the Annex for Chapter 5 were used as a guideline rather than a
checklist or criteria to define the quality of a study. The presence or absence of a
particular feature did not necessarily define a less informative study or preclude a study
from consideration in the ISA. Further, these aspects were not criteria for a particular
determination of causality in the five-level hierarchy. As described in the Preamble to the
ISAs (U.S. EP A. 2015b). causal determinations were based on judgments of the overall
strengths and limitations of the collective body of available studies and the coherence of
evidence across scientific disciplines and related outcomes. Where possible,
considerations such as exposure assessment and confounding (i.e., bias due to a
relationship with the outcome and correlation with exposures to SO2), were framed to be
specific to sulfur oxides. Thus, judgments of the strength of inference from a study can
vary depending on the specific pollutant being assessed.
Evaluation of the extent to which the science informs the understanding of uncertainties
related to the independent effect of sulfur oxides is of particular relevance in the review
process. Because examination of copollutant confounding is based largely on copollutant
models, the inherent limitations of such models are considered in drawing inferences
about independent associations for SO2. For example, collinearity potentially affects
model performance when highly correlated pollutants are modeled simultaneously, and
inference can also be limited if differences in the spatial distributions of SO2 and the
copollutant do not satisfy the assumptions of equal measurement error or constant
correlations for SO2 and the copollutant (Section 3.4.3). Correlations of short-term SO2
concentrations with other NAAQS pollutants are generally low to moderate, but may
vary by location (Section 3.5). Thus, the interpretation of copollutant model results
reported in epidemiologic studies depends on a variety of factors, which are discussed
throughout the chapter, generally in the context of a specific study and/or health
endpoint.
5.1.2.2 Integration of Scientific Evidence
Causal determinations are made by considering the strength of inference from individual
studies and on integrating multiple lines of evidence. As detailed in the Preamble to the
ISAs (U.S. EPA. 2015b). evidence integration involved evaluating the consistency and
coherence of findings within and across disciplines, as well as within and across related
outcomes. Cross-disciplinary integration often addresses uncertainties within a particular
discipline. Controlled human exposure and animal toxicological studies can provide
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direct evidence for health effects related to SO2 exposures. Coherence of experimental
evidence with epidemiologic findings can advance our understanding about whether
epidemiologic associations with health outcomes plausibly reflect an independent effect
of ambient SO2 exposure. For example, the coherence of effects observed in
epidemiologic studies with human clinical studies demonstrating direct effects of SO2 on
lung function (Section 5.2.1.2). is drawn upon to reduce uncertainties in epidemiologic
studies. Thus, the integration of evidence across a spectrum of related outcomes and
across disciplines was used to clarify the understanding of uncertainties for a particular
outcome or discipline due to chance, publication bias, selection bias, and confounding by
copollutant exposures or other factors.
The integration of the scientific evidence is facilitated through the presentation of data
from multiple studies within and across disciplines. To increase comparability of results
across epidemiologic studies, the ISA presents effect estimates for associations with
health outcomes scaled to the same increment of SO2 concentration.1 The increments for
standardization vary by averaging time. For 24-h avg, effect estimates were scaled to a
10-ppb increase for SO2. For 1-h daily max, effect estimates were scaled to a 40-ppb
increase for SO2. Effect estimates for long-term exposures to SO2 (i.e., annual or
multiyear averages) were scaled to a 5-ppb increase. Units of dose in toxicological
studies are typically presented in ppm; however, when toxicological data are summarized
in the context of epidemiologic findings, units are converted to ppb for comparability.
5.1.3 Summary
The subsequent sections review and synthesize the evidence of S02-related health effects
from multiple disciplines (e.g., exposure, animal toxicology, and epidemiology).
Information on dosimetry and modes of action (Chapter 4) provides the foundation for
understanding how exposure to inhaled SO2 may lead to health effects, providing
biological plausibility for effects observed in the health studies. The science related to
sources, emissions, and atmospheric concentrations (Chapter 2). as well as the potential
for human exposure to ambient sulfur oxides (Chapter 3). also informs the interpretation
of the health effects evidence. Integrative "Summary and Causal Determination" sections
for short- and long-term exposures follow the discussion of the evidence for each health
outcome category. These integrative summary sections include assessments of the
strength of inference from studies comprising the evidence base and integrate multiple
1 Versus reported effect estimates that are scaled to variable changes in concentration such as IQR for the study
period or an arbitrary unit.
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lines of evidence to characterize relationships between sulfur oxides and various health
effects.
5.2
Respiratory Effects
5.2.1
Short-Term Exposure
5.2.1.1
Introduction
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) concluded that there is a causal
relationship between respiratory effects and short-term exposure to SO2. The rationale for
this causal determination was heavily based on evidence from multiple, high-quality
controlled human exposure studies demonstrating decreased lung function and increased
respiratory symptoms following SO2 exposures of 5-10 minutes in exercising adults with
asthma.
There was also epidemiologic evidence indicating associations between short-term
increases in ambient SO2 concentration and respiratory effects in populations living in
locations with ambient concentrations below the previous 24-h avg NAAQS level of
140 ppb. Evidence was strongest for increased respiratory symptoms and
respiratory-related hospital admissions and ED visits, especially in children. Due to
inadequate examination, a key uncertainty was potential confounding by copollutants,
particularly PM. However, controlled human exposure studies of individuals with asthma
clearly show that respiratory effects are caused by 5-10 minute SO2 exposures.
In contrast with asthma exacerbation, there was little information to assess whether
short-term SO2 exposure exacerbated allergy or chronic obstructive pulmonary disease
(COPD) or increased risk of respiratory infection. However, there was some experimental
evidence for respiratory effects in healthy humans (>1,000 ppb) and animal models
(100 ppb) exposed to SO2. Epidemiologic evidence in healthy populations was limited
and inconsistent.
As described in the following sections, evidence from recent studies is generally
consistent with that in the 2008 ISA and 1982 AQCD for Sulfur Oxides (U.S. EPA.
2008d. 1982a). To clearly characterize differences in the weight of evidence and the
extent of coherence among disciplines and related outcomes, the sections are organized
by respiratory outcome group [asthma exacerbation (Section 5.2.1.2). allergy
exacerbation (Section 5.2.1.3). COPD exacerbation (Section 5.2.1.4). respiratory
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infection (Section 5.2.1.5). aggregated respiratory conditions (Section 5.2.1.6).
respiratory effects in the general population and healthy individuals (Section 5.2.1.7). and
respiratory mortality (Section 5.2.1.8)1. Epidemiologic studies comprise most of the
recent evidence base, and previous controlled human exposure and animal toxicological
studies form the basis for characterizing and integrating evidence across disciplines.
Recent epidemiologic evidence supports associations between ambient SO2
concentrations and asthma-related symptoms, hospital admissions, and ED visits, but
exposure measurement error and copollutant confounding remain uncertain. Recent
epidemiologic studies add information on allergy and COPD exacerbation, respiratory
infection, and respiratory effects in healthy populations, but relationships of these
outcomes with short-term SO2 exposure still are unclear because of inconsistent evidence
or limited coherence among disciplines.
5.2.1.2 Asthma Exacerbation
Asthma is a chronic lung disease with a broad range of characteristics and disease
severity. Its main features are airway obstruction that is generally reversible, airway
inflammation, and increased airway responsiveness. SO2 exposure has been demonstrated
to induce clinical features of asthma exacerbation, including decreased lung function
(e.g., decreased forced expiratory volume in 1 sec [FEVi] or increased specific airway
resistance [sRaw]), and increased symptoms (e.g., wheezing, cough, shortness of breath),
as well as some subclinical effects such as inflammation. This section describes evidence
for SC>2-associated lung function changes and respiratory symptoms in people with
asthma, hospital admissions and emergency department visits for asthma and related
respiratory conditions, and subclinical effects underlying asthma such as pulmonary
inflammation and oxidative stress.
As detailed in the previous 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d). controlled
human exposure studies reported increased respiratory symptoms and decreased lung
function after short-term exposures of 5-10 minutes to 0.2-0.6 ppm SO2 during exercise
or eucapnic hyperpnea (a rapid and deep breathing technique through a mouthpiece that
prevents an imbalance of CO2 due to hyperventilation) in adults and adolescents
(12-18 years) with asthma. In contrast, the majority of the controlled human exposure
studies evaluating the respiratory effects of SO2 in healthy adults demonstrated increased
airway resistance and decreased FEVi following exposures to concentrations
>1.0-5.0 ppm (Section 5.2.1.7). While children may be especially susceptible to the
respiratory effects of SO2 for dosimetric reasons (Section 4.2.2). there are no available
controlled human exposure studies in children under 12, partly due to ethical concerns.
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Coherent with controlled human exposure findings, epidemiologic evidence indicated
that short-term increases in ambient SO2 concentration were associated with
asthma-related hospital admissions, ED visits, and symptoms. The strongest evidence
was for children, which is consistent with their greater oral breathing and higher
ventilation rates relative to their size than adults and the consequent potential for them
receiving a higher SO2 dose to the tracheobronchial airways of the lower respiratory tract
(Section 4.1.2. Section 4.2.2). Epidemiologic evidence for S02-related lung function
decrements was inconsistent among both children and adults with asthma. A key
uncertainty in the epidemiologic evidence was whether findings reflected an independent
association for SO2 because the studies assigned exposure from central site monitors
(i.e., those used to determine attainment with the NAAQS, Section 3.3.1.1). Also, few of
the studies examined potential confounding by PM2 5 or other copollutants.
The 2008 SOx ISA (U.S. EPA. 2008d) also provided limited evidence for a relationship
between SO2 concentrations and allergic responses and inflammation in individuals with
asthma. Children and adults with atopy plus asthma were found to be at greater risk of
SCh-associated respiratory effects such as respiratory symptoms and lung function
decrements. In addition, animal toxicological studies demonstrated that repeated
exposure to SO2 enhanced inflammation and allergic responses in animal models of
allergic airway disease.
Together recent studies and the evidence presented in the 2008 ISA for Sulfur Oxides
link short-term SO2 exposure to asthma exacerbation. Most recent studies are
epidemiologic, which continue to show ambient SCh-associated increases in asthma
symptoms, hospital admissions, and ED visits among children. However, exposure
measurement error and copollutant confounding remain uncertainties in the
epidemiologic evidence. A few recent animal toxicological studies add support for
SCh-induced allergic inflammation. While there are no recent controlled human exposure
studies in individuals with asthma (see Section 5.2.1.7 for recent studies in healthy
individuals), previous evidence from controlled human exposure studies provides support
for an independent effect of SO2 exposure on asthma exacerbation.
Lung Function Changes in Populations with Asthma
The 2008 SOx ISA (U.S. EPA. 2008d) reported strong evidence for the effects of SO2
exposure on decrements in lung function in controlled human exposure studies in adults
with asthma under increased ventilation conditions. Controlled human exposure studies,
none of which are new since the 2008 SOx ISA (U.S. EPA. 2008d). also demonstrated a
subset of individuals (i.e., responders) within this population who are particularly
sensitive to the effects of SO2 exposure. This finding is most evident in the recent
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analysis of several published studies by Johns et al. (2010). Some additional data from
the previous studies has also become available since the 2008 SOx ISA and is
summarized in Table 5-2. Table 5-3. and Table 5-4. Recent epidemiologic findings are
inconsistent overall. A few recent epidemiologic studies add evidence for SO2 measured
at children's school or in copollutant models with PM, NO2, or O3, albeit with pollutants
measured at central site monitors. There is a paucity of evidence from animal
toxicological studies. While some animal toxicological studies of short-term exposure to
SO2 have examined changes in lung function, these experiments were conducted in naive
animals rather than in models of allergic airway disease, which share many phenotypic
features with asthma in humans.
Controlled Human Exposure Studies
Bronchoconstriction in individuals with asthma is the most sensitive indicator of
SCh-induced lung function effects. A characteristic feature of individuals with asthma is
an increased propensity of their airways to narrow in response to bronchoconstrictive
stimuli relative to nonatopic individuals without asthma. This characteristic is termed
airway hyperresponsiveness (AHR). Different kinds of stimuli can elicit
bronchoconstriction, but in general, they act on airway smooth muscle receptors (direct
stimuli, e.g., methacholine) or act via the release of inflammatory mediators (indirect
stimuli, e.g., allergens) (O'Bvme et al.. 2009). SO2 is a nonspecific bronchoconstrictive
stimulus that is not easily classified as a direct or indirect, as discussed in Section 4.3.1.
Bronchoconstriction, evidenced by decrements in lung function, is observed in controlled
human exposure studies after approximately 5-10-minute exposures and can occur at
SO2 concentrations as low as 0.2 ppm in exercising individuals with asthma; more
consistent decrements are seen at concentrations of 0.4 ppm and greater (U.S. EPA.
2008d). In contrast, healthy adults are relatively insensitive to the respiratory effects of
SO2 below 1 ppm (Section 5.2.1.7). In all individuals, bronchoconstriction is mainly seen
during conditions of increased ventilation rates, such as exercise or eucapnic hyperpnea.
This effect is likely due to a shift from nasal breathing to oral/nasal breathing, which
increases the concentration of SO2 reaching the airways (Section 4.2.2). The majority of
controlled human exposures to SO2 were conducted with adult volunteers, although a
limited number were also conducted with adolescents (12-18 years). Characteristics of
controlled exposure studies in individuals with asthma are summarized in Table 5-1.
Controlled exposure studies individuals without asthma are discussed in Section 5.2.1.7.
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Table 5-1 Study-specific details from controlled human exposure studies of
individuals with asthma.
Study
Disease Status;
n; Sex; (Agea)
Exposure Details
(Concentration; Duration)
Outcomes Examined
Balmes et al.
(1987)
Asthma; n = 8; 6 M, 2 F
(23-39 yr)
0, 0.5, or 1 ppm SO2 for 1, 3, and 5 min
during eucapnic hyperpnea (60 L/min)
sRaw
Bethel et al.
(1983)
Asthma; n = 10; 8 M,
2 F
(22-36 yr)
0 or 0.5 ppm SO2 for 5 min with exercise
750 kg m/min (125 watts)
sRaw
Bethel et al.
(1984)
Asthma; n = 7; 5 M, 2 F
(24-36 yr)
0.5 ppm SO2 for 3 min with room
temperature and cold air
sRaw
Bethel et al. Asthma; n = 19; 16 M, 0 or 0.25 ppm SO2 for 5 min during heavy sRaw
(1985) 3 F (22-46 yr) exercise [bicycle, 750 (n = 19) or
1,000 (n = 9) kg m/min; 125 or 167 watts,
respectively]
Gong et al. Asthma; n = 14; 12 M, 0 or 0.5, 1.0 ppm SO2 with light, medium, sRaw, FEV1, symptoms,
(1995) 2 F(18-50yr) and heavy exercise (average ventilation 30, psychophysical
36, and 43 L/min) for 10 min (stamina) changes
Gong et al. Asthma; n = 10; 2 M, 0 or 0.75 ppm SO2 for 10 min with exercise FEV1, symptoms
(1996) 8 F (19-49 yr) (29 L/min) at 1, 12, 18, and 24 h after
pretreatment with placebo or salmeterol
(long-acting B2-agonist)
Gong et al. Asthma; n = 12; 2 M, 0 or 0.75 ppm SO2 for 10 min with exercise sRaw, FEV1, symptoms,
(2001) 10 F (20-48 yr) (35 L/min) with orw/o pretreatment to eosinophil counts in
montelukast sodium (10 mg/d for 3 d) induced sputum
Horstman et al.
(1986)
(1) Asthma; n = 27;
27 M w/asthma and
sensitive to inhaled
methacholine (19-33 yr)
(2) n = 4 from study
population above
(1) 0, 0.25, 0.5, or 1.00 ppm SO2 for 10 min
with exercise (treadmill, 21 L/min per
m2 body surface area)
(2) 2 ppm SO2 for 10 min with exercise
(treadmill, 21 L/min per m2 body surface
area)
sRaw
Horstman et al.
(1988)
Asthma; n = 12; 12 M
(22-37 yr)
0 or 1.0 ppm SO2 for 0, 0.5, 1.0, 2.0, and
5.0 min with exercise (treadmill 40 L/min)
sRaw, symptoms
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Table 5-1 (Continued): Study specific details from controlled human exposure
studies of individuals with asthma.
Study
Disease Status;
n; Sex; (Agea)
Exposure Details
(Concentration; Duration)
Outcomes Examined
Jorres and
Maqnussen
(1990)
Asthma; n = 14; 10 M,
4 F (21-55 yr,
34 ± 14 yr)
0 or 0.25 ppm NO2, or 0.5 ppm SO2 at rest sRaw
followed by challenge with 0.75 ppm SO2
during voluntary eucapnic hyperpnea.
Ventilation increased in 15 L/min steps, each
lasting 3 min
Kehrl et al.
(1987)
Asthma; n = 10; 10 M
(20-30 yr)
0 or 1 ppm SO2 for 1 h with exercise
(3x10 min at 41 L/min on a treadmill)
sRaw
Koeniq et al.
(1980)
Asthma; n = 9; 7 M, 2 F 0 or 1 ppm SO2 with 1 mg/m3 of NaCI
(14-18 yr) droplet aerosol, 1 mg/m3 NaCI droplet
aerosol for 60 min exposure with
mouthpiece at rest
FEV1, RT, FRC, Vmaxso,
V max75, symptoms
Koeniq et al.
(1981)
Asthma; n = 8; 6 M, 2 F
(14-18 yr)
0 or 1 ppm SO2 with 1 mg/m3 of NaCI
droplet aerosol, 1 mg/m3 NaCI droplet
aerosol for 30 min exposure via mouthpiece
at rest followed by 10 min exercise on a
treadmill (six-fold increase in min vent)
FEV1, RT, FRC, Vmaxso,
V max75, symptoms
Koeniq et al.
(1983)
(1) Asthma w/EIB;
n = 9; 6 M, 3 F
(12-16 yr)
(2) Asthma w/EIB; n = 7
from study population
above
(1) 1 g/m3 of NaCI droplet aerosol, 1 ppm
SO2 + 1 mg/m3 NaCI, 0.5 ppm
SO2 + 1 mg/m3 NaCI for 30 min exposure via
mouthpiece at rest followed by 10 min
exercise on treadmill (five- to six-fold
increase in Ve)
(2) 0.5 ppm SO2 + 1 mg/m3 NaCI via a face
mask with no nose clip with exercise
conditions the same as above
FEV1, RT, FRC, Vmaxso,
V max75, symptoms
Koeniq et al.
(1987)
Allergic w/EIB; n = 10;
3 M 7 F (13-17 yr)
0 or 0.75 ppm SO2 (mouthpiece) with FEV1, RT, FRC,
exercise (33.7 L/min) for 10 and 20 min prior symptoms
pretreatment (placebo or 180 |jg albuterol)
Koeniq et al.
(1988)
Asthma w/EIB; n = 8;
2 M, 6 F (13-17 yr)
1.0 ppm SO210 min (mouthpiece, treadmill, FEV-i.RT
35 L/min) with pretreatment (placebo 20, 40,
60 mg cromolyn) 20 min prior, no control, air
exposure
Koeniq et al.
(1990)
Asthma w/EIB; n = 13;
8 M, 5 F (12-18 yr)
0.1 ppm SO2 for 15 min preceded by air or
0.12 ppm O3 for 45 min during intermittent
exercise (2><15 min at 30 L/min on a
treadmill), no control, air exposure
FEV1, RT, FRC, Vmaxso,
symptoms
Koeniq et al.
(1992)
Asthma; n = 8; 2 M, 6 F
(18-46 yr; 27.5 ± 9.6 yr)
1 ppm SO2 for 10 min with exercise
(VE = 13.4-31.3 L/min) with or w/o
pretreatment to theophylline
FEV1, RT
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Table 5-1 (Continued): Study specific details from controlled human exposure
studies of individuals with asthma.
Study
Disease Status;
n; Sex; (Agea)
Exposure Details
(Concentration; Duration)
Outcomes Examined
Lazarus et al.
(1997)
Asthma; n = 12; 7 M,
5 F (24-43 yr)
0, 0.25, 0.5, 1.0, 2.0, 4.0, or 8.0 ppm SO2
w/eucapnic hyperpnea (20 L/min) for 4 min
sequential exposures with pretreatment with
zafirlukast (placebo or 20 mg) 2 or 10 h
earlier
sRaw
Linn et al.
(1983b)
Asthma; n = 23; 13 M,
10 F (19-31 yr)
(1)0, 0.2, 0.4, or 0.6 ppm SO2 w/low
humidity or high humidity for 10 min
w/exercise (bicycle, 5 min 50 L/min)
(2) 0 or 0.6 ppm SO2 w/warm air or cold air
w/exercise (bicycle, 50 L/min, ~5 min)
sRaw, sGaw, FVC,
FEV1, symptoms
Linn et al.
(1983a)
Asthma; n = 23; 15 M,
8 F (18-30 yr, 23 ± 4 yr)
0 or 0.75 ppm SO2 with unencumbered
breathing and mouth only breathing (with
exercise 40 L/m, 10 min bicycle)
sRaw, thoracic gas
volume, symptoms,
FVC, FEV1, PEFR,
Vmax50, Vmax25
Linn et al.
(1984c)
Asthma; n = 24; 13 M,
11 F (19-31 yr)
0, 0.3, or 0.6 ppm SO2 at 21°, 7°, and -6°C,
rH 80% (bicycle 50 L/min, ~5 min)
sRaw, sGaw, symptoms
Linn et al.
(1984a)
Asthma: n = 14; 12 M,
2 F (18-33 yr)
0 or 0.6 ppm SO2 for 6 h with exercise on
day 1 and 2(2* 5-min exercise, bicycle,
50 L/min per exposure)
sRaw, sGaw, symptoms
Linn et al.
(1984b)
(1) Asthma; n = 8; 4 M,
4 F (19-29 yr)
(2) Asthma; n = 24;
17 M 7 F (18-30 yr)
(1)0, 0.2, 0.4, or 0.6 ppm SO2 at 5°C, 50
and 85% rH with exercise (5 min, 50 L/min)
(2) 0 or 0.6 ppm SO2 at 5 and 22°C, 85% rH
with exercise (5 min, 50 L/min)
sRaw, sGaw, FEV1,
symptoms
Linn et al.
(1985b)
Asthma; n = 22; 13 M,
9 F (18-33 yr)
0 or 0.6 ppm SO2 at 21 and 38°C and 20
and 80% rH with exercise (~5 min, 50 L/min)
sRaw, sGaw, symptoms
Linn et al.
(1985a)
COPD; n =24; 15 M,
9 F (49-68 yr)
0, 0.4, or 0.8 ppm SO2 for 1 h with exercise
(2 x 15 min, bicycle, 18 L/min)
sRaw, FVC, FEV1,
MMFR, symptoms
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Table 5-1 (Continued): Study specific details from controlled human exposure
studies of individuals with asthma.
Study
Disease Status;
n; Sex; (Agea)
Exposure Details
(Concentration; Duration)
Outcomes Examined
Linnetal. (1987)
Healthy; n = 24; 15 M,
9 F (18-37 yr)
Atopic; n = 21; 12 M,
9 F (18-32 yr)
Minimal or mild asthma;
n = 16; 10 M, 6 F
(20-33 yr)
Moderate or severe
asthma; n = 24; 10 M,
14 F (18-35 yr)
Moderate or severe
asthma; n = 24
0, 0.2, 0.4, or 0.6 ppm SO2
1 h exposures
3 x 10-min exercise (bicycle) periods
-40 L/min
Two rounds of exposures were conducted
Lung function measure
pre-exposure, -15 min
and -55 min into
exposure
sRaw, FVC, FEV1, peak
expiratory flow rate,
maximal midexpiratory
flow rate
Continuously—EKG
Midway—HR
Before, during, 1-d after,
and 1-wk after-symptom
score, self-rated activity
Immediately after
exposure—bronchial
reactivity percentage
change in FEV1 induced
by 3 min normocapnic
hyperpnea with cold, dry
air
Linnetal. (1988)
Asthma; n = 20; 13 M,
7 F (19-36 yr)
Three pretreatment groups
(1) metaproterenol sulfate (2) placebo (3) no
treatment
0, 0.3, or 0.6 ppm SO2
10 min with exercise (bike 50 L/min)
Lung function—pre,
post 60 min, 90 min
120 min,
Symptoms—pre, post,
20 min post, 60 min
post, 120 min post, 24
post, 1 wk post
Linnetal. (1990)
Asthma; n = 21; 6 M,
15 F (19-48 yr)
0, 0.3, or 0.6 ppm SO210 min with exercise
50 L/min
(1) low medication use; (2) normal; (3) high
(usual medication supplemented by inhaled
metaproterenol before exposure)
Lung function and
symptoms measured
before and after
exposure
Maqnussen et al.
(1990)
Asthma; n = 46; 24 M,
22 F(28± 14 yr)
Healthy; n = 12
(24 ± 5 yr)
0 or 0.5 ppm SO210 min tidal breathing
followed by 10 min of isocapnic
hyperventilation (30 L/min)
Histamine challenge—(8 mg/mL)
sRaw
Myers et al.
(1986a)
Asthma; n = 10; 7 M,
3 F (19-40 yr)
0, 0.25, 0.5, 1, 2, 4, or 8 ppm SO2 3 min
sequential exposures (mouthpiece,
40 L/min) with pretreatment 30 min prior with
cromolyn (placebo, 20, or 200 mg)
sRaw
Mvers et al.
(1986b)
(1) Asthma; n = 9; 7 M,
2 F (19-40 yr)
(2) Asthma; n = 7; 7 M
(19-40 yr)
0, 0.25, 0.5, 1, 2, 4, or 8 ppm SO2 3 min
sequential exposures (mouthpiece, eucapnic
hyperpnea 40 L/min) with pretreatment
30 min prior (1) atropine (2 mg) and
cromolyn (200 mg); (2) placebo and
cromolyn (200 mg); (3) atropine (2 mg) and
placebo; (4) placebo
sRaw
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Table 5-1 (Continued): Study specific details from controlled human exposure
studies of individuals with asthma.
Study
Disease Status;
n; Sex; (Agea)
Exposure Details
(Concentration; Duration)
Outcomes Examined
Roaer et al.
(1985)
Asthma; n = 28; 28 M
(19-33 yr)
75 min
0, 0.25, 0.5, or 1.0 ppm SO2
Three 10 min periods of exercise 42.4 L/min
Raw; sRaw; FVC, FEVi,
FEF25-75, FEFmax,
FEF50, FEF75,
Rubinstein et al.
(1990)
Asthma; n = 9; 5 M, 4 F
(23-34 yr)
0 or 0.3 ppm NO2 during exercise followed
by challenge with 0.25 to 4.0 ppm SO2, in
doubling dose increments, for 4 min each
until sRaw increased by 8 SRaw units above
baseline
sRaw, FVC, FEVi,
single-breath nitrogen
test
Sheppard et al.
(1983)
Asthma; n = 8; 4 M, 4 F
(22-36 yr)
0.5 ppm SO2 for 3 min eucapnic hyperpnea
sRaw, symptoms
Trenaa et al.
(1999)
Asthma; n = 47; 14 M,
33 F (18-39 yr)
0.5 ppm SO2 for 10 min during moderate
exercise
FEVi, FVC, FEV1/FVC,
PEF, FEF25-75,
symptoms ratings
Trenaa et al.
(2001)
Asthma; n = 17; 5 M,
12 F (19-38 yr)
0.1 or 0.25 ppm SO2 for 10 min w/moderate
exercise (treadmill)
FVC, FEVi, FEF25-75,
PEF, symptoms
Tunnicliffe et al.
(2003)
Asthma; n = 12
(adults, 35.7 yr)
Healthy; n = 12
(adults, 34.5 yr)
0 or 0.2 ppm SO2 at rest
Symptoms, FEVi, FVC,
MMEF, exhaled NO,
ascorbic and uric acid in
nasal lavage fluid
COPD = chronic obstructive pulmonary disease; EIB = exercise-induced bronchospasm; EKG = electrocardiogram;
F = female; FEV = forced expiratory volume; FEVi = forced expiratory volume in 1 sec; FVC = forced vital capacity;
FEF25 -75% - forced expiratory flow at 25-75% of forced vital capacity; FEFso% - forced expiratory flow at 50% of
forced vital capacity; FEF75% = forced expiratory flow at 75% of forced vital capacity; FEFmax = maximum forced
expiratory flow; FRC = functional residual capacity; HR = heart rate; M = male; MMEF = maximum midexpiratory
flow; MMFR = maximal midexpiratory flow rate; n = sample size; NaCI = sodium chloride; NO = nitric oxide;
NO2 = nitrogen dioxide; O3 = ozone; PEF = peak expiratory flow; PEFR = peak expiratory flow rates; ppm = parts
per million; Raw = airway resistance; rH = relative humidity; RT = total respiratory resistance; SD = standard
deviation; sGAW = specific airway conductance; sRaw = specific airway resistance; SO2 = sulfur dioxide;
Ve = minute volume; Vmax = maximal flow of expired vital capacity; Vmax75 = flow rate with 75% of FVC remaining to
be expired; Vmaxso = flow rate with 50% of FVC remaining to be expired; Vmax25 = flow rate with 25% of FC remaining
to be expired.
aRange or Mean ± SD.
1 Several investigators 11 -inn et al.. 1990; I inn et al.. 1988; I inn et al.. 1987; Bethel et al..
2 1985; Linn et al.. 1984a; Linn et al.. 1983b) demonstrated >100% increase in sRaw or
3 >15% decrease in FEVi after 5-10-minute exposures to low concentrations
4 (0.2-0.3 ppm) of SO2 in exercising adults with asthma, with effects being more
5 pronounced following 5-10-minute exposures to 0.4-0.6 ppm SO2 (Linn et al.. 1990;
6 Magnussen et al.. 1990; Linn et al.. 1988; Linn et al.. 1987; Roger et al.. 1985; Linn et al..
7 1983b).
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SCh-induced bronchoconstriction occurs rapidly and is transient with recovery following
cessation of exposure. Bronchoconstriction occurs in as little as 2 minutes from the start
of exposure in adults with asthma who have increased ventilation rates due to exercise or
eucapnic hyperpnea (Horstman et al.. 1988; Balmes et al.. 1987; Sheppard et al.. 1983).
During exposure to SO2 over a 30-minute period with continuous exercise, the response
to SO2 develops rapidly and is maintained throughout the 30-minute exposure (kehrl et
al.. 1987; Linn et al.. 1987; Linn et al.. 1984c). Linn et al. (1984a) reported decrements in
lung function in adults with asthma immediately after each exercise period (one early and
one late into the exposure) in two 6-hour exposures to 0.6 ppm SO2 on successive days.
The decrements in lung function observed in the early and late exercise periods were not
statistically significantly different from each other, and the response observed after the
second day of SO2 exposure was slightly less than the response observed after the first
day of SO2 exposure. These results demonstrate transient rather than cumulative
bronchoconstriction effects. These effects are generally observed to diminish to baseline
levels within 1 hour post exposure (Linn et al.. 1987).
Other factors that affect responses to SO2 include temperature and humidity.
The majority of controlled human exposure studies were conducted at 20-25°C and at
relative humidities ranging from -25-90%. Some evidence indicates that the respiratory
effects of SO2 are exacerbated by colder and dryer conditions (Linn et al.. 1985b; Bethel
et al.. 1984; I inn et al.. 1984b).
Responders versus nonresponders to SO2. At the time of the 2008 SOx ISA (U.S. EPA.
2008d). it was well documented that some individuals have a greater response to SO2
than others with similar disease status (Table 5-2) (Linn et al.. 1990; Magnussen et al..
1990; Linn et al.. 1988; Linn et al.. 1987; Horstman et al.. 1986; Bethel et al.. 1985;
Roger et al.. 1985; I inn et al.. 1984b; I inn et al.. 1983b).
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Table 5-2 Percentage of adults with asthma in controlled human exposure studies experiencing sulfur
dioxide-induced decrements in lung function and respiratory symptoms.
Cumulative Percentage of Responders
(Number of Subjects)3
dS sRaw 2100% -f- >200% + >300~ Respiratory Symptoms:
(ppm) (min) N (L/min) FEV, >150/,^ >20% * >30% 4* study Supporting Studies
0.2 5 23 -48 sRaw 9% (2)b 0 0 Linn et al. (1983b)
10 40 -40 sRaw 7.5% (3)c 2.5% (1)c 0C Linn et al. (1987)c
10 40 -40 FEV1 9% (3.5)c 2.5% (1)c 1 % (0.5)c Linn et al. (1987)c
0.25 5 19 -50-60 sRaw 32% (6) 16% (3) 0 Bethel et al. (1985)
Bethel et al. (1985)
5 9 -80-90 sRaw 22% (2) 0 0
10 28 -40 sRaw 4%(1) 0 0 Roger et al. (1985)
0.3 10 20 -50 sRaw 10% (2) 5%(1) 5%(1) Linn et al. (1988)d
10 21 -50 sRaw 33% (7) 10% (2) 0 Linn et al. (1990)d
10 20 -50 FEV1 15% (3) 0 0 Linn et al. (1988)
10 21 -50 FEV1 24% (5) 14% (3) 10% (2) Linn et al. (1990)
0.4 5 23 -48 sRaw 13% (3) 4%(1) 0 Linn et al. (1983b) Stronger evidence with some
statistically significant increases
10 40 -40 sRaw 24% (9.5)c 9% (3.5)c 4%(1.5)c Linn et al. (1987)° in respiratory symptoms: Balmes
et al. (1987)f. Gong etal. (1995)
10 40 -40 FEV1 27.5% (11 )c 17.5% (7)c 10%(4)c Linn et al. (1987)c (Linn et al. (1987); Linn etal.
(1983b)) Roger etal. (1985)
0.5 5 10 -50-60 sRaw 60% (6) 40% (4) 20% (2) Bethel et al. (1983)
10 28 -40 sRaw 18% (5) 4%(1) 4%(1) Roger et al. (1985)
10 45 -30 sRaw 36% (16) 16% (7) 13% (6) Magnussen et al. (1990)f
Limited evidence of SC>2-induced
increases in respiratory
symptoms in some people with
asthma: (Linn et al. (1990): Linn
etal. (1988): Linn et al. (1987):
Schachter et al. (1984): Linn et
al. (1983b))
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Table 5-2 (Continued): Percentage of adults with asthma in controlled human exposure studies experiencing
sulfur dioxide induced decrements in lung function and respiratory symptoms.
so2
Cone
(ppm)
Exposure
Duration
(min)
Ventil-
ation
(L/min)
Cumulative Percentage of Responders
(Number of Subjects)3
sRaw >100% t >200% t >300% t
FEVi >15% nU >20% * >30% <4/ study
Respiratory Symptoms:
Supporting Studies
0.6
5
23
-48
sRaw
39% (9)
26% (6)
17% (4)
Linn et al. (1983b)
10
40
-40
sRaw
34% (13.5)c
24% (9.5)c
19% (7.5)c
Linn et al. (1987)c
10
20
-50
sRaw
60% (12)
35% (7)
10% (2)
Linn et al. (1988)
10
21
-50
sRaw
62% (13)
29% (6)
14% (3)
Linn et al. (1990)
10
40
-40
FEVi
47.5% (19)c
39% (15.5)c
17.5% (7)c
Linn et al. (1987)°
10
20
-50
FEVi
55% (11)
55% (11)
5% (1)
Linn et al. (1988)
10
21
-50
FEVi
43% (9)
38% (8)
14% (3)
Linn et al. (1990)
1.0
10
28
-40
sRaw
50% (14)
25% (7)
14% (4)
Roaer et al. (1985)e
10
10
-40
sRaw
60% (6)
20% (2)
0
Kehrl et al. (1987)
Clear and consistent increases in
¦ S02-induced respiratory
symptoms: (Linn et al. (1990);
¦ Linn et al. (1988): Linn et al.
(1987): Linn et al. (1983b)). Gong
¦ et al. (1995), Horstman et al.
(1988)
Cone = concentration; FE\A| = forced expiratory volume in 1 sec; sRaw = specific airway resistance; S02 = sulfur dioxide.
aData 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, or a 15, 20, or 30% decrease in FEVi. Lung function decrements are adjusted for the effects of exercise in clean air (calculated as the difference between
the percent change relative to baseline with exercise/S02 and the percent change relative to baseline with exercise/clean air).
bNumbers in parenthesis represent the number of subjects experiencing the indicated effect.
°Responses of people with mild and moderate asthma reported in Linn et al. (1987) have been combined. Data are the average of the first and second round exposure responses
following the first 10 min period of exercise.
dAnalysis includes data from only people with mild Linn et al. (1988) and moderate Linn et al. (1990) asthma who were not receiving supplemental medication.
eOne subject was not exposed to 1 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.
'Indicates studies in which exposures were conducted using a mouthpiece rather than a chamber.
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38
Horstman et al. (1986) reported that individuals required different concentrations of SO2
to produce a doubling of sRaw (>100%) compared to clean air exposure [provocative
concentration of SO2, PC(SC>2)] (Figure 5-1). This study described the distribution of
individual bronchial sensitivity to SO2, measured by sRaw, in 27 subjects with asthma
that were sensitive to methacholine; nonsensitive volunteers were excluded from further
participation in the study. Individuals were exposed to concentrations of SO2 between 0
and 2 ppm for 10 minutes under exercising conditions (Ve = 42 L/minute). While six of
the subjects (22%) reached a PC(SC>2) below 0.5 ppm SO2, two subjects (7.4%)
experienced a moderate decrease <0.3 ppm (Figure 5-1). On the other end of the
spectrum, four subjects (14.8%) did not demonstrate >100% increase in sRaw even when
exposed to 2.0 ppm SO2 and eight (29.6%) subjects required an SO2 concentration
between 1.0 and 2.0 ppm to elicit a response. The authors noted that the effects of SO2 on
sRaw are similar to a variety of nonspecific bronchoconstrictive stimuli. However, they
observed only a weak correlation between airway responsiveness to SO2 and
methacholine (r = 0.31,/> = 0.12). This study demonstrates substantial interindividual
variability in sensitivity to the bronchoconstrictive effects of SO2 in exercising adults
with asthma.
Completed after the 2008 SOx ISA (U.S. EPA. 2008d). an analysis by Johns et al. (2010)
of publicly available primary data from published studies clearly demonstrates disparate
responses among 177 adults with asthma. Data from five studies of individuals with
asthma exposed to multiple concentration of SO2 for 5-10 minutes with elevated
ventilation rates (I inn et al.. 1990; I inn et al.. 1988; I inn et al.. 1987; Roger et al.. 1985;
Linn et al.. 1983b) were analyzed after classifying individuals by responder status.
Classification of responders versus nonresponders was based on the magnitude of sRaw
and FEVi changes in response to the highest SO2 concentration to which subjects were
exposed (0.6 or 1.0 ppm). Responders were defined as subjects experiencing >100%
increase in sRaw or >15% decrease in FEVi after exposure. Response status was assigned
separately for sRaw and FEVi. Among responders, significant decreases in FEVi were
observed for concentrations as low as 0.3 ppm SO2 (p = 0.005) (Table 5-3). In addition,
marginally significant increases in sRaw were demonstrated at 0.3 ppm SO2 (p = 0.009),
with statistically significant increases observed at 0.4 and 0.5 ppm (p < 0.001)
(Table 5-4). [Due to multiple comparisons, Johns et al. (2010) designated a critical
value of 0.005 as significant, using the Bonferroni multiple comparison correction.]
Overall, these data demonstrate a bimodal distribution of airway responsiveness to SO2 in
individuals with asthma, with one subpopulation that is insensitive to the
bronchoconstrictive effects of SO2 even at concentrations as high as 1.0 ppm, and another
subpopulation that has an increased risk for bronchoconstriction at low concentrations of
SO2. The Winterton et al. (2001) study suggests that a TNF-a promoter polymorphism in
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some individuals with asthma may be associated with increased airway responsiveness to
S02.
100-
— 75-
>»
o
c
CD
~
CT
CD
0)
_>
E
3
o
50.
25
0.25
X
X*
X
X
0.5
X
X
T
0.75 1.0
PC{S02) (ppm)
~1
2.0
5.0
10.0
PC = provocative concentration; S02 = sulfur dioxide.
Note: Each data point represents the PC(S02) for an individual subject.
Source: Horstman et al. (1986).
Figure 5-1 Distribution of individual airway sensitivity to sulfur dioxide.
The cumulative percentage of subjects is plotted as a function of
provocative concentration, which is the concentration of sulfur
dioxide that provoked a 100% increase in specific airway
resistance compared to clean air.
A recent analysis of four previously published studies (Horstman et al.. 1988; Horstman
et al.. 1986; Schachter et al.. 1984; Sheppard et al.. 1984) in which individuals with
asthma were exposed to multiple SO2 concentrations or had their response recorded over
multiple durations of SO2 exposure was provided by Goodman et al. (2015). However,
the analysis conducted by Goodman et al. (2015) did not consider the log-normal
distribution of airway responsiveness data and instead used an arithmetic mean and
standard deviation in their analysis. Eight of 56 individuals were identified as sensitive to
the effects of SO2 by Goodman et al. (2015).
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Table 5-3 Percent change in post- versus pre-exposure measures of forced
expiratory volume in 1 second relative to clean air control after
5-10-minute exposures to sulfur dioxide during exercise.
FEVi
qq2 95% Confidence Limits
Concentration Number of
ppm
Exposures
% Decrease
Lower
Upper
p-Value
Responders
0.2
37
-5.0
-8.9
-1.1
0.012
0.3
20
-7.6
-13.0
-2.3
0.005ab
0.4
37
-17.4
-21.3
-13.6
<0.001ab
Nonresponders
0.2
43
0.4
-4.3
5.2
0.854
0.3
21
-3.6
-9.6
2.5
0.252
0.4
43
-4.3
-9.2
0.6
0.086
FE\A| = forced expiratory volume in 1 sec; ppm = parts per million; S02 = sulfur dioxide.
A generalized linear latent and mixed models (GLLAMM) procedure was used that included study as a fixed effect, concentration
dummy variables as a covariate, and subject and the times a subject was exposed to a sequence of exposures as random
variables. Data were included from Linn et al. (1987). Linn et al. (1988). and Linn et al. (1990).
indicates significance at 0.05 level using the Bonferroni multiple comparison correction,
indicates significance at 0.05 level using Dunnett's test.
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Table 5-4 Percent change in post- versus pre-exposure measures of specific
airway resistance relative to clean air control after 5-10-minute
exposures to sulfur dioxide during exercise.
sRaw
qq2 95% Confidence Limits
Concentration Number of
ppm Exposures % Increase Lower Upper p-Value
Responders 0.2 36 10.2 -3.6 24.0 0.147
0.25
14
19.5
-4.0
43.1
0.104
0.3
25
25.4
6.5
44.3
0.009
0.4
36
75.7
53.4
98.0
<0.001ab
0.5
14
68.0
33.2
102.8
<0.001ab
Nonresponders 0.2 67 7.9 -4.9 20.7 0.227
0.25 14 12.6 -10.5 35.7 0.286
0.3
16
16.4
-5.2
38.1
0.137
0.4
67
16.2
1.8
30.6
0.028
0.5 14 14.7 -12.3 41.7 0.285
ppm = parts per million; sRaw = specific airway resistance; S02 = sulfur dioxide.
A A generalized linear latent and mixed models (GLLAMM) procedure was used that included study as a fixed effect,
concentration dummy variables as a covariate, and subject and the times a subject was exposed to a sequence of exposures as
random variables. Data were included from Linn etal. (1983b). Linn et al. (1987). Linn et al. (1988). Linn et al. (1990). and Roger et
al. (1985).
indicates significance at 0.05 p level, using the Bonferroni multiple comparison correction,
indicates significance at 0.05 level using Dunnett's test.
Effects of asthma severity on SCh-induced response. The influence of asthma severity
on the degree of responsiveness to SO2 exposure has been examined (Trenga et al.. 1999;
Linn et al.. 1987). One study involved exposure to SO2 under conditions of increased
ventilation (i.e., exercise) (Linn et al.. 1987). Adults with asthma were divided into two
groups, minimal/mild and moderate/severe, mainly based on the individual's use of
medication to control asthma. Individuals that did not regularly use asthma medication
were classified as minimal/mild; however, even the moderate/severe group consisted of
adults who had well-controlled asthma, were generally able to withhold medication, were
not dependent on corticosteroids, and were able to engage in moderate to heavy levels of
exercise. Thus, this moderate/severe group would likely be classified as moderate by
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today's classification standards (Johns et al.. 2010; Rcddcl. 2009). Linn et al. (1987)
found similar relative decrements in lung function in response to SO2 exposure between
the groups. However, the moderate/severe group demonstrated larger absolute changes in
lung function compared to the mild group (Linn et al.. 1987). This greater decrement in
lung function was attributable to a larger response to the exercise component of the
exposure protocol in the moderate/severe group compared with the mild group. Trenga et
al. (1999) found a correlation between asthma severity and response to SO2. Adults with
asthma were divided into four groups based on medication usage as an indicator of
asthma severity. The role of exercise was not determined in this study, so it unclear
whether individuals with more severe asthma had a greater response to exercise
compared to individuals with less severe asthma. However, both studies suggest that
adults with moderate/severe asthma may have more limited reserve to deal with an insult
compared with individuals with mild asthma.
Asthma with medication. Asthma medications have been shown to mitigate
SCh-induced bronchoconstriction (U.S. EPA. 2008d). Medications evaluated include
short-acting and long-acting beta-adrenergic bronchodilators (Gong et al.. 1996; Linn et
al.. 1990; I inn et al.. 1988; Koenig et al.. 1987). cromolyn sodium (Koenig et al.. 1988;
Myers etal. 1986b). theophylline (Koenig et al.. 1992). and leukotriene receptor
antagonists (Gong et al.. 2001; Lazarus et al.. 1997). While these therapies have been
shown to mitigate the respiratory effects of SO2, they did not completely eliminate these
effects in all studies.
Children and adolescents. Several studies have examined the responsiveness to SO2 of
adolescents (ages 12-18 years) with asthma or allergic with EIB (Koenig et al.. 1990;
Koenig et al.. 1988; Koenig etal.. 1987). Of these studies, only Koenig et al. (1987)
included a control air exposure, so that the bronchoconstrictive effects of SO2 itself
(rather than, e.g., due to EIB), can be assessed. On average, based on the data provided in
Table 1 of this paper, adolescents experienced a pre-to-post reduction in FEVi of 15.4%
following exposure to 0.75 ppm SO2 and a reduction in FEVi of 3.46% following air
exposure. Although the adolescents in this study were allergic with EIB, they did not
have extrinsic asthma. Nevertheless, they are discussed here because allergies affect
airway responsiveness (Burrows etal.. 1995) and because their response to SO2 is similar
to that observed in other studies of individuals with asthma. The pre-to-post reduction in
FEVi of 15.4% following 0.75 ppm SO2 observed by Koenig et al. (1987) is similar to the
pre-to-post reduction in FEVi of 13.9% found in adolescents with asthma following
exposure to 1.0 ppm SO2 observed by Koenig et al. (1988). For potential comparison to
the results of adolescents, three studies of adults with asthma were conducted at 0.75 ppm
(Gong et al.. 2001; Gong et al.. 1996; Linn et al.. 1983a). Of these, only Gong et al.
(2001) provided pre-to-post data for both exposures to air and SO2. Similar to the Koenig
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et al. (1987) results, Gong et al. (2001) observed a pre-to-post reduction of 15.8% in
FEVi following SO2 exposure in adults based on Table 2 of their paper. Adjusted for the
responses occurring with air exposure, Koenig et al. (1987) observed an 11.8% reduction
in FEVi in adolescents, similar to the 12.7% reduction observed in adults by Gong et al.
(2001). These two studies differ in that the adolescents were exposed via a mouthpiece,
whereas the adults were exposed in a chamber without a mouthpiece. Breathing on a
mouthpiece is expected to produce a somewhat larger FEVi decrement than
unencumbered breathing (Linn et al.. 1983a). Although generally similar effects of SO2
on adolescents and adults have been observed, exact comparisons of SO2 effects between
adolescents and adults are not possible given the available data.
There is also evidence that adolescents (ages 12-18 years) with asthma or atopy are
responsive to coexposures of SO2 and sodium chloride (NaCl) droplet aerosol (Koenig et
al.. 1983. 1981; Koenig et al.. 1980). Exposure concentrations in these studies ranged
from 0.1 to 1.0 ppm SO2. Koenig etal. (1983) observed average FEVi decrements of 15
and 23% in exercising adolescents (12 to 16 year old) with asthma after a 10-minute
exposure to 0.5 ppm SO2 or 1.0 ppm SO2 plus 1 mg/m3 NaCl droplet aerosols,
respectively. No significant changes were observed following exposure to the NaCl
droplet aerosol alone. However, the observed effect may be the result of the presence of
hygroscopic particles that carry SO2 deeper into the lung.
There are no controlled human exposure studies for children less than 12 years of age that
were exposed to SO2. However, the responsiveness of children to SO2 relative to
adolescents and adults may be inferred by the responses to other nonspecific
bronchoconstrictive stimuli. Horstman et al. (1986) noted that the effects of SO2 on sRaw
are similar to that of a variety of nonspecific bronchoconstrictive stimuli. Indeed, SO2 is a
nonspecific bronchial challenge agent that has been used to assess changes in airway
responsiveness of individuals with asthma following NO2 and O3 exposures (Trenga et
al.. 2001; Jorres and Magnussen. 1990; Rubinstein et al.. 1990). Airway responsiveness
to methacholine, a history of respiratory symptoms, and atopy were significant predictors
of airway responsiveness to SO2 in healthy adults Nowak et al. (1997). Thus, potential
differences in airway responsiveness of children to SO2 relative to adolescents and adults
may be gleaned from the literature on airway responsiveness to other nonspecific stimuli
such as methacholine.
A number of cross-sectional studies have assessed airway responsiveness of children with
and without asthma to methacholine [e.g., (Mochizuki et al.. 1995; Morikawa et al.. 1994;
A vital et al.. 1991; Hopp et al.. 1986; Hopp et al. 1985)1. Studies show a clear decrease
in airway responsiveness of healthy children with increasing age beyond 5-7 years of age
through adolescence (Mochizuki et al.. 1995; Hopp et al.. 1986; Hopp et al.. 1985). In
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studies of children with asthma, some have reported airway responsiveness increased
with asthma severity but was not affected by age (Avital et al.. 1991; Hopp et al.. 1986).
whereas others have found airway responsiveness to increase with asthma severity and
decrease with age beyond 6-7 years of age (Mochizuki et al.. 1995; Morikawa et al..
1994). The study by Mochizuki et al. (1995) suggested that airway responsiveness in both
healthy children and those affected by asthma increases from ages 2-3 years up to
6-7 years, after which airway responsiveness begins decreasing.
More confidence in the effect of age on airway responsiveness may be placed on data
from longitudinal studies than from the cross-sectional studies discussed above. In a
longitudinal study of methacholine responsiveness conducted at 9, 11, 13, and 15 years of
age, Le Souef et al. (1995) found that responsiveness (1) decreases with age; (2) is
greater in boys (n = 389) than girls (n = 429); and (3) is greater in those reporting
wheeze, although responsiveness decreased with age in these individuals as well. Asthma
prevalence and symptoms such as wheeze are greater in boys than girls during childhood
and become similar or reversed around the time of puberty (Almqvist et al.. 2008). In a
subset of the cohort as used by Le Souef et al. (1995). Burrows et al. (1995) investigated
the effects of age (n = 573, 49% female), atopy (n = 558), and serum IgE (n = 473) on
airway responsiveness. At 9 years of age, a larger fraction of boys experienced bronchial
responsiveness than did girls. By the age of 15 years, there was little to no difference in
responsiveness between the sexes. Relative to atopic children, those without atopy or
with only minimal atopy had lower airway responsiveness and showed a more evident
decrease in airway responsiveness with increasing age. In the most atopic children (41 of
558), about 40% experienced severe bronchial responsiveness, which did not decrease
with age. Across all ranges of serum IgE, there was a decrease in responsiveness from
age 9 to age 15 years. By 15 years of age, there was minimal bronchial reactivity in the
children having the lowest IgE levels, and bronchial reactivity increased with increasing
serum IgE levels (p < 0.0001). In biennial assessments of childhood responsiveness,
Burrows et al. (1995) observed considerable intra-individual variability in bronchial
reactivity, but they observed a statistically significant trend for the more allergic children
to experience persistent bronchial hyperresponsiveness among their biennial assessments.
Under the assumption that bronchial responsiveness to methacholine is an appropriate
surrogate for bronchial responsiveness to SO2, these studies suggest that greater airway
responsiveness to SO2 occurs in school-aged children, particularly boys, than in
adolescents. Additionally, the methacholine data also suggest that greater airway
responsiveness to SO2 in school-aged children and adolescents who are allergic or
experience wheeze is expected to occur than in those without these conditions. Children,
particularly boys, breathe more through the mouth than adults, and ventilation rates
relative to body mass are greater in children than adults (see Section 4.1.2). Allergic
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rhinitis can lead to increased nasal resistance, which also results in less nasal and more
oral breathing. Obese children also tend to have increased nasal resistance, increased oral
breathing, and increased ventilation rates relative to normal-weight children (see
Section 4.1.2). Oral breathing allows greater SO2 penetration into the lower airways,
where it may cause bronchoconstriction, than does nasal breathing (see Section 4.2.2).
Overall, school-aged children having asthma-like symptoms might be expected to
experience greater responsiveness (i.e., larger decrements in pulmonary function)
following exposure to SO2 than normal-weight adolescents and adults.
Mixtures effects. The health effects of SO2 can be potentially modified by the interaction
with other pollutants during or prior to exposure. A few controlled human exposure
studies have examined the interactive effects of O3 and S02both sequentially and in
combination. Exercising adolescents with asthma exposed to 0.1 ppm SO2 for 15 minutes
after a 45-minute exposure to 0.12 ppm O3 had a significant decrease (8%) in FEVi (8%)
(p < 0.05,), a significant increase in total respiratory resistance (Rt) (19%) (p < 0.05). and
a significant decrease in maximal flow at 50% of expired vital capacity (Vmaxso) (15%)
(p < 0.05). while air followed by SO2, and O3 followed by O3 exposures did not cause
significant changes in lung function (Roenig et al.. 1990). In a more recent study in
exercising adults with asthma, Trenga et al. (2001) observed greater decrements in lung
function after 45 minutes of exposure to 0.12 ppm O3 followed by 15 minutes of
0.25 ppm SO2 compared to air followed by SO2.
Jorres and Magnussen (1990) and Rubinstein et al. (1990) investigated the effects of prior
NO2 exposure on S02-induced bronchoconstriction in adults with asthma. While Jorres
and Magnussen (1990) observed that tidal breathing of NO2 increased airway
responsiveness to subsequent hyperventilation of SO2, Rubinstein et al. (1990) noted NO2
induced greater airway responsiveness to inhaled SO2 in only one subject.
While SO2 acts as a nonspecific bronchial challenge agent that causes reductions in lung
function in individuals with asthma after brief exposure, it can also increase airway
responsiveness to subsequent exposures involving other stimuli such as allergens or
methacholine. Two studies of adults with asthma provide evidence for AHRto allergens
when exposure to SO2 was in combination with NO2 (Rusznak et al.. 1996; Devalia et al..
1994). In the first of these studies, exposure to 0.2 ppm SO2 or 0.4 ppm NO2 did not
affect airway responsiveness to house dust mite allergen immediately after a 6-hour
exposure at rest. In considering the effect of SO2 alone, because volunteers were exposed
at rest, it is unlikely that enough SO2 reached the bronchial airways to cause an effect.
Following exposure to the two pollutants in combination, volunteers demonstrated an
increased response to inhaled allergen (Devalia et al.. 1994). Rusznak et al. (1996)
confirmed these results in a similar study and found that AHR to dust mites persisted up
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to 48-hours post-exposure. These results provide further evidence that SO2 may elicit
effects beyond the short time period typically associated with this pollutant.
Epidemiologic Studies
Unlike controlled human exposure studies, epidemiologic studies inconsistently indicate
SCh-related lung function decrements in populations with asthma. This applies to
previous (U.S. EPA. 2008d) and recent (Table 5-5 and Table 5-6) studies as well as
adults and children with asthma. Epidemiologic studies examined longer SO2 averaging
times and lags and had uncertainty in exposures estimated from central site monitors. For
the few findings of SCh-associated lung function decrements, confounding by moderately
to highly correlated PM and NO2 (r = 0.54-0.9) was not examined. A few recent studies
address some of these uncertainties, but they persist in the evidence overall.
Adults. Previous studies were limited to Europe and Asia. A recent study shows an
SCh-associated decrease in lung function in adults with asthma in the U.S. (Qian ct al..
2009b). Recent studies in Europe and Asia do not (Maestrelli et al.. 2011; Wiwatanadate
and Liwsrisakun. 2011; Canova et al.. 2010) (Table 5-5). Mean and upper percentile SO2
concentrations tended to be lower in recent studies than in previous studies (e.g., means
for 24-h avg 0.87-4.8 ppb vs. 1.6-90 ppb). However, lower concentrations do not appear
to account for the weak recent evidence in adults with asthma as previous studies with
mean SO2 concentrations of 5.2 to 90 ppb did not observe SCh-associated lung function
decrements (Park et al.. 2005; Peters et al.. 1996a). Recent studies did not differ in
temporal variability (e.g., ratio of the mean concentration to standard deviation) in SO2
concentrations, which is the basis of analysis in these repeated measure studies.
The U.S. multicity study provides supporting evidence but has the same uncertainty in
the exposure estimate as do other studies in adults with asthma. All studies estimated SO2
exposure from central site monitors, either a single monitor or average of many monitors.
Ambient SO2 concentrations tend to show high spatiotemporal variability within a city,
and correlations with personal exposure are poorly characterized (Section 3.4.1.3).
Studies did not discuss whether measurements at the monitors adequately represented the
spatiotemporal variability in ambient SO2 concentrations in the study area. Uncertainty is
high in the U.S. study, which averaged SO2 concentrations across monitors within 32 km
of subjects' ZIP code centroid (Qian et al.. 2009b). Ambient SO2 concentrations show
large, transient peaks (Section 2.5.3). which may be important based on results from
controlled human exposure studies showing that 5- to 10-minute exposures to
200-600 ppb SO2 induce rapid and short-lived lung function decrements. Epidemiologic
studies examined same-day (lag 0) SO2 concentrations, but the daily average. Daily
average SO2 concentrations may not represent peak exposures or capture the transient
effects of peak exposures implicated in controlled human exposure studies.
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Some recent studies that did not observe SCh-related lung function decrements had small
sample sizes (N = 19 or 32) (Maestrelli et al.. 2011; Canova et al.. 2010). However, it is
unclear whether sample size explains the inconsistency among adults with asthma
overall. Similarly sized studies (Boezen et al.. 2005; Neukirch et al.. 1998) observed
associations, and larger studies do not show evidence for association (Wiwatanadate and
Liwsrisakun. 2011; Park et al.. 2005; Peters et al.. 1996a). In panel studies, the number of
repeated measurements is also important, and Canova et al. (2010) measured lung
function for five 30-day periods. Many studies that had a large number of repeated
measurements examined lung function measured by subjects at home not supervised by a
trained technician. Results were inconsistent for both methodologies.
A few recent epidemiologic studies add information on response modification by asthma
phenotype but produce no clear finding. Previous results support an SO2 association with
decreased lung function or increased airway responsiveness in adults with asthma plus
atopy (Boezen et al.. 2005; Taggart et al.. 1996). but recent results do not (Maestrelli et
al.. 2011). A 10-ppb increase in 24-h avg SO2 was associated with a -2.1 point change
(95% CI: -6.6, 2.3) in percent predicted FEVi. Of note, the previous studies specified
examining adults with AHR. Similar to controlled human exposure studies,
epidemiologic studies do not clearly show that SCh-associated lung function decrements
depend on asthma severity. An association was observed in adults with mild to moderate
asthma (Neukirch et al.. 1998). and the results varied among populations with more
severe asthma (Maestrelli et al.. 2011; Canova et al.. 2010; Qian et al.. 2009b). In contrast
with the controlled human exposure studies, the U.S. asthma medication trial observed an
SCh-related decrease in lung function in adults randomized to daily inhaled corticosteroid
use [-8.4 L/minute change in PEF (95% CI: -13, -3.4) per 10-ppb increase in 24-h avg
SO2] (Qian et al.. 2009b). Decrements were not observed in the beta-agonist or placebo
groups (Table 5-5). These two groups had more frequent asthma exacerbation during the
study than the corticosteroid group but similar PEF and mean age (Lazarus et al.. 2001).
All three groups had persistent asthma. Thus, a clear explanation for the pattern of SO2
associations is not apparent. There is no clear rationale for attributing null findings to the
lack of analysis stratified by corticosteroid use, particularly for results that were adjusted
for such use (Maestrelli et al.. 2011; Canova et al.. 2010).
Across studies, the potential influence of copollutants is largely unaddressed. No study in
adults with asthma examined PM2 5 total mass, and previous studies observed lung
function decrements in association with larger sized PM metrics that were highly
correlated with SO2 concentrations (r = 0.8-0.9) and sulfate (Neukirch et al.. 1998; Peters
etal.. 1996a). That some cities had a coal-fired power plant or used coal for heating may
explain some of the high correlations with PM and moderate correlations with NO2
(r = 0.54) (Neukirch et al.. 1998; Taggart et al.. 1996). Copollutant interactions were not
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assessed. Only the recent U.S. study analyzed confounding, but the potential for
confounding is unclear. SO2 was moderately correlated with NO2 (r = 0.58, no report on
PM10) but was associated with PEF in different medication use groups than NO2 or PM10
(Oian et al.. 200%). SO2 was associated with PEF in the corticosteroid group, and effect
estimates decreased slightly with adjustment for PM10, NO2, or O3 (Table 5-5).
Associations for PM10 and NO2 were observed in the beta-agonist and placebo groups,
respectively, and were attenuated with SO2 adjustment. However, inference from the
results is weak due to numerous comparisons across pollutants, lags, and medication
groups and questionable reliability in the exposures estimated from monitors up to 32 km
away.
Children. As with adults, evidence from neither the 2008 ISA for Sulfur Oxides (U.S.
EPA. 2008d) nor recent studies (Table 5-6) consistently links increases in ambient SO2
concentration with lung function decrements in children with asthma, including recent
U.S. multicity studies (lerodiakonou et al.. 2015; O'Connor et al.. 2008).
The inconsistency does not appear to be explained by lung function measured under
supervised conditions or by subjects at home, asthma severity, or prevalence of asthma
medication use. In contrast to adults with asthma, S02-associated lung function
decrements were not observed in children with asthma who took inhaled corticosteroids
(lerodiakonou et al.. 2015; Liu et al.. 2009b). Among children with asthma in Windsor,
ON, the association was limited to nonusers (Liu et al. 2009b). For some recent studies,
including a U.S. multicity study, inference about an SO2 effect is weak because the
association was isolated to one lung function parameter or exposure lag among numerous
lung function parameters, lags, pollutants, and/or asthma medication groups examined
(lerodiakonou et al.. 2015; Wiwatanadate and Trakultivakom. 2010). A few recent
studies aimed to address uncertainty in the exposure estimates or copollutant confounding
(Greenwald et al.. 2013; Dales et al.. 2009; Liu et al.. 2009b) and provide limited
indication of S02-associated lung function decrements.
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Table 5-5 Recent epidemiologic studies of lung function in adults with asthma.
Study Population
and Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tQian et al. (2009b)
Boston, MA; New York, NY; Philadelphia, PA;
Madison, Wl; Denver, CO; San Francisco, CA;
1997-1999
N = 154, ages 12-65 yr. 100% persistent
asthma. 1/3 ICS use, 1/3 beta-agonist use, 1/3
placebo use.
Daily measures for 16 wk. Home PEF.
Recruited from clinics as part of an asthma
medication trial. Multiple comparisons—many
pollutants, lags, medication use analyzed.
Monitors averaged
within 32 km of
subject ZIP code
centroid.
Mean (SD): 4.8 (3.9)
75th percentile: 6.2
Max: 32
24-h avg Change in PEF (L/min)
0 All subjects:-0.12 (-3.0, 2.7)
ICS: -8.4 (-13, -3.4)
Beta-agonist: 4.4 (-0.49, 9.3)
Placebo: 3.3 (-1.4, 8.0)
0-2 avg All subjects: -1.9 (-5.6, 1.7)
ICS: -13 (-18, -6.4)
Beta-agonist: 6.4 (0.14, 13)
Placebo: 0.85 (-5.2, 6.9)
Copollutant model, ICS users, lag 0
with PM10: -7.3 (-15, 0)
with N02: -7.6 (-13, -1.8)
with 03: -6.5 (-12, -1.4)
PM10 association in placebo group,
NO2 in beta-agonist group. No
¦ association with O3. PM2.5 not
examined.
NO2 and PM10 associations
attenuated with SO2 adjustment.
SO2 moderately correlated with NO2,
r = 0.58. Correlation NR for PM10.
tMaestrelli et al. (2011)
Padua, Italy, 2004-2005
N = 32, mean (SD) age 40 (7.5) yr. 81%
persistent asthma. 69% ICS use. 90% atopy.
6 measures over 2 yr. Supervised spirometry.
Recruited from database of beta-agonist users
(>6 times per yr for 3 yr).
Two monitors in city 24-h avg
Medians across 0
seasons: 0.87-2.7
75th percentiles
across seasons:
1.3-4.1
Change in % predicted FEV1
All subjects: -2.1 (-6.6, 2.3)
Nonsmokers: -11 (-40, 18)
No copollutant model
CO associated with FEV1. No
association with personal or central
site PM2.5. No association for central
site PM10, NO2, O3.
Copollutant correlations NR.
tCanova et al. (2010)
Padua, Italy, 2004-2005
N = 19, ages 15-44 yr. 79% moderate/severe
asthma. 58% ICS use.
Daily measures for five 30-d periods over 2 yr.
Home PEF/FEV1. Part of same cohort as
Maestrelli et al. (2011) above.
Two monitors in city
Mean (SD): 1.4 (1.1)
Max: 4.9
24-h avg
0, 1, 2, 3, 0-1
avg, 0-3 avg
Quantitative effect estimates
NR. Figure shows negative
but imprecise associations for
PEF and FEV1 with wide 95%
CIs.
Copollutant model with CO
CO association with PEF not FEV1
robust to SO2 adjustment. No
association for PM10 or NO2. PM2.5
not examined.
SO2 moderately correlated with CO,
PM10, and NO2. Spearman r= 0.50,
0.51, 0.54.
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Table 5-5 (Continued): Recent epidemiologic studies of lung function in adults with asthma.
Study Population
and Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tWiwatanadate and Liwsrisakun (2011)
Chiang Mai, Thailand, 2005-2006
N = 121, ages 13-78 yr. 48% moderate/severe
persistent asthma.
Daily measures for 10 mo. Home PEF.
Recruited from allergy clinics.
Monitor within 10 km
of home
Mean (SD): 1.7 (0.62)
90th percentile: 2.4
Max: 3.9
24-h avg
4
NR
Only multipollutant models analyzed
SO2 increment and units of PEF NR.
with PM2.5 and NO2
Evening PEF: 0.90 (0.34, 1.5)
Average PEF: 0.48 (0, 0.96)
No associations with PM2.5, PM10,
CO, Os.
SO2 weakly correlated with NO2,
PM2.5. r= 0.23, -0.07.
CI = confidence interval; CO = carbon monoxide; FE\A| = forced expiratory volume in 1 sec; ICS = inhaled corticosteroid; N = sample size; N02 = nitrogen dioxide; NR = not reported;
03 = ozone; PEF = peak expiratory flow; PM2.5 = particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm; PM10 = particulate matter with a nominal
aerodynamic diameter less than or equal to 10 |jm; r = correlation coefficient; SD = standard deviation; S02 = sulfur dioxide.
aEffect estimates are standardized to a 10-ppb increase in 24-h avg S02.
fStudies published since the 2008 Integrated Science Assessment for Sulfur Oxides.
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For children in El Paso, TX, Greenwald et al. (2013) measured SO2 at schools, which
may better represent some component of exposure than a monitor not sited in a subject's
microenvironment. For children attending the school near a major road, a 10-ppb increase
in lag 0-3 avg SO2 was associated with a -31% change (95% CI: -52, -2.0) in FEVi.
This is the largest effect estimate among children or adults with asthma, but a 10-ppb
increase in 4-day avg SO2 is unlikely in the area [school mean 0.84 (SD: 0.54) ppb].
Results are inconsistent for 24-h avg SO2 assigned from monitors up to 2.3-50 km from
children's homes or schools (Amadeo et al.. 2015; lerodiakonou et al.. 2015; Dales et al..
2009; Liu et al.. 2009b; O'Connor et al.. 2008). Lung function decreased with increases in
SO2 concentrations at a monitor located a median distance of 2.3 km from children's
homes (O'Connor et al.. 2008) but not a monitor within 50 km of children's ZIP code
centroid (lerodiakonou et al. 2015) (Table 5-6). Studies did not describe the adequacy of
monitors at these distances to represent temporal variation in SO2 exposure. No
association was observed with the change in PEF after a 6-minute exercise (Amadeo et
al.. 2015). but this protocol does not mimic controlled human exposure studies because
PEF was examined in relation to 13-day avg SO2.
In children with asthma, associations with lung function were mixed for temporally
resolved SO2 metrics. However, the extent to which concentrations at monitors up to
4.8-10 km from homes represent children's 1- to 12-hour exposures is not known.
Previous studies observed an association with 1-h max SO2 (Delfino et al.. 2003b) but not
8-h max or 3-h avg (8-11 a.m.) SO2 (Delfino et al.. 2003a; Mortimer et al.. 2002). Recent
results also are mixed. Morning and bedtime FEVi were not associated with 8-hour or
12-hour overnight (12 a.m. or 8 p.m.-8 a.m.) or 12-hour daytime (8 a.m.-8 p.m.) avg
SO2 concentrations, but the diurnal change in FEVi decreased with an increase in 12-hour
daytime avg SO2 (Dales et al.. 2009) (Table 5-6). Previous studies associated lung
function decrements with lag 0 day SO2 concentrations (Delfino et al.. 2003b; Peters et
al.. 1996a). Recent studies point to associations with 3-to 5-day avg concentrations
(Greenwald et al.. 2013; Liu et al.. 2009b; O'Connor et al.. 2008). and effect estimates are
larger than those for lag 0 or 1 (Table 5-6). There is limited support from a controlled
human exposure study for lung function decreasing after exposure on 2 days. Repeated
SO2 exposures enhance allergic inflammation in rodents, and allergic
inflammation-mediated lung function decrements could explain associations with
multiday SO2 concentrations. Most studies did not report the prevalence of atopy, but a
U.S. multicity study observed an association in a population with 100% atopy and asthma
(O'Connor et al.. 2008). The results agree with previous findings in children with asthma
plus atopy (Segala et al.. 1998).
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Table 5-6 Recent epidemiologic studies of lung function in children with asthma.
Study
Population and Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tGreenwald et al. (2013)
El Paso, TX, Mar-Jun 2010
N = 38, mean age 10 yr. 47% daily asthma
medication use.
Weekly measures for 13 wk. Supervised
spirometry. Recruited from schools.
Monitor at school
A: residential area
B: 91 m from major
road
Mean (SD): 1.2 (0.44)
and 0.84 (0.54)
Upper percentiles NR.
24-h avg
0-3 avg
Percent change in FEVi
A: 15 (-60, 210)
B: -31 (-52, -2.0)
No copollutant model
Association with BC, NO2, BTEX,
cleaning product VOCs (a-pinene,
dichlorobenzene, d-limonene) at
school B. No association with PM2.5.
SO2 weakly correlated with BC, NO2,
BTEX, cleaning product VOCs.
Pearson r= -0.14, -0.22, -0.07, 0.14
tDales et al. (2009)
Windsor, ON, Oct-Dec 2005
N = 182, ages 9-14 yr. 37% ICS use,
35% beta-agonist use.
Daily measures for 4 wk. Home FEV1. Recruited Median: 4.5
from schools. Mean 1.6 and 2.2 h/d spent
outdoors for two study groups.
Two monitors
averaged
99% homes within
10 km of sites.
95th percentile: 16
12-h avg Percent change in FEV1
8 a.m.-8 p.m. Bedtime: 0 (-0.92, 0.93)
Diurnal: -1.41 (-2.73, -0.08)
8 p.m.-8 a.m. Bedtime: -0.17 (-0.98, 0.65)
8-h avg
12 a.m.-8 a.m.
Morning: 0.63 (-0.28, 1.55)
Copollutant model results in figure.
SO2 association with diurnal change
in FEV1 persists with adjustment for
PM2.5, NO2, or O3. NO2 and PM2.5
associations persist with adjustment
' for SO2. No association with O3.
SO2 moderately correlated with PM2.5,
weakly correlated with NO2. Pearson
¦r= 0.43, 0.31.
24-h avg
Bedtime: -0.14 (-1.03, 0.76)
tLiuetal. (2009b). Liu (2013)
Windsor, ON, Oct-Dec 2005
N = 182, ages 9-14 yr. 37% ICS use,
35% beta-agonist use.
Weekly measures for 4 wk. Supervised
spirometry. Same cohort as Dales et al. (2009)
above.
Two monitors
averaged
99% homes within
10 km of sites.
Median: 4.5
95th percentile: 16
24-h avg
0
0-2 avg
Percent change
FEV1: -0.46 (-2.0, 1.1)
FEF25-75%: -1.5 (-4.7, 2.0)
Change in percent predicted
FEV1: -2.0 (-4.6, 0.74)
FEF25-75%: -5.7 (-11, -2.2)
Copollutant model, lag 0-2 avg,
FEF25 -75%
with PM2.5: 7.2 (-2.8, 18)
with NO2: -2.4 (-8.7, 4.3)
with Os: -5.4 (-11, -0.19)
NO2 and PM2.5 associations persist
with adjustment for SO2. No
association with O3.
SO2 moderately correlated with PM2.5,
weakly correlated with NO2 and O3.
Spearman r= 0.56, 0.18, -0.02.
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Table 5-6 (Continued): Recent epidemiologic studies of lung function in children with asthma.
Study
Population and Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tO'Connor et al. (2008)
Inner-City Asthma Study cohort: Boston, MA;
Bronx, NY; New York, NY; Chicago, IL; Dallas,
TX; Tucson, AZ; Seattle, WA; 1998-2001
N = 861, ages 5-12 yr. 100% persistent
asthma. 100% atopy.
Daily measures for four 2-wk periods. Home
FEV-i/PEF. Recruited from intervention study.
Monitors averaged
close to home and not
near industry.
Median 2.3 km to site.
Quantitative SO2 data
NR.
24-h avg Change in percent predicted
1-5 avg FEV1: -1.29 (-2.04, -0.54)
PEF: -1.73 (-2.49, -0.96)
No association for lag 1.
No copollutant model
Associations observed with PM2.5,
NO2. Associations with CO and O3
imprecise with wide 95% CIs.
SO2 weakly correlated with PM2.5,
moderately correlated with NO2.
r= 0.37, 0.59.
tAmadeo et al. (2015)
Monitors in city
24-h avg
Change in prerun PEF (L/min)
No copollutant model
Pointe-a-Pitre, Guadeloupe, 2008-2009
Number and distance
0-13 avg
93 (-28, 214)
No association observed with PM10,
N = 71, ages 8-13 yr.
NR
Percent change post 6-min
NO2, or O3. PM2 5 not examined.
Cross-sectional. Supervised spirometry.
Mean (SD): 1.8 (1.4)
run
Copollutant correlations NR.
Recruited from schools.
Max: 4.9
-1.6 (-36, 33)
tlerodiakonou etal. (2015)
Childhood Asthma Management Program
cohort: Boston, MA; Baltimore, MD; St. Louis,
MO; Denver, CO; Albuquerque, NM; San Diego,
CA; Toronto, ON, 1993-1999
N = 1,003, ages, 5-12 yr. 100% mild/moderate
asthma. 30% ICS use. 30% mast cell inhibitor
use.
14 measures over 4 yr. Supervised spirometry.
Recruited from clinics. Multiple
comparisons—many pollutants, lags, exposure
durations, medication use analyzed.
Nearest monitor
within 50 km of ZIP
code centroid.
Medians across cities:
2-6
90th percentiles
across cities: 5-24
24-h avg Change in percent predicted
0 Prebronchodilator FEV1
All subjects 0.25 (-0.13, 0.63)
ICS: 0.38 (-0.30, 1.1)
Post-bronchodilator FEV1
ICS: 0 (-0.73, 0.75)
Change in methacholine that
induces a 20% drop in FEV1
Mast cell inhibitor:
-13% (-25, 1.3)
No copollutant model
Association with CO, not O3 or NO2.
PM2.5 not examined.
SO2 weakly to moderately correlated
with CO, O3, and NO2 across cities.
Spearman r= 0.19-0.34, -0.41 to
-0.05, 0.15-0.54.
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Table 5-6 (Continued): Recent epidemiologic studies of lung function in children with asthma.
Study
SO2 Averaging
SO2 Exposure
Time and Lag
Effect Estimate (95% CI)
Population and Methodological Details
Estimates (ppb)
Day
Single-Pollutant Modela
Copollutant Examination3
tWiwatanadate and Trakultivakorn (2010)
Monitor within 25 km
24-h avg
Change in PEF (L/min)
Copollutant model, lag 4, daily
Chiang Mai, Thailand, 2005-2006
of home
average PEF.
N = 31, ages 4-11 yr. 100% with symptoms in
Mean (SD): 1.7 (0.62)
0
Evening PEF
with O3, lag 5: -16 (-31, -1.1)
previous yr. 52% mild intermittent asthma
90th percentile: 2.4
4
-8.1 (-25, 9.2)
-21 (-38, -4.1)
O3 association persists with
Daily measures for 1 yr. Home PEF. Recruited
from allergy clinic. Multiple comparisons—many
Max: 3.9 ppb
adjustment for SO2. No association
with PM2.5, CO, NO2.
pollutants, lags, lung function parameters
0
4
Daily average PEF
-0.3 (-15, 15)
-18 (-32, -2.8)
SO2 weakly correlated with O3, PM2.5,
analyzed.
CO, NO2. r= -0.04, -0.07, 0.38, 0.23
BC = black carbon; BTEX = benzene, toluene, ethylbenzene, xylene; CI = confidence interval; CO = carbon monoxide; FEF25-75% = forced expiratory flow at 25-75% of forced vital
capacity; FE\A = forced expiratory volume in 1 sec; ICS = inhaled corticosteroid; L/min = liters per min; N = sample size; N02 = nitrogen dioxide; NR = not reported; 03 = ozone;
PEF = peak expiratory flow; PM25 = particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm; PM10 = particulate matter with a nominal aerodynamic
diameter less than or equal to 10 |jm; r = correlation coefficient; SD = standard deviation; S02 = sulfur dioxide; VOC = volatile organic compound.
aEffect estimates are standardized to a 10-ppb increase in 8-h to 24-h avg S02.
fStudies published since the 2008 Integrated Science Assessment for Sulfur Oxides.
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Where SO2 was associated with lung function decrements in children with asthma,
associations also were observed with PM25, PM10, sulfate, BC, OC, TSP, NO2, or various
VOCs (Greenwald et al.. 2013; Dales et al.. 2009; Liu et al. 2009b; O'Connor et al..
2008; Delfino et al.. 2003b; Peters et al.. 1996a). These copollutants were often
moderately to highly correlated with SO2 (r = 0.56-0.9), particularly in previous studies.
SO2 averaging times varied across studies, making it difficult to assess whether higher
correlations are due to higher air pollution levels in the past. Copollutant confounding
and interactions are poorly studied, and unstudied for children living near a coal-fired
power plant (Peters et al.. 1996a). O3 may not influence the associations observed with
SO2. SO2 and O3 measurements at central site monitors were not correlated (r = -0.02),
and SO2 associations persisted with adjustment for O3 (Dales et al.. 2009; Liu et al..
2009b). A recent study adds information on SO2 results adjusted for correlated
copollutants. Among children with asthma in Windsor, ON, the SO2 association persisted
with adjustment for PM2 5 or NO2 for 12-h avg SO2 (Dales et al.. 2009) but not 24-h avg
SO2 (Liu. 2013; Liu et al.. 2009b) (Table 5-6). Associations for PM2 5 were robust to SO2
adjustment, but inference about confounding is weak due to the moderate SO2-PM2 5
correlation (r = 0.56) and the potential differential exposure error for SO2 and PM2 5
measurements, which were made up to 10 km from subjects' homes. Weak inference also
applies to results in a Los Angeles, CA cohort showing an imprecise association for SO2
after adjustment for benzene [-34 L/minute change in PEF (95% CI: -120, 52) per
40-ppb increase in 1-h max SO2] (Delfino et al.. 2003b). SO2 was highly correlated with
benzene (r = 0.70), and pollutants were measured up to 4.8 km from home or school.
Summary of Lung Function Changes in Populations with Asthma
Controlled human exposure studies provide strong evidence for S02-induced lung
function decrements in adults with asthma under increased ventilation conditions.
Short-term exposures for 5-10 minutes to 0.2-0.3 ppm SO2 resulted in 5-30% of
exercising individuals with asthma experiencing moderate or greater decrements (defined
in terms of a >15% decrease in FEVi or >100% increase in sRaw; Table 5-2). Exposures
for 5-10-minutes to SO2 at concentrations >0.4 ppm results in moderate or greater
decrements in lung function in 20-60% of exercising individuals with asthma. A group
of responders (defined as having >15% decrease in FEVi after exposure to 0.6 or 1.0 ppm
SO2) showed statistically significant decrements in FEV1 following exposure for
5-10 minutes to 0.3 ppm SO2 (Table 5-3). Less evidence is available from controlled
human exposure studies to assess S02-induced lung function decrements in children with
asthma. However, school-aged children, particularly boys and perhaps obese children,
should be expected to experience greater responsiveness (i.e., larger decrements in lung
function) following exposure to SO2 than normal-weight adolescents and adults.
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For both adults and children with asthma, epidemiologic evidence is inconsistent for lung
function decrements associated with ambient SO2 concentrations (Table 5-5 and
Table 5-6). but most results indicate associations in populations with asthma plus atopy.
In the few controlled human exposure and epidemiologic studies, findings of increased
airway responsiveness could not be attributed to exposure to SO2 alone versus a
copollutant or mixture. A limitation across epidemiologic studies is the uncertainty in the
SO2 exposure estimates. A recent study observed an association with SO2 measured at
children's schools, but others used monitors located 2.3-50 km from subjects' homes or
schools. It is unclear whether the SO2 concentrations at central site monitors adequately
represent the variation in personal exposure, especially if peak exposures are important as
indicated by controlled human exposure studies. The influence of copollutants on
epidemiologic results remains largely uncharacterized, including associations in
populations with asthma plus atopy and populations living near SO2 sources. SCh-related
lung function decrements in adults and children with asthma are inconsistently observed
after adjustment for PM2 5, PM10, or NO2, but the implications of these results are unclear
because of uncertainty in the exposure estimates and potential differential exposure error.
Respiratory Symptoms in Populations with Asthma
The 2008 SOx ISA (U.S. EPA. 2008d) reported strong evidence for the effects of SO2
exposure on respiratory symptoms in controlled human exposure studies in individuals
with asthma under increased ventilation conditions. No new controlled human exposure
studies have been reported since the previous ISA. In contrast, previous and recent
epidemiologic evidence for S02-associated increases in respiratory symptoms is weak in
adults with asthma. However, epidemiologic evidence supports associations in children
with asthma, and recent studies add evidence for estimates of SO2 exposure at school
and/or home. Overall, the influence of copollutants remains largely unexamined.
Controlled Human Exposure Studies
As reviewed in the 2008 ISA for Sulfur Oxides and the 1986 Supplement to the Second
Addendum (U.S. EPA. 2008d. 1994). controlled human exposure studies demonstrate
increases in incidence or severity of respiratory symptoms (i.e., cough, chest tightness,
throat irritation) in individuals with asthma exposed to SO2 concentrations between 0.2
and 0.6 ppm for 5-10 minutes during exercise (Table 5-2 and Table 5-7). Statistically
significant increases are observed at SO2 concentrations >0.4 ppm.
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Table 5-7 Study-specific details from controlled human exposure studies of
respiratory symptoms.
Study
Disease Status;
n; Sex; (Agea)
Exposure Details
(Concentration; Duration)
Time of Symptom
Assessment
Gona et al. (1995)
Asthma;
n = 14; 12 M, 2 F;
(27 ±11 yr)
0, 0.5, or 1.0 ppm SO2 with light, medium,
and heavy exercise (average ventilation 30,
36, and 43 L/min) for 10 min
Before, during, and
immediately after
exposure
Gona et al. (1996)
Asthma;
n = 10; 2 M, 8 F;
(30.3 ± 9.2 yr)
0 or 0.75 ppm SO2 with exercise (29 L/min)
for up to 24 h with or w/o pretreatment with
salmeterol (long-acting B2-agonist)
Before and
immediately after
exposure
Gona et al. (2001)
Asthma;
n = 11; 2 M, 9 F;
(30.8 ±11.3 yr)
0 or 0.75 ppm SO2 for 10 min with exercise
(35 L/min) with or w/o pretreatment to
montelukast sodium (10 mg/d for 3 d)
Before, immediately
after, and 1 and 2 h
after exposure
Horstman et al. (1988)
Asthma;
n = 12 M;
(28.6 ± 5.5 yr)
0 or 1.0 ppm SO2 for 0, 0.5, 1.0, 2.0, and
5.0 min with exercise (treadmill, 40 L/min)
Before and
immediately after
exposure
Maanussen et al.
(1990)
Asthma;
n = 46; 21 M, 25 F;
(28 ± 14 yr)
0 or 0.5 ppm SO2 for 20 min. 10 min rest
followed by 10 min isocapnic
hyperventilation (30 L/min)
Before exposure
and immediately
after
hyperventilation
Kehrl et al. (1987)
Asthma;
n = 10 M;
(26.8 ± 4.4 yr)
0 or 1 ppm SO2 for 1 h with exercise
(3x10 min, 41 L/min, treadmill)
Before and during
exposure/exercise
Koenia et al. (1980)
Asthma;
n = 9; 7 M, 2 F;
(15.7 ± 1.1 yr)
0 or 1 ppm SO2 with 1 mg/m3 of NaCI
droplet aerosol, 1 mg/m3 NaCI droplet
aerosol for 60 min exposure with
mouthpiece at rest
Before, during, and
immediately after
exposure
Koenia et al. (1981)
Asthma;
n = 8; 6 M, 2 F;
(14-18 yr)
0 or 1 ppm SO2 with 1 mg/m3 of NaCI
droplet aerosol, 1 mg/m3 NaCI droplet
aerosol for 30 min exposure via mouthpiece
at rest followed by 10 min exercise on a
treadmill (sixfold increase in Ve)
Before, during, and
immediately after
exposure
Koenia et al. (1983) Phase 1:
Phase 1:
Before and
Asthma with EIB;
1 g/m3 of NaCI droplet aerosol, 1 ppm SO2,
immediately after
n = 9; 6 M, 3 F;
1 mg/m3 NaCI, 0.5 ppm SO2 + 1 mg/m3
exposure
(12-16 yr)
NaCI for 30 min exposure via mouthpiece at
Phase 2:
rest followed by 10 min exercise on
Asthma with EIB;
treadmill (five-to sixfold increase in Ve)
n = 7 (sex NR);
Phase 2:
(12-16 yr)
0.5 ppm SO2 + 1 mg/m3 NaCI via a face
mask with no nose clip with exercise
conditions the same as Koenia et al. (1981)
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Table 5-7 (Continued): Study specific details from controlled human exposure
studies of respiratory symptoms.
Study
Disease Status;
n; Sex; (Agea)
Exposure Details
(Concentration; Duration)
Time of Symptom
Assessment
Koenia et al.
CD
CO
Allergy with EIB;
n = 10; 3 M, 7 F;
(13-17 yr)
0 or 0.75 ppm SO2 (mouthpiece) with
exercise (33.7 L/min) for 10 min and 20 min
prior pretreatment (0 or 180 |jg albuterol)
Before and
immediately after
pretreatment and
exposure
Koenia et al.
(1990)
Asthma with EIB;
n = 13; 8 M, 5 F
(14.3 ± 1.8 yr)
0.1 ppm SO2 for 15 min preceded by air or
0.12 ppm O3 for 45 min during intermittent
exercise (2><15 min, 30 L/min, treadmill),
no control, air exposure
Before and
immediately after
exposure
Koenia et al. (1992) Asthma; 1 ppm SO2 for 10 min with exercise Before and
n = 8; 2 M, 6 F; (VE = 13.4-31.3 L/min) with or w/o immediately after
(27.5 ± 9.6 yr) pretreatment to theophylline exposure
Linn et al. (1983b) Asthma; 0, 0.2, 0.4, or 0.6 ppm SO2 with low Before and
n = 23; 13 M, 10 F; humidity or high humidity for 10 min with immediately after
(23.3 ± 4.4 yr) exercise (bicycle, 5 min 50 L/min) exposure
0 or 0.6 ppm SO2 with warm air or cold air
with exercise (bicycle, 50 L/min, ~5 min)
Linn et al. (1983a)
Asthma;
n = 23; 15 M, 8 F
(23 ± 4 yr)
0 or 0.75 ppm SO2 with unencumbered
breathing and mouth only breathing with
exercise (40 L/m, 10 min, bicycle)
Before and
immediately after
exposure
Linn et al. (1984a)
Asthma;
n = 14; 12 M, 2 F
(24.1 ± 4.7 yr)
0, 0.3, or 0.6 ppm SO2 at 21°, 7°, and -6°C, Before, during,
rH 80% with exercise (bicycle, 50 L/min, immediately after,
~5 min) and a week after
exposure
Linn et al. (1984c)
Asthma;
n = 24; 13 M, 11 F;
(24.0 ± 4.3 yr)
0, 0.3, or 0.6 ppm SO2 at 21°, 7 and -6°C
and 80% rH with exercise (5 min, 50 L/min)
Before, immediately
after, and 24 h after
exposure
Linn et al. (1984b)
Asthma;
Phase 1 (Pilot)
n = 8; 4 M, 4 F;
(24.5 ± 3.9 yr)
Phase 2
n = 24; 19 M, 5
(24.0 ± 4.3 yr)
Phase 1:
0, 0.2, 0.4, or 0.6 ppm SO2 at 5°C, 50, and
85% rH with exercise (5 min, 50 L/min)
Phase 2:
0 and 0.6 ppm SO2 at 5° and 22°C, 85% rH
with exercise (5 min, 50 L/min)
Phase 1:
before and
immediately after
exposure
Phase 2:
before, immediately
after, 1 d after, and
1 wk after exposure
Linn et al. (1985b)
Asthma;
n = 22; 13 M, 9 F;
(23.5 ± 4.0 yr)
0 or 0.6 ppm SO2 at 21 and 38°C, 20 and
80% rH with exercise (~5 min, 50 L/min)
Before, immediately
after, and 24 h after
exposure
Linn et al. (1985a)
Asthma with COPD;
n = 24; 15 M, 9 F;
(60 yr;
Range: 49-68 yr)
0, 0.4, or 0.8 ppm SO2 for 1 h with exercise Before, during,
(2><15 min, bicycle, 18 L/min) immediately after,
24 h after, and 7 d
after exposure
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Table 5-7 (Continued): Study specific details from controlled human exposure
studies of respiratory symptoms.
Study
Disease Status;
n; Sex; (Agea)
Exposure Details
(Concentration; Duration)
Time of Symptom
Assessment
Linnetal. (1987)
Healthy;
n = 24; 15 M, 9 F;
(18-37 yr)
Atopic (sensitive to
common airborne
allergens but no
asthma);
n = 21; 12 M, 9 F;
(18-35 yr)
Minimal or mild
asthma; n = 16; 10 M,
6 F;
(20-33 yr)
Moderate or severe
asthma;
n = 24; 10 M, 14 F;
(18-35 yr)
0, 0.2, 0.4, or 0.6 ppm SO2 for 1 h with
exercise (3 * 10-min, bicycle, -40 L/min)
Before and during
exposure (after first
exercise and after
last exercise)
Linnetal. (1988)
Asthma;
n = 20; 13 M, 7 F;
(28 ± 5 yr)
Three pretreatment groups
(1) metaproterenol sulfate, (2) placebo,
(3) no treatment
0, 0.3, and 0.6 ppm SO2 for 10 min with
exercise (bike, 50 L/min)
Before, immediately
after, 10 min,
30 min, 60 min,
120 min, 24 h, and
1 wk after exposure
Linnetal. (1990)
Asthma;
n = 21; 6 M, 15 F;
(34.8 ± 8.9 yr)
0, 0.3, or 0.6 ppm SO210 min with exercise
(50 L/min)
(1) low medication use, (2) normal, (3) high
usual medication supplemented by inhaled
metaproterenol before exposure
Before exposure,
after pretreatment,
immediately after,
30 min after, and
60 min after
exposure
Mvers et al. (1986a)
Asthma;
n = 10; 7 M, 3 F;
(27.6 ± 5.5 yr)
Three pretreatment groups
(1) 200 mg cromolyn, (2) 20 mg cromolyn,
(3) placebo
Doubling concentrations of SO2 during
sequential 3 min exposures, from 0.25 to
8 ppm
Before and after
each 3-min
exposure to an
increasing SO2
concentration
Sheppard et al. (1983)
Asthma;
n = 8; 4 M, 4 F;
(26.6 ± 4.3 yr)
0.5 ppm SO2 for 3 min eucapnic hyperpnea
Before and
immediately after
exposure
Trenaa et al. (1999)
Asthma;
n = 47; 14 M, 33 F;
(21.1 yr;
Range: 18-39 yr)
0.5 ppm SO2 for 10 min with moderate
exercise
Before and
immediately after
exposure
Trenaa et al. (2001)
Asthma;
n = 17; 5 M, 12 F;
(27.4 ± 6.3 yr)
0.5 ppm SO2 for 10 min with moderate
exercise (treadmill)
Before and
immediately after
exposure
COPD = chronic obstructive pulmonary disease; EIB = exercise-induced bronchospasm; F = female; M = male; n = sample size;
NaCI = sodium chloride; NR = not reported; 03 = ozone; ppm = parts per million; rH = relative humidity; SD = standard deviation;
S02 = sulfur dioxide; VE = minute volume.
aRange or Mean ± SD.
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Linn et al. (1983b) reported the severity of respiratory symptoms following 5-minute
exposures to 0, 0.2, 0.4, and 0.6 ppm SO2 in heavily exercising individuals with mild to
moderate asthma. Total symptom score changes were significant (0.01 0.4 ppm SO2. Subsequently, a similar study with a
slightly lower level of exercise demonstrated that 43% of subjects with asthma
experienced increases in respiratory symptoms after a 15-minute exposure to 0.6 ppm
SO2 (Linn et al.. 1987). Smith (1993) provided additional support for increasing
respiratory symptoms at concentrations as low as 0.4 ppm SO2.
Additional studies examining concentrations of >0.5 ppm SO2 demonstrated SC>2-induced
increases in respiratory symptoms. Total and lower respiratory symptom scores were
significantly increased with increasing SO2 concentrations (0, 0.5, and 1.0 ppm SO2)
following 10-minute exposures with varying levels of exercise (Gong et al.. 1995).
Trenga et al. (1999) confirmed these results, observing a significant correlation between
FEVi decrements and increases in respiratory symptoms following 10-minute exposures
to 0.5 ppm SO2 via mouthpiece. Respiratory symptoms have also been observed
following exposure durations as low as 3 minutes to 0.5 ppm SO2 via mouthpiece during
eucapnic hyperpnea (Ve = 0 L/minute), in which seven out of eight individuals with
asthma developed respiratory symptoms (Balmes etal.. 1987).
As with lung function, increased respiratory symptoms in response to short-term
exposure to SO2 in individuals with asthma is dependent on exercise. Linn et al. (1983b)
reported significant changes in total symptom scores after 0.2 ppm SO2 exposure in
heavily exercising individuals with asthma. In contrast, Tunnicliffe et al. (2003) found no
association between respiratory symptoms (i.e., throat irritation, cough, wheeze) and
1-hour exposures to 0.2 ppm SO2 in adults with asthma at rest.
Epidemiologic Studies
Compared with controlled human exposure studies, epidemiologic evidence for
SCh-associated increases in symptoms is variable, being supportive in children with
asthma but weak in adults with asthma. A recent study not restricted to a certain lifestage
does not support an association with asthma medication use but is limited by analysis of
beta-agonist levels in wastewater rather than use ascertained for individual subjects and
only reporting the lack of statistically significant associations (Fattore et al.. 2016)
(Table 5-8). The evidence base specifically in children with asthma is larger and more
informative, providing results for home and/or school SO2 exposure estimates and
temporally resolved SO2 metrics. Also, while they do not settle questions, studies in
children with asthma aim to assess copollutant confounding and interactions. Although
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the evidence overall is less consistent in recent than previous studies, the aforementioned
strengths are features of many recent studies of children with asthma.
Adults. SO2 concentrations were lower in recent than previous studies (0.87-2.7 ppb vs.
1.6-90 ppb for means), but this does not appear to explain the weak evidence because
previous results also are inconsistent [Supplemental Figure 5S-1 (U.S. EPA. 2016gYI. All
studies have uncertainty in the SO2 exposure estimates assigned from a single central site
monitor or averaged across multiple monitors. No study indicated whether measurements
at the monitors adequately represented the spatiotemporal variability in ambient SO2
concentrations in the study area or the temporal variation in people's exposures.
All epidemiologic studies of adults examined 24-h avg SO2 concentrations, longer than
the 5-10-minute exposures implicated in controlled human exposure studies (Table 5-2).
Similar to previous studies, recent epidemiologic evidence does not indicate associations
for respiratory symptoms with same-day (lag 0) SO2 concentrations (Anvenda et al..
2016; Maestrelli et al. 2011). Atopy was prevalent in Maestrelli et al. (2011) (90%);
previous findings supported an association in adults with atopy plus asthma (Boezen et
al.. 2005). A recent study linked an increase in SO2 concentration to an increase in
nighttime asthma symptoms with a 5-day lag (Wiwatanadate and Liwsrisakun. 2011). but
inference is weak because results were inconsistent among the many lags, pollutants, and
health effects examined. Also, SO2 exposures were assessed from a monitor up to 10 km
from subjects' homes. There is some consistency for SO2 concentrations lagged 2 or
5 days or averaged over 3 or 5 days, including recent results (Anvenda et al.. 2016)
[Supplemental Figure 5S-1 (U.S. EPA. 2016g)l. In these studies, symptoms were also
associated with moderately to highly correlated PM metrics (r = 0.60-0.9). Whether the
magnitude of copollutant correlations influences the consistency of association for SO2
with respiratory symptoms in adults with asthma cannot be determined in this small
evidence base. As examined only in a recent study, SO2 associations persisted with
adjustment for PAH or NO2 (Anvenda et al.. 2016). However, uncertainty in the
exposures estimated from a single central site monitor and a different site for PAH limits
inferences that can be drawn about an independent association for SO2. Controlled human
exposure studies show symptoms to resolve once exposure ends, but SC>2-induced allergic
inflammation could be a pathway by which SO2 exposure induces symptoms after several
days or over multiple days.
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Table 5-8 Recent epidemiologic studies of respiratory symptoms in populations with asthma.
SO2 Averaging
Study Population and SO2 Exposure Time and Effect Estimate (95% CI)
Methodological Details Estimates (ppb) Lag Day Single-Pollutant Modela Copollutant Examination3
Adults With Asthma
tMaestrelli et al. (2011)
Padua, Italy, 2004-2005
N = 32, mean (SD) age 40 (7.5 yr). 81%
persistent asthma. 69% ICS use. 90% atopy.
Six measures over 2 yr. Symptoms assessed in
clinic. Recruited from database of beta-agonist
users (>6 times per yr for 3 yr).
Two monitors in city 24-h avg
Medians across 0
seasons: 0.87-2.7
75th percentiles
across seasons:
1.3-4.1
Asthma control score
Increase = better control
All subjects: 0.77 (-1.1, 2.6)
Nonsmokers: 0.10 (-2.2, 2.4)
n = 22
No copollutant model
Association observed with CO and
personal PM10. No association with
personal or central site PM2.5. No
association with central site NO2, O3.
Copollutant correlations NR.
tWiwatanadate and Liwsrisakun (2011)
Chiang Mai, Thailand, 2005-2006
N = 121, ages 13-78 yr. 48% moderate/severe
persistent asthma.
Daily diary for 10 mo. Recruited from allergy
clinics. Multiple comparisons—many pollutants,
lags, health endpoints analyzed.
Monitor within 10 km
of home
Mean (SD): 1.7 (0.62)
90th percentile: 2.4
Max: 3.9
24-h avg SO2 increment NR. Results
reported only for statistically
significant lags.
2 Daytime symptoms
OR: 0.90 (0.81, 0.99)
5 Nighttime symptoms
OR: 1.16 (1.04, 1.29)
Copollutant model with NO2
SO2 and NO2 association reported
not statistically significant.
Quantitative results NR. Association
observed with PM10 but no
copollutant model. PM2.5 not
examined.
SO2 weakly correlated with NO2,
PM10. r= 0.23 for both.
tAnvenda et al. (2016)
One monitor in city
24-h avg
Cough
Copollutant model, lag 2
Kanazawa, Japan, Jan-June 2011
Mean (SD): 1.6 (1.3)
with PAH: 1.98 (1.31, 3.05)
N = 83, ages 23-84 yr. 54% atopy.
Max: 7.3
0
0.67 (0.34,
1.31)
with NO2: 1.94 (1.16, 3.58)
Daily diary for mean 153 d. Recruited from
hospital outpatients.
2
2.19 (1.34,
3.54)
Adjustment for SO2 does not alter
PAH association but attenuates NO2
association.
0-2 avg
2.53 (1.05,
6.08)
SO2 moderately correlated with PAH,
NO2. Spearman r= 0.60, 0.56.
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Table 5-8 (Continued): Recent epidemiologic studies of respiratory symptoms in populations with asthma.
SO2 Averaging
Study Population and SO2 Exposure Time and Effect Estimate (95% CI)
Methodological Details Estimates (ppb) Lag Day Single-Pollutant Modela Copollutant Examination3
Children With Asthma
tSpira-Cohen et al. (2011). Spira-Cohen (2013)
Monitor at school
1-h max (a.m.)
Cough
Copollutant model for cough
Bronx, NY, 2002-2005
Concentrations NR
0
RR: 1.60 (1.20, 2.12)
with school EC: 1.32 (0.93, 1.87)
N = 40, ages 10-12 yr. 44% with asthma ED
Most children walk to
Wheeze
No association with PM2.5. EC
visit or hospital admission in previous 12 mo.
school
RR: 1.81 (1.15, 2.84)
association robust to SO2 adjustment.
Daily diaries for 1 mo. Recruited from schools
Shortness of breath
School SO2 moderately correlated
by referrals from school nurses.
RR: 1.45 (0.90, 2.84)
with EC. r= 0.45.
tVelicka etal. (2015)
Five monitors and
24-h avg
Cough
No copollutant model
Ostrava, Czech Republic, Nov 2013-Feb 2014
dispersion model
0
OR: 0.92 (0.74, 1.17)
Associations observed with PM10 and
N = 147, ages 6-18 yr. 67% mild persistent
0.5 x 0.5 km
Breathing difficulty-wheeze
NO2. PM2.5 not examined.
asthma. 33% moderate persistent asthma. 79%
resolution
OR: 2.29 (1.55, 3.39)
Copollutant correlations NR.
atopy. 97% regular asthma medication use.
Weighted avg by time
Reliever inhaler use
Daily diaries for 4 mo. Recruited from clinics.
at home and school
OR: 1.84 (1.32, 2.56)
Median: 4.0
Restricted activities
75th percentile: 12
OR: 1.25 (1.00, 1.62)
tDales etal. (2009)
Two monitors
24-h avg
OR for SO2 S8.8 vs. <2.3 ppb
No copollutant model
Windsor, ON, Oct-Dec 2005
averaged
Chest tightness
Associations with PM2.5, NO2, O3
N = 182, ages 9-14 yr. 37% ICS use. 35%
99% homes within
1.30 (1.06, 1.58)
reported not statistically significant.
beta-agonist use.
10 km of sites
ORs for difficulty breathing,
Quantitative results NR.
Daily diaries for 4 wk. Recruited from schools.
Median: 4.5
cough, and wheeze reported
Mean 1.6 and 2.2 h/d spent outdoors.
95th percentile: 16
not statistically significant.
tO'Connor et al. (2008)
Monitors averaged
24-h avg
Wheeze-cough
No copollutant model
Inner-City Asthma Study cohort: Boston, MA;
close to home and not
1-19 avg
RR: 1.05 (0.89, 1.23)
Associations observed with NO2 and
Bronx, NY; New York, NY; Chicago, IL; Dallas,
near industry
Nighttime asthma
CO. PM2.5 associated with missed
TX; Tucson, AZ; Seattle, WA; 1998-2001
Median 2.3 km to site
RR: 1.11 (0.91, 1.36)
school. SO2 moderately correlated
N = 861, ages 5-12 yr. 100% persistent
Quantitative SO2 data
Slow play
with NO2, weakly with CO and PM2.5.
asthma. 100% atopy.
NR.
RR: 1.06 (0.88, 1.27)
r = 0.59, 0.32, 0.37.
Daily diaries for four 2-wk periods. Recruited
Missed school
from intervention study.
RR: 1.10 (0.82, 1.49)
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Table 5-8 (Continued): Recent epidemiologic studies of respiratory symptoms in populations with asthma.
Study Population and
Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and
Lag Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tGent et al. (2009)
New Haven county, CT, 2000-2004
N = 149, ages 4-12 yr. 45% intermittent
asthma.
Daily diaries reported monthly for 1 yr.
Recruited from larger cohort, clinic, and school.
Monitor 0.9-30 km of 24-h avg
home o
Mean 10 km to site
Concentrations NR
NR
Only multipollutant model analyzed
with six PM2.5 component factors
Wheeze: 1.04 (0.92, 1.19)
SO2 moderately correlated with motor
vehicle factor. r= 0.45.
Children and Adults with Asthma
tFattore et al. (2016)
Milan, Italy, Sep-Dec2013
N = 84 days
Daily wastewater samples for 84 days analyzed
for levels of the beta-agonist salbutamol.
3 monitors averaged
Mean (SD): 2.2 (1.3)
Max: 5.9
24-h avg
Oto 10
(single-day)
Beta-agonist levels in
wastewater
No quantitative results. RRs
reported not statistically
significant.
No copollutant model
Associations observed with PM2.5 and
PM10.
SO2 moderately correlated with PM2.5
and PM10. Pearson r= 0.66, 0.65.
CI = confidence interval; CO = carbon monoxide; EC = elemental carbon; ED = emergency department; ICS = inhaled corticosteroids; N = sample size; N02 = nitrogen dioxide;
NR = not reported; 03 = ozone; OR = odds ratio; PAH = polycyclic aromatic hydrocarbon; PM2 5 = particulate matter with a nominal aerodynamic diameter less than or equal to 2.5
|jm; PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm; RR = relative risk; SD = standard deviation; S02 = sulfur dioxide.
aEffect estimates are standardized to a 10-ppb increase in 24-h avg S02 and 40-ppb increase in 1-h max S02.
fStudies published since the 2008 Integrated Science Assessment for Sulfur Oxides.
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Children. As a whole, epidemiologic evidence indicates associations between higher SO2
concentrations and increased respiratory symptoms in children with asthma, particularly
when examined as a composite index of multiple symptoms (Figure 5-2). Associations
also are observed for asthma medication use or activity restriction but not consistently for
wheeze or cough. Results vary in magnitude and precision (Figure 5-2). In some study
areas, the SO2 concentrations were much lower (Spira-C'ohen et al.. 2011; Delfino et al..
2003a; Delfino et al.. 2003b) or higher (Mortimer et al.. 2002) than the 10-ppb increment
used to standardize effect estimates. Although recent studies give inconsistent results
(Table 5-8). associations are observed with SO2 measured or modeled for school or
home, which may represent exposure better than measurements at central site monitors.
Recent studies reported lower SO2 concentrations than many previous studies (for
24-h avg, median ~ 4 ppb vs. means 8.3 and 90 ppb). It is unclear whether the
inconsistency is due to lower concentrations; previous studies observed associations in
locations with similar SO2 concentrations [median 24-h avg 2.2-7.4 ppb in Schildcrout et
al. (2006). mean 8-h max 4.6 ppb in Delfino et al. (2003a). Delfino et al. (2003b)l.
Spira-C'ohen et al. (2011) is notable not only for monitoring SO2 at schools but also for
examining 1-h max concentrations. In the population of children in Bronx, NY, increases
in SO2 were linked to increased odds of cough and wheeze but not shortness of breath
(Table 5-8). Previous U.S. studies also associated symptoms with temporally resolved
SO2 metrics [i.e., 1-h max, 8-h max, 3-h avg (8-11 a.m.)] but had more uncertainty in
exposures estimated from monitors up to 4.8 km from children's homes/schools (Delfino
et al.. 2003a; Delfino et al.. 2003b) or monitors averaged across the city (Mortimer et al..
2002). Spira-C'ohen et al. (2011) did not report SO2 concentrations to compare to
previous studies but reported that most children walked to school, improving the
relevance of 1-h max SO2 concentrations at school to children's peak exposures. Velicka
et al. (2015) also aimed to improve exposure assessment for children in Ostrava, Czech
Republic. A dispersion model and five monitors were used to estimate SO2
concentrations at 0.5 km resolution and calculate a time-weighted 24-h avg for each child
based on the school and home location. SO2 was associated with breathing difficulty-
wheeze, reliever inhaler use, and restricted activities, but not cough (Table 5-8).
The study population had a high prevalence of atopy (79%); thus, results agree with
Boezen et al. (1999) and Segala et al. (1998) but may have less uncertainty in exposure
estimates (Section 3.5).
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Study
Wheeze
Exposure Assessment
fSpira-Cohen et al. (2011)School
Cough
fSpira-Cohen et al. (2011)School
fVelicka et al. (2015) Modeled home/school
Romieu et al. (1996) Monitor within 5 km
Segala et al. (1998) Average of 11 monitors
Composite of symptoms
fVelicka et al. (2015) Modeled home/school
Delfino et al. (2003a)
Delfino et al. (2003b)
Romieu et al. (1996)
Boezen et al. (1999)
Mortimer et al. (2002)
Segala et al. (1998)
Peters et al. (1996)
Schildcrout et al. (2006)
Monitor within 4.8 km
Monitor within 4.8 km
Monitor within 5 km
1 monitor
Average of city monitors
Average of 11 monitors
1 monitor
Monitors within 80 km
fO'Connor et al. (2008) Monitors within median 2.3
km
Asthma Medication
¦fVelicka et al. (2015)
Segala et al. (1998)
Modeled home/school
Average of 11 monitors
-20
129 (55,229)
100
Percent increase (95% confidence interval)3
Note: f and Red = recent studies published since the 2008 Integrated Science Assessment for Sulfur Oxides, black = studies from
the 2008 Integrated Science Assessment for Sulfur Oxides.
aEffect estimates are standardized to a 10-ppb increase in 24-h avg sulfur dioxide concentration and a 40-ppb increase in 1-h max
concentrations.
Study details are presented in Table 5-8. Results from Gent et al. (2009) are not presented in the figure because they are based on
a multipollutant model. Corresponding quantitative results are reported in Supplemental Table 5S-3 (U.S. EPA. 2016i).
Figure 5-2 Associations between short-term average ambient sulfur dioxide
concentrations and respiratory symptoms and asthma medication
use in children with asthma.
1 Other recent studies largely do not provide evidence for SC>2-associated increases in
2 respiratory symptoms in children with asthma (Dales et al.. 2009; Gent et al.. 2009;
3 O'Connor et al.. 2008). But, they have more questionable implications due to (1) the large
4 distance between the SO2 monitor and children's homes (e.g., up to 10 km, median
5 2.3 km, mean 10 km); (2) a lack of quantitative results (Dales et al.. 2009); (3) analysis of
6 19-day avg SO2 concentrations, which are more subject to residual temporal confounding
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(O'Connor et al.. 2008); or (4) analysis of SO2 only as part of a multipollutant model with
six PM2 5 component source factors (Gent et al.. 2009).
For the associations observed between SO2 and respiratory symptoms in children with
asthma, including those with atopy, the influence of copollutants is poorly addressed.
Symptoms were not associated with personal or school PM2 5 but with other PM metrics:
PM10, EC, OC, BS, and TSP. Associations also were observed with NO2, VOCs such as
benzene and xylene, and O3 (Table 5-8). Except for O3, these copollutants were
moderately to highly correlated with SO2 (r = 0.45-0.9). Correlations were highest in
previous studies, but recent studies did not report SO2 concentrations (Spira-Cohen et al..
2011) or copollutant correlations (Velicka et al.. 2015) to assess whether the magnitude
of correlation varied by SO2 levels. Copollutant models were analyzed in few studies and
for few copollutants. For a Los Angeles, CA cohort, no SO2-VOC interaction was
indicated, and SO2 associations persisted with adjustment for benzene, xylene, or toluene
for some but not all symptoms (Delfino et al.. 2003a; Delfino et al.. 2003b). Associations
for VOCs were attenuated as well, and copollutant model results are uncertain because of
the moderate to high correlations with SO2 (r = 0.58-0.78) and because exposures were
assessed from monitors 4.8 km from children's homes or schools. Potential exposure
error also limits inference from results showing associations for joint increases in SO2
with PM10, NO2, or CO that were similar to each single-pollutant association (Schildcrout
et al.. 2006). The recent Bronx, NY study analyzed copollutant models for school SO2
and EC, which may have more comparable exposure error. SO2 and EC were moderately
correlated (r = 0.45), consistent with the location in a high diesel truck traffic area (Spira-
Cohen etal.. 2011). In the copollutant model, the odds ratio for cough was robust for EC
but decreased in magnitude and precision for SO2 from 1.60 (95% CI: 1.20, 2.12) to 1.32
(95% CI: 0.93, 1.87) per 40-ppb increase in 1-h max SO2.
Summary of Respiratory Symptoms in Populations with Asthma
Controlled human exposure studies provide strong evidence for the effects of SO2
exposure on respiratory symptoms in adults with asthma under increased ventilation
conditions. Exposures for 5-10 minutes to 0.2-0.6 ppm SO2 induced respiratory
symptoms in exercising individuals with asthma, with the most consistent evidence from
exposures to 0.4-0.6 ppm SO2 (Table 5-2). Epidemiologic evidence in adults with asthma
is weak, but increases in ambient SO2 concentration are generally associated with
increased risk of asthma symptoms in children (Figure 5-2; Table 5-8). Assessing
coherence specifically with controlled human exposure studies of adolescents with
asthma is difficult because those studies lacked an appropriate control exposure. Limited
findings support associations in children and adults with asthma plus atopy.
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Epidemiologic results in children are less consistent in recent than previous studies but
support associations for 1-h max SO2 measured at schools or 24-h avg SO2 modeled for
school and home. School or home SO2 measures may better represent exposures than the
concentrations at central site monitors examined in most studies, particularly for 1-h max.
These SO2 metrics are longer than the 5-10 minutes SO2 exposures in controlled human
exposure studies, which show transient responses. And, the role of confounding or an
interaction with copollutants such as PM2 5, EC, NO2, and VOCs remains uncertain for
epidemiologic associations, including those for populations with asthma plus atopy and
for residents near a coal-fired power plant. However, evidence for allergic inflammation
enhanced by repeated 1-hour exposures, albeit 2 ppm SO2, to some extent supports the
biological plausibility of S02-associated increases in respiratory symptoms, especially in
populations with asthma plus atopy.
Hospital Admission and Emergency Department Visits for Asthma
Since the completion of the 2008 SOx ISA, epidemiologic studies have continued to
examine the association between short-term exposure to ambient SO2 concentrations and
respiratory-related hospital admissions and ED visits, but are primarily limited to
single-city studies. The sections within this chapter detailing the respiratory-related
hospital admissions and ED visits studies characterize recent studies in the context of the
collective body of evidence evaluated in the 2008 SOx ISA. The 2008 SOx ISA (U.S.
EPA. 2008d) included the first thorough evaluation of respiratory morbidity in the form
of respiratory-related hospital admissions and ED visits, including asthma. These studies
reported generally positive associations with short-term SO2 exposures, with associations
that are often larger in magnitude for children (Figure 5-3). Additionally, SO2
associations with asthma hospital admissions and ED visits were often attenuated, but
remained positive in copollutant models with PM, NO2, or O3.
Within this section focusing on asthma, as well as the rest of the chapter,
respiratory-related hospital admissions and ED visit studies are evaluated separately
because only a small percentage of respiratory-related ED visits result in hospital
admission. Additionally, when evaluating asthma ED visit and hospital admission studies
that focus on children (i.e., defined age ranges <18 years of age), it is important to note
that it is often difficult to reliably diagnose asthma in children <5 years of age, which
may add some uncertainty to the results including this age range (NAEPP. 2007).
For each of the studies evaluated in this section, Table 5-9 presents the air quality
characteristics of each city, or across all cities, the exposure assignment approach used,
and information on copollutants examined in each asthma hospital admission and ED
visit study. Other recent studies of asthma hospital admissions and ED visits are not the
December 2016
5-47
DRAFT: Do Not Cite or Quote
-------
1 focus of this evaluation because they were conducted in small single-cities, encompassed
2 a short study duration, had insufficient sample size, or did not examine potential
3 copollutant confounding. The full list of these studies, as well as study specific details,
4 can be found in Supplemental Table 5S-5 (U.S. EPA. 2016m).
Study
Sonetal. (2013)
Sonetal. fc013)
in et al. (2004)
Samofi e\ aL (loll)
Sheppard et al. (1999; 2003)
tSon et al. (2013)
- Sonetal. (20131
"Samolietal. (2011)
Sonetal. (20l3)
"Samolietal. (2011)
Wilson et al. (2005)
Ito et al. (2007)
Peel et al' (2005)
ATSDR (2006)
tStieb et al. (2009)
f Byers et al. (2015)
Villeneuve et al. (2007)
tAlhanti et al. (2015)
^Vilson et al. (2005)
tJalaludin et al. (2008)
flietal. (2011)
Strickland et al. (2010)
Byers etaL (2015)
Alhanti et al. (2015)
'ilson et al. (2^)05)
tAlhanti et al. (2015)
tByers et al. (2015)
tAlhanti et al. (2015)
Wilson et al. (2t)05)
tAlhanti et al. (2015")
Ito et al. (,2007)
tByers et al. (.2015)
Villeneuve et al. (2007)
tJalaludin et al. (.2008/
f Strickland et al. (2010)
JafFe et al. (2003.)
Ito et al. (2007)
tByers et al. (2015)
Villeneuve et al. (2007)
tJalaludin et al. (2008)
^Strickland et al. (2016)
Location
8 South Korean cities
8 South Korean cities
Bronx County, NY
Athens, Greece
Seattle, WA
8 South Korean cities
8 South Korean cities
Athens, Greece.
8 South Korean cities
Athens, Greece
Portland, ME
Manchester, NH
New York, NY
Atlanta, GA
Bronx, NY
Manhattan, NY
7 Canadian cities
Indianapolis, IN
Edmonton, Canada
3 U.S. cities
Portland, ME
Manchester, NH
Syndey, Australia
Detroit, MI
Atlanta, GA
Indianapolis, IN
3 U.S. cities
Portland, ME
Manchester, NH
3 U.S. cities
Indianapolis, IN
3 U.S. cities
Portland, ME
Manchester. NH
3 U.S. cities
New York, NY
Indianapolis, IN
Edmonton, Canada
Syndey, Australia
Atlanta, GA
3 Ohio cities
New York, NY
Indianapolis, IN
Edmonton, Canada
Syndey, Australia
Atlanta, GA
Age
Lag
All
0-3
0-14
0-3
0-14
NR
0-1
4
0
5-14
<65
0
75+
0-3
All
0-3
0-14
0
All
0-3
0-14
0
Al
0
Al
0
Al
0-1
Al
0-2
Al
0-4
Al
0-4
Al
2
Al
0-2
>2
0-4
0-4
0-2
0-14
0
0-14
0
1-14
0-1
2-1
8
Q-4a
G-4b
5-17
0-2
5-17
0-2
5-18
0-2
15-64
0
15-64
0
19-39
0-2
18-44
0-2
>44
0-2
40-64
0-2
65+
0
65+
0
65+
0-2
All
0-1
All
0-2
->9
0-4
1-14
0-1
5-17
0-2
5-34
NR
All
0-1
All
0-2
>~>
0-4
1-14
0-1
5-1
7
0-2
Hospital Admissions
-10.0 0.0 10.0 20.0 30.0
% Increase (95% Confidence Interval)
ED = emergency department.
Note: f and red = recent studies published since the 2008 ISA for Sulfur Oxides. Black = U.S. and Canadian studies evaluated in
the 2008 ISA for Sulfur Oxides; Circle = all-year; diamond = warm/summer months; square = cold/winter months, a = time-series
results; b = case-crossover results. Gray shading depicts studies that present results for children (i.e., <18 yr of age). Corresponding
quantitative results are reported in Supplemental Table 5S-4 (U.S. EPA. 2016i).
Figure 5-3 Percent increase in asthma hospital admissions and emergency
department visits from U.S. and Canadian studies evaluated in the
2008 SOx ISA and recent studies in all-year and seasonal
analyses for a 10-ppb increase in 24-h avg or 40-ppb increase in
1-h max sulfur dioxide concentrations.
December 2016
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DRAFT: Do Not Cite or Quote
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Table 5-9 Study-specific details and mean and upper percentile concentrations
from asthma hospital admission and emergency department visit
studies conducted in the U.S. and Canada and evaluated in the 2008
SOx ISA and studies published since the 2008 SOx ISA.
Upper
Mean Percentile
Location Exposure Concentration Concentrations Copollutants
Study Years Assignment Metric ppb ppb Examination
Hospital admissions
Lin et al. (2004) Bronx AvgofSC>2 24-h avg Cases: 16.8 NR NR
County, NY concentrations Controls'15 6
(1991-1993) from two
monitoring sites
(Sheppard (2003);
Sheppard et al.
(1999))
Seattle, WA
(1987-1994)
Avg of SO2
concentrations
from multiple
monitors
24-h avg 8.0
75th: 10.0
90th: 13.0
Correlation (r):
PM10: 0.31
PM2.5: 0.22
PM10-2.5: 0.34
Os: 0.07
CO: 0.24
Copollutant
models: none
tSon et al. (2013) Eight South Avg of hourly 24-h avg 3.2-7.3 NR Correlation (r):
Korean cities ambient SO2 PM10' 0 5
(2003-2008) concentrations
from monitors in 3'
each city NO2: 0.6
CO: 0.6
Copollutant
models: none
tZhena et al. (2015) Meta- NR 24-h avg 3.1-45.53 NR Correlations (r):
analysis NR
(1988-2014) Copollutant
models: none
tSamoli et al.
(2011)
Athens,
Greece
(2001-2004)
Avg of SO2
concentrations
across multiple
monitors
24-h avg 6.4
75th: 8.4
Correlation (r):
Os: -0.19
NO2: 0.55
Copollutant
models: PM10,
SO2, NO2, O3
December 2016
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DRAFT: Do Not Cite or Quote
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Table 5-9 (Continued): Study specific details and mean and upper percentile
concentrations from asthma hospital admission and
emergency department visit studies conducted in the U.S.
and Canada and evaluated in the 2008 SOx ISA and studies
published since the 2008 SOx ISA.
Upper
Mean Percentile
Location Exposure Concentration Concentrations Copollutants
Study Years Assignment Metric ppb ppb Examination
ED visits
Jaffe et al. (2003) Cincinnati, When more than 24-h avg Cincinnati:
Cleveland, one monitoring
and station operating
Columbus, in a day, monitor
OH reporting
(1991-1996) highest 24-h avg
SO2
concentration
used
13.7
Cleveland:
15.0
Columbus:
4.2
Max: Correlations (r)
Cincinnati: 50 (range across
Cleveland: 64 cities)
Columbus: 22 N°2: 0.07-0.28
Os: 0.14-0.26
PM10:
0.29-0.42
Copollutant
models: none
I to et al. (2007)
New York, Average SO2
NY concentrations
(1999-2002) across
19 monitors
24-h avg 7.8
75th: 10
95th: 17
Correlations (r):
NR
Copollutant
models: PM2.5,
NO2, O3, CO
ATSDR (2006)
Bronx and
Manhattan,
NY
(1999-2000)
SO2 concentra-
tions from one
monitor in Bronx
and one in
Manhattan
24-h avg Manhattan: 12 NR
Bronx: 11
Correlations (r):
Bronx:
Os: -0.49
NO2: 0.50
PM2.5: 0.39
Max PM10:
0.0.34
Manhattan:
Os: -0.40
NO2: 0.47
PM2.5: 0.26
PM10: 0.24
Copollutant
models: O3,
FRM and Max
PM2.5, NO2
December 2016
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DRAFT: Do Not Cite or Quote
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Table 5-9 (Continued): Study specific details and mean and upper percentile
concentrations from asthma hospital admission and
emergency department visit studies conducted in the U.S.
and Canada and evaluated in the 2008 SOx ISA and studies
published since the 2008 SOx ISA.
Study
Location
Years
Exposure
Assignment
Metric
Upper
Mean Percentile
Concentration Concentrations
PPb PPb
Copollutants
Examination
Peel et al. (2005)
Atlanta, GA
(1993-2000)
Average of SO2
concentrations
from monitors
for several
monitoring
networks
1-h max 16.5
90th: 39.0
Correlations (r):
PM2.5: 0.17
PM10: 0.20
PM10-2.5: 0.21
UFP: 0.24
PM2.5 water
soluble metals:
0.00
PM2.5 sulfate:
0.08
PM2.5 acidity:
-0.03
PM2.5 OC: 0.18
PM2.5 EC: 0.20
Oxygenated
HCs: 0.14
Os: 0.19
CO: 0.26
NO2: 0.34
Copollutant
models: none
Wilson et al. (2005)
Portland,
SO2 concentra-
24-h avg
Portland: 11.1
NR Correlation (r)
ME, and
tions from one
Manchester:
(Range across
Manchester,
monitor in each
16.5
cities):
NH
city
Os: 0.05-0.24
(1996-2000)
Copollutant
models: none
tStieb et al. (2009)
Seven
Average SO2
24-h avg
2.6-10.0
75th: 3.3-13.4 Correlations (r)
Canadian
concentrations
only reported
cities
across all
by city and
(1992-2003)
monitors in each
season
city. Number of
Copollutant
SO2 monitors in
models: none
each city ranged
from 1-11.
tOrazzo et al.
Six Italian
Average of SO2
24-h avg
All-year:
NR Correlations (r):
(2009)
cities
concentrations
2.1-8.1
NR
(1996-2002)
across all
Warm
Copollutant
monitors in each
(Apr-Sep):
models: none
city
1.3-9.0
Cold
(Oct-Mar):
2.6-7.3
December 2016
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Table 5-9 (Continued): Study specific details and mean and upper percentile
concentrations from asthma hospital admission and
emergency department visit studies conducted in the U.S.
and Canada and evaluated in the 2008 SOx ISA and studies
published since the 2008 SOx ISA.
Upper
Mean
Percentile
Location
Exposure
Concentration
Concentrations
Copollutants
Study
Years
Assignment
Metric ppb
ppb
Examination
tAlhanti et al.
Three U.S.
Population-
1-h max Atlanta: 10.7
NR
Correlations (r):
(2016)
cities
weighted
Dallas: 2.7
NR
Atlanta, GA
average using
St Louis' 10 7
Copollutant
(1993-2009)
data available
V_> I. LvU IO. 1 U. 1
models: none
Dallas, TX
from all monitors
(2006-2009)
measuring SO2
St. Louis,
MO
(2001-2007)
tZhena etal. (2015)
Meta-
NR
24-h avg 4.6-39.1®
NR
Correlations (r):
analysis
NR
(1988-2014)
Copollutant
models: none
tStrickland et al.
(2010)
Atlanta, GA
(1993-2004)
Population-
weighted
average using
data available
from all monitors
measuring SO2
1-h max All-year: 10.8 NR
Warm
(May-Oct): 9.6
Cold
(Nov-Apr):
12.0
Correlations (r):
NR
Copollutant
models: none
tLi et al. (2011)
Detroit, Ml
(2004-2006)
Average of SO2
concentrations
across two
monitors in
Detroit
metropolitan
area that
measure SO2
24-h avg 3.8
75th: 5.1
Max: 27.3
Correlations (r),
range across
monitors:
CO: 0.17-0.31
PM2.5:
0.40-0.53
NO2: 0.42-0.55
Copollutant
models: none
tBvers etal. (2015)
Indianapolis,
IN
(2007-2011)
Double-
1-h max All-year: 10.1
NR
Correlations (r):
weighted
Warm: 10.5
All-year:
average
Cold: 9.8
PM2.5: 0.34
(distance from
Warm:
1-h max O3:
0.45
monitor to ZIP
code centroid
and age-specific
census
8-h max O3:
population) of
0.42
two SO2
PM2.5: 0.38
monitors
Cold:
PM2.5: 0.29
December 2016
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Table 5-9 (Continued): Study specific details and mean and upper percentile
concentrations from asthma hospital admission and
emergency department visit studies conducted in the U.S.
and Canada and evaluated in the 2008 SOx ISA and studies
published since the 2008 SOx ISA.
Study
Location
Years
Exposure
Assignment
Metric
Mean
Concentration
PPb
Upper
Percentile
Concentrations
PPb
Copollutants
Examination
tVilleneuve et al.
Edmonton,
Average of SO2
24-h avg
Summer
Summer
Correlations (r):
(2007)
AB
concentrations
(Apr-Sep)
75th: 3.0
NR
(1992-2002)
across three
50th: 2.0
Winter
Copollutant
monitoring
Winter
75th: 4.0
models: NR
stations
(Oct-Mar)
50th: 3.0
tJalaludin et al.
(2008)
Sydney,
Australia
(1997-2001)
Average of SO2
concentrations
across
14 monitoring
stations
24-h avg
All-year: 1.07
Warm: 1.03
Cold: 1.1
Max
All-year: 4.1
Warm: 4.1
Cold: 3.9
Correlations (r):
(warm, cold)
PM10: 0.37,
0.46
PM2.5: 0.27,
0.46
03: 0.45, -0.04
CO: 0.46, 0.51
N02: 0.52, 0.56
Copollutant
models: PM10,
PM2.5, O3, CO,
NO2
tSmaraiassi et al.
Montreal,
SO2 concentra-
24-h avg Regional: 4.3
75th: NR
(2009)
QC
tions measured
East: 6.9
Regional: 5.3
(1996-2004)
at two
monitoring sites
Southwest: 4.4
East: 9.2
east and
AERMOD:
Southwest: 5.9
southwest of the
East + South-
AERMOD:
refinery
west: 3.0
East + South-
At-home
East: 3.7
west: 4.3
estimates of
Southwest: 2.4
East: 5.5
daily exposure
by estimating
Southwest: 3.0
SO2 concentra-
tions at centroid
of residential
postal codes
using AERMOD
December 2016
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Table 5-9 (Continued): Study specific details and mean and upper percentile
concentrations from asthma hospital admission and
emergency department visit studies conducted in the U.S.
and Canada and evaluated in the 2008 SOx ISA and studies
published since the 2008 SOx ISA.
Upper
Mean Percentile
Location Exposure Concentration Concentrations Copollutants
Study Years Assignment Metric ppb ppb Examination
tWinquist et al.
(2014)
Atlanta, GA, Population-
1-h max Warm
75th:
U.S.
(1998-2004)
weighted
average using
data available
from all monitors
measuring SO2
(May-Oct): 8.3 Warm: 11.4
Cold Cold: 14.6
(Nov-April):
10.8
Correlations (r):
Warm:
Os: 0.27
CO: 0.32
NO2: 0.44
PM2.5: 0.28
EC: 0.31
Sulfate: 0.24
Secondary
PM2.5: 0.24
Cold:
Os: 0.05
CO: 0.22
NO2: 0.41
PM2.5: 0.07
EC: 0.18
Sulfate: 0.02
Secondary
PM2.5: 0.08
Copollutant
models: none
tPearce et al. Atlanta, GA SO2 1-h max 14.6 NR Correlations (r):
(2015) concentrations NR
from one „ „ x x
monitor Copo Mutant
models: none
December 2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
Table 5-9 (Continued): Study specific details and mean and upper percentile
concentrations from asthma hospital admission and
emergency department visit studies conducted in the U.S.
and Canada and evaluated in the 2008 SOx ISA and studies
published since the 2008 SOx ISA.
Upper
Mean
Percentile
Location
Exposure
Concentration
Concentrations
Copollutants
Study Years
Assignment
Metric ppb
ppb
Examination
Outpatient and physician visits
tBurra et al. (2009) Toronto, ON Average of SO2 1-h max 9.7
(1992-2001) concentrations
across six
monitors
75th: 12.0 Correlations (r):
95th: 35.0 NR
Max: 62.0 Copollutant
models: none
tSinclair et al. Atlanta, GA, SO2 concentra- 1-h max 1998-2000: NR Correlations (r):
(2010) U.S. tions collected 19.3 NR
(1998-2002) as part of 2000-2002: Copollutant
AIRES at i7 g models: none
SEARCH „„„„
Jefferson street 1998 2002.
site 183
AERMOD = American Meteorological Society/U.S. EPA Regulatory Model; AIRES = Aerosol Research Inhalation Epidemiology
Study; CO = carbon monoxide; EC = elemental carbon; FRM = federal reference method; HCs = hydrocarbons; N02 = nitrogen
dioxide; NR = not reported; 03 = ozone; OC = organic carbon; PM10 = particulate matter with a nominal aerodynamic diameter less
than or equal to 10 |jm; PM2.5 = particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm;
PM-]0-2.5 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm and greater than 2.5 |jm;
SEARCH = Southeast Aerosol Research Characterization; S02 = sulfur dioxide; UFP = ultrafine particle.
aRange of mean concentrations across all studies included in the meta-analysis,
f = studies published since the 2008 SOx ISA.
Hospital Admissions
The 2008 SOx ISA identified only two U.S.-based studies and no Canadian studies that
examined the association between short-term SO2 exposures and asthma hospital
admissions. These studies reported positive associations; however, they were limited to
studies of individual cities (Figure 5-3). The asthma hospital admission studies averaged
SO2 concentrations over multiple monitors and only examined 24-h avg exposure
metrics, which may not adequately capture the spatial and temporal variability in SO2
concentrations (Section 3.4.2.2 and Section 3.4.2.3). While correlations between 24-h avg
and 1-h max SO2 concentrations are high (r > 0.75) at most monitors, lower correlations
may occur at some monitors and in individual studies, adding uncertainty to the ability of
24-h avg metrics to capture peak SO2 concentrations. Additionally, relatively few studies
have examined the potential confounding effects of other pollutants on the SCh-asthma
hospital admissions relationship.
To date a limited number of studies have been published since the 2008 SOx ISA that
focus on the relationship between short-term SO2 exposures and asthma hospital
December 2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
admissions. In a time-series study conducted in Athens, Greece, Samoli et al. (2011)
evaluated the association between multiple ambient air pollutants and pediatric asthma
hospital admissions for ages 0-14 years. In an all-year analysis, the authors reported a
positive association with SO2 [16.5 % (95% CI: 2.3, 32.6); lag 0 increase for a 10-ppb
increase in 24-h avg SO2 concentrations]. In copollutant analyses, the authors found SO2
risk estimates to be robust in models with PM10 [13.0% (95% CI: -1.5, 29.7)] and O3
[16.5 % (95% CI: 2.3, 32.6)]. However, in models with NO2 there was an increase in the
SO2 risk estimate [21.3% (95% CI: 1.1, 45.5)]. SO2 was low (r < 0.4) to moderately
(r ranging from 0.4-0.7) correlated with other pollutants examined in the study, with the
highest correlation with NO2 (r = 0.55).
The association between short-term SO2 exposures and asthma hospital admissions was
also examined by Son et al. (2013) in a study of eight South Korean cities. In addition to
focusing on asthma, the authors examined allergic disease hospital admissions, which
encompass asthma. For all ages, the authors reported a 5.3% increase (95% CI: -2.4,
13.0) in asthma hospital admissions for a 10-ppb increase in 24-h avg SO2 concentrations
and a 3.1% increase (95% CI: -3.7, 10.7) in allergic diseases hospital admissions. In
analyses focusing on children (ages 0-14) and older adults (>75 years of age), the authors
reported associations that were larger in magnitude, compared to all ages for both asthma
and allergic diseases hospital admissions (Figure 5-3).
The evidence from studies evaluated in the 2008 SOx ISA, as well as recent studies
indicating a positive association between short-term SO2 exposure and asthma hospital
admissions, is supported by a meta-analysis conducted by (Zheng et al.. 2015) that
focused on all studies examining air pollution and asthma hospital admissions and ED
visits published between 1988 and 2014. For SO2, the authors reported a 2.1% increase
(95% CI: 0.5, 3.70) in asthma hospital admissions for a 10-ppb increase in 24-h avg SO2
concentrations based on estimates from 31 studies. The results from Zheng et al. (2015)
are smaller in magnitude compared to the other asthma hospital admission studies
summarized in Figure 5-3. but this could be a reflection of the meta-analysis only
including single-day lag estimates from each of the studies. The results of the
meta-analysis were found to be robust in sensitivity analyses examining publication bias;
however, the publication bias analysis was not conducted separately for asthma hospital
admissions and ED visits results.
Emergency Department Visits
The majority of studies, examing respiratory-related hospital admissions and ED visits,
have focused on asthma ED visits. Studies evaluated in the 2008 SOx ISA were primarily
limited to single-city studies that provided generally positive associations between SO2
and asthma ED visits, with positive associations being reported in some study locations
December 2016
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
and evidence of no association in other locations (Figure 5-3). Additionally, there was
limited evidence for potential seasonal differences in SO2 associations with asthma ED
visits. As with the hospital admission studies, there has been limited analyses examining
the potential confounding effects of copollutants on the SCh-asthma ED visit relationship.
Recent studies that examined the association between short-term SO2 exposures and
asthma ED visits have primarily focused on either children or the entire population, with
a few studies examining whether effects differ by lifestage. Additionally, unlike the
hospital admission studies, the ED visit studies examined both 24-h avg and 1-h max
exposure metrics, which can provide some additional insight, on a population level, into
the short-term exposures that result in respiratory effects in controlled human exposure
and animal toxicological studies (see previous subsections of Section 5.2.1.2V
Strickland et al. (2010) examined the association between SO2 exposure and pediatric
asthma ED visits (ages 5-17 years) in Atlanta, GA, using air quality data over the same
years as Tolbert et al. (2007). who examined all respiratory ED visits. However, unlike
Tolbert et al. (2007). who used a single-site monitor, Strickland et al. (2010) used
population-weighting, a more refined exposure assignment approach, to combine daily
pollutant concentrations across monitors. As discussed in Section 3.4.2. a study by
Goldman et al. (2012) shows that the bias in health effect estimates decreases when using
population-weighted averages for assigning exposure instead the values from a central
site monitor. In Strickland et al. (2010). the authors developed a statistical model using
hospital-specific, time-series data that is essentially equivalent to a time-stratified,
case-crossover analysis (i.e., using interaction terms between year, month, and
day-of-week to mimic the approach of selecting referent days within the same month and
year as the case day). Strickland et al. (2010) observed a 4.2% (95% CI: -2.1, 10.8)
increase in ED visits for a 40-ppb increase in 1-h max SO2 concentrations at lag 0-2 days
in an all-year analysis. The potential confounding effects of other pollutants on the
SCh-asthma ED visit relationship was not assessed in this study, and correlations between
pollutants were not presented. However, when evaluating the correlation of pollutants
examined over the same study years in Tolbert et al. (2007). SO2 had a low correlation
with all pollutants (r < 0.36).
Positive associations between short-term SO2 exposures and pediatric asthma ED visits
were also observed in a study conducted by Li et al. (2011) in Detroit, MI that focused on
whether there was evidence of a threshold in the air pollution-asthma ED visit
relationship. In the main nonthreshold analysis, the authors conducted both time-series
and time-stratified case-crossover analyses. Li et al. (2011) observed similar results in
both analyses, which indicated an association between SO2 and asthma ED visits, [time
series: 20.5% (95% CI: 8.9, 33.2); lag 0-4 for a 10-ppb increase in 24-h avg SO2
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concentrations; case-crossover: 22.8% (95% CI: 12.6, 33.7); lag 0-4], The results of the
U.S.-based studies focusing on children conducted by Strickland et al. (2010) and Li et al.
(2011) are consistent with those of Jalaludin et al. (2008) in a study of children
1-14 years of age conducted in Sydney, Australia. In addition to conducting the analysis
focusing on ages 1-14, the authors also examined whether risks varied among age ranges
within this study population (Chapter 6). Jalaludin et al. (2008) examined single day lags
ranging from 0 to 3 days as well as the average of 0-1 days. In the 1-14 years of age
analysis, the authors observed slightly larger associations at lag 0-1 days [29.7% (95%
CI: 14.7, 46.5)] compared to lag 0 [22.0% (95% CI: 9.1, 34.5)] for a 10-ppb increase in
24-h avg SO2 concentrations. An examination of the potential confounding effects of
other pollutants was assessed in copollutant models with PM10, PM2.5, O3, CO, or NO2 at
lag 0. SO2 was found to be weakly to moderately correlated with these pollutants,
r = 0.27-0.52. Jalaludin et al. (2008) reported that the SCh-asthma ED visit association
was slightly attenuated, but remained positive in all copollutant models, with the
magnitude of the association ranging from a 13.2-16.1% increase in asthma ED visits.
Bvers et al. (2015) in a study conducted in Indianapolis, IN examined asthma ED visits
across all ages as well as various lifestages (i.e., 5-17, 18-44, and >45 years of age).
The authors used a double-weighted approach to assign exposure where they first
weighted air pollution concentrations by distance from a monitor to the ZIP code centroid
and then weighted concentrations by the age-specific census population. In an all-year
analysis for all ages, the authors reported a 0.4% increase in asthma ED visits (95% CI:
-3.6, 4.5) at lag 0-2 for a 40-ppb increase in 1-h max SO2 concentrations, with evidence
of a larger association when focusing on pediatric asthma ED visits [5.4% (95% CI: -3.2,
14.5); lag 0-2], which is consistent with Strickland et al. (2010). Li etal. (2011). and
Jalaludin et al. (2008). Although copollutant analyses were not conducted, SO2 was found
to have a low correlation with PM2 5 (/' < 0.4) in all-year and seasonal analyses, and
moderate correlation with 1-h max and 8-h max O3 in warm season analyses
(r = 0.42-0.45). Additionally, when examining SO2 concentrations across the entire study
period, the authors noted that only 36 days (i.e., 2.1% of days) had 1-h max SO2
concentrations that exceeded the NAAQS.
Alhanti et al. (2016) also used the approach of assigning exposure using
population-weighting similar to Strickland et al. (2010). but expanded the study area to
include two additional cities, Dallas, TX and St. Louis, MO, as well as Atlanta, GA.
The analysis focused on examining whether there was evidence of differential risk across
lifestages (i.e., 0-4, 5-18, 19-39, 40-64, and 65+ years of age) for asthma ED visits
across a number of air pollutants, including SO2. Analyses were conducted for each
individual city, and an overall estimate across all three cities was calculated by taking the
inverse-variance weighted average of the city-specific risk estimate. Across the
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individual cities, there was evidence of positive and negative associations for all age
categories examined except ages 5-18 where positive associations were observed across
all cities, which is consistent with the single-city studies detailed above. In the combined
analysis across the three cities, Alhanti et al. (2016) reported positive associations for
ages 0-4 [4.1% (95% CI: -0.8, 9.2); lag 0-2 for 40-ppb increase in 1-h max SO2
concentrations] and 5-18 [5.7% (95% CI: -0.8, 11.8); lag 0-2] (Samat. 2016). In
sensitivity analyses, the results were found to be robust to alternative model
specifications for both control for temporal trends and weather covariates.
As detailed in the asthma hospital admissions section, Zheng et al. (2015) conducted a
meta-analysis of asthma hospital admission and ED visit studies. In the analysis focusing
on ED visit studies, the authors reported a 3.5% increase (95% CI: 1.9, 5.1) in asthma ED
visits for a 10-ppb increase in 24-h avg SO2 concentrations based on single-day lag
estimates from 34 studies. This result is in the range of risk estimates reported in studies
that observed positive associations between short-term SO2 exposure and asthma ED
visits (Figure 5-3).
Although a number of recent studies add to the evidence from the 2008 SOx ISA
indicating a positive association between asthma ED visits and short-term SO2 exposures,
not all studies have reported positive associations. Both Stieb et al. (2009) and Villeneuve
et al. (2007). in studies conducted in seven Canadian cities and Edmonton, AB,
respectively, did not observe evidence of a positive association between short-term SO2
exposures and asthma ED visits (Figure 5-3). The evidence of no association was
observed over multiple lag structures (i.e., both single and multiday lags) (Stieb et al..
2009; Villeneuve et al.. 2007) as well as subdaily exposure metrics (i.e., 3-h avg pollutant
concentrations) (Stieb et al.. 2009).
Hospital Admissions and Emergency Department Visits for Respiratory
Conditions Associated with Asthma
As stated previously, asthma is difficult to diagnose in children less than 5 years of age
(NAEPP. 2007); however, asthma-like symptoms in children within this age range are
often presented in the form of transient wheeze. Although studies that examine ED visits
for wheeze do not directly inform upon the relationship between short-term SO2
exposures and asthma, they can add supporting evidence. Qrazzo et al. (2009) examined
the association between short-term SO2 exposures and wheeze ED visits, in children
(ages 0-2 years) in six Italian cities. In a time-stratified case-crossover analysis, Qrazzo
et al. (2009) examined associations for multiday lags ranging from 0-1 to 0-6 days.
The authors reported the strongest evidence for an association between short-term SO2
exposures and wheeze ED visits at lags of 0-3 to 0-6 days with estimates ranging from
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2.1 to 4.3%, respectively, for a 10-ppb increase in 24-h avg SO2 concentrations. Within
this study, copollutant analyses or correlations with other pollutants were not presented.
Smargiassi et al. (2009) also provided additional information on whether there is an
association between short-term SO2 exposures and health effects that may be closely
related to asthma. The distinction between asthma and asthma-related outcomes is made
in this case because the study focused on asthma hospital admissions and ED visits in
children 2-4 years of age. This age range may not necessarily represent an asthma
exacerbation in the same context as those studies discussed earlier in this section that
include older individuals in whom asthma is more easily diagnosed. Within this study,
the authors examined the influence of a point source of SO2 (i.e., stack emissions from a
refinery) in Montreal on asthma hospital admissions and ED visits using data from two
fixed-site monitors as well as estimates of SO2 concentrations from a dispersion model,
AERMOD. The authors examined both daily mean and daily peak SO2 concentrations.
When comparing SO2 concentrations at one monitoring site east of the refinery with
those obtained via AERMOD the authors observed a modest correlation (daily mean SO2,
r = 0.43; daily peak SO2, r = 0.36). An examination of hospital admissions and ED visits
for both monitor locations, east and southwest of the refinery, found that associations
were slightly larger in magnitude for the same-day daily peak [hospital admissions: 1.46
(95% CI: 1.10, 1.93); ED visits: 1.18 (95% CI: 1.05, 1.33) for a 40-ppb increase in
1-h max SO2 concentrations] compared to daily mean concentrations [hospital
admissions: 1.36 (95% CI: 1.05, 1.81); ED visits: 1.15 (95% CI: 1.02, 1.27) for a 10-ppb
increase in 24-h avg SO2 concentrations] in an unadjusted model at lag 0. When
examining associations using SO2 concentrations from the fixed monitoring sites,
Smargiassi et al. (2009) did not find consistent evidence of an increase in asthma hospital
admissions or ED visits, which is indicative of the fact that a monitor located far from a
point source may not adequately capture population exposures for residences of interest
located closer to that source (see Section 3.4.2). The authors also examined an adjusted
model to control for daily weather variables and all other regional pollutants (i.e., PM2 5,
SO2, NO2, and O3), but these results are not presented because, as discussed within this
ISA, the evaluation of potential copollutant confounding is limited to two-pollutant
models because the results from multipollutant models are difficult to interpret due to
multicollinearity between pollutants. However, the results from the unadjusted
(i.e., single-pollutant model) and adjusted models were generally similar.
Outpatient and Physician Visits Studies of Asthma
Several recent studies examined the association between ambient SO2 concentrations and
physician or outpatient (nonhospital, non-ED) visits for asthma. In Toronto, Burra et al.
(2009) examined asthma physician visits among patients aged 1-17 and 18-64 years in a
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study focusing on differences by sex and income within each age category. For children,
the authors reported evidence of consistent positive associations between short-term
increases in SO2 concentrations and asthma physician visits for most of the single and
multiday lags examined (i.e., 0, 0-1, 0-2, 0-3), with no evidence of an association for a
0-4 day lag. In the analysis of adults, a similar pattern of associations was observed;
however, there was no evidence of an association at the two longest lags examined, 0-3
and 0-4 days.
In a study conducted in Atlanta, GA, Sinclair et al. (2010) examined the association
between multiple respiratory outcomes, including asthma and outpatient visits from a
managed care organization. The authors separated the analysis into two time periods (the
first 25 months of the study period and the second 28 months of the study period) in order
to compare the air pollutant concentrations and relationships between air pollutants and
acute respiratory visits for the 25-month time period examined in Sinclair and Tolsma
(2004) (i.e., August 1998-August 2000), and an additional 28-month time period of
available data from the Atlanta Aerosol Research and Inhalation Epidemiology Study
(ARIES) (i.e., September 2000-December 2002). As detailed in Table 5-9. SO2
concentrations were relatively similar between periods, differing by less than 2 ppb.
A comparison of the two time periods indicated that risk estimates across outcomes
tended to be larger in the earlier 25-month period compared to the later 28-month period,
with evidence of consistent positive associations across the lags examined for asthma
(both child and adult), but confidence intervals were relatively large.
Examination of Seasonal Differences
In addition to examining the association between short-term SO2 exposures and asthma
hospital admissions and ED visits in all-year analyses, some studies also conducted
seasonal analyses. When evaluating these studies, it is important to note that the
difference in the geographic locations examined across studies complicates the ability to
draw overall conclusions regarding the seasonal patterns of associations.
In the study of eight South Korean cities, Son et al. (2013) examined potential seasonal
differences across respiratory hospital admission outcomes. For asthma and allergic
disease hospital admissions, the association with SO2 was largest in magnitude during the
summer, although confidence intervals were quite large [asthma: 19.1% (95% CI: -18.3,
73.9), lag 0-3; allergic disease: 21.9% (95% CI: -6.7, 58.6), lag 0-3 for a 10-ppb
increase in 24-h avg SO2 concentrations]. Across the eight cities, mean 24-h avg SO2
concentrations were lowest during the summer season (4.4 ppb compared to a range of
4.8 to 7.0 in the other seasons), which was also observed for NO2, PM10, and CO.
The seasonal asthma hospital admission results of Son et al. (2013) are similar to those
reported in Samoli et al. (2011) in a study conducted in Athens, Greece. Samoli et al.
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(2011) observed the largest magnitude of an association during the summer months
[46.6% (95% CI: -13.8, 149.3); lag 0 for a 10-ppb increase in 24-h avg SO2
concentrations], but also reported a similar association in the autumn months [42.6 %
(95% CI: -0.5, 104.4); lag 0], Although positive, associations for the winter and spring
months were smaller in magnitude, 20.2 and 31.8%, respectively.
The initial indication of larger associations during the summer for asthma hospital
admissions is further supported by the analysis of Strickland et al. (2010) examining
short-term SO2 exposures and pediatric asthma ED visits in Atlanta. The authors reported
evidence of asthma ED visit associations larger in magnitude during the summer [10.8%
(95% CI: 0.7, 21.7); lag 0-2 for a 40-ppb increase in 1-h max SO2 concentrations], with
no evidence of an association during the winter [0.4% (95% CI: -7.5, 9.0)]. These results
are consistent with (Bvers et al.. 2015). who reported associations larger in magnitude in
the summer for all ages [3.1% (95% CI: -2.6, 8.6); lag 0-2 for a 40-ppb increase in
1-h max SO2 concentrations], and particularly children 5-17 years of age [13.0% (95%
CI: 0.8, 26.8); lag 0-2], and no evidence of an association in the cold season across all
ages examined. However, in another study focusing on asthma physician visits in Atlanta,
Sinclair et al. (2010) reported inconsistent evidence of seasonal differences in risk
estimates, with the pattern of associations being different in each of the time periods
examined in the study. It is important to note that the results of Sinclair et al. (2010) may
be a reflection of the severity of asthma exacerbations requiring medical attention and
people proceeding directly to a hospital for treatment instead of first visiting a physician.
Therefore, the study may not be able to adequately capture associations, and specifically,
any potential seasonal differences.
The meta-analysis conducted by (Zheng et al.. 2015) provides some additional supporting
evidence for potential seasonal differences in S02-asthma hospital admission and ED
visit associations. In a combined analysis including both asthma hospital admission and
ED visit studies that reported seasonal results, Zheng et al. (2015) reported slightly larger
associations in the warm [4.8% (95% CI: 2.7, 7.0) for a 10-ppb increase in 24-h avg SO2
concentrations] compared to the cold season [3.2% (95% CI: 0.5, 5.9)], but confidence
intervals did overlap.
Although there is some evidence for larger associations during the summer, studies
conducted by Villeneuve et al. (2007) in Edmonton, AB and Jalaludin et al. (2008) in
Sydney, Australia present conflicting results. As stated above, Villeneuve et al. (2007)
did not find evidence of an association between short-term SO2 exposures and asthma ED
visits, including in seasonal analysis, while Jalaludin et al. (2008) reported evidence of
larger associations during the cold months (May-October) compared to the warm months
(November-April) (Figure 5-3).
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Overall, the results of Samoli etal. (2011). Son et al. (2013). Strickland et al. (2010). and
Bvers et al. (2015) suggest that associations are larger in magnitude during the summer
season, but this conclusion should be viewed with caution because the results of each
study are highly imprecise, as reflected by the wide confidence intervals for each
seasonal result. Additionally, the interpretation of results from these studies is
complicated by the lack of copollutant analyses, and the results from Villeneuve et al.
(2007) and Jalaludin et al. (2008) that do not find evidence of larger associations during
the summer or warm season.
Lag Structure of Associations
When examining associations between air pollution and a specific health outcome, such
as respiratory-related hospital admissions, it is informative to assess whether exposure to
an air pollutant results in an immediate, delayed, or prolonged effect on health. Recent
studies that examine both multiple single- and multiday lags can help provide information
on whether there is a specific exposure window(s) that contribute to SCh-related asthma
hospital admissions and ED visits.
Son etal. (2013) examined the lag structure of associations for multiple
respiratory-related hospital admissions, including asthma and allergic disease, by
analyzing both single- and multiday lags. Across single-day lags of 0 to 3 days, positive
associations were observed across each lag, but the magnitude of the association varied
across single-day lags for each outcome. For both asthma and allergic disease hospital
admissions, the largest association, in terms of magnitude, for SO2 was observed for each
of the multiday lags examined, with the largest occurring at lag 0-3 days [asthma: 5.3%
(95% CI: -2.4, 13.0); allergic disease: 3.1% (95% CI: -3.7, 10.7) for a 10-ppb increase in
24-h avg SO2 concentrations].
Studies conducted by Samoli etal. (2011) and Jalaludin et al. (2008) report evidence for
the strongest SCh-asthma hospital admission and ED visit associations occurring rather
immediately (lag 0) as well as over the first few days after exposure, average of lags from
0 up to 2 days. Samoli et al. (2011) in the examination of single- and multiday lags for
associations between SO2 and asthma hospital admissions in Athens, Greece found
associations of similar magnitude at lag 0 and a 0-2 day distributed lag, but the
distributed lag association was imprecise (i.e., larger confidence intervals) (quantitative
results not presented). The associations reported for single-day lags of 1 and 2 days were
small and close to null. Jalaludin et al. (2008) in a study in Sydney, Australia found when
examining single-day lags of 0 to 3 days that asthma ED visit associations were largest
for lag 0 [22.0% (95% CI: 9.1, 34.5) for a 10-ppb increase in 24-h avg SO2
concentrations] and 1 day [16.1% (95% CI: 5.1, 26.5)]. This is further reflected in the
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largest SO2 association being observed for the multiday lag of 0-1 days [29.7% (95% CI:
14.7, 46.5)].
Only a limited number of studies have examined the lag structure of associations and the
results across studies are not fully supported by the rest of the literature base. Villeneuve
et al. (2007). when studying asthma ED visits in seven Canadian cities, examined
single-day lags of 0 and 1 day, along with multiday lags of 0-2 and 0-4 days.
The authors reported no evidence of an association between short-term SO2 exposures
and asthma ED visits at any lag. Additionally, Qrazzo et al. (2009) in the study of wheeze
ED visits in six Italian cities, examined multiday lags ranging from 0-1 to 0-6 days.
Across the lags examined, the authors reported evidence of increasing magnitude of the
association as the length of the multiday lag increased, with lag 0-6 days showing the
largest association.
Exposure Assignment
Questions often arise in air pollution epidemiologic studies about the method used to
assign exposure (see Section 3.3.3). Strickland et al. (2011). using ED visit data from
Atlanta, GA, assessed the effect of various exposure assignment approaches on the
relationship between short-term air pollution exposures and asthma ED visits.
The authors used warm season data from Strickland et al. (2010) to examine the relative
influence of different exposure assignment approaches (i.e., central monitor, unweighted
average across available monitors, and population-weighted average) on the magnitude
and direction of associations between SO2 and pediatric asthma ED visits. SO2 exhibited
a relatively low chi-square goodness-of-fit statistic compared with other pollutants, which
the authors attributed to spatial heterogeneity in SO2 concentrations (Section 3.4.2.2).
Strickland et al. (2011) reported that effect estimates per IQR increase in SO2 were
similar across the metrics; however, based on a standardized increment (i.e., 20 ppb in the
study), the magnitude of the association between SO2 and pediatric asthma ED visits
varied [central monitor 3.0% (95% CI: -0.4, 8.4); unweighted average 12.8% (95% CI:
2.8, 23.4); population-weighted average 10.9% (95% CI: 0.8, 21.9) for a 40-ppb increase
in 1-h max SO2 concentrations at lag 0-2 days]. The difference in associations observed
across the various exposure assignment approaches when using the standardized
increment can be attributed to the value (i.e., a 1-h max SO2 concentration of 20 ppb) not
reflecting an increase in SO2 concentrations that is reflective of the SO2 distribution in
Atlanta (e.g., in the study the standardized increment for 1-h max SO2 is 20 ppb, but the
IQR, which is often used to calculate the relative risk, differs across the exposure
assignment approaches, varying from 9.6 to 13.9 ppb). Although the Strickland et al.
(2011) study was only conducted in one city, the study suggests that it is appropriate to
consider the distribution of air pollutant concentrations when calculating a relative risk
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(i.e., IQR), but also that the different approaches used to assign exposure across the
studies evaluated may alter the magnitude, not direction, of the associations observed.
Concentration-Response Relationship
To date, few studies have examined the C-R relationship between SO2 exposures and
respiratory morbidity. In recent studies, Strickland et al. (2010) and Li etal. (2011)
examined the shape of the SCh-pediatric asthma ED visit relationship using different
analytical approaches.
Strickland et al. (2010) examined the C-R relationship by conducting quintile and locally
weighted scatterplot smoothing (LOESS) C-R analyses. In the quintile analysis, SO2
associations were examined in both the warm and cold seasons; however, no associations
were observed for the cold season for any quintile. Focusing on the warm season, the
authors found evidence of an increase in the magnitude of the association for
concentrations within the range of 7 to <24.2 ppb, relative to the first quintile (i.e., SO2
concentrations <3.1 ppb). The smallest associations were observed for the 5th quintile,
which represented concentrations ranging from 24.2 to <149 ppb; however, this quintile
represented the extreme end of the distribution of SO2 concentrations where data density
was low. Additionally, the LOESS C-R relationship analysis provides evidence of a
linear relationship between short-term SO2 exposures and asthma ED visits along the
distribution of concentrations from the 5th (2.1 ppb) to 95th (21.5 ppb) percentile (Sacks.
2015) (Figure 5-4). Collectively, these analyses do not provide evidence of a threshold.
In a study conducted in Detroit, MI, Li et al. (2011) examined whether there is evidence
of a nonlinear C-R relationship for air pollutants and pediatric asthma ED visits.
Associations with SO2 were examined in both a time-series and time-stratified,
case-crossover study design assuming (1) a linear relationship and (2) a nonlinear
relationship starting at 8 ppb [i.e., the maximum likelihood estimate within the 10th to
95th percentile concentration where a change in linearity may occur (~91 st percentile)]. It
is important to note the analysis that assumed a nonlinear relationship did not assume
zero risk below the inflection point. The focus of the analysis was on identifying whether
risk increased above that observed in the linear models at SO2 concentrations above
8 ppb. In the analyses assuming linearity, the authors examined single-day lags of 3 and
5 days and multiday lags of 0-2 and 0-4 days. Positive associations were observed for all
lags examined and were relatively consistent across models, with the strongest
association for a 0-4 day lag [time series: 20.5% (95% CI: 8.9, 33.2); case-crossover:
22.8% (95% CI: 12.6, 33.7) for a 10-ppb increase in 24-h avg SO2 concentrations]. In the
models that assumed a nonlinear relationship, the authors did not observe evidence of
increased risk above ~8 ppb. However, it is important to note that the data density is low
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at concentrations greater than 8 ppb, as reflected by this value representing the ~91st
percentile of SO2 concentrations.
10
O _
=#=•
flj
o:
0)
nj
q;
8
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o
Sulfur Dioxide Warm Season
10 15
Concentration (ppb)
20
Note: solid line = smoothed concentration-response estimate. Dashed line = twice-standard error estimates.
Source: Reprinted with permission of the American Thoracic Society. Strickland et al. (2010).
Figure 5-4 Concentration-response for associations between 3-day average
(lag 0-2) sulfur dioxide concentrations and emergency
department visits for pediatric asthma at the 5th to 95th percentile
of sulfur dioxide concentrations in the Atlanta, GA area.
Sulfur Dioxide within the Muitipoiiutant Mixture
An important question often encountered during the review of any criteria air pollutant, is
whether the pollutant has an independent effect on human health. However, ambient
exposures to criteria air pollutants are in the form of mixtures, which make answering
this question difficult. Epidemiologic studies traditionally attempt to identify the
independent effect of a criteria air pollutant through the use of copollutant models, but
these methods do not consider the broader air pollution mixture. Recent studies
conducted by Winquist et al. (2014) and Pearce et al. (2015) using pediatric asthma ED
visits data from Atlanta assessed whether specific mixtures are more strongly associated
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with health effects compared to others. Although the primary objective of these types of
studies is not to directly assess the independent effects of a pollutant, they can inform the
understanding of the role of SO2 in the air pollution mixture (e.g., contributing to an
additive or synergistic effect).
Winquist et al. (2014) examined multipollutant mixtures by focusing on the joint effect
(i.e., the combined effect of multiple pollutants) of pollutants often associated with
specific air pollution sources. Associations between short-term SO2 exposures and
pediatric asthma ED visits (i.e., ages 5-17) were examined in single-pollutant models and
also in a multipollutant context in joint models for pollutant combinations representative
of irritant gases (i.e., O3, NO2, and SO2), power plants (i.e., SO2 and SO42 ), and NAAQS
pollutants (i.e., O3, CO, NO2, SO2, and PM2 5). It is important to note that the pollutant
combination analyses attempt to address a different question (i.e., what is the risk
associated with exposure to a combination of pollutants?) than a traditional copollutant
analysis, which focuses on identifying the independent effect of a pollutant. Using the
model detailed in Strickland et al. (2010). the authors examined the relationship between
each combination and pediatric asthma ED visits using a Poisson model in the context of
a time-referent case-crossover analysis. The authors reported results for an IQR increase
for lag 0-2 days in single-pollutant analyses as well as three types of joint effect models
[i.e., no interaction terms (primary), first-order multiplicative interactions between
pollutants (interactions), and nonlinear pollutant terms (nonlinear)] (Figure 5-5).
In single-pollutant analyses, SO2 associations were smaller in magnitude compared to the
other pollutants that comprised each pollutant combination, but the uncertainty
surrounding each SO2 estimate was relatively small. Across pollutant combinations that
contained SO2, joint effect models reported consistent positive associations with pediatric
asthma ED visits in the warm season. Additionally, for each pollutant combination the
association observed was larger in magnitude than any single-pollutant association,
including SO2, but not equivalent to the sum of each individual pollutant association for a
specific combination. In the warm season analyses, associations across the different joint
effect models were relatively similar. Overall, the results during the cold season were
more variable.
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SPE = single-pollutant model estimate.
Note: Interquartile range for 1-h max S02 concentrations = 10.51 ppb.
Source: (Winquist et al.. 2014).
Figure 5-5 Rate ratio and 95% confidence intervals for single-pollutant and joint effect models for each
pollutant combination in warm and cold season analyses for an interquartile range increase in
each pollutant at lag 0-2 days.
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Pearce et al. (2015) took a different approach to examining multipollutant mixtures by
using an unsupervised learning tool, the self-organizing map (SOM), which is similar to
cluster analysis. Using air pollution concentrations for 10 pollutants from a single
monitor, the authors identified nine distinct day types representative of air quality in
Atlanta during the study period. These unique days were then used as indicator variables
to examine associations with pediatric asthma ED visits using the same statistical
approach as Strickland et al. (2010) and Winquist et al. (2014). Across the nine SOMs,
some pollutant combinations represented days consisting of high single pollutant
extremes, which included a day with high 1-h max SO2 concentrations (i.e., mean
concentration of 48.8 ppb and concentrations ranging from 8.5-23.7 ppb for all other
SOMs). In analyses of all SOMs focusing on lag 1, the strongest associations were
observed for days representing above average concentrations for all pollutants, and for
days representing a collection of primary (i.e., CO, NO2, NOx, EC, and OC) or secondary
pollutants (i.e., O3, NH4 . and S042+) (Figure 5-6). Additional evidence of associations
with pediatric asthma ED visits was observed for days with single pollutant extremes,
including days with high SO2 concentrations and generally lower concentrations for all
other pollutants Figure 5-6). Interestingly, when comparing SOMs results with
single-pollutant results in sensitivity analyses, the authors reported a null association with
SO2 at lag 1. This result differs from that observed in Strickland et al. (2010) and
Winquist et al. (2014). but the difference could be due to the fact that Pearce et al. (2015)
focused only on lag 1 because they were examining distinct pollution profiles that often
do not occur on multiple days in a row. In contrast, Strickland et al. (2010) and Winquist
et al. (2014) examined associations over a multiday average of 0-2 days. Additionally,
the difference between the SOM and single-pollutant SO2 result could be because the
SOM with high SO2 concentrations was better able to capture the immediate respiratory
response due to higher peak concentrations, which would be consistent with the effects
observed in controlled human exposure and animal toxicological studies.
Although the single-pollutant results of Winquist et al. (2014) and Pearce et al. (2015)
differ due to the lags examined, the studies contribute to evidence that SO2 alone and in
combination with other pollutants is associated with asthma ED visits. The studies also
highlight the difficulty in separating out the independent effect of a pollutant that is part
of a mixture because multiple pollutants are often highly correlated.
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0£
cd
Di
u
cd
oi
[2,1] [2,2] [2,3]
SOM [x,y]
SOM = self-organizing map.
Note: [2,2] = days with high sulfur dioxide concentrations. [3,3] and [3,1] = days with primary and secondary pollutants, respectively.
[3,2] = days with above average concentrations for all pollutants.
Source: (Pearce et al.. 2015).
Figure 5-6 Rate ratio and 95% confidence interval for association between
self-organizing map-based multipollutant day type and pediatric
asthma emergency department visits at lag 1.
Summary of Asthma Hospital Admission and Emergency Department
Visits
1 Recent studies that examined the association between short-term SO2 exposure and
2 asthma hospital admissions and ED visits generally report positive associations in studies
3 examining all ages, children (i.e., <18 years of age), and older adults (i.e., 65 years of age
4 and older) (Figure 5-3). The pattern of associations observed across studies focusing on
5 all ages as well as age-stratified analyses is consistent with those studies evaluated in the
6 2008 SOx ISA. Across asthma hospital admission and ED visit studies that evaluated the
7 lag structure of associations, the most consistent evidence indicated that associations
8 were largest in magnitude for multiday lags that encompassed the first few days after
9 exposure (i.e., average of 0-2 and 0-3 day lags). This evidence generally supports the
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timing of SO2 effects observed in the controlled human exposure and animal
toxicological studies (Section 5.2.1.2V The examination of potential copollutant
confounding was rather limited in the body of studies that focused on asthma hospital
admissions and ED visits. Across studies, SO2 was found to be low (r < 0.4) to
moderately (r = 0.4-0.7) correlated with other pollutants examined. Evidence from these
studies is consistent with those studies evaluated in the 2008 SOx ISA and adds to the
body of evidence indicating that SCh-asthma hospital admission and ED visit associations
remain relatively unchanged in magnitude in copollutant models.
A number of recent studies also examined whether there was evidence that the
association between short-term SO2 exposures and asthma hospital admissions and ED
visits was modified by season or some other individual- or population-level factor
(Chapter 6). An examination of seasonal differences in S02-asthma hospital admission
and ED visit associations provide some evidence of SO2 effects being larger in magnitude
in the summer or warm season, but the lack of this pattern across all studies that
conducted seasonal analyses suggests that seasonal associations may vary by geographic
location. Studies of individual- and population-level factors provide evidence of
differences in associations by lifestage, with larger SO2 effects for children and older
adults, and more limited evidence for differences by sex (Chapter 6).
Additionally, some recent studies examined various study design issues, including model
specification and exposure assignment. An examination of model specification, as
detailed in Section 5.2.1.6. indicates that the relationship between short-term SO2
exposures and respiratory-related hospital admissions, including those for asthma and
allergic disease, are sensitive to using less than 7 degrees of freedom (df) per year to
account for temporal trends, but robust to alternative lags and df ranging from 3 to 6 for
weather covariates (Son et al.. 2013). The results of Son et al. (2013) are supported by the
sensitivity analyses examining model specification conducted by Alhanti et al. (2016) for
asthma ED visits where the results were relatively consistent when the number of df for
temporal trends was increased and alternative covariates for weather used. An
examination of various exposure assignment approaches, including single central site,
average of multiple monitors, and population-weighted average, suggests that each
approach may influence the magnitude, but not direction, of the S02-asthma ED visit risk
estimate (Strickland et al.. 2011).
Finally, a few recent studies examined whether the shape of the S02-asthma ED visits
relationship is linear or provides evidence of a threshold. These studies provide initial
evidence of a linear, no-threshold relationship between short-term SO2 exposures and
asthma ED visits (Li et al.. 2011; Strickland et al.. 2010).
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Subclinical Effects Underlying Asthma Exacerbation: Pulmonary
Inflammation and Oxidative Stress
Pulmonary inflammation is a key subclinical effect in the pathogenesis of asthma. It
consists of both acute and chronic responses and involves the orchestrated interplay of
the respiratory epithelium and both the innate and adaptive immune system.
The immunohistopathologic features of chronic inflammation involve infiltration of
inflammatory cells such as eosinophils, lymphocytes, mast cells, and macrophages and
the release of inflammatory mediators such as cytokines and leukotrienes. Oxidative
stress is also relevant to asthma exacerbation. For example, many transcription factors
regulating the expression of pro-inflammatory cytokines are redox sensitive.
This section characterizes the evidence on SO2 exposure effects on pulmonary
inflammation and oxidative stress in humans with asthma and in animal models of
allergic airway disease (see Section 5.2.1.7 for healthy humans and animal models).
The 2008 SOx ISA (U.S. EPA. 2008d) concluded that evidence from the limited number
of controlled human exposure, epidemiologic, and animal toxicological studies was
insufficient to determine that exposure to SO2 at current ambient concentrations was
associated with inflammation in the airway. However, several studies provided evidence
for subclinical effects related to allergic inflammation. There are no recent controlled
human exposure studies, but there is additional investigation in epidemiologic and animal
toxicological studies. Epidemiologic results are inconsistent for pulmonary inflammation
and oxidative stress, including those for SO2 measured at or near children's schools.
However, recent findings in rats link short-term SO2 exposure to allergic inflammation.
Controlled Human Exposure Studies
Pulmonary inflammation following 5-10 minute exposure to SO2 was discussed in the
previous ISA; no new studies were available for review. Briefly, Tunnicliffe et al. (2003)
measured levels of exhaled NO (eNO), an indirect marker for pulmonary inflammation,
in individuals with asthma before and after a 1 hour exposure to 0.2 ppm SO2 under
resting conditions. NALF levels of the antioxidants, ascorbic and uric acid, were also
measured pre- and post-exposure. No statistically significant differences were observed
between pre- and post-exposure for any of these indicators. Because subjects were
exposed at rest and exposed to low concentrations, it is unlikely that enough SO2 reached
the airways to cause an effect. Gong et al. (2001) evaluated the response of individuals
with asthma to 0.75 ppm SO2 during exercise. In addition to changes in lung function and
symptoms, there was a statistically significant increase in eosinophil count in induced
sputum 2 hours after a 10-minute exposure. This response was significantly dampened by
pretreatment with a leukotriene receptor antagonist. These results provided some
evidence that SO2 elicits an inflammatory response in the airways of individuals with
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asthma that extends beyond the immediate bronchoconstriction response typically
associated with SO2 exposure. Additionally, this study provides further evidence that the
bronchoconstriction response is only partially due to neural reflexes and that
inflammatory mediators play an important role (Section 4.3.IV
Epidemiologic Studies
Recent epidemiologic evidence is inconsistent for associations of short-term increases in
ambient SO2 concentration with pulmonary inflammation and oxidative stress in adults
and children with asthma (Table 5-10). Outcomes were assessed at varying frequency:
daily, weekly, or seasonally. All studies examined eNO. Higher eNO has been linked to
higher eosinophil counts (Brodv et al.. 2013) as well as prevalence and exacerbation of
asthma (Soto-Ramos et al. 2013; Carraro et al.. 2007; Jones et al.. 2001; Kharitonov and
Barnes. 2000). An SCh-associated increase in eNO was observed in a population of adults
with asthma with high prevalence of atopy (90%) (Maestrelli et al.. 2011) (Table 5-10).
Maestrelli et al. (2011) did not observe associations with lung function or asthma control
score, but their results for pulmonary inflammation agree with results for lung function
and symptoms in other populations with asthma plus atopy. Their results are also
supported by findings that allergic inflammation in rats persists 24 hours after SO2
exposures repeated over many days. The multicity U.S. asthma medication trial observed
imprecise associations for eNO with wide 95% CIs in the ICS, beta-agonist, and placebo
groups (Qian et al.. 2009a). Both studies of adults with asthma estimated SO2 exposure
from central site monitors. Neither indicated whether the measurements adequately
represented the spatiotemporal variability in SO2 concentrations in the study area, and the
U.S. study averaged concentrations from monitors within 32 km of each subject's ZIP
code centroid.
Two recent studies measured SO2 at or 0.65 km from children's schools (Greenwald et
al.. 2013; Lin et al.. 2011b). which may better represent some component of subjects'
exposure. Results are inconsistent. Percent changes in eNO were 31 (95% CI: -24, 119)
per 10-ppb increase in SO2 measured at a school in El Paso, TX (Greenwald et al.. 2013)
and 5.5 (95% CI: 2.7, 8.3) per 10-ppb increase in SO2 measured near a school in Beijing,
China before and after the 2008 Olympics (Lin et al.. 201 lb). Among children with
asthma not using ICS in Windsor, ON, SO2 concentrations at a monitor within 10 km of
homes were not associated with eNO but were associated with markers of oxidative stress
in exhaled breath condensate (EBC) (Liu et al.. 2009b). The school-based studies differed
in lags examined, and an association was observed with lag 0 SO2 (Lin et al.. 2011b) but
not lag 0-3 avg SO2 (Greenwald et al.. 2013). For SO2 measured at central site monitors,
associations were observed with both lag 0 and lag 0-2 avg concentrations (Liu et al..
2009b). Prevalence of atopy was not reported for the study populations of children.
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Copollutant confounding is an uncertainty in addition to inconsistent findings for SO2
associations with pulmonary inflammation and oxidative stress in children and adults
with asthma. Associations were observed with PM2 5, BC, CO, O3, and NO2 (Lin et al..
2011b; Maestrelli et al.. 2011; Liu et al.. 200%). Only Liu et al. (2009b) reported
S02-copollutant correlations, indicating the potential for confounding with PM2 5
(r = 0.56), less so with NO2 (r = 0.18), and likely not with O3 (r = -0.02). Maestrelli et al.
(2011) did not examine copollutant models, and results in children with asthma are
conflicting. For pollutants measured 0.65 km from school, SO2 associations with eNO
persisted with adjustment for PM2 5 or BC but nevertheless decreased (Lin et al.. 2011b).
The effect estimate decreased for PM2 5 but was robust for BC. Based on pollutants
measured up to 10 km from home, the SO2 association with oxidative stress decreased
with adjustment for NO2 and became imprecise with adjustment for PM2 5 (Liu et al..
2009b) (Table 5-10). However, inference about SO2 associations is weak because of
uncertainty in the SO2 exposure estimates and because PM2 5 and NO2 associations
decreased with SO2 adjustment.
Animal Toxicological Studies
The 2008 SOx ISA (U.S. EPA. 2008d) discussed several studies that investigated the
effects of exposure to SO2 on inflammatory responses. While one study failed to
demonstrate inflammation following a single subacute exposure to 1 ppm SO2 (U.S.
EPA. 2008d). other studies found that repeated SO2 exposure enhanced the development
of an allergic phenotype and altered physiologic responses in animal models of allergic
airway disease. These studies demonstrating effects of repeated SO2 exposures in models
of allergic airway disease are listed in Table 5-11 and described here. In addition, other
studies involving repeated SO2 exposures in naive rats, including studies that demonstrate
increased sensitivity to allergens, have been conducted and are described below in
Section 5.2.1.7.
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Table 5-10 Recent epidemiologic studies of pulmonary inflammation and oxidative stress in populations with
asthma.
SO2 Averaging
Study Population and SO2 Exposure Time and Effect Estimate (95% CI)
Methodological Details Estimates (ppb) Lag Day Single-Pollutant Modela Copollutant Examination3
Adults with Asthma
tQian et al. (2009b)
Boston, MA; New York, NY;
Philadelphia, PA; Madison, Wl;
Denver, CO; San Francisco, CA;
1997-1999
N = 119, ages 12-65 yr. 100%
persistent asthma. 1/3 ICS use, 1/3
beta-agonist use, 1/3 placebo use.
Examined every 2-4 wk for 16 wk.
Recruited from clinics as part of an
asthma medication trial. Multiple
comparisons—many pollutants, lags,
medication use analyzed.
Monitors averaged
within 32 km of subject
ZIP code centroid.
Mean (SD): 5.3 (4.4)
75th percentile: 7.6
Max: 27
24-h avg Change in eNO (ppb)
0 All subjects: 0.09 (-0.07, 0.25)
ICS: 0.17 (-0.11, 0.44)
Beta-agonist: 0.04 (-0.18, 0.27)
0-3 avg All subjects: 0.07 (-0.12, 0.26)
ICS: 0.15 (-0.13, 0.43)
Beta-agonist: 0.10 (-0.19, 0.38)
Copollutant model, all subjects, lag 0
with PM10: 0.16 (-0.08, 0.40)
with NO2: 0 (-0.18, 0.18)
with Os: 0.05 (-0.12, 0.22)
NO2 and PM10 associations persist with
SO2 adjustment. No association with O3.
SO2 moderately correlated with NO2,
r = 0.58. Correlation NR for PM10.
tMaestrelli et al. (2011)
Padua, Italy, 2004-2005
N = 32, mean (SD) age 40 (7.5) yr.
81% persistent asthma. 69% ICS use.
90% atopy.
Six measures over 2 yr. Recruited
from database of beta-agonist users
(>6 times per yr for 3 yr).
Two monitors in city 24-h avg
Medians across
seasons: 0.87-2.7
75th percentiles across
seasons: 1.3-4.1
0
Change in eNO (ppb)
All subjects: 55 (-2.3, 113)
Nonsmokers: 82 (3.1, 161)
Change in EBC pH
Decrease = more inflammation
All subjects: 0.46 (-0.20, 1.1)
Nonsmokers: 0.18 (-0.34, 0.69)
n = 22
No copollutant model
Association observed with CO and O3. No
association with personal or central site
PM2.5 or PM10. No association for central
site NO2.
Copollutant correlations NR.
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Table 5-10 (Continued): Recent epidemiologic studies of pulmonary inflammation and oxidative stress in
populations with asthma.
Study Population and
Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and
Lag Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
Children with Asthma
tGreenwald et al. (2013) Monitor at school 24-h avg
El Paso, TX, Mar-Jun 2010 A: residential area 0-3 avg
N = 38, mean age 10 yr. 47% daily B: 91 m from major
asthma medication use. road
Weekly measures for 13 wk. Mean (SD): 1.2 (0.44)
Recruited from schools. and 0.84 (0.54)
Upper percentiles NR.
Percent change in eNO No copollutant model
A: -59 (-89, 36) Association observed with BC, NO2,
B' 31 (-24 119) BTEX, cleaning product VOCs (a-pinene,
dichlorobenzene, d-limonene) at school B.
No association with PM2.5.
SO2 weakly correlated with BC, NO2,
BTEX, cleaning product VOCs. Pearson
r= -0.14, -0.22, -0.07, 0.14.
tLinetal. (2011b)
Beijing, China
N = 8, ages 9-12 yr
Daily measures for five 2-wk periods
before and after Olympics.
Recruitment from school.
Monitor 0.65 km from 24-h avg
school 0
Means across five
periods before and 1
after Olympics: 3.7-45
Percent change in eNO
5.5 (2.7, 8.3)
3.4 (1.4, 5.4)
Copollutant model with BC or PM2.5
Results presented only in a figure. SO2
associations persist but decrease in
magnitude with adjustment for BC or
PM2.5. BC association not altered by SO2
adjustment; PM2 5 association slightly
attenuated. Associations observed for CO
and NO2.
Copollutant correlations NR.
tLiu et al. (2009b). Liu (2013)
Windsor, ON
Oct-Dec 2005
N = 182, ages 9-14 yr. 37% ICS use.
35% beta-agonist use.
Weekly measures for 4 wk. Recruited
from schools. Mean 1.6 and 2.2 h/d
spent outdoors for two study groups.
Two monitors
averaged
99% homes within
10 km of sites
Median: 4.5
95th percentile: 16
24-h avg Percent change
0 eNO: 9.0 (-7.6, 29)
TBARS: 28 (0.46, 63)
8-lsoprostane: 23 (3.9, 44)
0-2 avg eNO: -5.6 (-28, 24)
TBARS: 77 (31, 131)
8-lsoprostane: -0.55 (-28, 38)
Copollutant model, lag 0-2 avg, TBARS
with PM2.5: 53 (-21, 158)
with NO2: 51 (0.93, 112)
with Os: 74 (26, 128)
1 PM25 and NO2 association attenuated
with SO2 adjustment.
SO2 moderately correlated with PM2.5,
weakly correlated with NO2 and O3.
Spearman r= 0.56, 0.18, -0.02.
BC = black carbon; BTEX = benzene, toluene, ethylbenzene, xylene; CI = confidence interval; CO = carbon monoxide; EBC = exhaled breath condensate; eNO = exhaled nitric
oxide; ICS = inhaled corticosteroid; N = sample size; N02 = nitrogen dioxide; 03 = ozone; PM2.5 = particulate matter with nominal aerodynamic diameter less than or equal to 2.5 |jm;
PM10 = particulate matter with nominal aerodynamic diameter less than or equal to 10 |jm; S02 = sulfur dioxide; TBARS = thiobarbituric acid reactive substances.
aEffect estimates are standardized to a 10-ppb increase in 24-h avg S02.
fStudies published since the 2008 Integrated Science Assessment for Sulfur Oxides.
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Table 5-11 Study-specific details from animal toxicological studies of
subclinical effects underlying asthma.
Study
Species (Strain); n;
Sex; Lifestage/Age
or Weight
Exposure Details
(Concentration; Duration)
Endpoints Examined
Li et al. (2007)
Rats (Wistar);
n = 6/group; M; age
NR
Sensitization by i.p. injection of 100 mg
ovalbumin followed by booster injection
of 10 mg ovalbumin after 7 d followed by:
(1) Challenge with 1% ovalbumin aerosol
for 30 min for 7 d beginning at 15 d,
(2) Exposure to 2 ppm SO2 for 1 h/d for
7 d, or
(3) SO2 exposure followed by ovalbumin
aerosol challenge for 7 d
Endpoints examined 24 h
following the last challenge
BALF—inflammatory cell
counts
Lung—histopathology,
immunohistochemistry
Lung and tracheal
tissue—mRNA and protein
levels of MUC5AC and
ICAM-1
Li et al. (2008)
Rats (Wistar);
n = 6/group; M; age
NR; 180-200 g
Sensitization by i.p. injection of 100 mg
ovalbumin followed by booster injection
of 10 mg ovalbumin after 7 d followed by:
(1) Challenge with 1% ovalbumin aerosol
for 30 min for 7 d beginning at 15 d,
(2) Exposure to 2 ppm SO2 for 1 h/d for
7 d, or
(3) SO2 exposure followed by ovalbumin
aerosol challenge for 7 d
Endpoints examined 24 h
following the last challenge
BALF—total and differential
cell counts, EGF
Lung tissue—histopathology
Lung and tracheal
tissue—mRNA levels of
EGF, EGFR, COX-2
Lung tissue—protein levels
of EGFR, COX-2
Xie et al. (2009) Rats (Wistar); Sensitization by i.p. injection of 100 mg
n = 6/group; M; age ovalbumin followed by booster injection
NR of 10 mg ovalbumin after 7 d followed by:
(1) Challenge with 1% ovalbumin aerosol
for 30 min for 7 d beginning at 15 d,
(2) Exposure to 2 ppm SO2 for 1 h/d for
7 d, or
(3) SO2 exposure followed by ovalbumin
aerosol challenge for 7 d
Endpoints examined 24 h
following the last challenge
Lung tissue—mRNA levels
of p53, bax, bcl-2
Lung—protein levels of p53,
bax, bcl-2
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Table 5-11 (Continued): Study specific details from animal toxicological studies of
subclinical effects underlying asthma.
Study
Species (Strain); n;
Sex; Lifestage/Age
or Weight
Exposure Details
(Concentration; Duration)
Endpoints Examined
Li etal. (2014)
Rats (Wistar);
n = 6/group; M; age
NR; 180-200 g
Sensitization by i.p. injection of 100 mg
ovalbumin followed by booster injection
of 10 mg ovalbumin after 7 d followed by:
(1) Challenge with 1% ovalbumin aerosol
for 30 min for 7 d beginning at 15 d,
(2) Exposure to 2 ppm SO2 for 1 h/d for
7 d, or
(3) SO2 exposure followed by ovalbumin
aerosol challenge for 7 d
Endpoints examined
BALF—inflammatory cell
counts and cytokines IL-4,
IFN-y, TNFa, IL-6
Serum—IgE
Lung—histopathology
Lung and tracheal
tissue—mRNA and protein
levels of NFkB, IkBq, IKK(3,
IL-6, IL-4, TNFa, FOXp3
EMSA NFkB binding activity
BALF = bronchoalveolar lavage fluid; bax = B-cell lymphoma 2-like protein 4; bcl-2 = B-cell lymphoma 2;
COX-2 = cyclooxygenase-2; EGF = epidermal growth factor; EGFR = epidermal growth factor receptor; EMSA = electrophoretic
mobility shift assay; FOXp3 = forkhead box p3 ICAM-1 = intercellular adhesion molecule 1 ;lFN-y = interferon gamma;
IgE = immunoglobulin E; IKK(B = inhibitor of nuclear factor kappa-B kinase subunit beta; IL-4 = interleukin-4; IL-6 = interleukin-6;
IkBo = nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha; i.p. = intraperitoneal; M = male;
MUC5AC = mucin 5AC glycoprotein; n = sample size; NFkB = nuclear factor kappa-light-chain-enhancer of activated B cells;
NR = not reported; p53 = tumor protein p53; SD = standard deviation; S02 = sulfur dioxide; TNF-or = tumor necrosis factor alpha.
Repeated exposure to SO2 promoted an allergic phenotype when ovalbumin sensitization
and challenge preceded SO2 exposure. As described in the 2008 SOx ISA (U.S. EPA.
2008d). Li et al. (2007) demonstrated that rats, which were first sensitized and challenged
with ovalbumin and subsequently exposed to 2 ppm SO2 for 1 hour/day for 7 days, had
an increased number of inflammatory cells in BALF and an enhanced histopathological
response compared with those treated with ovalbumin or SO2 alone. 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 in those treated with ovalbumin or SO2 alone. A follow-up study involving the
same exposure regimen (2 ppm SO2 for 1 hour) in the same allergic animal model (rats
sensitized and challenge with ovalbumin) also found that repeated SO2 exposure
enhanced inflammatory and allergic responses to ovalbumin (Li et al.. 2014). Numbers of
eosinophils, lymphocytes and macrophages were greater in BALF of SCh-exposed and
ovalbumin-treated animals than in animals treated only with ovalbumin. In addition, SO2
exposure enhanced upregulation and activation of NFkB, a transcription factor involved
in inflammation and upregulation of the cytokines IL-6 and IL-4 in lung tissue in this
model of allergic airway disease. Furthermore, BALF levels of IL-6 and IL-4 were
increased to a greater extent in SC>2-exposed and ovalbumin-treated animals compared
with ovalbumin treatment alone. These results indicate that repeated SO2 exposure
enhanced activation of the NFkB inflammatory pathway and upregulation of
inflammatory cytokines in ovalbumin-treated animals. Furthermore, SO2 exposure
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enhanced the effects of ovalbumin on levels of IFN^y (decreased) and IL-4 (increased) in
BALF and on IgE levels in serum (increased). Because levels of IL-4 are indicative of
Th2 status and levels of IFN-y are indicative of Thl status, these results suggest a shift in
Thl/Th2 balance away from Th2 in rats made allergic to ovalbumin, an effect
exacerbated by SO2 exposure. These Th2-related changes are consistent with the
observed increases in serum IgE and BALF eosinophils in ovalbumin-treated animals,
effects which were also enhanced by SO2 exposure. Alternatively, Th2-related changes
may reflect a Type 2 immune response mediated by group 2 innate lymphoid cells. Taken
together, these results indicate that repeated exposure to SO2 exacerbated inflammatory
and allergic responses in this animal model.
Two other follow-up studies by the same laboratory examined the effects of inhaled SO2
on the asthma-related genes encoding epidermal growth factor (EGF), epidermal growth
factor receptor (EGFR), and cyclooxygenase-2 (COX-2) and on apoptosis-related genes
and proteins in this same model based on sensitization with ovalbumin (Xie et al.. 2009;
Li et al.. 2008). While EGF and EGFR are related to mucus production and airway
remodeling, COX-2 is related to inflammation and apoptosis and may play a role in
regulating airway inflammation. SO2 exposure enhanced the effects of ovalbumin
challenge in this model, resulting in greater increases in mRNA and protein levels of
EGF, EGFR, and COX-2 in the trachea compared with ovalbumin challenge alone. SO2
exposure enhanced other effects of ovalbumin in this model, resulting in a greater decline
in mRNA and protein levels of p53 and bax and a greater increase in mRNA and protein
levels of bcl-2 in the lungs compared with ovalbumin challenge alone. The increased
ratio of bcl-2:bax, an indicator of susceptibility to apoptosis, observed following
ovalbumin challenge, was similarly enhanced by SO2. Thus, repeated exposure to SO2
may impact numerous processes that may be involved in inflammation and/or airway
remodeling in allergic airway disease.
Summary of Subclinical Effects Underlying Asthma Exacerbation
Whereas previous evidence was limited and inconsistent, recent evidence from
experimental studies supports a relationship between short-term exposure to SO2 and
allergic responses related to asthma. This includes findings of eosinophilic inflammation
in individuals with asthma exposed acutely to SO2. In addition, enhanced inflammation
and allergic responses were demonstrated in animals made allergic to ovalbumin and
exposed repeatedly to SO2. Epidemiologic findings are inconsistent overall, including
recent results based on SO2 measured at or near children's schools. However, coherent
with experimental studies, an S02-associated increase in pulmonary inflammation was
observed in adults with asthma plus atopy. Copollutant confounding is not addressed in
these results, but the evidence from animal toxicological studies provides some biological
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plausibility for an effect of SO2 exposure, particularly because effects in rats were shown
to occur with repeated exposures and 24 hours after exposure ended. The evidence for
SCh-related allergic inflammation also supports evidence across disciplines for SO2
effects on asthma symptoms, hospital admissions, and ED visits, as well as lung function
decrements in people with asthma.
Summary of Asthma Exacerbation
The 2008 ISA for Sulfur Oxides did not explicitly draw a conclusion about a relationship
between short-term SO2 exposure and asthma exacerbation but described strong support
from controlled human exposure studies for SCh-induced lung function decrements and
increases in respiratory symptoms in adults with asthma when ventilation rates were
increased. Such effects in adolescents with asthma are less clear due to a paucity of data,
but effects appear similar to adults. There are no laboratory studies of children exposed to
SO2; however, a number of studies have assessed airway responsiveness of children and
adults exposed to the bronchoconstrictive stimuli methacholine. Based largely on those
studies, school-aged children, particularly boys and perhaps obese children, might be
expected to have greater responses (i.e., larger decrements in lung function) following
exposure to SO2 than adolescents and adults.
In adults with asthma, short-term exposures for 5-10 minutes to 0.2-0.3 ppm SO2
resulted in 5-30% of exercising individuals experiencing moderate or greater decrements
(i.e., >15% decrease in FEVi or >100% increase in sRaw; Table 5-2). Decrements in
FEVi at 0.3 ppm SO2 were statistically significant in responsive individuals (defined as
those having an FEVi decrease of >15% after exposure to 0.6 or 1.0 ppm SO2;
Table 5-3). At concentrations greater than or equal to 0.4 ppm, 20-60% of asthmatics
experienced SC>2-induced decrements in lung function, which were frequently
accompanied by respiratory symptoms. There is a clear concentration-response
relationship for exposures to SO2 between 0.2 and 1.0 ppm, both in terms of increasing
severity of effect and percentage of asthmatics affected. These concentrations are in the
range of the highest 5-minute ambient SO2 concentrations in some U.S. cities during
2010-2012 (Table 2-9).
Epidemiologic evidence generally supports SCh-associated increases in asthma hospital
admissions and ED visits, particularly in children (Figure 5-3). and respiratory symptoms
in children with asthma (Figure 5-2; Table 5-8). Epidemiologic evidence is inconsistent
for SO2 associations with lung function decrements in adults and children with asthma
(Table 5-6 and Table 5-7). For the limited results from previous epidemiologic and
controlled human exposure studies on airway responsiveness (i.e., response to
methacholine), an independent effect of SO2 is unclear. Two controlled human exposure
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studies demonstrated increased airway responsiveness to subsequent allergen challenge
for at least 48 hours following SO2 exposure in combination with a copollutant
(i.e., NO2). Most epidemiologic studies estimated SO2 exposure from central site
monitors. A few recent studies aimed to address the uncertainty in exposure estimates and
observed asthma-related effects in association with SO2 measured or modeled at or near
school or homes. Studies did not statistically correct for measurement error, but in this
new research area, a method has not been reported for short-term SO2 exposure
(Section 3.4.4). As in the 2008 ISA for Sulfur Oxides, copollutant confounding is
unresolved in the epidemiologic evidence. Many recent studies continue to indicate that
SO2 associations with asthma hospital admissions and ED visits remain relatively
unchanged in magnitude in copollutant models, but SO2 associations with asthma
symptoms and pulmonary inflammation often did not persist after adjustment for PM2 5,
EC/BC, or NO2. The role of SO2 in ambient multipollutant mixtures is not clearly
elucidated. Controlled human exposure studies show asthma-related effects when SO2
exposure occurs with O3 orNCh, and limited epidemiologic examination shows
associations for multipollutant mixtures that contain SO2. However, associations for
mixtures containing SO2 are similar to those for SO2, CO, NO2, PM10, or PM2 5 or less
than the sum of single-pollutant effect estimates, indicating an overlap in associations for
copollutants.
Expanded evidence for S02-induced allergic inflammation supports an effect of SO2
exposure on asthma exacerbation. Epidemiologic findings of S02-associated increases in
pulmonary inflammation are inconsistent, but enhanced allergic inflammation and
allergic responses are demonstrated in a previous controlled human exposure study of
adults with asthma plus atopy and multiple recent studies from a single laboratory in rats
made allergic to ovalbumin and exposed repeatedly to 2 ppm SO2. These findings provide
some support for the epidemiologic associations for SO2 with decreased lung function as
well as increased airway responsiveness, respiratory symptoms, and pulmonary
inflammation observed in most studies of children and adults with asthma plus atopy.
Much of the epidemiologic evidence for S02-associated asthma exacerbation is for
24-h avg SO2 concentrations. Although 24-h avg and 1-h max SO2 concentrations are
correlated at the same monitor, it is not clear whether this correlation applies across a
community. Some recent studies add evidence for association for asthma symptoms and
ED visits with increases in 1-h max SO2 concentrations, including SO2 measured at
schools. For lung function decrements, pulmonary inflammation, and asthma hospital
admission and ED visit studies, several results indicate associations for 3- or 4-day avg
SO2 concentrations. The evidence for enhanced allergic inflammation, which is seen after
repeated 2 ppm SO2 exposures and 24 hours after exposure ended, somewhat supports the
biological plausibility of epidemiologic associations with asthma-related outcomes.
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Moreover, controlled human exposure studies clearly demonstrate that SO2 exposures of
0.2-0.6 ppm can induce effects related to asthma exacerbation.
5.2.1.3 Allergy Exacerbation
The evidence described in the preceding section for SO2 and allergen coexposure
enhancing inflammation in rodent models of allergic airway disease indicates that SO2
exposure may increase the sensitivity of people with allergic asthma to an allergen. This
evidence also suggests the potential for SO2 exposure to affect respiratory responses in
people with allergy but not asthma. The 2008 ISA for Sulfur Oxides did not make distinct
statements about a relationship with SO2 exposure, but relevant epidemiologic studies
had inconsistent findings. Recent epidemiologic evidence is also uncertain, including that
for school SO2 measurements.
Lung Function in Populations with Allergy
Previous epidemiologic studies examined children or adults with allergy but no asthma,
defined by high serum IgE levels but no bronchial hyperresponsiveness, and did not
indicate associations between short-term increases in ambient SO2 concentration and
decreases in lung function (Boezen et al.. 2005; Boezen et al.. 1999). The same studies
observed associations for groups with asthma plus allergy. Previous findings were based
on 24-h avg SO2 measured at a single site in each city. The only available recent study
measured SO2 at children's schools (Correia-Deur et al.. 2012). which may better
represent some component of subjects' exposures. Also, the temporally resolved 2-h avg
metric is more comparable to the exposure durations examined in experimental studies.
In this group of children with allergy in Sao Paolo, Brazil, SO2 had an imprecise
association with PEF with a wide 95% CI [-0.82% (95% CI: -1.9, 0.31) per 10-ppb
increase in 2-h avg SO2]. Results were similar for allergy defined by high serum IgE
levels alone like previous studies and by multiple criteria (i.e., high IgE levels, positive
skin prick test, and high blood eosinophil levels). There was evidence for an association
among all children (with and without allergy), but that was attenuated in copollutant
models with PM10, NO2, or CO. Correlations with SO2 were not reported.
Respiratory Symptoms and Physician Visits in Populations with Allergy
Limited to epidemiologic studies, evidence for an association between short-term SO2
exposure and allergy symptoms is inconsistent. Nonspecific upper and lower respiratory
symptoms were examined in children and adults with high IgE levels but no bronchial
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hyperresponsiveness, and associations with SO2 were inconsistent (Boezen et al.. 2005;
Boezen et al.. 1999). For symptoms specific to allergy, Villeneuve et al. (2006b)
observed an SCh-associated increase in physician visits for allergic rhinitis in older
adults. Recent findings for allergic rhinitis or eczema in children are mixed. However,
inference about an SO2 effect is weak both for results indicating an association (Kim et
al.. 2016a) and results not indicating an association (Annesi-Maesano et al.. 2012;
Linares et al.. 2010). Limitations include cross-sectional design (Annesi-Maesano et al..
2012; Linares et al.. 2010). analysis of a multipollutant model with NO2, O3, PM11, and
pollen (Kim et al.. 2016a; Annesi-Maesano et al.. 2012). lack of consideration of
confounding by meteorological factors (Kim et al.. 2016a). or inclusion of children with
and without allergy in analysis of eczema (Linares et al.. 2010). For results supporting a
relationship with allergy symptoms, associations were observed with same-day (lag 0)
24-h avg SO2 concentrations. These concentrations were from a single monitor in the
city, and information was not reported on the extent to which the measurements
represented the spatiotemporal variability in SO2 concentrations in the study area.
Associations were observed with copollutants such as NO2, PM10, and BS, although these
results were inconsistent as well (Villeneuve et al.. 2006b; Boezen et al.. 2005; Boezen et
al.. 1999). Correlations with SO2 concentrations were not reported, and copollutant
models were not analyzed. Thus, the extent to which the supporting findings may indicate
an independent association for SO2 is unclear.
Subclinical Effects Underlying Allergy Exacerbation
In addition to the animal toxicological evidence for SCh-enhanced allergic inflammation,
a previous epidemiologic study of children with atopy found an S02-associated decrease
in blood eosinophil number, which was presumed to reflect increased recruitment to the
airways (Sovseth et al.. 1995). Exposure assessment from a monitor 2 km from most
subjects' homes is an uncertainty, as is confounding by PM. The study was conducted in
a European city with an aluminum smelter that emitted SO2 and PM, and PM was not
examined for association with eosinophils.
5.2.1.4 Chronic Obstructive Pulmonary Disease Exacerbation
COPD is a lung disease characterized by deterioration of lung tissue and airflow
limitation. Reduced airflow can decrease lung function, and clinical symptoms
demonstrating exacerbation of COPD include cough, dyspnea, sputum production, and
shortness of breath. Severe exacerbation can lead to hospital admissions or ED visits.
This spectrum of outcomes has been evaluated in relation to short-term SO2 exposure,
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and evidence across outcomes and disciplines is inconsistent. This applies to the small
body of studies available for the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) as well
as the few available recent studies. Recent findings come from epidemiologic studies, and
most are for hospital admissions and ED visits.
Lung Function and Respiratory Symptoms
Evidence from a controlled human exposure study and epidemiologic studies does not
support an effect of SO2 exposure on lung function in adults with COPD. Recent
epidemiologic studies add information on respiratory symptoms and mostly do not
indicate an association with ambient SO2 concentrations.
Linn et al. (1985a) reported that a 15-minute exposure to 0.4 and 0.8 ppm SO2 had no
effect on lung function in older adults with physician-diagnosed COPD. These adults
were much older than the adults with asthma (Table 5-2) or healthy adults (Table 5-15)
examined in controlled human exposure studies. Also, the level of exercise in adults with
COPD (VE = 18 L/minute) was lower than that of individuals with asthma, which
effectively lowers the SO2 dose delivered to the lungs (Section 4.2.2). Neither the
previous nor recent epidemiologic study observed S02-associated decrements in lung
function in adults with COPD (Peacock et al.. 2011; Harre et al.. 1997). Both studies
estimated SO2 exposure from a central site monitor(s), and examined 24-h avg
concentrations lagged 1 day. Whereas previous results were based on a multipollutant
model (with PM10, NO2, O3), which often is unreliable, recent results were based on a
single-pollutant model. Associations were imprecise with wide 95% CIs
[e.g., 0.31 L/minute (95% CI: -0.10, 0.72) change in PEF per 10-ppb increase in SO2 and
OR 1.01 (95% CI: 0.89, 1.15) for PEF decrement greater than 20%] (Peacock et al..
2011). Mean and 75th percentile SO2 concentrations were 7.5 and 9.3 ppb, respectively.
SO2 mostly was not associated with dyspnea, sputum changes, wheeze/tight chest, or
other respiratory symptoms (Wu et al.. 2016; Peacock et al.. 2011). Wu et al. (2016)
examined a period of higher SO2 concentration (median 17 ppb and 75th percentile
27 ppb) and observed dyspnea to increase with an increase in 3- to 6-day avg SO2 (OR:
1.88 [95% CI: 1.06, 3.34] per 10-ppb increase in 3-day avg SO2). However, there was a
wide range of distance from subjects to the monitor (1.6-8.8 km), and associations also
were observed with moderately correlated (r = 0.51-0.68) PM2 5, PM10, and NO2.
Hospital Admissions and Emergency Department Visits
Of the studies evaluated in the 2008 SOx ISA, only one U.S. or Canadian-based study
examined the association between short-term SO2 exposure and COPD hospital
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admissions or ED visits (Figure 5-7). Recent studies add to the initial evidence, which
generally indicates no association between short-term SO2 exposures and COPD hospital
admissions and ED visits. Additionally, most studies averaged SO2 concentrations over
multiple monitors and examined 24-h avg exposure metrics, which, may not adequately
capture the spatial and temporal variability in SO2 concentrations (Section 3.4.2.). For
each of the studies evaluated in this section, Table 5-12 presents the air quality
characteristics of each city or across all cities, the exposure assignment approach used,
and information on copollutants examined in each COPD hospital admission and ED visit
study. Other recent studies of COPD hospital admissions and ED visits are not the focus
of this evaluation because of various study design issues, as initially detailed in
Section 5.2.1.2. but the full list of these studies, as well as study-specific details, can be
found in Supplemental Table 5S-5 (U.S. EPA. 2016m').
Study
Location
Age Lag
fQiuetal. (2013) Hong Kong All 0-3
fWong et al. (2009) Hong Kong All 0-1
65+ 0-1
Peel et al. (2005) Atlanta, GA All 0-2
f Stieb et al. (2009) 7 Canadian cities All 1
f Arbex et al. (2009) Sao Paulo, Brazil 40+ 0-3 DL
Hospital Admissions
ED Visits
-+-
-10.0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0
% Increase (95% Confidence Interval)
ED = emergency department.
Note: f and red = recent studies published since the 2008 ISA for Sulfur Oxides; black = U.S. and Canadian studies evaluated in the
2008 ISA for Sulfur Oxides. Corresponding quantitative results are reported in Supplemental Table 5S-6 (U.S. EPA. 2016n).
Figure 5-7 Percent increase in chronic obstructive pulmonary disease
hospital admissions and emergency department visits from U.S.
and Canadian studies evaluated in the 2008 SOx ISA and recent
studies in all-year analyses for a 10-ppb increase in 24-h avg or
40-ppb increase in 1-h max sulfur dioxide concentrations.
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Table 5-12 Study-specific details and mean and upper percentile concentrations
from chronic obstructive pulmonary disease hospital admission and
emergency department visit studies conducted in the U.S. and
Canada and evaluated in the 2008 SOx ISA and studies published
since the 2008 SOx ISA.
Study
Location
Years
Exposure
Assignment
Metric
Upper
Mean Percentile
Concentration Concentrations Copollutants
ppb ppb Examined
Hospital admissions
t(Qiu etal. (2013b);
Ko et al. (2007a))
Hong Kong,
China
(1998-2007)
Average of SO2
concentrations
from
10 monitoring
stations
24-h avg
7.4
NR
Correlations (r):
Os: 0.173
Copollutant
models: PM10
tWonq et al. (2009)
Hong Kong, Average of SO2 24-h avg
China concentrations
(1996-2002) from eight
monitoring
stations
6.8
75th: 8.4
Max: 41.8
Correlations (r):
NR
Copollutant
models: none
ED visits
Peel et al. (2005) Atlanta, GA
Average of SO2 1-h max
16.5
90th: 39.0
Correlations (r):
concentrations
PM2.5: 0.17
across monitors
PM10: 0.20
for several
PM10-2.5: 0.21
monitoring
networks
UFP: 0.24
PM2.5 water
soluble metals:
0.00
PM2.5 sulfate:
0.08
PM2.5 acidity:
-0.03
PM2.5 OC: 0.18
PM2.5 EC: 0.20
Oxygenated
HCs: 0.14
Os: 0.19
CO: 0.26
NO2: 0.34
Copollutant
models: none
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Table 5-12 (Continued): Study specific details and mean and upper percentile
concentrations from chronic obstructive pulmonary
disease hospital admission and emergency department
visit studies conducted in the U.S. and Canada and
evaluated in the 2008 SOx ISA and studies published
since the 2008 SOx ISA.
Upper
Mean
Percentile
Location
Exposure
Concentration
Concentrations
Copollutants
Study
Years
Assignment
Metric
PPb
PPb
Examined
tfStieb et al. (2009))
Seven
Average SO2
24-h avg
2.6-10.0
75th: 3.3-13.4
Correlations (r)
Canadian
concentrations
only reported
cities
across all
by city and
(1992-2003)
monitors in
season.
each city.
Copollutant
Number of SO2
models: none
monitors in
each city
ranged from
1-11.
t(Arbex et al. Sao Paulo, Average of SO2 24-h avg 5.3 75th: 6.6
(2009)) Brazil concentrations Max'16-
(2001-2003) across
13 monitoring
stations
Correlations (r):
PM10: 0.77
NO2: 0.63
CO: 0.52
Copollutant
models: none
CO = carbon monoxide; EC = elemental carbon; HC = hydrocarbon; NR = not reported; 03 = ozone; OC =organic carbon;
N02 = nitrogen dioxide; PM10 = particulate matter with nominal aerodynamic diameter less than or equal to 10 |jm;
PM2.5 = particulate matter with nominal aerodynamic diameter less than or equal to 2.5 |jm; PM^-2.5 = particulate matter with a
nominal aerodynamic diameter less than or equal to 10 |jm and greater than a nominal 2.5 |jm; r= correlation coefficient;
S02 = sulfur dioxide; UFP = ultrafine particle.
f = Studies published since the 2008 ISA for Sulfur Oxides.
Hospital Admissions
Of the studies evaluated in the 2008 SOx ISA, relatively few examined the association
between short-term SO2 exposure and COPD hospital admissions, and evidence of an
association was inconsistent across studies. Although several recent studies assessed the
relationship between short-term SO2 exposures and COPD hospital admissions, the
overall body of evidence remains limited.
Wong et al. (2009) in a study that examined the potential modification of the relationship
between air pollution and respiratory-related hospital admissions by influenza, also
focused on cause-specific respiratory hospital admissions, including COPD. When
focusing on the baseline effect of short-term SO2 exposures on COPD hospital
admissions, the authors found limited evidence of an association at lag 0-1 days for a
10-ppb increase in 24-h avg SO2 concentrations in analyses of both all ages [0.8% (95%
CI: -1.5, 3.1)] and individuals over the age of 65 [0.5% (95% CI: -2.0, 3.0)].
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In an additional study conducted in Hong Kong, Qiu et al. (2013b) focused on whether
there is evidence of modification of the air pollution-COPD hospital admissions
relationship by season and humidity. Compared to Wong et al. (2009). Qiu et al. (2013b)
included 5 additional years of recent data through the year 2007. In single-pollutant
models focusing on the association between short-term SO2 exposures and COPD
hospital admissions, for amultiday lag of 0-3 days, the authors reported a 1.6% increase
(95% CI: 0.1, 3.1) for a 10-ppb increase in 24-h avg SO2 concentrations. The magnitude
of the SO2 association was found to differ between Qiu et al. (2013b) and Wong et al.
(2009). but the reason for the difference remains unclear, considering that similar data
sources were used in each study. It is important to note that neither study conducted
copollutant analyses for the entire study duration nor provided detailed information on
the correlation between the air pollutants examined to help in the assessment of whether
SO2 has an independent effect on COPD hospital admissions.
Emergency Department Visits
The 2008 SOx ISA identified relatively few studies that examined the association
between short-term SO2 exposure and COPD ED visits, and across studies there was
inconsistent evidence of an association. Although recent studies continued to assess the
relationship between short-term SO2 exposures and COPD ED visits, the overall body of
evidence remains limited.
In the seven Canadian cities study discussed previously, and consistent with the asthma
ED visits results, Stieb et al. (2009) did not find any evidence of associations between
24-h avg SO2 and COPD ED visits at single-day lags of 0 to 2 days. Additionally, there
was no evidence of consistent associations between any pollutant and COPD ED visits at
subdaily time scales (i.e., 3-h avg of ED visits vs. 3-h avg pollutant concentrations).
Arbex et al. (2009) also examined the association between COPD and several ambient air
pollutants, including SO2, in a single-city study conducted in Sao Paulo, Brazil for
individuals over the age of 40 years. The authors examined associations between
short-term SO2 exposures and COPD ED visits in both at single-day lags (0 to 6 days)
and in a polynomial distributed lag model (0-6 days). The authors found evidence that
the magnitude of the association was larger at multiday lags compared to single-day lags,
with the lag of 0-3 days from the distributed lag model [49.4% (95% CI: 4.1, 113.7) for a
10-ppb increase in 24-h avg SO2 concentrations] most representative of the pattern of
associations across single-day lags. Although the 0-6-day distributed lag model had the
largest risk estimate, it was not supported by the single-day lag results that showed the
strongest associations at lags of 0 and 1 day. It is important to note that Arbex et al.
(2009) did not conduct copollutant analyses, but unlike correlations with SO2 observed in
other locations, SO2 was highly correlated with PM10 (r = 0.77) and moderately
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correlated with NO2 (r = 0.63) and CO (r = 0.52) in this study. The results of Arbex et al.
(2009) provide evidence of a potentially prolonged SO2 effect on COPD ED visits;
however, the results should be viewed with caution because effect estimates are not
precise, time series is short, and there is potential for copollutants confounding.
Seasonal Analyses
Traditionally, epidemiologic studies have examined potential seasonal differences in
associations by stratifying by season. In the study of air pollution and COPD hospital
admissions in Hong Kong, Qiu et al. (2013b) examined potential seasonal differences in
associations by this traditional approach but also examined whether the combination of
season and humidity modify the air pollution-health effect association. In seasonal
analyses, the authors found a stronger association at lag 0-3 for a 10-ppb increase in
24-h avg SO2 concentrations during the cool season (November-April) [2.7% (95% CI:
0.5,4.9)] compared to the warm season (May-October) [0.6% (95% CI: -1.1. 2.3)1. Qiu
et al. (2013b) then examined whether the seasonal differences in associations observed
were due to low humidity days (i.e., relative humidity <80%) or high humidity days
(i.e., relative humidity >80%) by examining the interaction between the various
combinations of season and humidity. When focusing on the combined effect of season
and humidity, SO2 concentrations were found to be highest on days with low humidity in
both seasons. In the warm season, there was no evidence of an association regardless of
whether the interaction between season and low or high humidity days were examined. In
the cold season, at lag 0-3 for a 10-ppb increase in 24-h avg SO2 concentrations, Qiu et
al. (2013b) reported the strongest association during days with low humidity [5.3% (95%
CI: 2.4, 8.3)] compared to high humidity [0.5% (95% CI: -2.6, 3.7)], suggesting that the
combination of season and humidity plays a role in the relationship between air pollution
and health effects. However, when examining copollutant models with PM10, associations
in all season and humidity combinations were attenuated, with only the association in the
cool season and low humidity combination remaining positive, albeit with large
uncertainty estimates [0.8% (95% CI: -2.1, 3.9); lag 0-3 for a 10-ppb increase in
24-h avg SO2 concentrations]. The results from Qiu et al. (2013b) are consistent with
evidence from controlled human exposure studies demonstrating that SO2 responses are
exacerbated in colder and dryer conditions (Section 5.2.1.2). However, these studies
focused on lung function changes in people with asthma and it is unclear how these
results correspond to results from an epidemiologic study of COPD hospital admissions.
Additionally, it is important to note the potential influence of geographic location on the
results from studies that examine the seasonal patterns of associations.
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Lag Structure of Associations
Only a limited number of studies examined the lag structure of associations for
SCh-related COPD hospital admissions and ED visits. Oiu et al. (2013b) in the
examination of air pollution and COPD hospital admissions in Hong Kong conducted
analyses to evaluate associations with SO2 at both single-day and multiday lags of
0-3 days. The authors found the strongest evidence for an SO2-COPD hospital admission
association at a multiday lag of 0-3 days, with additional evidence of positive
associations at single-day lags of 1 day and 3 days.
Arbex et al. (2009). when examining associations between SO2 exposure and COPD ED
visits in Sao Paulo, Brazil, focused on both single-day lags (0 to 6 days) and a polynomial
distributed lag (0-6 day) model. The authors found evidence that the magnitude of the
association was larger at multiday lags compared to single-day lags, and the magnitude of
the association increased as the number of lag days examined increased, specifically
across lags of 0-1, 0-2, and 0-5 days. However, the 0-5-day distributed lag model
results were not supported by the single-day lag results, which indicated that the effect of
SO2 on COPD ED visits was rather immediate, occurring in the range of lag 0 and 1 days.
Collectively, the results of Oiu et al. (2013b) and Arbex et al. (2009) provide initial
evidence suggesting a potential prolonged effect of SO2 on COPD hospital admissions
and ED visits. However, the collective evidence indicating a potential association
between short-term SO2 exposures and COPD hospital admissions and ED visits remains
relatively small.
Summary of Chronic Obstructive Pulmonary Disease Exacerbation
Across disciplines and outcomes, evidence from previous and recent studies does not
clearly support a relationship between short-term SO2 exposure and COPD exacerbation.
The evidence base is relatively small and mostly comprises epidemiologic studies.
Neither the single controlled human exposure study nor the few epidemiologic studies
indicate S02-related lung function changes in adults with COPD, and recent
epidemiologic studies mostly reported no association with an array of respiratory
symptoms, including sputum changes and dyspnea, which are characteristic of COPD
exacerbation. There is similarly inconsistent evidence for association between short-term
increases in ambient SO2 concentration and hospital admissions and ED visits for COPD
(Figure 5-7). Hospital admissions, ED visits, lung function, and symptoms were
examined in relation to 24-h avg SO2 concentrations, but an association was not observed
with 1-h max SO2 either. The supporting evidence is limited largely to an association of
COPD hospital admissions and ED visits with same-day and 4-day avg SO2
concentrations. All epidemiologic studies estimated SO2 exposure from central site
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monitors. SO2 generally has low to moderate spatial correlations across urban
geographical scales, and the potential error in the exposure estimates in adequately
representing the spatiotemporal variability is uncharacterized in the evidence
(Section 3.4.2.2V The uncertainty in exposure estimates especially applies to 1-h max
SO2. COPD hospital admissions were associated with PM10, NO2, and O3. PM10 was
highly correlated with SO2 (r = 0.77) or when analyzed in a copollutant model, attenuated
the SO2 association and produced wide 95% CIs. The copollutant model results have
unclear implication due to uncertainty in the exposure estimates and unreported
SO2-PM10 correlation. Overall, there is inconsistent evidence for an effect of SO2
exposure on COPD exacerbation, and for the limited supporting evidence, an effect of
SO2 exposure that is independent of copollutants is unclear.
5.2.1.5 Respiratory Infection
The respiratory tract is protected from exogenous pathogens and particles through various
lung host defense mechanisms that include mucociliary clearance, phagocytosis by
alveolar macrophages, and innate and adaptive immunity. There is a paucity of evidence
related to host defense from animal toxicological experiments using ambient-relevant
concentrations of SO2. Several studies of short-term exposure to SO2 were reported in the
1982 AQCD (U.S. EPA. 1982a) and discussed in the 2008 SOx ISA (U.S. EPA. 2008d).
Findings of short-term studies included some effects of 0.1-1 ppm SO2 on the clearance
of labeled particles. No new animal studies of the effects of SO2 exposure on lung host
defense have been conducted since the previous review. A small number of previous
epidemiologic studies reported S02-associated increases in respiratory infections as
self-reported or indicated by hospital admissions and ED visits. However, many results
were noted as being unreliable because they were based on statistical methods prone to
bias.
Recent contributions to the evidence are limited to epidemiologic studies, and the
evaluation of this evidence focuses on hospital admissions and ED visits. There are recent
studies of self-reported infections, and they inconsistently show associations with
ambient SO2 concentrations, [Supplemental Figure 5S-2 (U.S. EPA. 2016h)|. Results
based on school or home SO2 exposure estimates are limited by their cross-sectional
design or examination of nonspecific symptoms such as fever. Other studies do not
provide insight over studies of hospital admissions and ED visits on issues such as
exposure measurement error, copollutant confounding, or potentially relevant exposure
durations and concentrations. Recent studies of respiratory infection hospital admissions
and ED visits provide some evidence for association with ambient SO2 concentrations.
However, copollutant confounding remains an uncertainty.
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Hospital Admissions and Emergency Department Visits
The 2008 SOx ISA contained limited evidence of an association between short-term SO2
concentrations and respiratory conditions other than asthma or COPD. Although some
studies evaluated respiratory infections, including respiratory tract infections and
pneumonia, the majority of studies used generalized additive models with default
convergence criteria in the analysis, and this statistical approach was shown to
inaccurately calculate effect estimates and to underestimate standard errors. Additionally,
of the studies evaluated in the 2008 SOx ISA, only one study was conducted in the U.S.
or Canada [i.e., (Peel et al.. 2005)1. Recent studies have examined a variety of outcomes
indicative of respiratory infection; however, none have examined the same respiratory
infection outcome. Additionally, most studies averaged SO2 concentrations over multiple
monitors and examined 24-h avg exposure metrics, which may not adequately capture the
spatial and temporal variability in SO2 concentrations (Section 3.4.2). For each of the
studies evaluated in this section, Table 5-13 presents the air quality characteristics of each
city, or across all cities, the exposure assignment approach used, and information on
copollutants examined in each respiratory infection hospital admission and ED visit
study. Other recent studies of respiratory infection hospital admissions and ED visits are
not the focus of this evaluation because of various study design issues, as initially
detailed in Section 5.2.1.2. but the full list of these studies, as well as study specific
details, can be found in Supplemental Table 5S-5 (U.S. EPA. 2016m).
Hospital Admissions
Although recent studies have continued to examine the association between short-term
SO2 exposures and respiratory infection hospital admissions, the overall evidence
remains limited, primarily due to the variety of respiratory infection outcomes examined.
In a study conducted in Ho Chi Minh City, Vietnam Mehtaetal. (2013) and HEI (2012)
examined the association between short-term air pollution exposures and pediatric (ages
28 days-5 years) hospital admissions for acute lower respiratory infections (ALRI,
including bronchiolitis and pneumonia). In a time-stratified, case-crossover analysis
focusing only on the average of a 1-6 day lag, the study authors reported a positive
association, with large uncertainty estimates, between SO2 and ALRI hospital admissions
in the all-year analysis [7.0% (95% CI: -3.0, 19.1) for a 10-ppb increase in 24-h avg SO2
concentrations]. A larger association was observed in the time-series analysis (HEI.
2012) (Figure 5-8). When examining copollutant models with PM10 and O3, SO2
associations increased slightly, with the percent increase ranging from 7.5-8.0%,
respectively. However, in models with NO2, the SO2 association was attenuated, but
remained positive [4.9% (95% CI: -6.0, 17.0) for a 10-ppb increase in 24-h avg SO2
concentrations].
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Table 5-13 Study-specific details and mean and upper percentile concentrations from respiratory infection
hospital admission and emergency department visit studies conducted in the U.S. and Canada and
evaluated in the 2008 SOx ISA and studies published since the 2008 SOx ISA.
Study
Location (Years)
Type of Visit
(ICD 9/10)
Exposure
Assignment
Metric
Upper Percentile
Mean of
Concentration Concentrations Copollutants
ppb ppb Examined
Hospital admissions
tHEl (2012)
Mehta et al. (2013)
Ho Chi Minh City,
Vietnam
(2003-2005)
Acute lower
respiratory
infection
(J 13—16, 18, 21)
Average of SO2
concentrations
across nine
monitors
24-h avg
8.2
Max: 30.5
Correlations (r):
Dry season:
PM10: 0.32
Os: 0.19
NO2: 0.29
Rainy season:
PM10: 0.36
Os: 0.65
NO2: 0.01
Copollutant
models: NO2,
PM10, O3
tSeqala et al.
(2008)
Paris, France
(1997-2001)
Bronchiolitis
Average SO2
concentrations
across
30 monitors
24-h avg
4.0
Max: 27.4
Correlations (r):
BS: 0.76
PM10: 0.73
NO2: 0.78
Copollutant
models: none
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Table 5-13 (Continued): Study specific details and mean and upper percentile concentrations from respiratory
infection hospital admission and emergency department visit studies conducted in the
U.S. and Canada and evaluated in the 2008 SOx ISA and studies published since the 2008
SOx ISA.
Study
Type of Visit Exposure
Location (Years) (ICD 9/10) Assignment
Metric
Upper Percentile
Mean of
Concentration Concentrations Copollutants
ppb ppb Examined
ED visits
Peel et al. (2005)
Atlanta, GA
(1993-2000)
Pneumonia
(480-486)
Average of SO2
concentrations
from monitors for
several monitoring
networks
1-h max
16.5
90th: 39.0
Correlations (r):
PM2.5: 0.17
PM10: 0.20
PM10-2.5: 0.21
UFP: 0.24
PM2.5 water
soluble metals:
0.00
PM2.5 sulfate: 0.08
PM2.5 acidity:
-0.03
PM2.5 OC: 0.18
PM2.5 EC: 0.20
Oxygenated HCs:
0.14
Os: 0.19
CO: 0.26
NO2: 0.34
Copollutant
models: none
tStieb et al. (2009)
Seven Canadian
cities (1992-2003)
Respiratory
infection
(464, 466,
480-487)
Average SO2
concentrations
across all
monitors in each
city. Number of
SO2 monitors in
each city ranged
from 1-11.
24-h avg
2.6-10.0
75th: 3.3-13.4
Correlations (r)
only reported by
city and season.
Copollutant
models: none
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Table 5-13 (Continued): Study specific details and mean and upper percentile concentrations from respiratory
infection hospital admission and emergency department visit studies conducted in the
U.S. and Canada and evaluated in the 2008 SOx ISA and studies published since the 2008
SOx ISA.
Study
Location (Years)
Type of Visit
(ICD 9/10)
Exposure
Assignment
Metric
Mean
Concentration
PPb
Upper Percentile
of
Concentrations
PPb
Copollutants
Examined
tSeaala et al.
(2008)
Paris, France
(1997-2001)
Bronchiolitis
Average SO2
concentrations
across
30 monitors
24-h avg
4.0
Max: 27.4
Correlations (r):
BS: 0.76
PM10: 0.73
NO2: 0.78
Copollutant
models: none
tZemek et al.
(2010)
Edmonton, AB
(1992-2002)
Otitis media
(382.9)
Average of SO2
concentrations
across three
monitors
24-h avg
All-year: 2.6
Warm (Apr-Sep):
2.1
Cold (Oct-Mar):
3.1
All-year
75th: 3.5
Correlations (r):
NR
Copollutant
models: none
Outpatient and physician visits
tSinclair et al.
(2010)
Atlanta, GA
(1998-2002)
Upper respiratory
infection
Lower respiratory
infection
SO2
concentrations
collected as part
of AIRES at
SEARCH
Jefferson Street
site
1-h max
1998-2000: 19.3
2000-2002: 17.6
1998-2002: 18.3
NR
Correlations (r):
NR
Copollutant
models: none
AIRES = Aerosol Research Inhalation Epidemiology Study; BS = black smoke; CO = carbon monoxide; EC = elemental carbon; HC = hydrocarbon; ICD = International Classification
of Diseases; ISA = Integrated Science Assessment; N02 = nitrogen dioxide; 03 = ozone; OC = organic carbon; PM = particulate matter; NR = not reported; r= correlation coefficient;
SEARCH = Southeast Aerosol Research Characterization; S02 = sulfur dioxide; UFP = ultrafine particle.
f = studies published since the 2008 ISA for Sulfur Oxides.
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Study
Age
tHEI (2012); Mehtaetal. (2013) Ho Chi Minh, Vietnam 28 days - 5 years
28 days - 5 years
tSegala et al. (2008)
Peel et al. (2005)
tStieb et al. (2009)
tSegala et al. (2008)
Peel et al. (2005)
tZemek et al. (2010)
Paris, France
Atlanta, GA
7 Canadian cities
Paris, France
Atlanta, GA
Edmonton, Canada
All
All
<3
All
1-3
Lag
l-6a
1 -6b
0-4a
0-4b
0-2
2
0-4a
0-4b
0-2
4
Hospital Admissions
Respiratory Infection
Bronchiolitis
^
ED Visits
Respiratory Infection
Bronchiolitis
—g ^
Pneumonia
Otitis media
0.0 10.0 20.0 30.0
) Increase (95% Confidence Interval)
ED = emergency department.
Note: f and red = recent studies published since the 2008 ISA for Sulfur Oxides; Black = U.S. and Canadian studies evaluated in
the 2008 ISA for Sulfur Oxides; circles = all-year results, diamonds = warm season results, squares = cold season results.
Corresponding quantitative results are found in Supplemental Table 5S-7 (U.S. EPA. 2016k).
Figure 5-8 Percent increase in respiratory infection hospital admissions and
emergency department visits from U.S. and Canadian studies
evaluated in the 2008 SOx ISA and recent studies in all-year and
seasonal analyses for a 10-ppb increase in 24-h avg or 40-ppb
increase in 1-h max sulfur dioxide concentrations.
In another study that also examined respiratory infections (i.e., bronchiolitis) in children,
Seaala et al. (2008) focused on associations with winter (October-January) air pollution
because that is when respiratory syncytial virus (RSV) activity peaks. It has been
hypothesized that air pollution exposures may increase the risk of respiratory infections,
including bronchiolitis due to RSV (Segala et al. 2008). Focusing on children <3 years of
age in Paris, France, the study authors conducted a bidirectional case-crossover analysis
along with a time-series analysis to examine air pollution associations with bronchiolitis
hospital admissions and ED visits (see ED visits section below). Although the authors
specified that the bidirectional case-crossover approach was used to "avoid time-trend
bias," it must be noted that the bidirectional approach has been shown to bias results
(Seaala et al.. 2008; Lew et al.. 2001). In the case-crossover analysis, SO2 was associated
with bronchiolitis hospital admissions at lag 0-4 days for a 10-ppb increase in 24-h avg
SO2 concentrations [34.8% (95% CI: 19.5, 47.8)] with a similar risk estimate observed
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forthe time-series analysis [31.6% (95% CI: 13.7, 51.2)]. Although a positive association
was observed, the authors did not conduct copollutant analyses. This omission
complicates the interpretation of the results because SO2 was highly correlated with the
other pollutants examined, with correlations ranging from r = 0.73-0.87.
Emergency Department Visits
Similar to respiratory infection hospital admissions, recent studies have examined
respiratory infection ED visits; however, these studies overall have not consistently
examined the same respiratory infection outcomes (Figure 5-8). In their study of seven
Canadian cities, Stieb et al. (2009) also examined the association between short-term SO2
exposure and respiratory infection ED visits. The authors reported a positive association
at a 2-day lag [1.2% (95% CI: -2.5, 5.2) for a 10-ppb increase in 24-h avg SO2
concentrations], but there was uncertainty surrounding this result and there was no
evidence of an association at single-day lags of 0 and 1 days. However, Sena la etal.
(2008). in addition to examining bronchiolitis hospital admissions, also examined
bronchiolitis ED visits. The authors reported evidence of an association between
short-term SO2 exposures and bronchiolitis ED visits [34.7% (95% CI: 25.5, 44.5); lag
0-4 for a 10-ppb increase in 24-h avg SO2 concentrations]. However, as mentioned
previously, the interpretation of these results is complicated by the lack of copollutant
analyses and the high correlation between the pollutants examined (r = 0.73 to 0.87),
along with the use of a bidirectional case-crossover approach.
In an additional study conducted in Edmonton, AB, Zemek et al. (2010) examined a new
outcome for SO2, otitis media (i.e., ear infections) ED visits, for ages 1-3 years.
Associations were examined for single-day lags of 0 to 4 days in all-year as well as
seasonal analyses. The authors found no evidence of an association between short-term
SO2 exposures and increases in ED visits for otitis media at any single-day lag in the
all-year analysis.
Physician/Outpatient Visits
In a study conducted in Atlanta, GA as discussed in Section 5.2.1.2. Sinclair et al. (2010)
examined the association between air pollution and respiratory infection (e.g., upper
respiratory infections, lower respiratory infections) outpatient visits from a managed care
organization. As detailed previously, the authors separated the analysis into two time
periods - the first 25 months of the study period (i.e., August 1998-August 2000) and the
second 28 months of the study period (i.e., September 2000-December 2002).
A comparison of the two time periods indicated that risk estimates across outcomes
tended to be larger in the earlier 25-month period compared to the later 28-month period.
An examination of the respiratory infection outcomes found no evidence of an
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association for upper respiratory infections at any lag and a positive association for lower
respiratory infections for only lag 0-2.
Multiday Lags
In the case of respiratory infection hospital admission and ED visit studies, none of the
studies evaluated conducted an extensive analysis of the lag structure of associations.
However, Sena la et al. (2008) in a study of acute bronchiolitis examined multiday lags of
0-1 and 0-4 days, which does provide some indication of the lag structure of
associations. The authors found relatively similar associations for both multiday lags, but
the association was slightly larger for lag 0-4 days (i.e., 31.6 vs. 34.8%). These initial
results indicate a potential prolonged effect of SO2 that could lead to a respiratory
infection hospital admission or ED visit.
Seasonal Analyses
A few of the recent studies that examined respiratory infection-related hospital
admissions and ED visits also examined whether there was evidence of seasonal
differences in associations. It should be noted that interpreting the results from these
studies is complicated by the different geographic locations as well as the respiratory
infection outcome examined in each study. Mehta et al. (2013) in the study of ALRI
hospital admissions in Vietnam examined potential seasonal differences in associations
by dividing the year into the dry (November-April) and rainy seasons (May-October).
Within these seasons, SO2 concentrations differed drastically, with mean 24-h avg SO2
concentrations being 10.1 ppb in the dry season and 5.7 ppb in the rainy season. In
seasonal analyses, Mehta et al. (2013) reported that SO2 was consistently associated with
ALRI hospital admissions in the dry season [16.1% (95% CI: 1.2, 33.3) for a 10-ppb
increase in 24-h avg SO2 concentrations, lag 1-6 day avg], with no evidence of an
association in the rainy season. Of the other pollutants that were found to be positively
associated with ALRI hospital admissions during the dry season (i.e., PM10 and NO2),
none were associated during the rainy season. In copollutant analyses for the dry season,
SO2 was robust to the inclusion of PM10 and O3 in the model, with the magnitude of the
effect remaining similar, 15.0 and 15.8%, respectively. However, in models with NO2,
the SO2-ALRI hospital admission association was attenuated, but remained positive with
large uncertainty estimates [10.0% (95% CI: -4.6, 26.9) for a 10-ppb increase in 24-h avg
SO2concentrations, lag 1-6 day avg].
Additionally, Zemek et al. (2010) in the study of otitis media ED visits in Alberta,
reported that the magnitude of the association was larger, albeit with wide confidence
intervals, in the warm months (April-September), 9.0% (95% CI: -8.4, 34.2), compared
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to the cold months, (October-March), -4.3% (95% CI: -16.30, 9.0) at lag 4 for a 10-ppb
increase in 24-h avg SO2 concentrations.
Summary of Respiratory Infection
Recent evidence, which comes from epidemiologic studies, expands on that presented in
the 2008 ISA for Sulfur Oxides and provides some, but not entirely consistent, support
for an association between ambient SO2 concentrations and respiratory infection.
Whereas cross-sectional studies do not consistently link SO2 exposures estimated for
school or home to respiratory infections self-reported by children [Supplemental
Figure 5S-2 (U.S. EPA. 2016h)I. some evidence points to an association with hospital
admission and ED visits (Figure 5-8). Associations are observed for all respiratory
infections combined and bronchiolitis but not pneumonia or otitis media. The lack of
multiple studies examining the same respiratory infection outcome complicates the
interpretation of the collective body of evidence, specifically because the etiologies of
upper and lower respiratory infections are vastly different.
Most supporting evidence points to associations with 24-h avg SO2 concentrations
averaged over 3 to 7 days, but an association was observed with temporally resolved
1-h max as well. The relatively small number of studies does not provide a strong basis
for drawing inferences about the lag structure of associations with respiratory infection or
potential seasonal differences in associations. An examination of potential factors that
could modify the S02-respiratory infection hospital admission or ED visit association
finds differences by SES but inconsistent differences by sex (Chapter 6). Recent studies
continued to rely on central site monitors. SO2 generally has low to moderate spatial
correlations across urban geographical scales, which could contribute to some degree of
exposure error (Section 3.4.2.2). Another uncertainty that persists in the recent evidence
is copollutant confounding. Respiratory infection hospital admissions and ED visits were
associated with PM2 5, PM10, BS, and NO2. High S02-copollutant correlations were
observed (r = 0.73-0.78). Correlations were low in some locations (r = 0.17-0.34)
(Table 5-13). but these may not adequately reflect correlation in exposure due to
differential measurement error, particularly for copollutants with different averaging
times. New information from copollutant models shows an SO2 association that is
attenuated and made imprecise with adjustment for NO2, but uncertainty in the exposure
estimates weakens inference about independent associations. Information to assess the
biological plausibility of epidemiologic findings is limited. There is some evidence in
rodents that SO2 exposures of 0.1-1 ppm diminish clearance of particles, but responses to
infectious agents have not been examined in relation to ambient-relevant exposures.
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5.2.1.6
Aggregated Respiratory Conditions
In addition to individual respiratory conditions, epidemiologic studies examined
respiratory effects as an aggregate of multiple respiratory conditions (e.g., asthma,
COPD, respiratory infections). Epidemiologic studies examining the association between
short-term SO2 exposures and respiratory-related hospital admissions or ED visits,
including those discussed earlier in this chapter, were not available until after the
completion of the 1986 Supplement to the Second Addendum of the 1982 SOx AQCD
(U.S. EPA. 1994V Therefore, the 2008 SOx ISA (U.S. EPA. 2008d) included the first
thorough evaluation of respiratory morbidity in the form of respiratory-related hospital
admissions and ED visits. Of the studies evaluated, the majority consisted of single-city,
time-series studies that primarily examined all respiratory disease or asthma hospital
admissions or ED visits, with a more limited number of studies examining other
respiratory outcomes, as discussed in previous sections. Additionally, most studies
averaged SO2 concentrations over multiple monitors and examined 24-h avg exposure
metrics, which may not adequately capture the spatial and temporal variability in SO2
concentrations (Section 3.4.2V The studies that examined all respiratory disease hospital
admissions and ED visits generally reported positive associations (Figure 5-9). These
associations were found to remain generally positive with some evidence of an
attenuation of the association in models with gaseous pollutants (i.e., NO2 and O3) and
particulate matter (U.S. EPA. 2008d).
Since the completion of the 2008 SOx ISA, recent studies have examined the association
between short-term exposure to ambient SO2 and all respiratory disease hospital
admissions and ED visits. For each of the studies evaluated in this section, Table 5-14
presents the air quality characteristics of each city or across all cities, the exposure
assignment approach used, and information on copollutants examined in each hospital
admission and ED visit study that examined all respiratory diseases. Other recent studies
that have examined all respiratory disease hospital admissions and ED visits are not the
focus of this evaluation because of various study design issues, as initially detailed in
Section 5.2.1.2. but the full list of these studies, as well as study specific details, can be
found in Supplemental Table 5S-5 (U.S. EPA. 2016m).
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Study
Location
Age
Lag
Cakmak et al. (2006)
10 Canadian cities
All
2.6
tSonetal. (2013)
8 South Korean cities
All
0-3
t Atkinson et al. (2012)
Meta-analysis (Asia)
All
NR
Burnett et al. (1997)
Toronto, CAN
All
0-3
tWong et al. (2009)
Hong Kong
All
0-1
Dales et al. (2006)
11 Canadian cities
0-27 days
2
Yang et al. (2003)
Vancouver, CAN
<3
2
tSonetal. (2013)
8 South Korean cities
0-14
0-3
Schwartz (1995)
Tacoma, WA
65+
0
Yang et al. (2003)
Vancouver, CAN
65+
0
Schwartz (1995)
New Haven, CT
65+
2
Schwartz et al. (1996)
Cleveland, OH
65+
0-1
tWong et al. (2009)
Hong Kong
65+
0-1
Fung et al. (2006)
Vancouver, CAN
65+
0-6
tSonetal. (2013)
8 South Korean cities
75+
0-3
tWong et al. (2009)a
Hong Kong
All
0-1
0-14
0-1
Wilson et al. (2005)
Portland, ME
All
0
Manchester, NH
All
0
Peel et al. (2005)
Atlanta, GA
All
0-2
Tolbert et al. (2007)
Atlanta, GA
All
0-2
Wilson et al. (2005)
Portland, ME
0-14
0
Manchester, NH
0-14
0
Portland, ME
15-64
0
Manchester, NH
15-64
0
Portland, ME
65+
0
Manchester, NH
65+
0
Hospital Admissions
ED Visits
i
¦f
i
i 1 1 1 1 1 1
-10 0 10 20 30 40 50
% Increase (95% Confidence Interval)
ED = emergency department.
Note: f and red = recent studies published since the 2008 ISA for Sulfur Oxides; Black = U.S. and Canadian studies evaluated in
the 2008 ISA for Sulfur Oxides. Corresponding quantitative results are found in Supplemental Table 5S-8 (U.S. EPA. 20160). a =
(Wong et al.. 2009) also presented results for acute respiratory disease hospital admissions, which is a subset of total respiratory
hospital admissions.
Figure 5-9 Percent increase in respiratory disease hospital admissions and
emergency department visits from U.S. and Canadian studies
evaluated in the 2008 SOx ISA and recent studies in all-year and
seasonal analyses for a 10-ppb increase in 24-h avg or 40-ppb
increase in 1-h max sulfur dioxide concentrations.
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Table 5-14 Study-specific details and mean and upper percentile concentrations
from respiratory disease hospital admission and emergency
department visit studies conducted in the U.S. and Canada and
evaluated in the 2008 SOx ISA and studies published since the 2008
SOx ISA.
Upper Percentile
Mean of
Location Exposure Concentration Concentrations Copollutants
Study Years Assignment Metric ppb) ppb) Examined
Hospital admissions
Cakmak et al. 10 Canadian Average of 24-h avg 4.6 Max: 14-75 Correlations (r):
(2006) cities SO2 NR
(1993-2000) concentrations Copollutant
across all models: none
monitors in
each city
Dales et al. (2006) 11 Canadian Average of 24-h avg 4.3a 95th: 3.5-23.5 Correlations (r):
cities SO2 PM10: -0.09 to
(1986-2000) concentrations 0.61
across all 03: -0.41 to
monitors in
each city
0.13
NO2: 0.20 to
0.67
CO: 0.19 to
0.66
Copollutant
models: none
Burnett et al. (1997) Toronto, ON
(1992-1994)
Average of 1-h max 7.9
SO2
concentrations
from 4-6
monitors
during the
course of the
study
75th: 11 Correlations (r):
95th: 18 H+: 0.45
Max: 26 s°4: 0 42
PM10: 0.55
PM2.5: 0.49
PM10-2.5: 0.44
COH: 0.50
Os: 0.18
NO2: 0.46
CO: 0.37
Copollutant
models: COH,
PM10, PM10-2.5,
PM2.5
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Table 5-14 (Continued): Study specific details and mean and upper percentile
concentrations from respiratory disease hospital
admission and emergency department visit studies
conducted in the U.S. and Canada and evaluated in the
2008 SOx ISA and studies published since the 2008 SOx
ISA.
Upper Percentile
Mean
of
Location
Exposure
Concentration
Concentrations
Copollutants
Study
Years
Assignment
Metric
PPb)
PPb)
Examined
Funa et al. (2006)
Vancouver,
Average of
24-h avg
3.46
Max: 12.5
Correlations (r):
BC
SO2
CO: 0.61
(1995-1999)
concentrations
COH: 0.65
across all
Os: -0.35
monitors
within
NO2: 0.57
Vancouver
PM10: 0.61
PM2.5: 0.42
PM10-2.5: 0.57
Copollutant
models: none
Schwartz (1995)
New Haven,
Average of
24-h avg
New Haven:
New Haven:
Correlations (r):
CT
SO2
29.8
75th: 38.2
NR
Tacoma,
concentrations
Tacoma: 11.5
90th: 60.7
Copollutant
WA
across all
Tacoma:
models: PM10,
(1988-1990)
monitors in
75th: 21.4
O3
each city
90th: 28.2
Schwartz et al.
Cleveland,
Average of
24-h avg
35.0
75th: 45.0
Correlations (r):
(1996)
OH
SO2
90th: 61.0
NR
(1988-1990)
concentrations
Copollutant
across all
models: none
monitors
Yana et al. (2003b)
Vancouver,
Average of
24-h avg
4.8
75th: 6.3
Correlation (r):
BC
SO2
Max: 24.0
Os: -0.37
(1986-1998)
concentrations
Copollutant
across four
models: O3
monitors
tSon et al. (2013)
Eight South
Average of
24-h avg
3.2-7.3
NR
Correlation (r):
Korean
hourly
PM10: 0.5
cities
ambient SO2
Os: -0.1
(2003-2008)
concentrations
NO2: 0.6
from monitors
Copollutant
in each city
models: none
tAtkinson et al.
Meta-
NR
24-h avg
NR
NR
Correlation (r):
(2012)
analysis
NR
(Asia)
Copollutant
(1980-2007)
models: none
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Table 5-14 (Continued): Study specific details and mean and upper percentile
concentrations from respiratory disease hospital
admission and emergency department visit studies
conducted in the U.S. and Canada and evaluated in the
2008 SOx ISA and studies published since the 2008 SOx
ISA.
Upper Percentile
Mean of
Location Exposure Concentration Concentrations Copollutants
Study Years Assignment Metric ppb) ppb) Examined
tWona et al. (2009)
Hong Kong,
China
(1996-2002)
Average of
SO2
concentrations
from eight
monitoring
stations
24-h avg
6.8
75th: 8.4
Max: 41.8
Correlation (r):
NR
Copollutant
models: none
ED visits
Peel et al. (2005) Atlanta, GA
Average of 1-h max
16.5
90th: 39.0
Correlations (r):
SO2
PM2.5: 0.17
concentrations
PM10: 0.20
from monitors
for several
PM10-2.5: 0.21
monitoring
UFP: 0.24
networks
PM2.5 water
soluble metals:
0.00
PM2.5 sulfate:
0.08
PM2.5 acidity:
-0.03
PM2.5 OC: 0.18
PM2.5 EC: 0.20
Oxygenated
HCs: 0.14
Os: 0.19
CO: 0.26
NO2: 0.34
Copollutant
models: none
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Table 5-14 (Continued): Study specific details and mean and upper percentile
concentrations from respiratory disease hospital
admission and emergency department visit studies
conducted in the U.S. and Canada and evaluated in the
2008 SOx ISA and studies published since the 2008 SOx
ISA.
Upper Percentile
Mean
of
Location
Exposure
Concentration
Concentrations
Copollutants
Study
Years
Assignment
Metric
PPb)
PPb)
Examined
Tolbert et al. (2007)
Atlanta, GA
Average of
1-h max
14.9
75th: 20.0
Correlations (r):
(1993-2004)
SO2
90th: 35.0
PM10: 0.21
concentrations
Os: 0.21
from monitors
for several
NO2: 0.36
monitoring
CO: 0.28
networks
PM10-2.5: 0.16
PM2.5: 0.17
PM2.5 S04:
0.09
PM2.5 EC: 0.22
PM2.5 OC: 0.17
PM2.5 TC: 0.19
PM2.5 water
soluble metals:
0.06
Organic
hydrocarbon:
0.05
Copollutant
models: none
Wilson et al. (2005)
Portland,
SO2
24-h avg
Portland:
NR
Correlation (r):
ME
concentrations
11.1
Portland
Manchester,
from one
Manchester:
Os: 0.05
NH
monitor in
16.5
Manchester
(1996-2000)
each city
Os: 0.01
Copollutant
models: none
CO = carbon monoxide; COH = coefficient of haze; EC = elemental carbon; H+ = hydrogen ion; HC = hydrocarbon; OC = organic
carbon; N02 = nitrogen dioxide; NR = not reported; 03 = ozone; PM10 = particulate matter with a nominal aerodynamic diameter
less than or equal to 10 |jm; PM25 = particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm;
PM10 -2.5 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm and greater than a nominal 2.5 |jm;
r= correlation coefficient; S02 = sulfur dioxide; S04 = sulfate; TC = total hydrocarbon; UFP = ultrafine particle,
f studies published since the 2008 SOx ISA.
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Hospital Admissions
A recent multicity study conducted in Korea (Son et al.. 2013) and a single-city study
conducted in Hong Kong (Wong et al. 2009) provide additional insight into the
relationship between short-term SO2 exposures and hospital admissions for all respiratory
diseases.
Son etal. (2013) examined the association between short-term exposures to air pollution
and respiratory-related hospital admissions in eight South Korean cities. It is important to
note that South Korea has unique demographic characteristics with some indicators more
in line with other developed countries (e.g., life expectancy, percent of population living
in urban areas), but because it represents a rapidly developing Asian country, it is likely
to have different air pollution, social, and health patterns than less industrialized Asian
nations or Western nations that developed earlier (Son et al.. 2013). In a time-series
analysis using a two-stage Bayesian hierarchical model, Son et al. (2013) examined both
single-day lags and multiday lags up to 3 days (i.e., lag 0-3). For a lag of 0-3 days the
authors reported a 5.6% increase (95% CI: 1.4, 10.0) in respiratory disease hospital
admissions for a 10-ppb increase in 24-h avg SO2 concentrations. The authors did not
conduct copollutant analyses; however, SO2 was found to be moderately correlated with
PM10 (r = 0.5), NO2 (r = 0.6), and CO (r = 0.6). The results of Son et al. (2013) add
additional support to the results from the multicity studies evaluated in the 2008 SOx ISA
[i.e., (Cakmak et al. (2006); Dales et al. (2006))! in terms of the lag in which the strongest
associations were observed and the magnitude of the association (Figure 5-9).
A greater degree of variability in the magnitude of the association between short-term
SO2 exposures and all respiratory hospital admissions was observed when evaluating
single-city studies in the 2008 SOx ISA (Figure 5-9). Wong et al. (2009) in a study
conducted in Hong Kong reported results consistent with these earlier single-city studies
for individuals over the age of 65 [1.0% (95% CI: -0.8, 2.8) for a 10-ppb increase in
24-h avg SO2 concentrations at lag 0-1], However, compared to studies that examined all
ages, the magnitude of the association was much smaller [0.8% (95% CI: -0.6, 2.3) for a
10-ppb increase in 24-h avg SO2 concentrations at lag 0-11. Wong et al. (2009) also
examined acute respiratory disease, which represents a smaller subset of outcomes within
all respiratory diseases. When focusing on only acute respiratory disease, Wong et al.
(2009) reported no evidence of an association at a 0-1 day lag for all ages [-2.0% (95%
CI: -4.4, 0.4) for a 10-ppb increase in 24-h avg SO2 concentrations].
The all-respiratory-disease hospital admissions results of Son et al. (2013) and Wong et
al. (2009) are supported by the results of a meta-analysis conducted by Atkinson et al.
(2012) that focused on studies conducted in Asian cities since 1980. The six estimates
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from studies that examined the association between SO2 and all respiratory hospital
admissions were included in a random effects model, which yielded a 1.3% increase in
respiratory hospital admissions (95% CI: -0.4, 3.2) for a 10-ppb increase in 24-h avg SO2
concentrations. However, Atkinson et al. (2012) found some evidence of publication bias
for associations between SO2 and respiratory hospital admissions.
Emergency Department Visits
The 2008 SOx ISA evaluated a few studies that examined the association between
short-term SO2 exposures and all respiratory ED visits I Figure 5-9. Supplemental
Table 5S-8 (U.S. EPA. 20160)1. These studies reported evidence of a positive
association, but the magnitude of the association varied across study locations. However,
these studies were limited in that they did not examine copollutant confounding. Recent
studies that examined the association between air pollution and all respiratory ED visits
have not examined associations with SO2.
Model Specification—Sensitivity Analyses
A question that often arises when evaluating studies that examine the association between
air pollution and a health effect is whether the statistical model employed adequately
controls for the potential confounding effects of temporal trends and meteorological
conditions. Son et al. (2013). in the study of eight South Korean cities, conducted
sensitivity analyses to identify whether risk estimates changed depending on the df used
to control for temporal trends and meteorological covariates (i.e., temperature, humidity,
and barometric pressure). The authors reported that the association between short-term
SO2 exposures and all of the respiratory hospital admission outcomes examined (i.e., all
respiratory diseases, allergic disease, and asthma) was sensitive to using less than 7 df per
year, indicating inadequate control for temporal trends, but was stable when using
7-10 df per year. These results suggest that at least 7 df per year are needed to adequately
account for temporal trends when examining the relationship between short-term SO2
exposures and respiratory disease hospital admissions. However, additional studies have
not systematically examined this issue for SO2.
In an additional sensitivity analysis focusing on meteorological covariates
(i.e., temperature, relative humidity, and barometric pressure), Son et al. (2013) examined
whether risk estimates were sensitive to the degree of smoothing used and to the lag
structure. The authors found that when varying the number of df for each covariate from
3 to 6 df and varying the lag structure (i.e., lag 0 and lag 0-3 days), the SO2 association
remained robust for all respiratory hospital admission outcomes.
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Lag Structure of Associations
As stated previously, when examining associations between air pollution and a specific
health outcome, it is informative to assess whether there is a specific exposure window
for SO2 that results in the strongest association with the health outcome of interest. In the
examination of all respiratory disease hospital admissions, Son et al. (2013) focused on
both single-day and multiday lags to address whether there is evidence of an immediate
or persistent effect of SO2. Across single-day lags of 0 to 3 days, positive associations
were observed across each lag with the magnitude of the association being relatively
similar across each lag (i.e., 2.4% for lag 0 and 2.1% for lags 1 to 3 days for a 10-ppb
increase in 24-h avg SO2 concentrations). When examining multiday lags of 0-1, 0-2,
and 0-3 days, the authors reported an increase in the magnitude of the association as the
length of the multiday lag increased with a 3.5% increase reported at lag 0-1 and a 5.6%
increase reported for lag 0-3 days. Therefore, the limited evidence suggests that SO2
effects occur within the first few days after exposure, but also that SO2 effects on
respiratory disease hospital admissions may persist over several days.
Examination of Seasonal Differences
Of the studies that examined all respiratory disease hospital admissions or ED visits, only
Son etal. (2013) in the analysis of eight South Korean cities examined potential seasonal
differences in SO2 associations. However, it is important to note the potential influence of
geographic location on the results from studies that examine potential seasonal
differences in associations. For all outcomes examined, including respiratory diseases,
the association with SO2 was largest in magnitude during the summer, although
confidence intervals were quite large [respiratory diseases: 21.5% (95% CI: -0.7, 48.3),
lag 0-3, for a 10-ppb increase in 24-h avg SO2 concentrations] with additional evidence
of a positive association in the fall [8.9% (95% CI: -1.4, 20.7), lag 0-3, for a 10-ppb
increase in 24-h avg SO2 concentrations]. There was no evidence of an association
between short-term SO2 exposures and respiratory disease hospital admissions in either
the spring or winter seasons. Across the eight cities, mean 24-h avg SO2 concentrations
were lowest during the summer season (4.4 ppb compared to a range of 4.8 to 7.0 in the
other seasons) as was also the case for NO2 and CO.
Summary of Aggregate Respiratory Conditions
Recent studies add to the evidence detailed in the 2008 SOx ISA that indicated a
generally positive association between short-term SO2 exposures and respiratory disease
hospital admissions and ED visits (Figure 5-9). These recent studies provide some insight
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into previously identified limitations (i.e., model specification, lag structure of
associations, and potential seasonal differences) in the S02-respiratory disease hospital
admission and ED visits relationship. Initial evidence from a limited number of studies
suggests that SO2 associations are robust to alternative model specifications for weather
covariates and that SO2 associations are relatively stable in the range of df per year
indicative of reasonable control for temporal trends (i.e., 7-10 df per year); however,
more studies are needed to confirm these findings. Additionally, an examination of the
lag structure of associations is in line with the results reported in studies that focused on
a priori lags [i.e., associations tend to be strongest within the first few days after
exposure, primarily within the range of 0 to 3 days (Figure 5-9)1. The potential seasonal
patterns in SO2 associations remain unclear due to the variability in SO2 associations
observed across different geographic locations, as reflected in studies of other respiratory
hospital admission and ED visit outcomes. Some studies have also examined whether
there is evidence that specific factors modify the S02-respiratory disease hospital
admission or ED visit relationship and have found some evidence for potential
differences by lifestage and influenza intensity (see Chapter 6). Studies of all respiratory
hospital admissions and ED visits have not conducted extensive analyses to examine
potential copollutant confounding. However, studies that reported SO2 correlations with
other pollutants found low (r < 0.4) to moderate (r = 0.4-0.7) correlations. Overall, the
results of recent studies are limited in that they do not further inform the understanding of
potential confounding by copollutants on the relationship between short-term SO2
concentrations and respiratory disease hospital admissions and ED visits.
5.2.1.7 Respiratory Effects in General Populations and Healthy Individuals
The 2008 SOx ISA (U.S. EPA. 2008d) reported respiratory effects of SO2 in general
populations and healthy individuals but did not make specific conclusions about the
relationship. Respiratory effects were demonstrated in healthy individuals following SO2
exposures >1.0 ppm in controlled human exposure studies. Animal toxicological studies
demonstrated bronchoconstriction after a single SO2 exposure and increased airway
responsiveness and inflammation after repeated SO2 exposures. Epidemiologic evidence
was weak. The few recent toxicological studies corroborate previous results, but recent
epidemiologic and controlled human exposure studies provide inconsistent results,
including new results for pulmonary inflammation.
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Lung Function Changes in General Populations and Healthy Individuals
Compared with evidence for lung function changes in individuals with asthma, evidence
for SC>2-induced lung function effects in healthy individuals is weak. Most of the
controlled human exposure studies evaluating these effects in healthy individuals were
discussed in the 1982 SOx AQCD (U.S. EPA. 1982a). While some studies showed that
transient decreases in lung function can occur at concentrations of 1.0 ppm SO2 under
exercising or forced oral breathing conditions, the evidence was more consistent for
exposures >1.0 ppm (U.S. EPA. 2008d). Epidemiologic associations between ambient
SO2 concentrations and lung function continue to be inconsistent in children. While
recent results indicate associations in adults, inferences about SO2 exposure still are weak
because of uncertainty in the exposure estimates and copollutant confounding.
Controlled Human Exposure Studies
Evidence from controlled human exposure studies evaluating SC>2-induced lung function
changes in healthy adults was extensively discussed in the 1982 AQCD (U.S. EPA.
1982a). In general, these studies demonstrated respiratory effects such as increased
airway resistance and decreased FEVi following exposures to concentrations
>1.0-5.0 ppm, while some studies demonstrated respiratory effects at 1.0 ppm.
Lung function changes in response to SO2 exposure in controlled human exposure studies
have been investigated since the early 1950s. Respiratory effects including increased
respiration rates, decrements in peak flow, bronchoconstriction, and increased airway
resistance have been observed in healthy human volunteers at concentrations >1.0 ppm
(Lawtheret al.. 1975; Andersen et al.. 1974; Snell and Luchsinger. 1969; Abe. 1967;
Frank etal.. 1962; Sim and Pattle. 1957; Lawther. 1955; Amdur et al.. 1953). Although
bronchoconstriction was observed in healthy subjects exposed to concentrations
>5.0 ppm, shallow rapid respiration and increased pulse rate, decreased maximum
expiratory flow from one-half vital capacity, and increased sRaw were observed
following exposures as low as 1.0 ppm (Lawther et al.. 1975; Snell and Luchsinger. 1969;
Amdur et al.. 1953). Overall, only these few studies have reported SC>2-induced
respiratory effects in healthy individuals for 5-10-minute exposures at concentrations
>1.0 ppm SO2.
A limited number of studies examined lung function changes in healthy populations in
response to >1 hour exposures to SO2. Controlled human exposure studies examining
lung function changes in healthy individuals exposed to SO2 are summarized in
Table 5-15. Andersen et al. (1974) reported that exposures of up to 6 hours to 1.0 ppm
SO2 in resting healthy adults induced decreases in FEF25-75 and to a lesser extent FEVi.
Another human exposure study (van Thriel et al.. 2010) reported that healthy subjects
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exposed to SO2 concentrations of 0.5, 1.0, or 2.0 ppm for 4 hours while exercising did not
show changes in FEVi. However, lung function measurements in this study were not
performed between 40-100 minutes after exercise and more sensitive measures such as
shallow rapid respiration or FEF25 75 were not reported. Healthy individuals at rest or
exercising exhibited no changes in several measures of lung function following a 1 hour
exposure to 0.2-0.6 ppm SO2 (Tunnicliffe et al.. 2003; Linnetal.. 1987).
The interaction of SO2 exposure with O3 was reported in two studies. Hazucha and Bates
(1975) demonstrated that a combined 2 hours exposure to low concentrations of O3
(0.37 ppm) and SO2 (0.37 ppm) has a greater effect on lung function than exposure to
either agent alone in exercising adults. However using a similar study design, Bedi et al.
(1979) did not observe a greater effect of the combined exposures compared with
exposure to only O3; exposure to SO2 alone had no effect.
Epidemiologic Studies
Previous epidemiologic evidence was inconsistent for an association between ambient
SO2 concentrations and lung function in healthy adults or children and people recruited
from the general population (U.S. EPA. 2008d). Studies mostly estimated SO2 exposure
from central site monitors and did not report whether the measurements well captured the
spatiotemporal variability in the study areas. Some recent studies measured SO2 at
subjects' locations and observed associations with lung function decrements in adults but
not consistently in children. Most studies examined 24-h avg SO2 concentrations, which
are much longer than the 5-10 minute exposures inducing lung function decrements in
experimental studies. Inconsistency also is observed among recent results for temporally
resolved metrics such as 1-h max and 1- to 10-h avg SO2 concentrations, which is similar
to controlled human exposure findings for 1- to 6-hour exposures to SO2.
Adults. Among previous studies, an SC>2-associated decrease in lung function was
observed in adults in Beijing, China where coal was used for domestic heating (Xu et al..
1991). Recent results are based on much lower SO2 concentrations [means 7.3-8.6 ppb
vs. 6.8-49 ppb in Xu et al. (1991)1. Associations are observed with lung function
decrements in adults without respiratory disease (Table 5-16). with some based on
relatively good exposure characterization (Dales et al.. 2013).
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Table 5-15 Study-specific details from controlled human exposure studies of
lung function and respiratory symptoms in healthy adults.
Disease Status;
n; Sex; Age Exposure Details
Reference (mean ± SD) (Concentration; Duration) Endpoints Examined
Andersen et Healthy; n = 15; 15 M; 0, 1, 5, or 25 ppm SC>2for6 h at Nasal mucociliary flow
al. (1974) 20-28 yr rest Area of the nasal airway
Airway resistance (FEV-i, FEF25-75%)
Nasal removal of SO2
Discomfort level symptoms
Lung function measure pre-exposure,
-15 min, and -55 min into exposure
sRaw, FVC, FEV1, peak expiratory flow
rate, maximal mid expiratory flow rate
Continuously EKG
Midway-HR
Before, during, 1-d after, and 1 wk
after-symptom score, self-rated activity
Immediately after exposure-bronchial
reactivity percent change in FEV induced
by 3 min normocapnic hyperpnea with
cold, dry air
Raulf-
Healthy; n = 16; I
3 M,
0, 0.5, 1.0, or 2.0 SO2 for 4 h
Exhaled NO, biomarkers of airway
Heimsoth et
8 F; 19-36 yr
with exercise for 15 min (bicycle,
inflammation in EBC and NALF
al. (2010)
75 Watts) two times during each
session
Tunnicliffe et
Asthma; n = 12
0 or 0.2 ppm SO2 for 1 h at rest
Symptoms, FEV1, FVC, MMEF, exhaled
al. (2003)
adults, 35.7 yr
NO, ascorbic and uric acid in nasal
Healthy; n = 12
lavage fluid
adults, 34.5 yr
van Thriel et
Healthy; n = 16; I
3 M,
0, 0.5, 1.0, or 2.0 ppm SO2 for
Symptoms, FEV1
al. (2010)
8 F; M: 28.4 ± 3.9 yr,
4 h with exercise for 15 min
F: 24.3 ± 5.2 yr
(bicycle, 75 Watts) two times
during each session
EBC = exhaled breath condensate; EKG = electrocardiogram; F = female; FEF25-75% = forced expiratory flow at 25-75% of exhaled
volume; FEV = forced expiratory volume; FE\A| = forced expiratory volume in 1 sec; FVC = forced vital capacity; HR = heart rate;
M = male; MMEF = maximum midexpiratory flow; n = sample size; NALF = nasal lavage fluid; NO = nitric oxide; SD = standard
deviation; S02 = sulfur dioxide; sRaw = specific airway resistance.
1 The exposure characterization of Dales et al. (2013) is judged to be good because SO2
2 was measured on site of adults' scripted exposures near (0.87 km) and away from
3 (4.5 km at a college campus) a steel plant in Ontario. Another strength was the
4 well-defined 8-hour exposure duration and lag between exposure and lung function
5 testing. Higher SO2 concentrations averaged over 10 hours (8 a.m.-6 p.m.) were
December 2016 5-112 DRAFT: Do Not Cite or Quote
Linn et al. Healthy; n = 24; 1
(1987) 9 F; 18-37 yr
M, 0, 0.2, 0.4, or 0.6 ppm SO2
1 h exposures
3 x 10-min exercise (bicycle)
periods -40 L/min
Exposures were repeated for a
total of eight
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associated with decreases in several lung function parameters measured just after
exposure (Table 5-16). For example, a 10-ppb increase in SO2 was linked to a -0.50%
FEVi change (95% CI: -1.0, 0.05). Son et al. (2010) also examined air pollution from
industry, in this case a petrochemical complex in Ulsan, South Korea. Ambient SO2
concentrations across the study area were highly variable. Between-monitor correlation
varied widely (0-0.8), even for those 5 km apart, and the mean decreased from about 0.4
to 0.2 with increasing distance up to 20 km. Investigators aimed to capture this
spatiotemporal variability by combining SO2 measurements across monitors with inverse
distance weighting or kriging. These metrics and that for the nearest monitor to the
subjects' home, all 24-h avg SO2, were associated with FVC but not FEVi (Table 5-16).
The implications overall are unclear because many subjects lived far from a monitor, and
potential confounding by meteorological factors and season were not considered. Both
studies observed associations with copollutants among PM2 5, PM10, UFP, CO, NO2, and
O3. Correlations among copollutants and analyses of confounding or interactions were
not reported for personal exposures near the steel plant (Dales et al.. 2013). For the study
near the petrochemical complex, the decrease in FEVi for kriged SO2 was larger after CO
adjustment (Son et al.. 2010) (Table 5-16). The effect estimate for CO became null, but
the range of between-monitor correlations was 0-0.8. The effect estimate for SO2 was
attenuated with adjustment for O3, which could be influenced by differential exposure
measurement error. Between-monitor correlations were 0.4 to 0.8 for O3.
Other studies reported S02-associated lung function decrements, but inference about SO2
is weaker (Steinvil et al.. 2009; Min et al.. 2008a'). Associations were observed for SO2
after adjustment for NO2 or CO, but correlations with SO2 were 0.62-0.70, and
single-pollutant associations for SO2 were in opposing directions across lags and limited
to lags of 3 or more days (Steinvil et al.. 2009). Associations were observed with 1-h avg
SO2 concentrations lagged 5-30 hours, but confounding by meteorological factors was
not considered (Min et al. 2008a). Also, both studies had cross-sectional design and
estimated SO2 exposure from monitors up to 11 km or unspecified distance from homes.
Children. Similar to previous studies, many recent studies of children examined
populations with high prevalence (8-35%) of respiratory disease, such as asthma, and
populations outside the U.S. and Canada. As examined in several recent studies, SO2 at
schools was inconsistently associated with lung function (Table 5-17). Previously,
1-h max SO2 concentrations at school were not associated with lung function. Additional
results for temporally resolved SO2 metrics, both school and central site, are inconsistent.
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Table 5-16 Recent epidemiologic studies of lung function in healthy adults and adults in the general population.
Study Population and
Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tDales etal. (2013)
Sault Ste. Marie, ON, May-Aug 2010
N = 61, mean age 24 yr. 100% healthy.
Cross-over, with scripted outdoor exposures
near and away from steel plant. Five
consecutive 8-h days at each site, with 9-d
washout period in between. Supervised
spirometry. Recruited from university.
Required not to live in neighborhood bordering
steel plant.
Monitor on site of
outdoor exposures
Mean (SD)
Near steel plant
7.8 (13)
College campus
1.6 (4.2)
10-h avg
(8 a.m.-6 p.m.
Lag 0 h
Percent change
FEVi: -0.50 (-1.0, 0.05)
FVC: -0.45 (-1.1, 0.19)
FEVi/FVC: -0.15 (-0.31, 0.01)
FEF25-75%: -0.44 (-0.74, -0.14)
Total lung capacity
-0.42 (-0.70, -0.13)
Residual volume
-2.1 (-4.1, -0.18)
No copollutant model
Associations observed with PM2.5,
UFP, NO2, and O3. All pollutants
higher at steel plant than at college
campus.
Copollutant correlations NR.
tSon etal. (2010)
Ulsan, South Korea, 2003-2007
N = 2,102, ages 7-97 yr. Mean age 45 yr.
Mean percent predicted FEV1 83%.
Cross-sectional. Supervised spirometry.
Recruited from a meeting of residents near a
petrochemical complex. Did not examine
confounding by meteorological factors or
season.
13 monitors in city
Mean (SD), 75th
percentile, max
Kriging
8.3 (4.4), 9.6, 25
Nearest monitor
7.3 (5.9), 9.5, 34
IDW
8.4 (5.3), 11, 29
Average of 13 monitors
8.6 (4.1), 10, 24
24-h avg
0-2 avg
Change in percent predicted
FVC
Kriging
-6.2 (-8.2, -4.2)
IDW
-5.3 (-7.1, -3.5)
Nearest monitor
-5.6 (-7.4, -3.9)
Average of 13 monitors
-7.0 (-9.0, -4.8)
FEV1
Kriging
-0.08 (-0.76, 0.60)
IDW
0.31 (-0.32, 0.95)
Nearest monitor
0.35 (-0.21, 0.92)
Average of 13 monitors
-0.15 (-0.89, 0.58)
Copollutant model, lag 0-2 avg
FVC
Kriging
with Os: -1.8 (-4.0, 0.46)
with CO: -8.8 (-11, -6.3)
O3 association persists with SO2
adjustment. CO association
attenuated. Association also
observed with PM10 and NO2 but no
copollutant model. PM2.5 not
examined.
Copollutant correlations NR.
December 2016
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Table 5-16 (Continued): Recent epidemiologic studies of lung function in healthy adults and adults in the general
population.
Study Population and
Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tSteinvil et al. (2009)
Three monitors within
24-h avg
Change in FEV1 (mL)
Copollutant model, lag 5,
Tel Aviv, Israel, 2002-2007
11 km of home
0
93 (-90, 277)
FEV1 (mL)
N = 2,380, mean age 43 yr. 100% healthy.
Mean (SD): 2.8 (1.2)
5
-300 (-487, -113)
with Os: -220 (-413, -33)
Cross-sectional. Supervised spirometry.
75th percentile: 3.4
0-6 avg
-447 (-750, -143)
with NO2: -280 (-527, -33)
Recruited from ongoing survey of individuals
attending health center.
Max: 9.4
0
Change in FVC (mL)
53 (-167, 273)
with CO: -247 (-473, -20)
NO2 and CO association
5
0-6 avg
-373 (-600, -147)
-560 (-927, -193)
Percent change in FEV1/FVC
attenuated with SO2 adjustment.
No association with O3.
SO2 highly correlated with NO2,
moderately correlated with CO,
0
716 (-6.5, 4,233)
weakly correlated with O3. r= 0.70,
5
237 (-79, 2,195)
0.62, -0.24.
0-6 avg
220 (-217, 657)
tMin et al. (2008a) Monitors in city 1-h avg
South Korea, 2006 Number and distance Lag 1 h
NR
N = 867, ages 20-86 yr. 100% no serious
medical conditions. Mean: 6
Cross-sectional. Supervised spirometry.
Recruitment not described. Did not examine
confounding by meteorological factors.
CI = confidence interval; CO = carbon monoxide; FEF25-75% = forced expiratory flow at 25-75% of forced vital capacity; FENA = forced expiratory volume in 1 sec; FVC = forced vital
capacity; IDW = inverse distance weighting; max = maximum; mL = millilitres; N = sample size; N02 = nitrogen dioxide; NR = not reported; r = correlation coefficient; 03 = ozone;
PM2.5 = particulate matter with nominal aerodynamic diameter less than or equal to 2.5 |jm; PM10 = particulate matter with nominal aerodynamic diameter less than or equal to 10 |jm;
SD = standard deviation; S02 = sulfur dioxide, UFP = ultrafine particles.
aEffect estimates are standardized to a 10-ppb increase in 1-h to 24-h avg S02.
fStudies published since the 2008 Integrated Science Assessment for Sulfur Oxides.
Results presented only in figure. No copollutants examined.
Associations observed only in
smokers. FEV1 and FVC
decrease after lag of 5-6 h. No
association after 30 h.
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Table 5-17 Recent epidemiologic studies of lung function in healthy children and children in the general
population.
Study Population and
Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tCorreia-Deur et al. (2012)
Monitor at school
2-h avg
Percent change in PEF
Copollutant model for group that
Sao Paolo, Brazil, Apr-Jul 2004
Mean (SD): 8.8 (3.3)
0
-0.24 (-0.96, 0.49)
included 65 children with atopy.
N = 31, ages 9-11 yr. 100% no allergic
sensitization.
75th percentile: 11
90th percentile: 13
24-h avg
-0.20 (-1.4, 0.96)
SO2 association near null with
adjustment for PM10, NO2, or CO.
Daily measures for 15 d. Supervised
spirometry. Recruited from schools.
0
No association for 3-, 5-, 7-,
10-d avg
or SO2 highly correlated with PM10,
moderately correlated with NO2 &
CO. Pearson r= 0.75, 0.60, 0.60
tAltua et al. (2014)
Monitor at school
24-h avg
Relative ratio for change
No copollutant model
Eskisehir, Turkey, Feb-Mar 2007
Mean and max
0-6 avg
Subjects without URS
No association with O3 or NO2.
N = 535, ages 9-13 yr
Suburban: 21, 29
FVC: 1.00 (0.97, 1.03)
PM2.5 and PM10 not examined.
Cross-sectional. Supervised spirometry.
Recruited from schools from participants of a
larger study.
Urban: 29, 44
Urban-traffic: 22, 27
FEV1: 1.00 (0.97, 1.03)
PEF: 1.00 (0.97, 1.03)
MMEF: 1.00 (0.92, 1.08)
SO2 moderately correlated with
NO2, negatively correlated with O3
in winter. r= 0.49, -0.40.
Subjects with URS
FVC: 1.00 (0.97, 1.03)
FEV1: 1.00 (0.97, 1.03)
PEF: 1.00 (0.97, 1.03)
MMEF: 1.03 (0.95, 1.11)
December 2016
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Table 5-17 (Continued): Recent epidemiologic studies of lung function in healthy children and children in the
general population.
Study Population and
Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tAltuq etal. (2013)
Eskisehir, Turkey, Jan 2008-Mar 2009
N = 1,880, 9-13 yr. 7% asthma. 11% hay fever
Two measures: summer and winter.
Supervised spirometry. Recruited from
schools. Did not examine confounding by
meteorological factors.
Monitor at school
Mean and max
Summer
Suburban: 8.5, 16
Urban: 10, 16
Urban-traffic: 6.3, 8.9
Winter
Suburban: 21, 29
Urban: 29, 44
Urban-traffic: 22, 33
24-h avg OR for impaired lung function
0-6 avg (predicted values <85% for
FEVi or FVC or <75% for PEF
or MMEF)
Summer
Girls: 1.22 (0.72, 2.09)
Boys: 0.83 (0.47, 1.45)
Winter
Girls: 1.00 (0.76, 1.32)
Boys: 0.83 (0.61, 1.11)
Copollutant model, girls, summer
with 03: 1.08 (0.63, 1.91)
with N02: 1.14 (0.65, 1.99)
O3 association persists with SO2
adjustment. No association for
NO2 overall. PM2.5 and PM10 not
examined.
SO2 moderately correlated with
NO2 and negatively correlated
with O3 in winter. r= 0.49, -0.40.
Summer correlations NR.
tCastro et al. (2009)
Rio de Janeiro, Brazil, 2004
N = 118, ages 6-15 yr. 18% asthma.
Daily measures for 6 wk. Supervised PEF.
Recruited from schools.
Monitor at school
Mean (SD): 7.1 (6.8)
90th percentile: 16
Max: 37
24-h avg Change in PEF (L/min)
1 -0.73 (-2.5, 0.99)
2 -0.99 (-2.6, 0.61)
3 0.34 (-1.1, 1.8)
0-1 avg -1.8 (-3.8, 0.17)
0-2 avg -1.5 (-3.4, 0.46)
No copollutant model
Associations observed with PM10
and CO but not NO2. PM2.5 not
examined.
Copollutant correlations NR.
tChanq etal. (2012b)
Taipei, Taiwan, 1996-1997
N = 2,919, ages 12-16 yr.
Cross-sectional. Supervised spirometry.
Recruited from schools.
Five monitors averaged
within 2 km of schools
Means across districts
4-h avg (8 a.m.-12 p.m.'
4.6-10
10-h avg (8 a.m.-6 p.m.'
1.8-5.4
1-h max:
5.9-35
4-h avg
0
10-h avg
1
Change in FEV1 (mL)
0.4 (-32, 33)
-117 (-193, -42)
No copollutant model
Associations observed with PM10,
NO2, CO, O3. PM2.5 not examined.
Copollutant correlations NR.
1-h max
0
1
3.6 (-21, 28)
-85 (-129, -41)
December 2016
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Table 5-17 (Continued): Recent epidemiologic studies of lung function in healthy children and children in the
general population.
Study Population and
Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tLinares et al. (2010)
Salamanca, Mexico, Mar 2004-Feb 2005
N = 464, ages 6-14 yr. 0.6% asthma.
Daily measures for 20 d in each season.
Monitors within 2 km of 24-h avg
school o
Means spring-winter
School 1: 12, 12, 10, 9.8
Supervised spirometry. Recruited from schools School 2: 9.1, 8.7, 10, 13
Units not reported
FVC: -0.06 (-0.13, 0)
FEVi: -0.01 (-0.01, -0.00)
PEF: -0.03 (-0.05, 0)
FEVi/FVC: -0.07 (-0.18, 0.03)
No copollutant model
Associations observed with PM10
and O3 but not NO2. PM2.5 not
examined.
Copollutant correlations NR.
tReddv et al. (2012)
Durban, South Africa, 2004-2005
N = 129, ages 9-11 yr. 37% asthma.
Daily measures for 3 wk each season.
Supervised spirometry. Recruited from
schools. Did not examine confounding by
meteorological factors except season.
Monitor at school
Mean (SD): 5.8 (0.2)
Max: 41
24-h avg Percent change FEV1 diurnal
variability (increase = poorer
function)
0-4 avg By GSTM1 gene variant
Null: -1.2 (-3.0, 0.54)
Positive: 1.1 (0.45, 2.7)
3 By GSTP1 gene variant
AG/GG: 3.1 (1.6, 4.7)
AA: -0.73 (-2.2, 0.70)
No copollutant model
Association observed with PM10 in
GSTP1 AG/GG group. NO2
association in AA group. PM2.5 not
examined.
Copollutant correlations NR.
tMakamure et al. (2016a)
Durban, South Africa, 2004-2005
N = 71, ages 9-11 yr. 35% asthma.
Part of the same cohort as Reddv et al. (2012)
above.
Daily measures for 3 wk each season.
Supervised spirometry. Recruited from
schools. Did not examine confounding by
meteorological factors except season.
Monitor at school 24-h avg
Mean (SD): 5.8 (0.2) 1
Max: 41
Percent change FEV1 diurnal
variability (increase = poorer
function)
All subjects: 1.6 (-0.03, 3.3)
By CD14 gene variant
CC: -1.5 (-3.4, -0.37)
CT/TT: -3.6 (-7.1, -0.17)
No copollutant model
Association observed with PM10 in
CD14 CC group. No association
with NO2. PM2.5 not examined.
Copollutant correlations NR.
December 2016
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Table 5-17 (Continued): Recent epidemiologic studies of lung function in healthy children and children in the
general population.
Study Population and
Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and Lag
Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination3
tMakamure et al. (2016b)
Durban, South Africa, 2004-2005
N = 104, ages 9-11 yr. 39% asthma.
Part of the same cohort as Reddv et al. (2012)
above.
Daily measures for 3 wk each season.
Supervised spirometry. Recruited from
schools. Did not examine confounding by
meteorological factors except season.
Monitor at school
Mean (SD): 5.8 (0.2)
Max: 41
24-h avg Percent change FEVi diurnal
variability (increase = poorer
function)
1 By TNF-a gene variant
AA/GA: 2.3 (-0.29, 5.0)
GG: 0.83 (-1.32, 3.0)
2 AA/GA: 2.7 (0.52, 4.8)
GG: 0.24 (-1.19, 1.68)
No copollutant model
Association observed with NO2 at
lag 1 and NO at lag 2. No
association with PM10 in AA/GA
group. PM2 5 not examined.
Copollutant correlations NR.
tAmadeo et al. (2015)
Pointe-a-Pitre, Guadeloupe, 2008-2009
N = 354, ages 8-13 yr. 17% asthma.
Cross-sectional. Supervised spirometry.
Recruited from schools.
Monitors in city
Number and distance NR
Mean (SD): 1.8 (1.4)
Max: 4.9
1-h max All subjects
0 Percent change post 6-min run
43 (-3,787, 3,873)
24-h avg
0-13 avg
Children without asthma
Change in prerun PEF (L/min)
18 (-84, 119)
Percent change post 6-min run
4.5 (-24, 33)
No copollutant model
Association observed with
24-h avg O3 measured at central
site not PM10 or NO2. PM2.5 not
examined.
Copollutant correlations NR.
CI = confidence interval; CO = carbon monoxide; FE\A| = forced expiratory volume in 1 sec; FVC = forced vital capacity; MMEF = maximum midexpiratory flow; N = sample size;
N02 = nitrogen dioxide; NR = not reported; 03 = ozone; OR = odds ratio; PEF = peak expiratory flow; PM25 = particulate matter with nominal aerodynamic diameter less than or equal
to 2.5 |jm; PM10 = particulate matter with nominal aerodynamic diameter less than or equal to 10 |jm; r= correlation coefficient; SD = standard deviation; S02 = sulfur dioxide;
TNF-a = tumor necrosis factor-alpha; URS = upper respiratory symptoms.
aEffect estimates are standardized to a 10-ppb increase in 1-h to 24-h avg S02 or a 40-ppb increase in 1-h max S02.
fStudies published since the 2008 Integrated Science Assessment for Sulfur Oxides.
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For SO2 measured at schools, there is no evidence for association with lung function in
groups of children without respiratory disease or symptoms in Turkey or Brazil (Altug et
al.. 2014; Correia-Deur et al.. 2012). Altug et al. (2014) examined only 1-wk avg SO2,
but Correia-Deur et al. (2012) was noteworthy for examining multiple averaging times
and lags (i.e., 3- to 10-day avg). PEF also was measured at school and analyzed with the
preceding 2-h avg SO2 concentrations. The association was imprecise [-0.24% change
(95% CI: -1.4, 0.96) in PEF per 10-ppb increase in SO2]. Another strength of this study
over similar ones is its repeated-measures design and clinical assessment of children's
respiratory health status. Among the studies of school SO2, an association with lung
function was observed in another cohort of children from Brazil (Castro et al.. 2009).
The impact of the 18% of children with asthma on these results is unknown. The effect
estimate was largest for 2-day avg SO2 concentrations and imprecise for lag 1 and 2
(Table 5-17). Missing SO2 concentration data for 52% of days could be one reason for the
imprecision.
Some results for SO2 measured at children's schools have more ambiguous implication
(Makamure et al.. 2016a. b; Altug et al.. 2013; Reddv et al.. 2012) (Table 5-17). For
children in Turkey, lung function was analyzed dichotomously based on a cutpoint of 85
or 75% of the predicted value (Altug et al.. 2013). Healthy children may not experience
such decrements, and the 7% of the cohort with asthma may influence results. In a South
African cohort, results were in opposing directions across the many comparisons made
among lung function parameters, pollutants, exposure lags, and gene variants (Makamure
et al.. 2016a. b; Reddv et al.. 2012). For example, an association for SO2 was found in
children with the GSTP1 variant with reduced oxidative metabolism activity but children
with the GSTM1 variant with normal activity (Table 5-17 and Section 6.4). Confounding
by meteorology was not considered in either cohort.
For exposures estimated from central site monitors, lung function associations were
inconsistent for 1-h max SO2 (Amadeo et al.. 2015; Chang et al.. 2012b). which may be
more variable within a community and subject to greater exposure error. For children in
Taiwan, a 40-ppb increase in 1-h max SO2 lagged 1 day was associated with a -85 mL
(95% CI: -129, -41) change in FEVi (Chang et al.. 2012b). SO2 concentrations were
averaged from five monitors within 2 km of children's schools. For children in
Guadeloupe, West Indies, the distance to monitors was not reported. Daily 1-h max SO2
concentrations were not associated with PEF (Amadeo et al.. 2015). Although PEF was
measured before and after a 6-minute exercise period, which is akin to procedures in
controlled human exposure studies, the SO2 metric was not likely matched temporally
with PEF measurements. Lung function in populations of children with low or no
prevalence of asthma was inconsistently associated with 24-h avg SO2 measured at
central site monitors (Amadeo et al.. 2015; Linares et al.. 2010). although the null
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findings are for 13-day avg SO2 (Amadeo et al. 2015). Airway responsiveness increased
with increases in 24-h avg SO2 in a population of children with 8% asthma and 18%
atopy (Sovseth et al.. 1995). SO2 exposures were estimated from monitors within 2 km of
homes, which is similar to studies observing associations with 24-h avg and 1-h max SO2
(Chang et al.. 2012b; Linares et al. 2010).
For the few associations observed for SO2 with lung function or airway responsiveness,
the potential for copollutant confounding or interactions is not addressed, including the
study conducted near an aluminum smelter that also emitted PM (Sovseth et al.. 1995).
Associations were observed for PM10, CO, NO2, and O3 measured at schools and central
site monitors, but neither correlations with SO2 nor copollutant model results were
reported (Chang et al.. 2012b; Linares et al.. 2010; Castro et al.. 2009). Altug et al. (2014)
reported a moderate correlation with NO2 of 0.49 and observed no association for either
NO2 or SO2. Copollutant models were analyzed for long-term SO2, which was not
associated with lung function decrements in single-pollutant models (Linares et al..
2010). Importantly, none of the studies examined PM2 5
Animal Toxicological Studies
Lung function was examined in numerous studies reported in the 1982 SOx AQCD (U.S.
EPA. 1982a) and the 2008 SOx ISA (U.S. EPA. 2008d). The majority of these were
conducted in naive animals rather than in animal models of allergic airway disease.
Bronchoconstriction, indicated by increased pulmonary resistance, was identified as the
most sensitive indicator of lung function effects of acute SO2 exposure, based on the
observation of increased pulmonary resistance in guinea pigs that were acutely exposed
to 0.16 ppm S02 (U.S. EPA. 2008d. 1982a). The 2008 SOx ISA (U.S. EPA. 2008d)
reported a few additional studies conducted at concentrations below 2 ppm. Animal
toxicological studies examining lung function changes in naive animals exposed to SO2
are summarized in Table 5-18. Increased pulmonary resistance and decreased dynamic
compliance were observed in conscious guinea pigs exposed to 1 ppm SO2 for 1 hour
(Amdur et al.. 1983). Effects were seen immediately after exposure and were not present
1 hour 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 hours/day for 6 days (Conner et al.. 1985). No changes were observed in lung
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, Barthelemv et al.
(1988) demonstrated a 16% increase in airway resistance following a 45-minute exposure
of anesthetized rabbits to 0.5 ppm SO2 via an endotracheal tube. This latter exposure is
more relevant to oronasal than to nasal breathing.
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Table 5-18 Study-specific details from animal toxicological studies of lung
function.
Species (Strain); n; Sex; Exposure Details
Study Lifestage/Age or Weight (Concentration; Duration) Endpoints Examined
Amdur et al. (1983) Hartley guinea pig; ~1 ppm (2.62 mg/m3); head Endpoints examined during
n = 8-23/group; M; age only for 1 h exposure and up to 1 h
NR; 200-300 g; post-exposure.
Lung function—pulmonary
resistance, dynamic
compliance, breathing
frequency, tidal volume, and
min volume
Endpoints examined 1, 24,
and 48 h after the sixth
exposure.
Lung function—residual
volume, functional residual
capacity, vital capacity, total
lung capacity, respiratory
frequency, tidal volume,
pulmonary resistance,
pulmonary compliance,
diffusing capacity for CO, and
alveolar volume
Barthelemv et al. (1988)
Rabbit; n = 5-9/group;
sex NR; adult; mean
2.0 kg; rabbits were
mechanically ventilated
0.5 ppm (1.3 mg/m3) for
45 min; intratracheal
Endpoints examined 5 min
before and up to 1 h
post-exposure.
Lung function—pulmonary
resistance
Amdur et al. (1988)
Guinea pig; n = 8
1 ppm for 1 h
Endpoints examined 2 h
following exposure
Airway responsiveness to
acetylcholine
Bronchial obstruction
determined by examination of
the respiratory loop
measured by whole-body
plethysmography in
spontaneously breathing
animals after each bronchial
provocation.
4 groups:
Control
0.1 ppm SO2
4.3 ppm SO2
16.6 ppm SO2
Conner et al. (1985) Hartley guinea pig; 1 ppm (2.62 mg/m3); nose
n < 18/group/time point; only for 3 h/d for 6 d
M; age NR; 250-320 g;
Riedel et al. (1988) Guinea pigs (Perlbright- 0.1, 4.3, and 16.6 ppm whole
White); n = 5-14; M; age body; 8 h/d for 5 d
NR; 300-350 g Animals were sensitized to
ovalbumin (ovalbumin
aerosol) on the last 3 d of
exposure
Bronchial provocation every
other day with aerosolized
0.1% ovalbumin began at
1 wk after the last exposure
to SO2 and continued for 14 d
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Table 5-18 (Continued): Study specific details from animal toxicological studies of
lung function.
Study
Species (Strain); n; Sex; Exposure Details
Lifestage/Age or Weight (Concentration; Duration)
Endpoints Examined
Park etal. (2001)
Guinea pigs
(Dunkin-Hartley);
n = 7-12/group; M; age
NR; 250-350 g
0.1 ppm whole body; 5 h/d for
5 d
Animals were sensitized to
ovalbumin (0.1% ovalbumin
aerosol) on the last 3 d of
exposure
Bronchial challenge with
1% ovalbumin aerosol
occurred at 1 wk after the last
exposure to SO2
4 groups:
Control
Ovalbumin
Bronchial
obstruction—measurement of
Penh by whole-body
plethysmography
CO = carbon monoxide; n = sample size; NR = not reported; M = male; Penh = enhanced pause; SD = standard deviation;
S02 = sulfur dioxide.
The 2008 SOx ISA (U.S. EPA. 2008(1) also described studies that examined airway
responsiveness following SO2 exposure. In several different animal species, a single
exposure to SO2 at a concentration up to 10 ppm failed to increase airway responsiveness
to a challenge agent. These studies were mainly conducted in naive animals rather than in
models of allergic airways disease. Only one was conducted at a SO2 concentration of
less than 2 ppm. This study found no change in airway responsiveness to acetylcholine
measured 2 hours following a 1-hour exposure in guinea pigs to 1 ppm SO2 (Amduret
al.. 1988). However, two toxicological studies (Park et al.. 2001) (Riedel et al.. 1988)
described in the 2008 SOx ISA (U.S. EP A. 2008d). provide evidence that repeated SO2
exposure of guinea pigs to concentrations as low as 0.1 ppm enhanced AHR following
subsequent sensitization and challenge with ovalbumin.
Summary of Lung Function Changes in General Populations and
Healthy Individuals
Across disciplines, there is limited evidence that short-term SO2 exposure induces lung
function changes in healthy people. Evidence from controlled human exposure studies of
healthy individuals shows that transient decreases in lung function can occur at
concentrations of 1.0 ppm SO2 under exercising or forced oral breathing conditions, but
the evidence is more consistent for exposures >1.0 ppm. Animal toxicological studies
demonstrated that acute exposure of guinea pigs to 0.16-1.0 ppm SO2 results in increased
airway resistance and repeated exposure of guinea pigs to concentrations of SO2 as low as
0.1 ppm led to an enhancement of AHR following sensitization and challenge with an
allergen. Epidemiologic studies do not clearly indicate S02-associated decreases in lung
function in healthy adults or children or groups from the general population with varying
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prevalence of respiratory disease. Results are mixed for SO2 measured at subjects'
locations and at central site monitors. Similar to experimental studies in healthy humans
and animals without allergen challenge plus 1- to 6-hour SO2 exposures, epidemiologic
findings are mixed for temporally resolved metrics such as 1-h max or 1- to 4-h avg SO2.
Associations were observed for populations living in locations with steel, aluminum, or
petrochemical industry or coal heating, but SO2 was one of many pollutants implicated.
Respiratory Symptoms in General Populations and Healthy Individuals
Respiratory symptoms in relation to short-term SO2 exposure have been investigated in a
limited number of studies of general populations or healthy individuals. The 2008 SOx
ISA (U.S. EPA. 2008d) described some controlled human exposure and epidemiologic
studies of respiratory symptoms among children or adults without asthma. Most
controlled human exposure studies reported no respiratory symptoms at concentrations up
to 2.0 ppm. Evidence from both previous and recent epidemiologic studies is
inconsistent.
Controlled Human Exposure Studies
Controlled human exposure studies examining respiratory symptoms in healthy
individuals exposed to SO2 are summarized in Table 5-15. Briefly, Tunnicliffe et al.
(2003) found no association between respiratory symptoms (i.e., throat irritation, cough,
and wheeze) and 1-hour exposures at rest to 0.2 ppm SO2 in either healthy adults or those
with asthma. Similarly, Andersen et al. (1974) reported no change in respiratory
symptoms in resting adults exposed to 1.0 ppm SO2 for 6 hours. A more recent study in
which exercising healthy adults were exposed to SO2 concentrations as high as 2.0 ppm
for 4 hours confirms these null findings (van Thriel et al.. 2010).
Epidemiologic Studies
Associations for ambient SO2 with respiratory symptoms in populations of healthy adults
and children are inconsistent. Most results are from Europe and Asia. There are more
studies of children than adults, but studies of adults focus on healthy individuals. Many
previous studies of children examined populations with 5-81% chronic wheeze, asthma,
or atopy, although results were inconsistent for healthy children as well (Boezen et al..
1999; Neas et al.. 1995). Some recent studies examine populations of children with low
(0.6-4%) prevalence of respiratory disease, but like previous studies do not consistently
associate increases in SO2 concentrations with respiratory symptoms (Table 5-19).
Previous results were largely based on 24-h avg SO2 concentrations measured at central
site monitors. Many recent studies have improved exposure assessment, examining
temporally resolved 1-hour SO2 concentrations for adults or SO2 concentrations at
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children's schools. These associations with respiratory symptoms also are inconsistent.
Other uncertainties include confounding by meteorological factors and copollutants.
For adults, a study on Miyakejima Island, Japan 5 years after a volcano eruption provided
information on effects related to SO2 concentrations and durations comparable to those
examined in experimental studies (Tshigami et al.. 2008). Incidence of many symptoms
increased at 1-h avg SO2 concentrations above 100 ppb and 1-h max concentrations
above 600 ppb than concentrations less than 10 ppb (reference category) (Table 5-19).
Although temporally resolved metrics were analyzed, inference about an SO2 effect is
weak. SO2 concentrations were measured within 2 km of volunteer workers' home and
work site, no other air pollutants or other potential confounders were examined, and 80%
of concentrations were in the reference category. Results linking long-term air pollution
from volcanoes to respiratory symptoms also are uncertain because they are based on
ecological comparisons of areas with low and high air pollution mixtures in which SO2 is
one constituent (Section 5.2.2.1).
For children, associations with SO2 concentrations were inconsistent within studies
among the array of symptoms examined (Table 5-19). Results across studies were
consistent for wheeze, an asthma symptom that is less likely to be experienced by healthy
children. A study in South Korea has many limitations including estimating SO2 exposure
from central site monitors at an unspecified distance from children and observing only a
few isolated associations among the numerous pollutants, symptoms, exposure lags, and
cities examined (Moon et al.. 2009). Other studies had cross-sectional design and
measured SO2 at school or within 2 km from school (Altug et al.. 2014; Linares et al..
2010; Zhao et al.. 2008). A study in China examined high SO2 concentrations similar to
those in the Japanese volcano study. Mean school SO2 concentrations were 101 ppb
indoors and 271 ppb outdoors. Indoor, but not outdoor, 1-wk avg SO2 concentrations
were associated with symptoms (Zhao et al.. 2008) (Table 5-19). Temporal mismatch is
likely between current SO2 measurements and symptoms at any time in the preceding
12 months. The other study with 1-wk avg school SO2 measures, conducted in Turkey,
observed an association with any shortness of breath or wheeze in the previous 7 days but
not throat symptoms, runny nose, or medication use concurrently or in the previous
7 days (Altug et al.. 2014). It is not clear whether the single positive association applied
to the entire population, the 7% with asthma, or 27% with hay fever. Among mostly
healthy children (0.6% asthma) in Mexico, lag 0 SO2 concentration was associated with
wheeze, but SO2 was measured up to 2 km from children's schools (Linares et al.. 2010).
SO2 concentrations were not associated with runny nose or difficulty breathing.
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Table 5-19 Recent epidemiologic studies of respiratory symptoms in healthy adults and children and groups in
the general population.
Study Population
and Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and
Lag Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination
Adults
tlshiaami et al. (2008)
Miyakejima Island, Japan, 2005
N =611, ages >15 yr, 100% healthy
Daily diaries for 1-15 d. Recruited from
volunteers working on an active volcanic
island 5 yr after eruption. Did not examine
potential confounding factors.
Monitors within 2 km of 1-h avg
residence/work area
Means across monitors
0-3,550
Max across monitors
3,790-10,320 1-h max
Cough crude incidence rate, males
< 10 ppb: 4.8, 10-20 ppb: 1.4,
20-30 ppb: 2.9, 30-100 ppb: 6.6,
> 100 ppb: 19.3. p for trend <0.01
< 10 ppb: 4.7, 10-20 ppb: 4.3,
20-60 ppb: 8.1, 60-2,000 ppb: 16.4,
> 2,000 ppb: 58.3. p fortrend < 0.01
No copollutant model
No copollutants examined.
Children
tZhao et al. (2008)
Taiyuan, China, Dec 2004
N = 1,993, ages 11—15 yr. 2% asthma. 4%
with furry pet or pollen allergy.
Cross-sectional. Recruited from schools.
Likely temporal mismatch between current
SO2 concentrations and symptoms
assessed as any occurrence in preceding
12 mo.
Monitor at school
Mean (SD) and max
Outdoor: 271 (72), 386
Indoor: 101 (53), 244
24-h avg Outdoor SO2
0-6 avg Wheeze
OR: 1.01 (0.98, 1.04)
Daytime attacks of breathlessness
OR: 0.99 (0.97, 1.01)
Nocturnal attacks of breathlessness
OR: 1.01 (0.96, 1.06)
Indoor SO2
Wheeze
OR: 1.04 (1.01, 1.08)
Daytime attacks of breathlessness
OR: 1.02 (0.99, 1.04)
Nocturnal attacks of breathlessness
OR: 1.07 (1.01, 1.13)
No copollutant model
Indoor NO2 and
formaldehyde associated
with symptoms. PM2.5 not
examined.
SO2 highly correlated with
NO2. r= 0.74.
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Table 5-19 (Continued): Recent epidemiologic studies of respiratory symptoms in healthy adults and children and
groups in the general population.
Study Population
and Methodological Details
SO2 Exposure
Estimates (ppb)
SO2 Averaging
Time and
Lag Day
Effect Estimate (95% CI)
Single-Pollutant Modela
Copollutant Examination
tAltuq etal. (2014)
Eskisehir, Turkey, Feb-Mar 2007
N = 605, ages 9-13 yr. 7% asthma, 44%
eczema.
Cross-sectional. Recruited from schools
from participants of a larger study.
Monitor at school
Mean and max
Suburban: 21, 29
Urban: 29, 44
Urban-traffic: 22, 27
24-h avg Complaints of the throat in last 7 d
0-6 avg RR: 0.83 (0.59, 1.15)
Complaints of the throat at the moment
RR: 1.03 (0.72, 1.47)
Runny nose in last 7 d
RR: 0.95 (0.74, 1.22)
Runny nose at the moment
RR: 0.92 (0.69, 1.23)
Shortness of breath/wheeze in last 7 d
RR: 1.72 (1.05, 2.81)
Medication for shortness of breath/
wheeze in last 7 d RR: 1.44 (0.69, 2.99)
Shortness of breath/wheeze today
RR: 1.79 (0.90, 3.58)
Medication for shortness of breath/
wheeze today RR: 0.74 (0.16, 3.33)
No copollutant model
O3 and NO2 not associated
with symptoms. PM2.5 not
examined.
SO2 weakly correlated with
O3, moderately correlated
with NO2. r= 0.40, 0.49.
tLinares et al. (2010)
Monitors within 2 km of
24-h avg
No copollutant model
Salamanca, Mexico, Mar 2004-Feb 2005
school
0
Wheezing OR: 1.06 (1.00, 1.11)
PM10 and O3 but not NO2
N = 464, ages 6-14 yr. 0.6% asthma.
Cross-sectional. Recruited from schools.
Means spring-winter
School 1: 12, 12, 10, 9.8
School 2: 9.1, 8.7, 10, 13
Rhinorrhea OR: 0.98 (0.92, 1.05)
Dyspnea OR: 1.02 (0.97, 1.07)
associated with symptoms.
PM2.5 not examined.
Copollutant correlations NR.
tMoon et al. (2009)
Seoul, Incheon, Busan, Jeju, South Korea,
2003
N = 696, ages < 13 yr
Daily diaries for 2 mo. Recruited from
schools.
Monitors in city 24-h avg
Number and distance NR 0
Means NR
Max: 38
LRS OR: 1.00 (0.93, 1.08)
URS OR: 1.11 (1.03, 1.20)
No copollutant model
PM10 and CO associated
with symptoms. PM2.5 not
examined.
Copollutant correlations NR.
CI = confidence interval; CO = carbon monoxide; LRS = lower respiratory symptoms; N = sample size; N02 = nitrogen dioxide; NR = not reported; 03 = ozone; OR = odds ratio;
PM2 5 = particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm; PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10
|jm; r = correlation coefficient; RR = relative risk or ratio; SD = standard deviation; S02 = sulfur dioxide; URS = upper respiratory symptoms.
aEffect estimates are standardized to a 10-ppb increase in 1-h avg and 24-h avg S02 and a 40-ppb increase in 1-h max S02.
fStudies published since the 2008 Integrated Science Assessment for Sulfur Oxides.
1
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For the few observations of SCh-associated increases in respiratory symptoms in healthy
adults and children, the potential for copollutant confounding was not examined. PMio,
CO, and formaldehyde were also associated with symptoms; PM2 5 was not examined
(Table 5-19). Most studies did not report copollutant correlations, and none examined
copollutant models. Symptoms were not associated with outdoor NO2 (Altug et al.. 2014;
Linares et al.. 2010; Zhao et al.. 2008). but an association was observed with indoor NO2
(Zhao et al.. 2008). Indoor school SO2 and NO2 were highly correlated (r = 0.74), and it
is not clear the extent to which the association with breathlessness can be attributed
independently to SO2 or NO2 or to a combined effect of those and other copollutants.
Summary of Respiratory Symptoms in General Populations and Healthy
Individuals
There is little evidence for an effect of short-term SO2 exposure on respiratory symptoms
in healthy individuals. Controlled human exposure studies of healthy adults did not
demonstrate effects for 1- to 6-hour SO2 exposures up to 2 ppm, and epidemiologic
findings are inconsistent for healthy adults and children. For epidemiologic studies, there
is uncertain representativeness of SO2 exposures estimated from central site monitors.
However, as shown in recent studies, respiratory symptoms are also inconsistently
associated with SO2 measured at children's schools. A biological explanation for
associations observed with 1-wk avg SO2 concentrations is unclear. For associations
observed with 1-h avg or max concentrations and the evidence overall, potential for
confounding by PM2 5, PM10, NO2, CO, and formaldehyde is not addressed.
Subclinical Respiratory Effects in Healthy Individuals
Pulmonary inflammation is a key subclinical effect in the pathogenesis of asthma and
other respiratory diseases. It consists of both acute and chronic responses and involves
the orchestrated interplay of the respiratory epithelium and both the innate and adaptive
immune system. The immunohistopathologic features of chronic inflammation involve
the infiltration of inflammatory cells such as eosinophils, lymphocytes, mast cells, and
macrophages and the release of inflammatory mediators such as cytokines and
leukotrienes. The 2008 ISA for Sulfur Oxides described limited evidence from animal
toxicological studies for S02-induced pulmonary inflammation and allergic sensitization
in rodents exposed to allergen. Recent controlled human exposure and epidemiologic
studies add to the evidence base and do not clearly support S02-related pulmonary
inflammation in healthy populations.
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Controlled Human Exposure Studies
A recent controlled human exposure study examined eNO and other biomarkers of
pulmonary inflammation in the NALF and EBC after exposures to 0, 0.5, 1, and 2 ppm
SO2 for 4 hours in exercising healthy adults (Raulf-Hcimsoth et al.. 2010). Data
demonstrated no statistically significant changes in eNO; leukotriene B4, prostaglandin
E2, and 8-iso-prostaglandin F2 alpha in EBC; or substance P, interleukin-8 (IL-8), and
brain derived neurotrophic factor in NALF after SO2 exposures, compared to air.
Epidemiologic Studies
Unlike the study reviewed in the 2008 ISA for Sulfur Oxides (Adamkiewicz et al.. 2004).
recent studies measured SO2 near subjects' homes, schools, or work. SO2 concentrations
at a site within 1 km of most homes were not associated with pulmonary inflammation in
a population of children with high prevalence (33%) of asthma or atopy (Chen et al..
2012a). Previous results were similar for a population of older adults that included people
with respiratory disease. Recent examination of healthy adults and children in Beijing,
China indicates S02-associated increases in pulmonary inflammation or oxidative stress.
These recent studies were conducted before, during, and after the 2008 Olympics (Rov et
al.. 2014; Lin et al.. 2011b). Concentrations of SO2 and other pollutants were lower
during the Olympics than before or after (e.g., mean 24-h avg 3.0 vs. 7.5 and 6.8 ppb).
During a winter 2007 period, mean 24-h avg SO2 concentrations were 45 ppb (Lin et al..
2011b). Pollutants were measured 0.65 km from the school that study children attended
and the hospital where most of the study adults worked. A 10-ppb increase in lag 0
24-h avg SO2 was associated with a 7.6% (95% CI: 5.9, 9.3) increase in eNO of children
(Lin etal.. 2011b) and, in adults, a 0.67 standard deviation (95% CI: 0.48, 0.86) increase
in an index of pulmonary inflammation and oxidative stress combining eNO and EBC
markers (Rov et al.. 2014). Associations were also observed with PM2 5, sulfate, EC/BC,
CO, NO2, and OC. Copollutant models were analyzed for children, in which SO2 effect
estimates remained positive but decreased substantially with adjustment for PM2 5 or BC
(Lin etal.. 2011b). Conversely, the effect estimate for BC was robust to adjustment for
SO2. Correlations with SO2 concentrations were not reported, but inference from
copollutant models is likely better for pollutants measured close to school than at central
site monitors due to more comparable exposure measurement error. Confounding by
other copollutants was not examined.
Animal Toxicological Studies
The 2008 SOx ISA (U.S. EPA. 2008d) described several animal toxicological studies that
examined the effects of repeated exposure to SO2 on inflammation. These and other
animal toxicological studies examining inflammation in naive animals exposed to SO2
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are summarized in Table 5-20. Repeated exposure to SO2 was found to promote allergic
sensitization and enhanced allergen-induced bronchial obstruction in guinea pigs. In the
first of these studies, Riedel et al. (1988) examined 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 hours/day for 5 days. During the last 3 days, SO2 exposure
was followed by exposure to nebulized ovalbumin for 45 minutes. Following bronchial
provocation with inhaled ovalbumin (0.1%) 1 week later, bronchial obstruction was
measured by examining the respiratory loop obtained by whole-body plethysmography.
In addition, specific antibodies against ovalbumin were measured in serum and BALF.
Results showed significantly higher bronchial obstruction in animals exposed to both
SO2, at all concentration levels, and ovalbumin compared with animals exposed only to
ovalbumin. In addition, significant increases in anti-ovalbumin IgG antibodies were
detected in BALF 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 and ovalbumin compared with controls
exposed only to ovalbumin. These results demonstrated that repeated exposure to SO2
enhanced allergic sensitization and bronchial obstruction in the guinea pig at a
concentration as low as 0.1 ppm.
In the second study, guinea pigs were exposed to 0.1 ppm SO2 for 5 hours/day for 5 days
and sensitized with 0.1% ovalbumin aerosols for 45 minutes on days 4 and 5 (Park et al..
2001). One week later, animals were subjected to bronchial challenge with 0.1%
ovalbumin and lung function was evaluated 24 hours later by whole-body
plethysmography. The results demonstrated a significant increase in enhanced pause
(Penh), a measure of airway obstruction, in animals exposed to both SO2 and ovalbumin
but not in animals treated with ovalbumin or SO2 alone. In animals treated with both SO2
and albumin, increased numbers of eosinophils were found in lavage fluid. In addition,
infiltration of inflammatory cells, bronchiolar epithelial cell damage, and plugging of the
airway lumen with mucus and cells were observed in bronchial tissues. These cellular
changes were not observed in animals treated with ovalbumin or SO2 alone. Results
indicate that repeated exposure to near-ambient levels of SO2 may play a role in allergic
sensitization and in exacerbating allergic inflammatory responses in the guinea pig.
Furthermore, increases in bronchial obstruction suggest that SO2 exposure induced an
increase in airway responsiveness in the animals subsequently made allergic to
ovalbumin.
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Table 5-20 Study-specific details from animal toxicological studies of
subclinical effects.
Study
Species (strain);
n; Sex;
Lifestage/Age or
Weight
Exposure Details
(Concentration; Duration)
Endpoints Examined
Conner et al. (1989) Guinea pigs
(Hartley); n = 4; M;
age NR;
250-300 g;
1 ppm nose only; 3 h/d for 1-5 d
BAL performed each day.
BALF—total and differential cell
counts
Riedeletal. (1988)
Guinea pigs
(Perlbright-White):
n = 5-14/group- *'
age NR;
300-350 g;
M;
0.1, 4.3, and 16.6 ppm whole body;
8 h/d for 5 d
Animals were sensitized to ovalbumin
(ovalbumin aerosol) on the last 3 d of
exposure
Bronchial provocation every other day
with 0.1% ovalbumin aerosol began at
1 wk after the last exposure to SO2
and continued for 14 d
Endpoints examined 48 h after
the last provocation.
Serum—anti IgG levels
BALF—anti IgG levels
Four groups:
Control
0.1 ppm SO2
4.3 ppm SO2
16.6 ppm SO2
Park et al. (2001)
Guinea pigs
(Dunkin-Hartley);
n = 7-12/group; M;
age NR;
250-350 g;
0.1 ppm whole body; 5 h/d for 5 d
Animals were sensitized to ovalbumin
(0.1% ovalbumin aerosol) on the last
3 d of exposure
Bronchial challenge with 1%
ovalbumin aerosol occurred at 1 wk
after the last exposure to SO2
Four groups:
Control
Ovalbumin
SO2
Ovalbumin/SC>2
Endpoints examined 24 h after
the bronchial challenge.
BALF—differential cell counts
cells
Lung and bronchial
tissue—histopathology
Li et al. (2007)
Rats (Wistar);
n = 6/group; M;
NR
2 ppm SO2 for 1 h/d for 7 d
age
Endpoints examined 24 h
following the last exposure
BALF—inflammatory cell counts
Lung—histopathology and
immunohistochemistry
Lung and tracheal
tissue—mRNA and protein
levels of MUC5AC and ICAM-1
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Table 5-20 (Continued): Study specific details from animal toxicological studies of
subclinical effects.
Study
Species (strain);
n; Sex;
Lifestage/Age or
Weight
Exposure Details
(Concentration; Duration)
Endpoints Examined
Li et al. (2014) Rats (Wistar); 2 ppm SO2 for 1 h/d for 7 d
n = 6/group; M; age
NR; 180-200 g
Endpoints examined
BALF—inflammatory cell counts
and cytokines IL-4, IFN-y, TNFa,
IL-6
Serum—IgE
Lung—histopathology,
Lung and tracheal
tissue—mRNA and protein
levels NFkB, IkBq, IKK(3, IL-6,
IL-4, TNFa, FOXp3,
EMSA NFkB binding activity
BAL = bronchoalveolar lavage; BALF = bronchoalveolar lavage fluid; EMSA = electrophoretic mobility shift assay;
FOXp3 = forkhead box p3; ICAM-1 = intercellular adhesion molecule 1; IFN-y = interferon gamma; IgE = immunoglobulin E;
IgG = immunoglobulin G; IKK(B = inhibitor of nuclear factor kappa-B kinase subunit beta; IL-4 = interleukin-4; IL-6 = interleukin-6;
IkBo = nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha; i.p. = intraperitoneal; M = male;
MUC5AC = mucin 5AC glycoprotein; n = sample size; NFkB = nuclear factor kappa-light-chain-enhancer of activated B cells;
NR = not reported; SD = standard deviation; S02 = sulfur dioxide; TNFa = tumor necrosis factor alpha.
Park et al. (2001) demonstrated that repeated exposure of guinea pigs to 0.1 ppm SO2
alone did not lead to allergic inflammation or morphologic changes in the lung although
it enhanced the allergic inflammation due to subsequent sensitization and challenge with
ovalbumin. Conner et al. (1989) found no changes in total cells and neutrophils in BALF
from guinea pigs exposed repeatedly to 1 ppm SO2. In contrast, found that repeated
exposure of rats to 2 ppm SO2 resulted in mild pathologic changes in the lung, including
inflammatory cell influx and smooth muscle hyperplasia (Li et al.. 2014; Li et al.. 2007).
Several other indicators of inflammation and immune response were not changed by
exposure to SO2 alone.
Summary of Subclinical Respiratory Effects in Healthy Individuals
There is limited evidence for inflammatory and other subclinical respiratory effects in
healthy populations following short-term exposure to SO2, primarily from animal
toxicological studies involving allergen sensitization. As newly informed by recent
studies, SO2 is not clearly related to pulmonary inflammation in healthy populations in
controlled human exposure or epidemiologic studies. Associations were observed in
some epidemiologic studies, but confounding by PM2 5, sulfate, BC, or NO2 is not well
addressed. Studies in animals demonstrated that repeated exposure of guinea pigs to 0.1
or 1 ppm SO2 had no effect on inflammation. However, when followed by sensitization
with an allergen, exposure of guinea pigs to 0.1 ppm SO2 enhanced allergic sensitization,
allergic inflammatory responses, and airway responsiveness to that allergen. These results
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point to the potential for SO2 exposure to increase sensitivity to an allergen, which differ
from the inflammatory responses examined in healthy humans. In addition, repeated
exposure of rats to 2 ppm SO2 resulted in inflammation and smooth muscle hyperplasia,
early indicators of airway remodeling.
5.2.1.8 Respiratory Mortality
Studies evaluated in the 2008 SOx ISA that examined the association between short-term
SO2 exposure and cause-specific mortality found consistent positive associations with
respiratory mortality using a 24-h avg exposure metric with some evidence indicating that
the magnitude of the association was larger compared to all-cause and cardiovascular
mortality. Recent multicity studies conducted in Asia (Chen et al.. 2012b; Kan et al..
2010b) and Italy (Bellini et al.. 2007), a meta-analysis of studies conducted in Asia
(Atkinson et al.. 2012). and a four-city study conducted in China that focused specifically
on COPD mortality (Meng et al.. 2013) add to the initial body of evidence indicating
larger respiratory mortality effects (Section 5.5.1.3. Figure 5-18).
Studies evaluated in and prior to the 2008 SOx ISA that examined the association
between short-term SO2 exposures and respiratory mortality focused exclusively on
single-pollutant analyses. Therefore, questions arose regarding the independent effect of
SO2 on respiratory mortality, and whether associations remained robust in copollutant
models. A few recent multicity studies conducted in China (Meng et al.. 2013; Chen et
al.. 2012b) and multiple Asian cities (Kan et al.. 2010b) examined both of these
questions. Chen et al. (2012b) found that the S02-respiratory mortality association was
attenuated, but remained positive in copollutant models with PM10 [2.03% (95% CI: 0.89,
3.17) for a 10-ppb increase in 24-h avg SO2 concentrations at lag 0-1 days] and NO2
[1.16% (95% CI: -0.03, 2.37) for a 10-ppb increase in 24-h avg SO2 concentrations at lag
0-1 days]. These results are similar to what the authors reported when examining the
SCh-total mortality association in models with PM10 (i.e., -40% reduction), but more
attenuation was observed in models with NO2 (i.e., -80% reduction for total mortality
and 65% reduction for respiratory mortality) (Section 5.5.1.4). Kan et al. (2010b). as part
of the Public Health and Air Pollution in Asia (PAPA) study, also examined the effect of
copollutants (i.e., NO2, PM10, and O3), but only in each city individually. The study
authors found that although the S02-respiratory mortality association remained positive
in copollutant models, there was evidence of an attenuation of the association in models
with PM10 and more so in models with NO2 (Figure 5-10). Meng et al. (2013) in a
four-city analysis of COPD mortality in China reported evidence consistent with Chen et
al. (2012b) and Kan et al. (2010b). The authors observed a 3.7% (95% CI: 2.4, 4.9)
increase in COPD mortality for a 10-ppb increase in 24-h avg SO2 concentrations at lag
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0-1 days. However, compared to the results for respiratory mortality from copollutant
models reported in Chen et al. (2012b). Meng et al. (2013) found a larger degree of
attenuation in models with PMio, -50% reduction [1.9% (95% CI: 0.3, 3.5)] and NO2,
-99% reduction [0.0% (95% CI: -1.8, 1.9)] compared to the SO2 results from the single
pollutant model. The larger degree of attenuation of the SO2-COPD mortality association
in Meng et al. (2013). compared to respiratory mortality in Chen et al. (2012b) could be a
reflection of the smaller sample size and smaller number of cities included in the
analysis. Overall, the studies that examined the potential confounding effects of
copollutants on the S02-respiratory mortality relationship show results consistent with
what has been observed for total mortality. However, the overall assessment of potential
copollutant confounding remains limited, and it is unclear how the results observed in
Asia translate to other locations, specifically due to the unique air pollution mixture and
higher concentrations observed in Asian cities.
Of the studies evaluated, only Bellini et al. (2007) (in a multicity study conducted in
Italy) examined potential seasonal differences in the S02-cause-specific mortality
relationship. Bellini et al. (2007) reported that risk estimates for respiratory mortality
were dramatically increased in the summer from 4.1 to 12.0% for a 10-ppb increase in
24-h avg SO2 concentrations at lag 0-1, respectively, with the all-year and winter results
being similar. These results are consistent with the seasonal pattern of SO2 associations
observed in Bellini et al. (2007) for total and cardiovascular mortality. However, it
remains unclear whether this seasonal pattern of S02-respiratory mortality associations is
observed in other locations.
An uncertainty that often arises when examining the relationship between short-term air
pollution exposures and cause-specific mortality is whether the lag structure of
associations and the C-R relationship is consistent with what is observed for total
mortality. Meng et al. (2013) addressed both the lag structure of associations and the C-R
relationship in a study of short-term air pollution exposures and COPD mortality in four
Chinese cities. Although not explicitly part of the China Air Pollution and Health Effects
Study (CAPES) study, Meng et al. (2013) focused on four CAPES cities over the same
time period as Chen et al. (2012b). In comparison to Chen et al. (2012b). who found a
steady decline in risk estimates at single-day lags of 0 to 7 days with the largest effect at
lag 0-1, Meng et al. (2013) observed a steady decline over single lag days, but some
indication of larger associations, although highly uncertain, at longer multiday lags
(i.e., 0-4 and 0-7 days) (Figure 5-10). Note that Chen et al. (2012b) did not examine
multiday lags longer than 0-1 days, but the magnitude of the association for all
respiratory mortality [3.3% (95% CI: 2.1, 4.6) for a 10-ppb increase in 24-h avg SO2
concentrations] is similar to that reported in Meng et al. (2013) for COPD [3.7% (95%
CI: 2.4,4.9)].
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%
4.0
3.5
>,
cn
t
o
E
Q
0.
O
0
c
n
'£
(0
b
c
1
5
Q_
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
——r
1 2 3 4 5 6 7 01 04
S02
07
Lag
COPD = chronic obstructive pulmonary disease; S02 = sulfur dioxide.
Source: Adapted from Meng et al. (2013).
Figure 5-10 Percent increase in chronic obstructive pulmonary disease
mortality associated with a 10 |jg/m3 (3.62 ppb) increase in
24-h avg sulfur dioxide concentrations at various single and
multiday lags.
1 Meng et al. (2013) also examined the shape of the SO2-COPD mortality C-R relationship.
2 To examine the assumption of linearity, the authors modeled the relationship between air
3 pollution exposures and COPD mortality using a natural spline with 3 df. Meng et al.
4 (2013) then computed the difference between the deviance of the linear and spline
5 models to assess whether there was evidence of nonlinearity in the SO2-COPD
6 relationship. As depicted in Figure 5-11. the authors found no evidence that the spline
7 model resulted in a better fit of the SC^-mortality relationship compared to the linear
8 model. However, the authors did not present confidence intervals for each of the C-R
9 curves, which complicates the interpretation of the results.
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o «
E
o
Q_
o
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o °
•IT
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>
(-1
* J
?
o;
o
Beijing
Shanghai
Guangzhou
Hongkong
i mi ^minimi mill
50 100
S02 concentration at lag 01 day
Hi
150
COPD = chronic obstructive pulmonary disease; S02 = sulfur dioxide.
Source: Adapted from Meng et al. (2013).
Figure 5-11 City-specific concentration-response curves for short-term sulfur
dioxide exposures and daily chronic obstructive pulmonary
disease mortality in four Chinese cities.
Overall, recent multicity studies report evidence of consistent positive associations
between short-term SO2 concentrations and respiratory mortality, which is consistent
with those studies evaluated in the 2008 SOx ISA. Unlike studies evaluated in the 2008
SOx ISA, recent studies examined whether copollutants confound the relationship
between short-term SO2 concentrations and respiratory mortality. Overall, these studies
reported evidence that the S02-respiratory mortality association was attenuated in models
with NO2 and PM10, but the analyses are limited to Asian cities where the air pollution
mixture and concentrations are different than those reported in other areas of the world.
Additional analyses focusing on seasonal patterns of associations, lag structure of
associations, and the C-R relationship are limited in number, but suggest evidence of:
larger associations in the summer/warm season, larger and more precise associations at
shorter lag periods (in the range of 0 and 1 days), and a linear, no threshold C-R
relationship, respectively. However, for both total and cause-specific mortality, the
overall assessment of linearity in the C-R relationship is based on a very limited
exploration of alternatives.
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5.2.1.9 Summary and Causal Determination
Strong evidence indicates that there is a causal relationship between short-term SO2
exposure and respiratory effects, particularly for respiratory effects in the at-risk
population of individuals with asthma. This determination is based on the consistency of
SCh-induced bronchoconstriction in exercising individuals with asthma in controlled
human studies, coherence of asthma-related effects among multiple lines of evidence, and
biological plausibility for effects specifically related to asthma exacerbation. There is
limited support for a relationship between short-term SO2 exposure and other respiratory
effects, including exacerbation of COPD, allergy exacerbation, respiratory infection,
respiratory effects in healthy populations, and respiratory mortality. The limited and
inconsistent evidence for these nonasthma-related respiratory effects does not contribute
heavily to the causal determination.
The determination of a causal relationship is the same as the conclusion of the 2008 SOx
ISA (U.S. EPA. 2008d). The evidence for this conclusion was heavily based on
controlled human exposure studies that showed lung function decrements and respiratory
symptoms in adult individuals with asthma exposed to SO2 for 5-10 minutes under
increased ventilation conditions. These findings are consistent with the current
understanding of biological plausibility described in the mode of action section
(Section 4.3.6). Previous epidemiologic studies provided supporting evidence indicating
associations between short-term increases in ambient SO2 concentration and
respiratory-related ED visits and hospital admissions as well as respiratory symptoms.
The evidence for a causal relationship is detailed below using the framework described in
the Preamble to the ISAs (U.S. EPA. 2015b). While new evidence adds to the existing
body of evidence, the determination remains largely based on previous controlled human
exposure studies. The key evidence as it relates to the causal framework is presented in
Table 5-21.
Evidence for Asthma Exacerbation
A causal relationship between short-term SO2 exposure and respiratory effects is
primarily supported by evidence from controlled human exposure studies of respiratory
effects in adults with asthma. These studies consistently demonstrated that the majority of
individuals with asthma experience a moderate or greater decrement in lung function, as
defined by a >100% increase in sRaw or >15% decrease in FEVi. This decrement is
frequently accompanied by respiratory symptoms following exposures of 5-10 minutes,
with elevated ventilation rates at concentrations of 0.4-0.6 ppm (Johns et al.. 2010; Linn
et al.. 1990; Linn et al.. 1988; Balmes et al.. 1987; Linn et al.. 1987; Horstman et al..
1986; Linn et al.. 1983b). A fraction of the population with asthma (-5-30%) has also
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been observed to have decrements in lung function at lower SO2 concentrations
(0.2-0.3 ppm) (I inn et al.. 1990; I inn et al.. 1988; I inn et al.. 1987; Bethel et al.. 1985V
Although the degree of lung function decrements are considered moderate, they are less
likely to be accompanied by respiratory symptoms at these lower concentrations (Linn et
al.. 1990; Linn et al.. 1988; Linn et al.. 1987; Roger et al.. 1985; Linn et al.. 1983b).
A group of responders (defined as having >15% decrease in FEVi after exposure to 0.6 or
1.0 ppm SO2) showed statistically significant decrements in FEVi following 5-10 minute
exposure to 0.3 ppm SO2 (Johns et al.. 2010) (Table 5-3). While SO;-induced respiratory
effects have been examined in individuals classified as having mild and moderate asthma,
these individuals are relatively healthy. Thus, extrapolating to individuals with severe
asthma is difficult because such individuals cannot be tested in an exposure chamber due
to the severity of their disease. Therefore, it is unknown whether people with severe
asthma are at increased risk to respiratory effects due to short-term SO2 exposure.
The same may be said about children with asthma. There are no laboratory studies of
children exposed to SO2, but a number of studies have assessed airway responsiveness of
children and adults exposed to the bronchoconstrictive stimuli methacholine. Based
largely on those studies, school-aged children, particularly boys and perhaps obese
children, would be expected to have greater responses (i.e., larger decrements in lung
function) following exposure to SO2 than adolescents and adults.
The coherence of epidemiologic findings (Section 5.2.1.2) is supporting evidence for a
causal relationship. Epidemiologic evidence for lung function changes in adults and
children with asthma is inconsistent. However, short-term increases in ambient SO2
concentration are associated with increases in asthma hospital admissions and ED visits
among all ages, children (i.e., <18 years of age) and older adults (i.e., 65 years of age and
older) (Figure 5-3). as well as asthma symptoms in children (Velickaet al. 2015; Spira-
Cohen etal.. 2011). Epidemiologic associations between short-term increases in ambient
SO2 concentration and respiratory mortality provide support for a potential continuum of
effects between respiratory morbidity and respiratory mortality.
Most epidemiologic studies indicating associations between short-term SO2 exposures
and asthma exacerbation assigned exposure using SO2 concentrations measured at central
site monitors. The use of central site monitors to assign exposure, particularly to 1-h max
SO2, may introduce exposure measurement error if the spatiotemporal variability in SO2
concentrations is not captured. Studies did not statistically correct for measurement error,
but in this new research area, a method has not been reported for short-term SO2 exposure
(Section 3.4.4). A few recent results reduce the uncertainty with SO2 measured or
modeled at or near children's school or home (Velicka et al.. 2015; Spira-Cohen et al..
2011). Additional uncertainty exists regarding potential copollutant confounding. In
many studies, SO2 was moderately to highly correlated with PM2 5, larger sized PM,
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EC/BC, NO2, and VOCs (r = 0.4-0.9). The few available results show association with
sulfate. A small number of studies examined copollutant models. Some associations were
relatively unchanged in magnitude after adjustment for a copollutant; others did not
persist. However, inference from copollutant models is limited given potential differences
in exposure measurement error for SO2 compared to NO2, CO, PM, and O3 and in many
cases, high copollutant correlations. Copollutant interactions are not well studied. Some
controlled human exposure studies demonstrate increased asthma-related effects with
coexposure to SO2 and NO2 or O3. Limited epidemiologic evidence shows increased
asthma-related effects with joint increases in SO2 and copollutants but does not clearly
show a joint association that is greater than a single-pollutant association.
There is supportive evidence for a relationship between short-term SO2 exposure and
airway responsiveness and pulmonary inflammation. Limited epidemiologic evidence
points to associations with increased airway responsiveness in adults with asthma plus
atopy (Taggart et al.. 1996). Gong et al. (2001) demonstrated an increase in airway
eosinophils in adults with asthma 2 hours after a 10-minute exposure to 0.75 ppm SO2.
This effect, along with bronchoconstriction, was attenuated by pretreatment with a
leukotriene receptor antagonist. Other pharmacologic studies have demonstrated the
importance of inflammatory mediators in mediating SO2 exposure-induced
bronchoconstriction in people with asthma (Section 4.3.1). Further support for an
important role of airway inflammation, including allergic inflammation, is provided by
animal toxicological studies of repeated SO2 exposure in allergic animals that are used to
model the asthmatic phenotype (Li et al.. 2014; Li et al.. 2007). In addition, repeated
exposure of naive animals promoted allergic sensitization and enhanced allergic
inflammation and airway responsiveness to an allergen (Park et al.. 2001; Riedel et al..
1988). These latter studies point to a possible increased sensitivity to allergens following
SO2 exposure.
Evidence for Other Respiratory Effects
Epidemiologic studies demonstrate some associations of ambient SO2 concentrations
with hospital admissions and ED visits for all respiratory causes combined (Figure 5-9).
While these results suggest that the respiratory effects of short-term SO2 exposure could
extend beyond exacerbation of asthma, evidence across disciplines is inconsistent and/or
lacks biological plausibility for conditions such as allergy exacerbation (Section 5.2.1.3).
COPD exacerbation (Section 5.2.1.4). and respiratory infection (Section 5.2.1.5). Where
epidemiologic associations were found, potential copollutant confounding is uncertain.
For COPD exacerbation, a controlled human exposure study demonstrated no effect of
SO2 exposure, and epidemiologic associations are inconsistent for lung function,
respiratory symptoms, hospital admissions, and ED visits. Some evidence supports
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SCh-associated increases in hospital admissions and ED visits due to respiratory
infections. However, the lack of multiple studies examining the same respiratory
infection outcome, inconsistent findings for self-reported infections in children, and the
lack of evidence from controlled human exposure and animal toxicological studies
produces uncertainty as to whether a relationship exists. Controlled human exposure
studies in healthy individuals provide evidence for transient decreases in lung function
with >1 ppm SO2 exposures for 5-10 minutes under exercising or a forced oral breathing
condition with no evidence for increased respiratory symptoms. Epidemiologic evidence
is inconsistent for SO2 associations with lung function, respiratory symptoms, and
pulmonary inflammation in healthy children and adults.
Conclusion
The evidence integrated across disciplines supports a causal relationship between
short-term SO2 exposure and respiratory effects, particularly asthma exacerbation. This
determination is primarily based on decreased lung function and increased respiratory
symptoms observed in controlled human exposure studies in adults with asthma.
Epidemiologic studies of asthma hospital admissions and ED visits and asthma symptoms
in children provide supporting evidence. Supportive evidence for a relationship between
short-term SO2 exposure and pulmonary inflammation and AHR, is provided by
controlled human exposure, epidemiologic, and toxicological studies. Evidence for an
effect of SO2 exposure on allergy exacerbation, COPD exacerbation, respiratory
infection, respiratory effects in healthy populations, and respiratory mortality is
inconsistent within and across disciplines and outcomes, and there is uncertainty related
to potential confounding by copollutants. The limited and inconsistent evidence for these
nonasthma-related respiratory effects does not contribute heavily to the causal
determination.
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Table 5-21 Summary of evidence for a causal relationship between short-term
sulfur dioxide exposure and respiratory effects.
Rationale for Causal
Determination3 Key Evidence13
Key References'3
SO2
Concentrations
Associated with
Effects0
Asthma exacerbation
Consistent evidence Decreased lung function following exposures
from multiple, of 5-10 min in exercising individuals with
high-quality controlled asthma
Section 5.2.1.2
Table 5-2
400-600 ppb
studies rules out a group of responders (defined as having
chance, confounding, >150/,, decrease in FEVi after exposure to
and other biases g.6 or 1.0 ppm SO2) showed statistically
significant decrements in FEV1 following 5-
10 min of exposure to 0.3 ppm SO2
Section 5.2.1.2
Table 5-3
300 ppb
Decreased lung function following exposures Section 5.2.1.2 200-300 ppb
of 5-10 min in 5-30% of exercising Table 5-2
individuals with asthma
Increased respiratory symptoms following Section 5.2.1.2 400-1,000 ppb
exposure of 5-10 min in exercising Table 5-2
individuals with asthma
Generally supporting
evidence from
multiple
epidemiologic studies
at relevant SO2
concentrations
Increase in asthma hospital admissions and
ED visits in single- and multi-city studies,
among all ages, children and older adults
Section 5.2.1.2
1-h max:
9.6-10.8 ppb
24-h avg:
1.03-36.9 ppb
Limited evidence for respiratory symptoms in
children with asthma with school and/or
home SO2 exposure estimates
tSpira-Cohen et al.
(2011). tVelicka et al.
(2015)
Section 5.2.1.2
24-h avg:
median 4.0 ppb
Uncertainty regarding
exposure
measurement error
SO2 exposures estimated from central site
monitors may not capture spatiotemporal
variability of SO2 across a community
Section 3.4.2
Uncertainty regarding Some SO2 associations were relatively
potential copollutant
confounding
unchanged in magnitude in copollutant
models with NO2, PM2.5, or PM10. Others
were attenuated. Differential exposure
measurement error limits inference. SO2
showed a wide correlation with copollutants
across studies (r= 0.4-0.9).
Attenuated: tSpira-
Cohen et al. (2011)
Section 5.2.1.2,
Section 3.4.3
Neural reflexes and/or inflammation lead to
bronchoconstriction.
Section 4.3.6
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Table 5-21 (Continued): Summary of evidence for a causal relationship between
short-term sulfur dioxide exposure and respiratory effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2
Concentrations
Associated with
Effects0
Evidence for key
events in proposed
mode of action
Increased airway eosinophils in adults with
asthma exposed to SO2
Enhanced allergic inflammation in rats
previously sensitized with an allergen and
then repeatedly exposed to SO2.
Gong et al. (2001). Li et
al. (2007). tJJetaL
(2014)
750-2,000 ppb
Enhancement of allergic sensitization,
Park et al. (2001). Riedel
100 ppb
allergic inflammation and airway
et al. (1988)
responsiveness in guinea pigs exposed to
SO2 repeatedly over several days and
subsequently sensitized and challenged with
an allergen
Allergic inflammation leads to increased
Taaaart et al. (1996)
24-h avg: max
airway responsiveness. Association with
39 ppb
airway responsiveness among adults with
asthma plus atopy
Other respiratory effects
Limited and
Inconsistent evidence for allergy
Section 5.2.1.3.
inconsistent evidence
exacerbation, COPD exacerbation,
Section 5.2.1.4,
across disciplines
respiratory infection, respiratory diseases,
Section 5.2.1.5,
and outcomes
hospital admissions and ED visits, and
Section 5.2.1.6, and
respiratory effects in healthy individuals
Section 5.2.1.7
Respiratory mortality
Consistent
Increases in respiratory mortality in multicity
Section 5.2.1.8 and
Mean 24-h avg:
epidemiologic
studies conducted in the U.S., Canada,
Section 5.5.1.3
U.S., Canada,
evidence from
Europe, and Asia
Fiaure 5-8 and
Europe:
multiple studies at
Fiaure 5-16
0.4-28.2d ppb
relevant SO2
Asia:
concentrations
0.7->200 ppb
Table 5-39
Uncertainty regarding
No copollutant models with PM2.5. SO2
Section 5.2.1.8.
potential confounding
associations remained positive but
Section 3.4.3
by copollutants
decreased in magnitude with adjustment for
PM10 or NO2, suggesting confounding.
Studies limited to areas with high SO2
concentrations, which complicates the
interpretation of independent association for
SO2.
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20
Table 5-21 (Continued): Summary of evidence for a causal relationship between
short-term sulfur dioxide exposure and respiratory effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2
Concentrations
Associated with
Effects0
Uncertainty regarding
SO2 exposures estimated from central site
Section 3.4.2
exposure
monitors may not capture spatiotemporal
measurement error
variability of SO2 across a community.
COPD = chronic obstructive pulmonary disease; ED = emergency department; N02 = nitrogen dioxide; PM10 = particulate matter
with a nominal aerodynamic diameter less than or equal to 10 |jm; PM2.5 = particulate matter with a nominal aerodynamic diameter
less than or equal to 2.5 |jm; r = correlation coefficient; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Table I and Table II of the
Preamble to the ISAs (U.S. EPA. 2015b).
bDescribes the key evidence and references, supporting or contradicting, contributing most heavily to causal determination and,
where applicable, to uncertainties or inconsistencies. References to earlier sections indicate where the full body of evidence is
described.
°Describes the S02 concentrations with which the evidence is substantiated (for experimental studies, below 2,000 ppb).
dThe value of 28.2 represents the median concentration from Katsouvanni et al. (1997).
fStudies published since the 2008 Integrated Science Assessment for Sulfur Oxides.
5.2.2 Long-Term Exposure
The 2008 SOx ISA (U.S. EPA. 2008(1) reviewed the epidemiologic and toxicological
evidence for long-term exposure to SO2 and respiratory effects and concluded that the
evidence was inadequate to infer a causal relationship. Although some positive
associations with asthma prevalence, bronchitis, symptoms, and lung function were
observed among children, uncertainties made it difficult at that time to assess the
evidence as a whole. Uncertainties related to assessing the consistency of findings across
a diverse set of respiratory outcomes, the potential for exposure measurement error to
influence results, and the lack of information available to assess the impact of copollutant
confounding were cited in the document. The studies of long-term exposure to SO2 and
respiratory morbidity that were considered in the last review are found in Supplemental
Table 5S-9 (U.S. EPA. 2015f). Animal toxicological studies of the effects of long-term
exposure to SO2, which were reviewed in the 2008 SOx ISA (U.S. EPA. 2008d).
examined lung function, morphology, and host defense. Most of these studies involved
SO2 concentrations well above 2 ppm. Recent toxicological studies add to this database.
Both older and more recent epidemiologic and toxicological studies that evaluate the
relationship between long-term SO2exposure and asthma (Section 5.2.2.1). allergy
(Section 5.2.2.2). lung function (Section 5.2.2.3). respiratory infection (Section 5.2.2.4).
other respiratory diseases (Section 5.2.2.5). and respiratory mortality (Section 5.2.2.6) are
discussed below. Recent cohort studies of asthma incidence (Nishimura et al.. 2013;
Clark et al.. 2010) use a longitudinal design, a methodological enhancement over the
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cross-sectional studies of asthma prevalence available in the 2008 SOx ISA (U.S. EPA.
2008(1). A recent study (Terodiakonou et al.. 2015) using a longitudinal design provides
the first epidemiological report relating SO2 exposure to AHR in human subjects with
asthma. Uncertainties related to exposure estimates based on IDW concentrations or other
estimates based on monitors (see Section 3.3.1) may limit the inferences that can be made
for these recent studies. The majority of other recent and earlier epidemiologic studies
used cross-sectional designs evaluating prevalence. Results were generally positive,
although the strength of the associations varied across studies. The designs used
(i.e., ecological, cross-sectional) limit the contribution of these studies to possible
inferences about causality of relationships between long-term SO2 exposure and
respiratory effects. The caution expressed in the 2008 SOx ISA (U.S. EPA. 2008d)
related to the limitation of attributing an independent effect to SO2 (due to the
relationship of SO2 levels to PM levels) is still a concern. The evidence base does not
include studies evaluating concentration-responses, and few studies provide copollutant
model analyses. The 2008 SOx ISA (U.S. EPA. 2008d) found that animal toxicological
studies did not provide sufficient evidence to assess the effects of long-term SO2
exposure on lung function, morphology, or host defense. The one new subchronic animal
toxicological study that is discussed in this review found effects of SO2 exposure on
airway responsiveness, airway remodeling, and allergic inflammation. Short-term
toxicological studies also provide some evidence for these responses to SO2 exposure.
5.2.2.1 Development and Severity of Asthma
Development of Asthma
Asthma is described by the National Heart, Lung, and Blood Institute (NHLBI NAEPP.
2007) as a chronic inflammatory disease of the airways that develops overtime.
Pulmonary inflammation can induce AHR, resulting in bronchoconstriction (bronchial
smooth muscle contraction), and in turn, episodes of shortness of breath, coughing,
wheezing, and chest tightness. When asthma advances in its development to the stage
when the symptoms lead people to seek medical treatment, a diagnosis of asthma can
result. Epidemiologic studies of SO2 used self- or parental report of a diagnosis to define
asthma. Epidemiologic studies reviewed in the 2008 SOx ISA (U.S. EPA. 2008d) were
limited to those with cross-sectional designs [Supplemental Table 5S-9 (U.S. EPA.
2015f)l. The majority of these studies reported positive associations of long-term SO2
exposure with asthma prevalence. A few recent longitudinal epidemiologic studies
support associations with asthma incidence and provide coherent evidence for
associations with respiratory symptoms in healthy populations. Uncertainty remains in
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the adequacy of SO2 exposure estimates and copollutant confounding. However, some
support for an effect of SO2 exposure comes from a recent toxicological study showing
SCh-induced AHR.
Epidemiologic Studies
A strength of recent epidemiologic studies of asthma development is their longitudinal
design (see Table 5-22). The follow-up of children over time to mark the first record of a
physician diagnosis with no prior record of diagnosis can better characterize the temporal
sequence between SO2 exposure and the incidence of asthma. In this regard, longitudinal
studies can better distinguish between onset of asthma and the exacerbation of asthma. In
a large multicity study (N = 4,320 from Chicago, IL, Bronx, NY, Houston, TX, San
Francisco Bay Area, CA, and the territory of Puerto Rico), Nishimura et al. (2013)
observed that for SO2 exposures during the first year of life the OR and 95% CI for
asthma incidence was 0.95 (0.59-1.47) per 5 ppb change. SO2 exposure during the first
3 years of life produced an OR and 95% CI for asthma incidence of 1.16 (0.73-1.84) per
5 ppb SO2. SO2 exposures were estimated using the IDW average of the four monitors
within 50 km of the subject's residence. Selection bias due to differential loss to
follow-up is not an issue given the retrospective design.
In a study of the British Columbia Birth Cohort (n =3,394 asthma cases), Clark et al.
(2010) used IDW estimate-based concentrations from the three closest monitors within
50 km of the participants postal code to estimate SO2 exposure. These authors observed
an adjusted OR (95% CI) per 5 ppb of 1.48 (1.3-1.9) due to average exposures both
during pregnancy and the first year of life. Conducted in Southwest British Columbia, the
study had 14 SO2 monitors available to provide data. Clark et al. (2010) conducted a
quartile analysis to explore the exposure-response relationship and observed that the
trend across quartiles was not linear (i.e., for the first-year exposure model the second
quartile was smaller, negative with confidence intervals less than 1.0, than the positive
first and last quartiles), lessening the strength of the association. In this nested
case-control study (n = 37,401), medical records of children ages 3-4 years (born
1999-2000) were reviewed for asthma diagnosis (Clark et al.. 2010). Selection bias due
to differential loss to follow-up is not an issue, because of the records-based analysis
used.
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Table 5-22 Selected epidemiologic studies of long-term exposure to SO2 and the
development of asthma and intervention studies/natural
experiments.
Location
Study/Population (Years)
Mean SO2
PPb
Exposure
Assessment
Selected Effect
Estimates (95% Cl)a
Longitudinal studies of the development of asthma
tNishimura et al. Chicago, IL; Bronx, 4.0
(2013)
GALA II and
SAGE II cohorts
(Latinos and
African Americans
NY; Houston, TX;
San Francisco Bay
Area, CA; and the
territory of Puerto
Rico
8-21 yr) N = 4,320) (2006-2011)
IDW avg of
monitors within
50 km of
residence;
annual avg and
concentration
during first 3 yr
of life.
Copollutant
correlations: NR
0.95 (0.59-1.47)—annual
avg
1.16 (0.74-1.84—early
life exposure
Covariate adjustment:
age, sex, ethnicity, and
composite SES.
tClark et al. (2010) Southwest British
British Columbia
Birth Cohort
(N = 37,401)
Columbia
1999-2000
In utero
Controls: 5.11
Cases: 5.22
1st yr of life:
Controls: 5.22
Cases: 5.37
IDW avg of three
monitors within
50 km of postal
code centroid.
Concentrations
for in utero and
1st yr of life
estimated.
Copollutant
correlations: NR
1.47 (1.30-1.89) (both in
utero/1st yr of life)
Covariate adjustment:
native status,
breast-feeding, maternal
smoking, income quartile,
birth weight, and
gestational length.
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Table 5-22 (Continued): Selected epidemiologic studies of long term exposure to
SO2 and the development of asthma and intervention
studies/natural experiments.
Study/Population
Location
(Years)
Mean SO2
PPb
Exposure
Assessment
Selected Effect
Estimates (95% Cl)a
t(Chianq et al.
(2016a). 2016b))
Recruited
587 children aged
between 11 and
14 yr from junior
high schools in
each of 9
townships.
N = 587
Incidence rates for
asthma (ICD-9;
493) were obtained
from the Taiwan
Health Insurance
Database.
Taiwan, near a
petrochemical
complex which
yields a diverse
pollution mix.
1999 to 2010
The three-year average of
the 99th percentage of SO2
levels in high and low
exposure areas after 2003
was 137.3 ppb and
32.0 ppb in the HE and LE
areas respectively between
2003 and 2006. From 2003
to 2010, There were 138 h
with hourly SO2
concentrations above
75 ppb each year in the HE
areas and 2 hours in LE
areas.
Two air quality
monitoring
stations, part of
the Taiwan
Environmental
Protection
Administration
(TEPA),
provided the
SO2 levels in the
HE and LE
areas. One is
located 8.1 km
south of the
complex, and
the other
16.2 km east
and south of the
complex. Three
exposure
periods were
reported since
opening of the
complex.
Copollutant
correlations NR.
The incidence rate of
asthma in the HE group
(18.5%) was significantly
higher than that in the LE
group (11.0%) in the first
4 yr after the complex
began its operations.
A difference in the
incidence of asthma
between the two groups
emerged after 12 mo, and
the maximum difference
appeared at 40 mo.
The hazard ratios of the
incidences of asthma,
during the different study
periods were adjusted for
group, age, gender, living
near roads, incense
burning and passive
smoking exposure. In
example for the third
study period
(1999-2010), HR (CI):
1.29 (0.91 to 1.83) for the
difference between Hi
and Low exposure areas.
Intervention studies and natural experiments
Peters et al. (1996b)
Children
N = 3,521
Hong Kong,
China
(Kwai Tsing and
Southern
districts)
Period of study:
1989-1991
Annual avg (|jg/m3):
Southern
1989: 11
1990
1991
Kwai Tsing
1989
1990
1991
111
67
23
Pre- and
post-regulation
concentrations
compared in
natural
experiment; SO2
emissions were
reduced by 80%
post-regulation.
Associations between
respiratory symptoms and
living in polluted areas
observed and greater
decline in symptoms
post-regulation.
Covariate adjustment:
age, gender,
environmental tobacco
smoking in the family
home, housing and
father's education.
tWonq et al. (1998)
Children (9-
N =423
-12 yr)
Hong Kong, China
(Kwai Tsing and
Southern districts)
Period of study:
1989-1991
Annual avg (|jg/m3)
Southern
1989: 11
1990
1991
Kwai Tsing
1989
1990
1991
111
67
23
Pre- and
post-regulation
concentrations
compared in
natural
experiment; SO2
emissions were
reduced by 80%
post-regulation.
Decreased bronchial
responsiveness observed
post-intervention.
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Table 5-22 (Continued): Selected epidemiologic studies of long term exposure to
SO2 and the development of asthma and intervention
studies/natural experiments.
Study/Population
Location
(Years)
Mean SO2
PPb
Exposure
Assessment
Selected Effect
Estimates (95% Cl)a
tlwasawa et al.
(2009)
Miyake adults
(N= 823)
Miyakejima
Island, Japan,
near Mt. Oyama
volcano
2004-2006
31, post volcano (range:
19-45)
Inhabited areas were
classified into one lower
SO2 and three higher SO2
areas to gauge exposure.
Seven monitors
in residential
areas used to
estimate 2 yr
avg; Natural
experiment
comparing
symptom
prevalence pre-
and
post-volcano
eruption.
Copollutant
correlations: NR
Minor health effects on
the respiratory system
observed.
Phlegm higher in higher
exposure areas.
Note: no consistent
differences in lung
function observed.
Logistic regression model
used. Covariate
adjustment: sex, age,
current smoking status,
residential area, and
hyper-susceptibility.
tlwasawa et al.
(2015)
120 Miyake school
children
Feb. 2005 to Nov.
2011
Average concentrations
(ppb) of SO2 decreased
year-by-year and ranged
from 11.3 to 2.47 in low
area, from 32.2 to 12.2 in
high area-1, and from 75.1
to 12.1 in high area-2.
Six monitors in
residential areas
used to estimate
post-volcano
eruption
concentrations
in different
residential
areas.
Other volcanic
gases were
measured and
considered to be
unlikely to cause
the health
effects seen in
the study.
Prevalence of respiratory
symptoms (cough,
phlegm, wheeze,
shortness of breath) was
increased in areas with
higher post-volcano SO2
concentrations compared
to areas with lower
concentrations.
Exposure-dependent
increases in symptoms
observed (no effects
observed at
concentrations lower than
30 ppb).
Logistic regression model
used. Covariate
adjustment: age, sex, and
hyper-susceptibility.
tLonao et al. (2008)
tLonqo (2009)
Adults (>20 yr)
N = 115 exposed
N = 110 unexposed
Kilauea volcano,
Hawaii
Apr. to Jun. 2004
24.5 (exposed)
0.7 (unexposed).
The emission pattern of the
volcanic plume is carried
over the exposed by the
Pacific trade winds.
The unexposed area is
located at the extreme end
of the island from the
volcano.
Ambient and
indoor SO2
concentrations
measured using
a network of
70 passive
samplers over a
3 wk sample
period.
Copollutant
correlations: NR
Cough on most days for
3 consecutive months or
more (acute bronchitis)
per year increased in
areas with higher levels.
Note: associations with
other symptoms also
reported.
Logistic regression model
used. Covariate
adjustment: age, sex,
race, smoking, dust and
body mass index.
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Table 5-22 (Continued): Selected epidemiologic studies of long term exposure to
SO2 and the development of asthma and intervention
studies/natural experiments.
Study/Population
Location
(Years)
Mean SO2
PPb
Exposure
Assessment
Selected Effect
Estimates (95% Cl)a
tTametal. (2016)
1,836 4th/5th
graders mean age
10,1 yr
Kilauea volcano,
Hawaii
2002 to 2005
SO2, PM2.5, and particulate
acid in four exposure
zones. Mean (SD) SO2
across zones ranged from
0.3 to 10.1 ppb.
SO2 measured
by passive
diffusion for 1- to
4-wk intervals to
determine zone
levels at
representative
sites in each
zone.
Strongly acidic respirable
particulates associated
with cough. SO2 not
evaluated specifically but
included in the area mix
which was not related to
cough.
Cross-sectional study
with adjustments for age,
race, sex, sitting height,
BMI, premature birth,
maternal smoking during
pregnancy, current
smokers in the home, and
visible mold in the home.
BMI = body mass index; CI = confidence interval; FE\A| = forced expiratory volume in 1 second; FVC = forced vital capacity;
IDW = inverse distance weighting; N = population number; NR = not reported; PM2.5 = particulate matter with a nominal
aerodynamic diameter less than or equal to 2.5 |jm; SD = standard deviation; SES = socioeconomic status; S02 = sulfur dioxide.
aEffect estimates are standardized per 5-ppb increase in S02 concentrations unless otherwise noted.
fStudies published since the 2008 ISA for Sulfur Oxides.
Asthma incidence for school children from the Taiwan Health Insurance Database was
evaluated contrasting high and low air pollution areas near a petrochemical complex for
three time periods after the opening of the complex. The areas were indexed by 3-year
annual average levels of the 99th % of SO2 levels and periods above 75 ppb (Chiang et al..
2016a. b). The HRs were positive with wide confidence intervals for the three periods.
Caution is required in inferences about an SO2 effect because the areas examined
represent complicated mixes from petrochemical complexes, the uncertainty for exposure
error is high to include area comparisons rather than individual level comparisons, and
the absence of evaluation for potential asthma risk factors.
The use of questionnaires in these studies to ascertain parents' report of
physician-diagnosed asthma, a strength of the study design (Burr. 1992; Ferris. 1978).
adds to the strength of inference about associations with SO2. A limitation of these
longitudinal studies include the potential for measurement error related to the use of IDW
for SO2 exposure estimates and comparison of high and low concentration areas (see
Section 3.3.2). Validation of SO2 exposures was not discussed for these studies.
The standard increment used in the current ISA, 5 ppb for an annual average, is larger
than the mean exposures in these studies, especially so for Clark et al. (2010) where the
mean exposure and SD are 1.98 (0.97) ppb. Additionally, the strongest associations
observed in both studies were with NO2 concentration. Correlations between pollutant
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concentrations were not reported by (Chiang et al. (2016a); Nishimura et al. (2013)).
while Clark et al. (2010) noted that correlations between pollutant concentrations were
generally high, but did not provide quantitative data. These studies suggest the potential
for a relationship between long-term SO2 exposure and the development of asthma.
However, these results do little to reduce uncertainty related to potential copollutant
confounding.
These studies considered confounding by asthma risk factors, which may be related to
PM2 5 exposure. All used information on maternal smoking. Clark et al. (2010) and
Nishimura et al. (2013) examined parental education level. Nishimura et al. (2013)
considered family history of allergy. These are key risk factors for asthma (Paaso et al.
2014). Other potentially important risk factors that do not appear to have been considered
in these studies include respiratory infections, dampness, gas stove, pets, and daycare
attendance (Gehring et al.. 2010). Obesity identified as a potential risk factor for asthma
in children (Gilliland et al.. 2003; Gold et al.. 2003) was not evaluated in these studies.
However Borrell et al. (2013) examined obesity in the cohorts studied by Nishimura et al.
(2013) in a nonpollution study.
Several recent studies presented in Supplemental Table 5S-10 (U.S. EPA. 2016p) also
examine the association of long-term exposure to SO2 with the prevalence of asthma in
cross-sectional designs with various SO2 exposure estimates as discussed in the table.
While these studies involve uncertainties, most (Liu et al.. 2016; Deng et al.. 2015a; Liu
et al.. 2014a; Dong et al.. 2013c; Dong et al.. 2013b; Kara et al.. 2013; Deger et al.. 2012;
Portnov et al.. 2012; Akinbami et al.. 2010; Sahsuvaroglu et al.. 2009). but not all
(Portnov et al.. 2012). reported positive associations. These studies are consistent with
similar studies in the 2008 SOx ISA (U.S. EPA. 2008d). Deng et al. (2015a) used
multipollutant models and reported that adjusting SO2 for PM10 only slightly changes
asthma risk. However, adjusting SO2 for NO2 substantially changed the SO2 result. In
addition, Liu et al. (2016) found that adjusting the effect in the single adjusted model for
SO2 was attenuated when further adjusted for NO2 and PM10. No longitudinal study of
asthma incidence evaluates copollutant models. Thus, within the recent epidemiologic
evidence base, studies provide limited new data to reduce the uncertainty related to
whether the effect was from SO2 or another pollutant. Studies of asthma incidence
strengthen the inference by addressing the temporality of exposure and response.
Supportive evidence for a relationship between long-term SO2 exposure and the
development of asthma is provided by cross-sectional studies of respiratory symptoms
related to asthma. In the 2008 SOx ISA (U.S. EPA. 2008d). studies examining an array of
respiratory symptoms related to SO2 exposure are presented in Supplemental Table 5S-11
(U.S. EPA. 2016q) and others are noted in the text of the 2008 SOx ISA (U.S. EPA.
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2008d; Ware et al.. 1986; Chapman et al.. 1985; Dodge et al.. 1985). These
cross-sectional studies used fixed site monitors for the SO2 exposure estimate. While
associations were generally positive, some inverse or null associations were also
observed. Recent studies evaluating the relationship between long-term SO2 exposure and
the prevalence of asthma symptoms [Supplemental Table 5S-10 (U.S. EPA. 2016p) I also
found positive associations (Altug et al. 2013; Pan et al.. 2010; Amedo-Pena et al..
2009).
Additional epidemiologic evidence for a link between long-term exposure to SO2 and the
development of asthma may come from intervention or natural experiment studies (see
Table 5-22). Physicians diagnose asthma, in part, based on the occurrence or
exacerbation of asthma symptoms, such as cough and wheeze, and the level of bronchial
hyperreactivity (BHR) in the subjects. Decline in such symptoms and BHR in relation to
a decline of a pollutant level may support a relationship between asthma development
and exposure to pollutants such as SO2. Decreases in respiratory symptoms, including
any wheeze or asthmatic symptoms, wheezing, and cough and sore throat, in
3,521 healthy children (mean age of 9.51 years) were associated with decreases in SO2
concentrations in Hong Kong due to a government restriction of sulfur content of fuels as
discussed in the 2008 SOx ISA [see Peters et al. (1996b). within U.S. EPA (2008d)l .
During the same period, Wong et al. (1998) examined the effect of the same decrease in
SO2 concentrations on BHR in children aged 9-12 who were non-wheezing and did not
have asthma at study entry. In the cohort analysis, which compared measurements made
before the intervention and 1 year afterwards, BHR declined. The subjective health
measures seen in Peters et al. (1996b) were corroborated by the objective data of the
histamine challenge test in Wong et al. (1998). These results should be interpreted with
caution given the uncertainty of whether changes in BHR and respiratory symptoms were
independently related to SO2 in light of the concomitant decline in sulfate respirable
suspended particles (RSP) (<10 Over the study period, SO2 declined about 80%
(from about 111 to 23 |_ig/m3 while annual mean sulfate concentrations in RSP fell from
12.5 to 7.7 (ig/m3. It is difficult to determine whether one was more important than the
other. However, these studies add to the information base relating long-term SO2
exposure and asthma-related outcomes.
Recent cross-sectional studies that estimated long-term SO2 exposure from volcano
emissions in Japan and Hawaii were conducted (Table 5-22). Iwasawa et al. (2009)
observed increased frequencies of phlegm and minor effects on the respiratory system
among both adults and children residing near the Mt. Oyama volcano in Japan across four
inhabitant areas with varying SO2 levels. Iwasawa et al. (2015) further followed the
children yearly from 2006 to 2011, finding the prevalence of respiratory symptoms
(cough, phlegm, wheeze, shortness of breath) to be related to the higher SO2 exposure.
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Studies conducted near the Kilauea volcano in Hawaii observed an adjusted increase in
cough on most days for 3 consecutive months or more per year in children and adults
(Longo. 2009; Longo and Yang. 2008; Longo et al.. 2008). Tarn et al. (2016) related
cough to a mixture containing acidic respirable particulates, but not to SO2 exposure
directly, in children near the Kilauea volcano. As a whole, these studies are supportive of
a link between SO2 exposure and respiratory symptoms. However, such studies compare
areas of high volcano emissions to areas of lower emissions (indexed by SO2
concentration) and thus, results may be confounded by copollutant exposures.
Severity of Asthma
NHLBI NAEPP (2007) identifies stages of asthma such as mild, moderate,
moderate-persistent, and severe. When going from mild to severe, the likelihood of acute
exacerbations increases. Stages of worsening of asthma are usually based on severity
scores as used in the following studies [Supplemental Table 5S-10, (U.S. EPA. 2016p)l.
Rage et al. (2009) examined severity of asthma in adults. Long-term SO2 exposure was
correlated with a higher asthma severity score. Ozone showed the strongest relationship
while NO2 was unrelated. In 17-year-old male military recruits, Greenberg et al. (2016)
related asthma severity to SO2 measured as low, intermediate, and high. The observed
associations between asthma severity and air pollution support the notion that air
pollutants may increase asthma severity. However, the uncertainty related to these effects
potentially being influenced by short-term exposure needs to be examined. Degeretal.
(2012) examined the prevalence of active and poor asthma control in children and
observed an association with long-term SO2 exposure among children with active asthma
and a more marked association among children with poor asthma control. No other
pollutants were examined. Adjusting for child's age and sex, parental atopy and
environmental tobacco smoke exposure slightly decreased the association, and
stratification according to age (<6 years and >6 years) showed that associations with SO2
were mainly observed in the older age group. Adjusting for socioeconomic status
(i.e., household income and maternal educational level) had limited influence on the
results of the analyses (<5%).
AHR is a key component of asthma. In a recent study, long-term exposures to SO2 were
associated with increased methacholine responsiveness determined by FEVi decreasing
by 20% or more [provocative concentration 20 (PC20)] (lerodiakonou et al.. 2015). but
results have uncertain inference because exposures were estimated from monitors up to
50 km from subjects' ZIP code centroid. Further, a very large number of comparisons
were made among pollutants, exposure lags, lung function parameters, cities, and asthma
medication groups, and there is higher probability that the few associations observed are
due to chance. The PC20 percent change per interquartile range (2 ppb 4-month moving
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average) was -6% (95% CI, -ll%to -1.5%) in 2,661 observations in the Childhood
Asthma Management Program (CAMP), a randomized clinical trial involving eight cities
in North America. The PC20 standardized to per 5 ppb is -15% (-27.5 to -3.75%).
Four-month average SO2 was not associated with changes in lung function measured
before or after bronchodilator treatment. Health outcome results for 1-day and 1-week
exposure periods are discussed earlier in Section 5.2.1.2; only the 4-month moving
average results are discussed here. The original health study, a longitudinal prospective
cohort study with repeated measures but without a pollution component, was designed to
examine the long-term safety and effectiveness of daily inhaled anti-inflammatory
medication in children with mild to moderate asthma diagnosed and was sponsored by
the NHLB. The children were 5 to 12 years of age and hyperresponsive to methacholine
at study entry. Recruitment occurred from late December 1993 to early September 1995
(CAMP Research Group. 1999; Cherniack et al.. 1999) at two HMO's and six academic
institutions.
Monitoring data on 24-h avg concentrations of pollutants ozone, CO, NO2, and SO2 were
obtained for each metropolitan area from the Aerometric Information Retrieval System
for the U.S. cities and from the Air Quality and Reporting Unit for Toronto were linked
to the ZIP code of the subject's address at study entry. There is uncertainty in the
measurement estimate and a potential for measurement error. Distance or proximity of
sites to subjects is not known. For long-term studies bias can go in either direction. Thus,
the evidence base for a relationship between long-term SO2 exposure and AHR is limited.
Animal Toxicological Studies
A single animal study of chronic SO2 exposure-related effects on lung morphology was
discussed in the 2008 SOx ISA (U.S. EPA. 2008d). Study characteristics are summarized
in Table 5-23. Smith et al. (1989) found that rats exposed to 1 ppm of SO2 had an
increased incidence of bronchiolar epithelial hyperplasia and increased numbers of
nonciliated epithelial cells after 4 months of exposure. However, these effects were not
present at 8 months of exposure, suggesting that repair and/or adaptation may have taken
place.
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Table 5-23 Study-specific details from animal toxicological studies.
Study
Species (strain); n;
Sex; Lifestage/Age
Exposure Details
(Concentration; Duration)
Endpoints Examined
Smith et al. (1989)
Rats
(Sprague-Dawley);
n = 12-15 per data
point; M; young adult;
normal or
elastase-impaired
1 ppm (2.62 mg/m3) SO2
whole body; 5 h/d, 5 d/wkfor
4 or 8 mo
8-mo exposure group
sacrificed immediately or
3 mo after exposure ended
Endpoints examined prior to
sacrifice
Lung function—residual volume,
functional residual capacity,
quasi-static compliance,
residual volume/total lung
capacity, N2 washout
Morphological effects
Lung function—residual volume,
functional residual capacity,
quasi-static compliance,
residual volume/total lung
capacity, N2 washout
Endpoints examined after
sacrifice
Morphology
Song et al. (2012)
Rats
(Sprague-Dawley);
n = 10/group; M; 4 wk
old neonates
Sensitization by i.p. injection
of 10 mg ovalbumin followed
by booster injection of 10 mg
ovalbumin after 7 d
Challenge with 1% ovalbumin
aerosol for 30 min daily for
4 wk beginning at 15 d
Exposure to 2 ppm SO2 for
4 h/d for 4 wk beginning at
15 d
Exposure groups:
(1) Control
(2) SO2 alone
(3) Ovalbumin alone
(4) Ovalbumin + SO2
Endpoints examined 24 h after
challenge
Lung function—whole body
plethysmography (MCh
challenge)
BALF-IL-4, IFN-y
Serum-IL-4, IFN-y
Lung—histopathology
In vitro culture of airway smooth
muscle cells from
experimentally treated
animals—stiffness and
contractility
BALF = bronchoalveolar lavage fluid; IFN-y = interferon gamma; IL-4 = interleukin-4; i.p. = intraperitoneal; M = male;
MCh = methacholine; n = sample size; N2 = nitrogen; SD = standard deviation; S02 = sulfur dioxide.
1 No studies on airway responsiveness or pulmonary inflammatory responses to long-term
2 exposure to SO2 concentrations of 2 ppm and lower were discussed in the 2008 SOx ISA
3 (U.S. EPA. 2008d). One new animal toxicological study of subchronic SO2 exposure has
4 become available since the last review. Key findings are discussed here, and study
5 characteristics are summarized in Table 5-23. Song et al. (2012) found that airway
6 responsiveness was enhanced in a model of allergic airways disease using rats that were
7 first sensitized and challenged with ovalbumin and then exposed to 2 ppm SO2 for
8 4 hours/day for 28 days. Airway responsiveness was not changed with exposure to SO2
9 alone in naive rats. However, Song et al. (2012) observed hyperemia in the lung
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parenchyma and inflammation in the airways of naive rats exposed only to SO2. SO2
exposure also increased the inflammatory responses in rats made allergic to ovalbumin.
Airway remodeling was found in ovalbumin-treated rats with and without exposure to
SO2. A more pronounced increase in the airway smooth muscle layer was found in the
ovalbumin/SCh group compared to the ovalbumin group. The authors concluded that the
effects of SO2 on airway responsiveness and airway remodeling were dependent on
ovalbumin sensitization and challenge. Song et al. (2012) also measured concentrations
of IL-4 and IFN-y in the BALF and serum of rats exposed to SO2, with and without prior
sensitization and challenge with ovalbumin. Concentrations of IL-4 in the BALF were
increased in the ovalbumin and the SO2 groups, with the greatest increase occurring in
the combined ovalbumin/S02 group. An increase in IL-4 in serum occurred only in the
ovalbumin/SCh group. Concentrations of IFN-y in the BALF were decreased in the
ovalbumin, SO2, and ovalbumin/SCh groups. A decrease in serum IFN-y was observed in
the ovalbumin and ovalbumin/S02 groups. IL-4 is a Th2 cytokine associated with allergic
responses, while IFN-y is a Thl cytokine. An increase in the ratio of Th2 to Thl
cytokines indicates Th2 polarization (or possibly a Type 2 immune response mediated by
group 2 innate lymphoid cells), a key step in allergic sensitization. As discussed in prior
sections, these findings provide evidence that repeated SO2 exposure enhances allergic
responses, airway remodeling, and airway responsiveness in this model of allergic airway
disease. Furthermore, repeated SO2 exposure in naive rats increased levels of the Th2
cytokine IL-4, decreased levels of the Thl cytokine IFN-y in the BALF, and increased
airway inflammation suggesting that SO2 exposure may on its own induce allergic
sensitization. Because allergic sensitization, airway remodeling, and AHR are key events
(or endpoints) in the proposed mode of action for the development of asthma
(Section 4.3.6). these results suggest that long-term exposure to SO2 may lead to the
development of an asthma-like phenotype in this animal model involving newborn rats.
Summary of Asthma Development and Severity
Recent epidemiologic evidence from a limited number of longitudinal studies report
associations between asthma incidence among children and long-term SO2 exposures.
Additional supportive evidence for a link between long-term SO2 exposure and the
development of asthma is provided by cross-sectional studies of asthma prevalence.
The longitudinal studies help reduce the uncertainty associated with the temporality of
exposure and response that is inherent in cross-sectional study designs. This evidence is
coherent with animal toxicological evidence of inflammation, allergic sensitization and
other allergic responses, airway remodeling, and AHR, which are key events (or
endpoints) in the proposed mode of action for the development of asthma (Section 4.3.6).
The animal toxicological evidence provides support for an independent effect of SO2 and
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strengthens the link between long-term exposure to SO2 and the development of asthma
in children. Additional evidence supportive of this link comes from cross-sectional
studies of respiratory symptoms and respiratory allergies among children and from
natural experiments. Thus, multiple lines of evidence suggest that long-term SO2
exposure results in a coherent and biologically plausible sequence of events that
culminates in the development of asthma, especially allergic asthma, in children.
The potential for a relationship between long-term SO2 exposure and severity of asthma
has been examined in a few studies. One study in adults correlated exposure with higher
asthma severity scores. A study in children found a more marked association in those
with poor asthma control. AHR, measured as PC20, worsened with long-term SO2
exposure in a multicity cohort of children. Thus, evidence of asthma control and
increased AHR provides suggestive but limited support for this relationship.
5.2.2.2 Development of Allergy
There is some evidence for a potential relationship between long-term SO2 exposure and
indicators or respiratory allergies and inflammation among children. Several recent
cross-sectional studies examined the prevalence of respiratory allergies using different
markers for respiratory allergies including IgE antibodies, rhinitis, eczema, sensitization
to pollen, and hay fever related to long-term SO2 exposure (Liu et al.. 2016; Chan et al..
2013; Bhattacharvva and Shapiro. 2010; Penard-Morand et al.. 2010; Parker et al.. 2009;
Nordling et al.. 2008) [see Supplemental Table 5S-11 (U.S. EPA. 2016qYI. Positive
results were observed for children using these various indicators of allergy. Further, a
very weak relationship was found Dales etal. (2008) between long-term SO2 exposure
and eNO, an indicator of inflammation [see Supplemental Table 5S-11 (U.S. EPA.
2016a)].
Recent studies examine two-pollutant models for allergic rhinitis prevalence. Results for
allergic rhinitis prevalence based on responses from ISAAC questionnaire data in
Changsha China (Chan et al.. 2013) did not find an association for SO2 for site-specific
background SO2 and allergic rhinitis in children 3-6 year old, but did find an association
for age-related accumulative exposure in a single pollutant model using the closest
monitor to kindergartens. The two-pollutant model with PM10 was attenuated. For SO2
exposures during the first year of life in Shanghai, China, Liu et al. (2016) found an
association with allergic rhinitis in children at age 6 which was attenuated when adjusted
for other pollutants using district monitors. These findings suggest the possibility that
chronic exposure to SO2 may play a role in the development of allergic conditions based
on results for various allergic markers. The cross-sectional design of these studies makes
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these relationships uncertain and the exposure estimates from monitors is subject to the
possibility of measurement error and uncertainties informing the representativeness of the
exposure estimates in the studies as discussed in Section 3.4.2. Thus, the evidence base
for a relationship between long-term SO2 exposure and allergic rhinitis response is
limited and two-pollutant model begin to characterize the role of SO2 exposure.
5.2.2.3 Lung Function
Epidemiologic Studies
Longitudinal epidemiologic studies examine associations between long-term SO2
exposure and decrements in lung function. Lung function grows through early adulthood
with growth and development, then declines with aging (Stanoievic et al.. 2008; Zeman
and Bennett. 2006; Thurlbeck. 1982). Thus, a relationship between long-term SO2
exposure and decreased lung function over time in school-age children into early
adulthood would be an indicator of decreased lung development.
As discussed in the 2008 SOx ISA (U.S. EPA. 2008d). earlier cross-sectional studies
(Dockerv et al.. 1989; Schwartz. 1989) found no association between long-term SO2
exposure and lung function in U.S. children. A longitudinal cohort study (Frischer et al..
1999) reported that long-term SO2 exposure was associated with decrements in lung
function in the summer but not in the winter. In Poland, a prospective cohort study of
children (Jedrvchowski et al.. 1999) found decrements in lung function growth related to
a polluted area where concentrations of both TSP and SO2 were high compared to a
cleaner area where concentrations of both TSP and SO2 were low, thus not providing
results specifically for SO2. In a cross-sectional study in adults in Switzerland,
Ackermann-Liebrich et al. (1997) observed an association between SO2 concentration
and lung function, but after controlling for PM10, this association was no longer evident.
In the former East Germany from 1992 to 1999, Frve et al. (2003) reported improvements
in lung function associated with declines in SO2 concentrations in 2,493 children over
three cross-sectional surveys. These studies are presented in Supplemental Table 5S-9
(U.S. EPA. 2015f).
Recent studies in children and adults add to this evidence base [see Supplemental
Table 5S-12 (U.S. EPA. 2016r)l. In a repeated measure prospective study of the TCHS
cohort, Hwang et al. (2015a) examined lung function growth for a 2 year period from age
12 to 14 years. No association was found for SO2 exposure and FEVi or FVC for boys
and girls, but a deficit was observed for boys for FEF25 75. A single measure longitudinal
study in several U.S. cites observed for first year of life exposures a suggestive
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association for SO2 and FEVi. Neophvtou et al. (2016) examined the same cohort that
Nishimura et al. (2013) did as discussed earlier in this section for asthma incidence in the
same cities with the same SO2 exposure method evaluating the same confounding factors
plus obesity. For each 1 ppb increase of SO2 percent change in FEVi and the 95% CI
were-1.01 (-3.25, 1.27).
In a cross-sectional, longitudinal repeated-measures study of children, Linares et al.
(2010) reported a decline in FEV1 related to long-term SO2 exposure in the entire study
group. This study included children from two schools in different locations relative to a
petrochemical zone. In an analysis of the children by sex, in one- and two-pollutant
analysis of PM10 and O3, the outcome was attenuated. In a cross-sectional study of
children in 14 communities in Taiwan, Lee etal. (201 lc) found a reduction in FEVi
related to long-term SO2 exposure with larger reductions related to NO2 and CO
exposure. Yogev-Baggio et al. (2010) related the effect of the interaction, NOx x SO2
"event," to reduction in FEVi in children in Israel near a coal-fired power plant. In a
cross-sectional study of 32,712 adults in England, Forbes et al. (2009c) related FEVi
effects to exposure to SO2, PM10, and NO2, but not O3. A U.K. study of
alpha-1-antitrypsin deficiency and COPD (Wood et al.. 2010) found reduced FEVi in
relation to SO2 concentration but a more rapid decline in relation to PM10 concentration.
Dales et al. (2008) found a weak decline in FEVi and FVC related to long-term SO2
exposure in school children in Windsor, ON using a cross-sectional prevalence design.
The majority of the recent studies and earlier studies used cross-sectional designs. Some
studies took into account potentially confounding covariates detailed in the Supplemental
Table 5S-12 (U.S. EPA. 2016r). Neophvtou et al. (2016) controlled for age, height, and
calendar time, allowing for nonlinear effects, indicator variables for sex, race/ethnicity,
and continuous variables for SES (composite score variable), and numbers of smokers in
the household and also assessed effect modification by sex, obesity, SES, atopy, and
parental asthma. The designs used in most of the recent studies (i.e., ecological,
cross-sectional, single measure) limit the possible inferences about the relationship
between long-term SO2 exposure and lung function. The evidence does not include
studies evaluating concentration-responses. The one study conducting a copollutant
analysis found attenuation of the effect with adjustment for PM10. Thus, recent studies do
not add information that changes conclusions made in the 2008 SOx ISA (U.S. EPA.
2008d) that there is not clear evidence that long-term SO2 exposure is related to lung
function changes.
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Animal Toxicological Studies
A single long-term study with SO2 exposure concentrations at or below 2 ppm was
discussed in the 2008 SOx ISA (U.S. EPA. 2008d). Study characteristics are summarized
in Table 5-23. Smith et al. (1989) found that rats exposed to 1 ppm SO2 for 4 months had
decreased residual volume and quasi-static compliance when treated with saline (control).
Rats treated with elastase (a model of emphysema) and exposed to 1 ppm SO2 for
4 months had a decreased ratio of residual volume to total lung capacity and decreased
alveolar plateau of the single-breath nitrogen (N2) washout (N2-slope), indicating a
worsening of the emphysema. However, Smith et al. (1989) concluded that the effects of
SO2 on lung function measurements were very minor in the saline (control) group and
likely due to chance alone (residual volume) or to unusually high control values
(quasi-static compliance).
Summary of Lung Function
Several studies evaluated the relationship between long-term SO2 exposure and
decrements in lung function. Evidence supporting this relationship is limited because
associations were inconsistent and because both PM and SO2 were at high concentrations
in the same areas, which does not allow determination of individual SO2 effects. Potential
confounding of long-term SO2 exposure-related decrements in lung function and lung
development by other pollutants, especially PM, was evaluated in only one study. This
study found an attenuation of the effect in copollutant analyses. No changes in lung
function were found in long-term animal toxicological studies at relevant SO2
concentrations. The recent studies support conclusions of no association between
long-term SO2 exposure and lung function in children made in the 2008 SOx ISA (U.S.
EPA. 2008d).
5.2.2.4 Respiratory Infection
Epidemiologic Studies
Studies have also examined the association of long-term exposure to SO2 with infant
bronchiolitis, otitis media, and pneumonia in children, hospital admission for
community-acquired pneumonia in adults aged 65 years or more, and tuberculosis in
adults. Infant bronchiolitis was examined in British Columbia by Karr et al. (2009).
These authors observed an association with lifetime exposure to SO2 after adjustment for
an array of confounders [Supplemental Table 5S-11 (U.S. EPA. 2016q)l. The largest
associations were observed with NO2 and CO concentrations. Maclntvre et al. (2011)
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found no increased risk for otitis media in relation to long-term SO2 exposure in a study
of children up to the age of 2 in British Columbia, while Bhattacharvva and Shapiro
(2010) found a strong relationship with long-term SO2 exposure in the U.S. National
Health Interview Survey of 126,060 children ages 3-6 years. Lu et al. (2014) observed
that the prevalence of pneumonia in children 3 to 6 year old was related to long-term SO2
exposure. Liu et al. (2016) reported that doctor-diagnosed pneumonia in children
4-6 years old was related to SO2 exposure during the first year of life. Neupane et al.
(2010) estimated long-term SO2 exposure at the residence for both the case and control
subjects with bicubic splined (SPL) and IDW methods for the 2-yr avg for 2001 and
2002, obtaining means of 4.65 ppb and 5.80 ppb, respectively, but with a twofold greater
range for SPL. Adjusted estimates of associations for SO2 with hospitalization from
community-acquired pneumonia were positive for SPL but not for IDW. The incidence of
tuberculosis was associated with an increase of SO2 in adult males (Hwang et al.. 2014)
but not in a study in California (Smith et al.. 2016). Although limited in number, by
inconsistency, and by their cross-sectional design, these studies suggest a potential
relationship between long-term exposure to SO2 and respiratory infections due to various
infectious agents.
Animal Toxicological Studies
No new animal studies of the effects of long-term SO2 exposure on lung host defense
have been conducted since the previous review. Several studies of short- and long-term
exposure to SO2 were reported in the 1982 AQCD (U.S. EPA. 1982a) and discussed in
the 2008 SOx ISA (U.S. EPA. 2008d). Short-term exposure studies found some effects of
0.1-1 ppm SO2 on the clearance of labeled particles. Long-term exposure studies found
decreased tracheal mucus flow at a concentration of 1 ppm SO2, but no effects on
susceptibility to bacterial infection or alterations in the pulmonary immune system at
concentrations of 2 ppm or less.
Summary of Respiratory Infection
Evidence for prevalence of infant bronchiolitis and/or respiratory infections consists of
generally positive associations found in cross-sectional studies. Thus, they provide a
limited evidence base in number and design. While some animal toxicological studies
reported alterations in specific host defense mechanisms, there is no evidence to support
increases in bacterial or viral infections in animals exposed to SO2 at relevant
concentrations.
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5.2.2.5 Development of Other Respiratory Diseases: Chronic Bronchitis, Chronic
Obstructive Pulmonary Disease, and Acute Respiratory Distress Syndrome
Chronic bronchitis consists of symptoms, including daily cough and/or congestion or
phlegm for 3 months in a row. While these symptoms may have started with acute
exacerbation, they are likely to represent chronic indolent symptoms. As discussed in the
2008 SOx ISA (U.S. EPA. 2008d). earlier cross-sectional studies observed positive
relationships between long-term SO2 exposure estimates derived from fixed site monitors
and chronic bronchitis as presented in Supplemental Table 5S-11 (U.S. EPA. 20160).
Recent cross-sectional studies of the association of long-term exposure to SO2 with the
prevalence of bronchitis also observed positive relationships after adjustment for
potential confounders. In addition, a recent COPD incidence study in a national English
cohort (Atkinson et al.. 2015). discussed in Supplemental Table 5S-11 (U.S. EPA.
2016q). reported a positive association in an adjusted HR model with SO2 exposure
averaged over 3 years determined by dispersion models. Assessment of model validity
using national network sites and separate verification sites yielded poor If values for SO2
of 0 and 0.39, respectively. Other limitations of this study include a short follow-up time
and the failure to confirm the 36% of incident hospital admissions for COPD by a general
practitioner diagnosis.
A relationship between Acute Respiratory Distress Syndrome (ARDS) and long-term SO2
exposure has recently been studied (Ware et al.. 1986) as discussed in Supplementary
Table 5S-11 (U.S. EPA. 2016q). SO2 and PM2 5 were not associated with ARDS.
5.2.2.6 Respiratory Mortality
Recent studies provide some evidence that respiratory mortality may be more
consistently associated with long-term exposure to SO2 than other causes of death
(Section 5.5.2 and Figure 5-27). There is uncertainty in the small, positive associations
between long-term exposure to SO2 and respiratory mortality observed in these studies,
because the exposure assessment and statistical methods are not adequate for studying a
highly spatially and temporally heterogeneous pollutant like SO2. Additionally, there is
little evidence of respiratory health effects in adults in relation to long-term SO2 exposure
that could provide coherence with the observed associations with respiratory mortalities.
5.2.2.7 Summary and Causal Determination
Overall, the evidence is suggestive of, but not sufficient to infer, a causal relationship
between long-term SO2 exposure and respiratory effects, mainly the development of
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asthma in children. This conclusion represents a change from "inadequate to infer a
causal association" for respiratory effects as stated in the 2008 SOx ISA (U.S. EPA.
2008d).
Recent epidemiologic evidence from a limited number of longitudinal studies report
associations between asthma incidence among children and long-term SO2 exposures.
The longitudinal studies address the temporality of exposure and response and help to
reduce the uncertainty associated with temporality that is inherent in cross-sectional study
designs. The evidence from longitudinal studies is coherent with animal toxicological
evidence of allergic sensitization, airway remodeling, and enhanced airway
responsiveness, which are key events (or endpoints) in the proposed mode of action for
the development of asthma. The animal toxicological evidence provides support for an
independent effect of SO2 and a possible relationship between long-term exposure to SO2
and the development of asthma in children. Some evidence of a link between long-term
exposure to SO2 and respiratory symptoms and/or respiratory allergies among children
further supports this relationship. The potential for SO2 to serve as an indicator for other
pollutants or mixture related to PM is an uncertainty that applies to the new body of
epidemiologic evidence across the respiratory effects examined.
The key evidence supporting the causal determination is detailed below using the
framework described in Table I of the Preamble to the ISAs (U.S. EPA. 2015b') and is
presented in Table 5-24.
Evidence for the Development of Asthma
A limited number of longitudinal studies demonstrate associations between ambient SO2
concentrations measured in the first year of life and/or over the first 3 years of life in
children and asthma incidence such as (Clark et al.. 2010) and (Nishimura et al.. 2013)
(Section 5.5.2.1). Results are fairly consistent between studies with one based on several
different locations across the U.S., another over a large area in Canada, and one in
Taiwan, involving a large number of participants. Uncertainties and the potential for
measurement error related to the use of IDW and area comparisons in these studies may
limit inferences that can be made (Section 3.4.2). Additional supportive evidence for a
link between long-term SO2 exposure and the development of asthma is provided by
cross-sectional studies of asthma prevalence, respiratory symptoms, and markers of
respiratory allergies among children (Section 5.2.2.2). Findings of studies evaluating
respiratory symptoms are supportive of the development of asthma; however, they may
also reflect other respiratory conditions. Intervention and natural experiment studies also
indicate a possible relationship between long-term exposure to SO2 and the development
of asthma.
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Table 5-24 Summary of evidence for a suggestive of, but not sufficient to infer, a
causal relationship between long-term sulfur dioxide exposure and
respiratory effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
so2
Concentrations
Associated with
Effects0
Development and severity of asthma
Evidence from
epidemiologic studies
is generally supportive
but not entirely
consistent
Evidence for increases in asthma
incidence in cohorts of children in U.S.
and Canada. Adequate adjustment for
confounding by asthma risk factors.
Some inconsistency regarding time
window
Nishimura et al. (2013)
Clark etal. (2010)
Mean (SD)
across five cities
4.0 (3.4) ppb
1.98 (0.97) ppb
Supporting cross-sectional studies of
asthma prevalence among children but
uncertainty regarding the temporal
sequence between exposure and the
development of asthma
Section 5.2.2.1
Supporting evidence for respiratory
symptoms and markers of respiratory
allergies among children in
cross-sectional studies
Section 5.2.2.1 and
Section 5.2.2.2
Supporting evidence from intervention
studies and natural experiments
Section 5.2.2.1
Evidence for increases in asthma severity
as indicated by asthma severity score,
degree of asthma control, and AHR
Section 5.2.2.1
Uncertainty regarding
potential for
measurement error in
exposure estimates
Use of IDW in asthma incidence studies
and fixed monitoring sites in
cross-sectional studies
Section 3.4.2
Uncertainty regarding
potential confounding
by copollutants
No copollutant models analyzed in
asthma incidence studies; limited
evidence from cross-sectional studies
that observed effects are robust to
copollutant adjustment
Section 3.4.3
(Liu et al. (2016); Dens et
al. (2015aY)
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Table 5-24 (Continued): Summary of evidence for a suggestive of, but not
sufficient to infer, a causal relationship between long
term sulfur dioxide exposure and respiratory effects.
SO2
Concentrations
Rationale for Causal
Associated with
Determination3
Key Evidence13
Key References'3
Effects0
Limited animal
Th2 polarization (or other Type 2 immune
Sona et al. (2012)
2,000 ppb
toxicological evidence
responses) and airway inflammation
provides coherence
following repeated exposure of naive
and biological
newborn rats for 28 d
plausibility
Evidence for enhanced inflammation,
airway remodeling and AHR following
repeated exposure of allergic newborn
rats for 28 d
Coherence with
Inflammation and morphologic responses
Li et al. (2007)
2,000 ppb
evidence from
indicative of airway remodeling following
Li et al. (2014)
short-term animal
repeated exposures of naive rats over
toxicological studies
several days
Enhancement of allergic sensitization,
Riedel et al. (1988)
100 ppb
allergic inflammation, airway
Park et al. (2001)
100 ppb
responsiveness in guinea pigs exposed
repeatedly over several days and
subsequently sensitized and challenged
with an allergen
Enhanced inflammation and allergic
Li et al. (2007)
2,000 ppb
responses in rats previously sensitized
Li et al. (2014)
with an allergen and then repeatedly
exposed
Some evidence for key
Inflammation, allergic sensitization, AHR,
Section 4.3.6
events in proposed
airway remodeling
mode of action
Development of allergy
Limited epidemiologic
Generally positive associations with
Section 5.2.2.2
evidence but
different markers for allergies in
uncertainty regarding
cross-sectional studies in children.
SO2 independent
Uncertainty in temporality and exposures
effects
estimated from central site monitors;
copollutant confounding examined on a
limited basis remains uncertain
Lung function
Inconsistent
epidemiologic
evidence among
children from quality
studies and
uncertainty regarding
SO2 independent
effects
In cohort studies, associations
inconsistent with adjustment for PM and
by season
Neophvtou et al. (2016)
Jedrvchowski et al. (1999)
Frischer et al. (1999)
Inconsistent results from cross-sectional
studies
Dockerv et al. (1989)
Schwartz (1989)
Ackermann-Liebrich et al.
(1997)
Frve et al. (2003)
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Table 5-24 (Continued): Summary of evidence for a suggestive of, but not
sufficient to infer, a causal relationship between long
term sulfur dioxide exposure and respiratory effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2
Concentrations
Associated with
Effects0
Respiratory infection
Limited epidemiologic
evidence; uncertainty
regarding SO2
independent effects
Generally positive associations in
cross-sectional studies. Uncertainty in
temporality, exposures estimated from
monitors in the community, and
copollutant confounding
Section 5.2.2.4
Limited animal
toxicological evidence
Altered clearance of particles and
decreased tracheal mucus flow
U.S. EPA (1982a)
0.1-1 ppm
Lack of evidence for
key events in
proposed mode of
action
Changes in specific host defense
mechanisms but no evidence of greater
infectivity
Development of other respiratory diseases
Limited epidemiologic
evidence but
uncertainty regarding
SO2 independent
effects
Generally positive associations for
chronic bronchitis in cross-sectional
studies. Uncertainty in temporality,
exposures estimated from monitors in the
community, and copollutant confounding
Section 5.2.2.5
Respiratory mortality
Generally consistent
epidemiologic
evidence
Small, positive associations between
long-term exposure to SO2 and
respiratory mortality in several cohorts,
even after adjustment for common
potential confounders
Hart et al. (2011). Nafstad
etal. (2004). Elliott etal.
(2007). Cao etal. (2011).
Carev et al. (2013), Dona
et al. (2012), Katanoda et
al. (2011)
2.4-41.4
No coherence
between respiratory
morbidity in and
respiratory mortality
No evidence for a relationship between
long-term exposure and respiratory
mortality to support the observed
associations with respiratory morbidity
Section 5.2.2.6
AHR = airway hyper-responsiveness; IDW = inverse distance weighting; PM = particulate matter; SD = standard deviation;
S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Table I and Table II of
the Preamble to the ISAs (U.S. EPA. 2015b).
bDescribes the key evidence and references, supporting or contradicting, contributing most heavily to causal determination, and
where applicable, to uncertainties or inconsistencies. References to earlier sections indicate where the full body of evidence is
described.
°Describes the S02 concentrations with which the evidence is substantiated (for experimental studies, <2,000 ppb).
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Epidemiologic studies of asthma development in children have not clearly characterized
potential confounding by other pollutants or mixtures of pollutants. This uncertainty was
present in the previous review, and there is no new information from incidence studies to
help reduce this uncertainty. No studies of asthma incidence have evaluated copollutant
models to address copollutant confounding, making it difficult to evaluate the
independent effect of SO2 within the epidemiologic evidence base for incidence.
A limited number of recent cross-sectional studies of asthma prevalence involving
two-pollutant models provide preliminary information to characterize the role of
long-term SO2 exposure. In studies that examined both SO2 and PM2 5, positive
associations were observed between PM2 5 concentrations and asthma development; the
effects were similar in magnitude to those for SO2 (Nishimura et al.. 2013; Clark et al..
2010). Correlations between SO2 and PM2 5 were not reported in these studies. Thus,
results from these two studies do not reduce the uncertainty related to potential
copollutant confounding.
The uncertainties in the epidemiologic evidence base is reduced, in part, by the biological
plausibility provided by findings from experimental studies that demonstrate
SCh-induced effects on key events or endpoints that are part of the proposed mode of
action for development of asthma [i.e., allergic sensitization, airway remodeling and
AHR (Section 4.3.6)1. An experimental study in newborn rats, which were not previously
sensitized and challenged with an allergen (i.e. naive animals), found that repeated acute
SO2 exposures over several weeks led to airway inflammation and Th2 polarization (or
other Type 2 immune responses), important steps in allergic sensitization I (Song et al..
2012); (see Section 5.2.2.1)1. Repeated SO2 exposure in the newborn rats, which were
previously sensitized and challenged with an allergen (i.e., allergic animals), resulted in
enhanced allergic airway inflammation and some evidence of airway remodeling and
AHR. Additional evidence comes from experimental studies in adult animals involving
short-term exposure to SO2 over several days. In naive rats, airway inflammation and
morphologic responses indicative of airway remodeling were seen (Section 5.2.1.7).
Furthermore, enhancement of allergic sensitization and other inflammatory responses
were observed along with AHR in guinea pigs exposed repeatedly to SO2 for several days
and subsequently sensitized and challenged with an allergen (Section 5.2.1.7). Similarly,
SO2 exposure enhanced airway inflammation in rats previously sensitized with an
allergen (Section 5.2.1.2).
Evidence for the Severity of Asthma
A few studies provide evidence for a potential relationship between long-term SO2
exposure and the severity of asthma, as indicated by asthma severity scores, asthma
control, and AHR (Section 5.2.2.1).
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Evidence for the Development of Allergies
Epidemiologic evidence from a few long-term studies provides a link between long-term
SO2 exposure and respiratory allergies and allergic rhinitis among children
(Section 5.2.2.2V However, uncertainties remain given the cross-sectional design of these
studies. Two pollutant models have begun to address the role of SO2 exposure in the
development of allergic rhinitis.
Evidence for Lung Function
Several studies evaluated the relationship between long-term SO2 exposure and
decrements in lung function (Section 5.2.2.3). Evidence supporting this relationship is
limited because associations were inconsistent and because both PM and SO2 were at
high concentrations in the same areas, precluding determination of individual SO2 effects.
Potential confounding of long-term SO2 exposure-related decrements in lung function
and lung development by other pollutants, especially PM, was evaluated in only one
study. This study found an attenuation of the effect in two-pollutant analyses. No changes
in lung function were found in long-term animal toxicological studies at relevant SO2
concentrations. The recent studies support conclusions made in the 2008 SOx ISA (U.S.
EPA. 2008d) that the available evidence was inadequate to infer a causal relationship
between long-term exposure to SO2 at ambient concentrations and changes in lung
function.
Evidence for Respiratory Infection
Respiratory infection related to long-term SO2 exposure is discussed in Section 5.2.2.4.
A limited number of the cross-sectional studies examined indicate associations between
long-term SO2 exposure and bronchitis or respiratory infection due to various infectious
agents; findings were generally positive. While some animal toxicological studies
reported alterations in specific host defense mechanisms, there is no evidence to support
increases in bacterial or viral infections in animals exposed to SO2 at relevant
concentrations.
Evidence for the Development of Other Respiratory Diseases
Evidence for prevalence of bronchitis and/or COPD consists of generally positive
associations found in cross-sectional studies (Section 5.2.2.5).
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Evidence for Respiratory Mortality
Small positive associations between long-term exposure to SO2 and respiratory mortality
among adults were found in several cohort studies after adjustment for common potential
confounders (Section 5.2.2.6V There is little evidence of respiratory health effects in
adults in relation to long-term SO2 exposure that could provide coherence with the
observed associations with respiratory mortality among adults.
Conclusion
Taken together, epidemiologic and animal toxicological studies provide evidence that is
suggestive of, but not sufficient to infer, a causal relationship between long-term SO2
exposure and respiratory effects (see Table 5-24). The strongest evidence is provided by
coherence of findings of epidemiologic studies showing associations between long-term
SO2 exposure and increases in asthma incidence among children and findings of animal
toxicological studies that provide a pathophysiologic basis for the development of
asthma. These latter studies demonstrated that repeated SO2 exposure over several weeks
resulted in Th2 polarization (or other Type 2 immune responses) and airway
inflammation, key steps in allergic sensitization, in naive newborn animals. In addition,
repeated SO2 exposure over several weeks resulted in enhanced airway inflammation and
some evidence of airway remodeling and AHR in allergic newborn animals.
Toxicological studies involving repeated exposure to SO2 over several days provide
additional evidence of these effects. However, because the animal toxicological evidence
is limited, particularly for long-term exposure, some uncertainty remains regarding an
independent effect of long-term SO2 exposure on the development of asthma. In addition,
potential confounding by other pollutants is unexamined, and largely unavailable, for
epidemiologic studies of asthma among children. However, multiple lines of evidence
suggest that long-term SO2 exposure results in a coherent and biologically plausible
sequence of events that culminates in the development of asthma, especially allergic
asthma, in children.
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5.3
Cardiovascular Effects
5.3.1 Short-Term Exposure
5.3.1.1 Introduction
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008(1) reviewed studies published through
2006 and concluded that "the evidence as a whole is inadequate to infer a causal
relationship" between short-term exposure to SO2 and cardiovascular health effects.
Specifically, the 2008 ISA for Sulfur Oxides found a lack of consistency with regard to
short-term exposure to SO2 and markers of HRV, cardiac repolarization, discharges of
implantable cardioverter defibrillators (ICDs), blood pressure, blood markers of
cardiovascular disease risk, the triggering of a myocardial infarction, or ED visits or
hospital admission for cardiovascular diseases. This section reviews the published studies
pertaining to the cardiovascular effects of short-term exposure (i.e., up to 1 month) to
SO2 in humans and animals. There are no toxicological studies evaluating cardiovascular
effects following 5-10 minute exposures to SO2. With few exceptions, most
epidemiologic studies model the association of 24-h avg SO2 concentration with
cardiovascular outcomes. With the existing body of evidence serving as the foundation,
emphasis has been placed on studies published since the 2008 ISA for Sulfur Oxides
(U.S. EPA. 2008d).
To clearly characterize the evidence underlying causality, the discussion of the evidence
is organized into groups of related outcomes [myocardial infarction and ischemic heart
disease (Section 5.3.1.2). arrhythmia and cardiac arrest (Section 5.3.1.3). cerebrovascular
disease (Section 5.3.1.4). hypertension (Section 5.3.1.5). venous thromboembolism
(Section 5.3.1.6). heart failure (Section 5.3.1.7). aggregated cardiovascular disease
(Section 5.3.1.8). and cardiovascular mortality (Section 5.3.1.9)1. Evidence for
subclinical effects (e.g., heart rate variability, blood biomarkers of cardiovascular effects)
of short-term exposure to SO2 that potentially underlie the triggering or indication of
various clinical events are discussed in Section 5.3.1.10. and may provide biological
plausibility for multiple outcomes. When considered with the evidence reviewed in the
2008 ISA for Sulfur Oxides, recent epidemiologic studies add to the evidence for effects
of SO2 exposure on a broader array of cardiovascular effects and mortality. Still,
substantial uncertainties remain concerning exposure measurement error, the lack of
mechanistic evidence to describe a role for SO2 in the initiation of key events in a
proposed mode of action, and potential confounding by copollutants. The majority of the
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recent evidence is from epidemiologic studies, which examined the association of SO2
exposure with MI, cerebrovascular disease and other cardiovascular effects.
The previous ISA included a small number of animal toxicological studies of blood
pressure (Section 5.3.1.5). HR and HRV (Section 5.3.1.10). and arrhythmia frequency
(Section 5.3.1.3) and controlled human exposure studies that examined effects on the
autonomic nervous system (Section 5.3.1.10) from short-term exposure to SO2. Since the
2008 ISA for Sulfur Oxides (U.S. EPA. 2008d). no controlled human exposure studies
and few animal toxicological studies have investigated the effects of short-term SO2
exposure on the cardiovascular system. Results from the experimental studies included in
the past and current reviews that evaluated cardiovascular effects of short-term SO2
exposures of less than 2,000 ppb are summarized in the relevant outcome section and
additional study details are summarized in Supplemental Table 5S-13 (U.S. EPA. 2016s).
Studies examining cardiovascular effects of sulfite exposure (via i.p., i.v., etc.) are not
included in this section because these studies generally involve exposures to sulfite that
are higher than what is expected to occur following inhalation of SO2 at ambient relevant
concentrations. Some studies using prolonged exposures to 300 ppb and higher
concentrations of SO2 reported measurable changes in the concentrations of
sulfite/S-sulfonate in plasma and tissues. A positive correlation was found between the
concentration of inhaled SO2 and plasma sulfite/S-sulfonate levels in humans exposed
continuously to SO2 (300-6,000 ppb) (Gunnison and Palmes. 1974). Similarly, a recent
report in mice exposed to 5,000-20,000 ppb SO2 for 7 days found a
concentration-dependent increase in sulfite/S-sulfonate levels in lung, heart, and brain
compared to controls (Meng et al.. 2005b). These studies suggest that prolonged exposure
to SO2 at concentrations higher than typically found in ambient air may increase
circulating sulfite, but these changes would be expected to be far less following ambient
exposures of shorter duration. The literature on the distribution and metabolism of sulfite
is discussed in Section 4.2.3 and Section 4.2.4. The potential role of sulfite in the
induction of systemic effects, such as effects of the cardiovascular system, is discussed in
Section 4.3.4.
5.3.1.2 Myocardial Infarction and Ischemic Heart Disease
Several lines of evidence are discussed in evaluating the relationship between short-term
SO2 exposure and MI. An MI, or heart attack, occurs as a consequence of IHD, resulting
in insufficient blood flow to the heart that overwhelms myocardial repair mechanisms
and leads to muscle tissue death. ICD codes for MI are classified within the group of
IHDs, thus studies in which IHD is evaluated will include any patients diagnosed with an
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MI. Finally, acute MI may be characterized by ST-segment depression, a nonspecific
marker of myocardial ischemia. The evaluation of evidence supporting a relationship
between short-term SO2 exposure and the triggering of an MI includes hospitalization and
ED visits for MI or IHD and ST-segment amplitude changes.
The epidemiologic data available for review by the 2008 ISA for Sulfur Oxides (U.S.
EPA. 2008d) did not indicate an association between SO2 and risk of MI. A number of
additional studies based on administrative data of hospital admissions or ED visits or on
clinical data are now available in Figure 5-12. The air quality characteristics of the city,
or across all cities, and the exposure assignment approach used in each Mi-related
hospital admission and ED visit study evaluated in this section are presented in
Table 5-25. The recent clinical registry studies provide inconsistent evidence for an
association between MI and ambient SO2, while multicity and single-city hospital
admission and ED visit studies provide generally consistent evidence of an association.
However, potential copollutant confounding and limited mechanistic evidence are still
key uncertainties that make it difficult to interpret the results of these studies.
Additionally, most studies examined 24-h avg exposure metrics for SO2, which may not
adequately capture the spatial and temporal variability in SO2 concentrations
(Section 3.4.2).
Some studies rely on clinical registries, which are generally less susceptible to
misclassification of the outcome. Using data from the Myocardial Ischaemia National
Audit Project (MINAP) clinical registry, Bhaskaran et al. (2011) reported that hourly
ambient SO2 concentrations were not associated with risk of MI in a case-crossover study
of 15 conurbations in England and Wales between 2003 and 2006. While no associations
were reported in the population overall, there was some evidence of an association in
subgroup analyses within older age groups (60-69, 70-79, and 80+) at inconsistent lag
times. This study is unique because it included detailed data on the timing of MI onset in
more than 79,000 patients, which allowed examination of the association with ambient
SO2 in the hours preceding MI. Miloievic et al. (2014) also used data from MINAP, from
2003 to 2009, and observed stronger evidence of an association between SO2
concentrations and MI [4.3% (95% CI: -0.25, 8.8%) increase in risk of MI per 10-ppb
increase in 24-h avg SO2 at lag 0-4], Turin et al. (2012) did not observe any association
using data from the Takashima County Stroke and Acute Myocardial Infarction Registry
in central Japan, although this study was likely underpowered to detect an association of
the expected magnitude. None of the clinical registry studies examined copollutant
models.
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Study
Outcome
Lag
tBhaskaran et al, (2011)
Ml
1-6 h
tMilojevic etal. (2014)
Ml
0-4
fTurin et al. (2012)
Ml
1
Ballesteret al. (2006)
1HD
0-1
fRichetal. (2010)
Ml
0
tCheng et al. (2009)
Ml
0-2
0-2
j-Hsiehetal. (2010)
Ml
0-2
0-2
fSteib et al. (2009)
Ml/Angina
1
Certdon et al. (2006)
Ml
0-7
tThach etal. (2010)
IHD
0-1
fTsai et al. (2013)
Ml
0-2
0-2
tQui etal. (2013)
IHD
0-3
0-3
0-3
fTam et at. (2015)
IHD
0-3
tBelletal. (2008)
IHD
0-3
Notes
>25"C
< 25'C
a 23° C
< 23° C
Warm Days
Cool Days
All Year
Warm Days
Cool Days
All Monitors
City Monitors
Correlated Monitors
1 ~
' O
I
I
>
i
-T—•"
0.5
1
1.5
Risk or Odds Ratio (95% CI)
CI = confidence interval.
1 Note: Studies in red are recent studies. Studies in black were included in the 2008 ISA for Sulfur Oxides. All-year
2 associations = circles; summer/warm-days associations = diamonds; winter/cold-days associations = squares.
3 Relative risks are standardized to a 10-ppb or 40-ppb increase in sulfur dioxide for 24-h avg and 1-h max metrics,
4 respectively. Lag times are reported in days, unless otherwise noted. Corresponding quantitative results are reported
5 in Supplemental Table 5S-14 (U.S. EPA. 2016t). All results are from single pollutant models.
Figure 5-12 Results of studies of short-term sulfur dioxide exposure and
hospital admissions for ischemic heart disease.
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Table 5-25 Mean and upper percentile concentrations of sulfur dioxide from
ischemic heart disease hospital admission and emergency
department visit studies.
Mean/Median Upper Percentile
Exposure Concentration of Concentrations
Study Location Years Assignment Metric ppb ppb
tBhaskaran et al.
(2011)
15 conurbations Central site 1-h max
in England and monitor from each
Wales conurbation
(2003-2006) (aggregated when
more than one
monitor)
Mean: 1.9
75th: 3.4
tMiloievic et al.
(2014)
230 acute
hospitals in
England and
Wales
(2003-2009)
Nearest monitor
within 50-km
distance from
residence location
24-h avg
Median: 1.2
75th: 2.3
tTurin et al. (2012)
Takashima
County, Japan
(1988-2004)
Nearest monitor to
Takashima County
(20 km)
24-h avg
Mean: 3.9
75th: 4.8
Ballester et al.
(2006)
14 Spanish
cities
(1995-1999)
Citywide average
for each city
24-h avg
Mean: 2.9-15.6
across cities
90th: 4.8-28.8
across cities
tRich et al. (2010)
New Jersey
(2004-2006)
Closest of
14 monitor (those
>10 km from
monitor excluded)
24-h avg
NR
NR
tChena et al.
(2009)
Kaohsiung,
Taiwan
(1996-2006)
Average across six
monitoring stations
24-h avg
Mean: 9.33
75th: 11.69
Max: 31.26
tHsieh et al. (2010)
Taipei, Taiwan
(1996-2006)
Average across six
monitoring stations
24-h avg
Mean: 4.36
75th: 5.48
Max: 17.82
tStieb et al. (2009)
Seven Canadian
cities
(1992-2003)
Citywide average
for each city
24-h avg
Mean: 2.6-10.0
across cities
75th: 3.3-13.4
across cities
Cendon et al.
(2006)
Sao Paulo,
Brazil
(1998-1999)
Average across
13 monitoring
stations
24-h avg
Mean: 5.6
95th: 12.1
tThach et al. (2010)
Hong Kong,
China
(1996-2002)
Average across
eight monitoring
stations
24-h avg
Mean: 6.8
NR
tTsaietal. (2012)
Taipei, Taiwan
(1999-2009)
Average across six
monitoring stations
24-h avg
Mean: 3.94
75th: 5.01
Max: 12.7
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Table 5-25 (Continued): Mean and upper percentile concentrations of sulfur
dioxide from ischemic heart disease hospital admission
and emergency department visit studies.
Study
Location Years
Exposure
Assignment
Metric
Mean/Median
Concentration
PPb
Upper Percentile
of Concentrations
PPb
tQiu et al. (2013a)
Hong Kong,
China
(1998, 2007)
Average across
14 monitoring
stations
24-h avg
Mean: 7.4
NR
tSan Tarn et al.
(2015)
Hong Kong,
China
(2001-2010)
Average across
13 monitoring
stations
24-h avg
Mean: 7.6
75th: 9.3
Max: 51.9
tBell et al. (2008)
Taipei, Taiwan
(1995-2002)
Average across
13 monitoring
stations; 5 within
city limits; or 6 with
correlations >0.75
24-h avg
Mean: 4.7
Max: 26.9
NR = not reported.
fStudies published since the 2008 ISA for Sulfur Oxides.
One prominent study from the previous 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d)
was conducted in 14 cities across Spain and found a 4.5% (95% CI: 1.3, 8.1%) increase
in hospital admissions per 10-ppb shift in SO2 for the composite endpoint of IHD,
arrhythmias, and heart failure (Ballester et al.. 2006). This association was still positive,
but attenuated and no longer statistically significant after adjustment for CO or NO2. It
was lessened in magnitude, but more precise, with adjustment for TSP or O3 in
copollutant models (no quantitative results; results presented graphically). Several
additional ED visit and hospital admission studies are now available. In a study of
hospitalization in New Jersey, Rich et al. (2010) did not report strong evidence for an
association between SO2 and risk of hospital admissions for MI [OR: 1.05 (95% CI: 0.84,
1.29) per 10-ppb increase in 24-h avg SO2 on the same day]. The inclusion of PM25 in a
copollutant model did not reveal a positive association for SO2 [OR: 0.91 (95% CI: 0.69,
1.21)]. In Kaohsiung, Taiwan, Cheng et al. (2009) reported an association between SO2
concentrations and hospital admissions for MI, but only on days when the mean ambient
temperature was <25°C. However, in copollutant models adjusting for PM10, NO2, or CO,
SO2 was no longer associated with increased admissions. Conversely, in Taipei, Taiwan,
Hsieh et al. (2010) only observed an association between SO2 and MI on warm days
(>23°C). Similar to the findings of Cheng et al. (2009). this association was no longer
positive after adjustment for PM10, NO2, O3, or CO in copollutants models. Most other
studies have not considered copollutant models.
A study using data from 14 hospitals in seven Canadian cities found a 4.2% (95% CI: 0.4,
8.0%) increase in risk of ED visits for the composite endpoint of acute MI or angina per
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10-ppb increase in SO2 on the previous day (Stieb et al.. 2009). Most (San Tam et al..
2015; Oiu et al.. 2013a; Tsai et al.. 2012; Thach et al.. 2010; Condon et al.. 2006; Martins
et al.. 2006) but not all (Bell et al.. 2008) studies using data from individual cities have
found associations between SO2 concentrations and risk of hospital admissions or ED
visits for ischemic heart disease or MI. None of the single-city studies evaluated potential
copollutant confounding, and all of the studies in this section used fixed site monitors to
measure ambient SO2. The limitations of these monitors in capturing spatial variation in
SO2 has been noted previously (Section 3.4.2).
ST-Segment Changes
ST-segment changes (either ST-segment elevation or depression) on the
electrocardiogram are considered a nonspecific marker of myocardial ischemia. While
the 2008 ISA for Sulfur Oxides did not review any epidemiologic studies of ambient SO2
concentrations and markers of myocardial ischemia, one subsequent study reported an
association. Chuang et al. (2008) conducted a repeated-measures study in adults with a
history of coronary heart disease (CHD) and examined the association between ambient
pollutants and ST-segment level changes. This study found an odds ratio of 3.0 (95% CI:
1.8, 5.5) for ST-segment depression of>0.1 mm per 10-ppb increase in SO2 over the
previous 24 hours. This finding was generally unchanged after additional control for
PM2 5 and BC in copollutant models.
Summary of Ischemic Heart Disease and Myocardial Infarction
In summary, while evidence from epidemiologic studies suggests a potential association
between ambient SO2 concentrations and rates of hospital admissions or ED visits for MI
or ischemic heart diseases in single-pollutant models, these associations may be the result
of confounding by other pollutants. While three studies based on clinical data report
inconsistent evidence regarding associations between ambient SO2 concentrations and
risk of MI, the majority of studies relying on MI hospital admission and ED visit data
observed either seasonal or year-round associations with SO2. However, some of these
associations were either attenuated or no longer present after controlling for potential
copollutant confounding (Hsieh et al.. 2010; Cheng et al.. 2009; Ballester et al.. 2006).
leaving uncertainties regarding the independent effect of short-term SO2 exposure. In
congruence with the evidence from hospital admission and ED visit studies, there was
limited evidence from a single study indicating that SO2 may be associated with
ST-segment changes on the electrocardiogram in patients with a history of coronary heart
disease. Most studies examined 24-h avg exposure metrics for SO2, which may not
adequately capture the spatial and temporal variability in SO2 concentrations
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(Section 5.2.1.2). No experimental studies have been conducted to evaluate measures of
ischemic heart disease or MI following short-term SO2 exposure. Overall, despite some
epidemiologic evidence of an association between short-term exposure to SO2 and
hospital admissions and ED visits for ischemic heart disease and MI, uncertainties
regarding copollutant confounding continue to impede the determination of an
independent SO2 effect.
5.3.1.3 Arrhythmias and Cardiac Arrest
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) concluded that the evidence available
at the time did not suggest that SO2 has an effect on cardiac arrhythmias. There continues
to be essentially no epidemiologic or toxicological evidence suggestive of such a
relationship.
Metzger et al. (2007) examined 518 patients with ICDs with 6,287 tachyarrhythmic
event-days over a 10-year period in Atlanta, Georgia and found no association between
SO2 concentrations and the risk of tachyarrhythmias, either overall or in analyses limited
to more severe tachyarrhythmic events, or stratified by season or the presence of a recent
past arrhythmic event (results for this study and other studies in this section can be found
in Table 5-26). A similar study in London, England also found limited evidence of an
association between SO2 concentrations and arrhythmic risk (Anderson et al.. 2010).
Anderson etal. (2010) reported an increase in risk of ICD activations corresponding to an
increase in ambient SO2, but the association was imprecise [OR: 1.35 (95% CI: 0.75,
2.41) per 10-ppb increase in SO2 at lag days 0-1], Similarly, a study in Boston,
Massachusetts observed an association between ambient SO2 and ICD activations that
was even more imprecise [32.0% (95% CI: -48.5, 336.2%) increase in ICD activations
per 10-ppb increase in SO2 concentrations at lag 1] (Link et al.. 2013). Additionally, a
multicity study in Canada (Stieb etal.. 2009) and a large single-city study in Taipei,
Taiwan (Tsai et al.. 2009) have reported finding no association between SO2 and ED
visits for arrhythmias, while a large single-city study in Shanghai, China reported a
positive association that was attenuated and no longer positive in a copollutant model
adjusted for NO2 (Zhao et al.. 2014).
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Table 5-26 Epidemiologic studies of arrhythmia and cardiac arrest.
Study
Location and
Years
(Sample Size)
Mean and Upper
Concentration
SO2 (ppb)
Exposure
Assessment
Selected Effect Estimates3
(95% CI)
tMetzaer et al.
(2007)
Atlanta, GA
1993-2002
(n = 518)
1-h max: 15.5
90th percentile:
36
Max: 149
Central All tachyarrhythmic events (OR); year
monitor round
Lag 0: 1.00 (0.94, 1.08)
Warm season
Lag 0: 1.06 (0.98, 1.25)
Cold season
Lag 0: 0.97 (0.91, 1.05)
Cardiac pacing or defibrillation (OR): Lag
0: 0.98 (0.88, 1.09)
Defibrillation (OR):
Lag 0: 1.01 (0.98, 1.24)
tAnderson et al.
(2010)
London, U.K.
1995-2003
[n = 705
(5,462 device
activations)]
24-h avg: 1.03
75th percentile:
1.15
Max: 2.67
Citywide avg ICD activations (OR);
Lag 01: 1.35 (0.75, 2.41)
Lag 05: 1.71 (0.69, 4.27)
Correlations: PM10: 0.48, PM2.5: 0.42, BS:
0.35, SO42": 0.19, PNC: 0.29, NO2: 0.60,
NO: 0.44, NOx: 0.49, O3: -0.36
tLinketal. (2013)
Boston, MA
24-h avg: 3.2
Citywide avg
ICD activations (percent change);
2006-2010
75th percentile: 4
Lag 1: 32.0 (-48.5, 336.2)
[n = 176
Correlations: CO:-0.06 to 0.75, NO2: 0.05
(328 atrial
to 0.69, Os: -0.52 to -0.18, PM10: 0.27 to
fibrillation
0.55, PM2.5: 0.01 to 0.67
episodes
>30 sec)]
tStieb et al. (2009)
Seven
24-h avg: 2.6 to
Citywide avg
Dysrhythmia ED visits (percent change);
Canadian
10 across cities
for each city
Lag 0: -1.4 (-6.0, 3.4)
cities
75th percentile:
Lag 1: 0.8 (-6.4, 8.6)
1992-2003
3.3 to 13.4 across
Lag 2: -5.0 (-9.2, -0.6)
(n = 45,160 ED
visits)
cities
Correlations: PM10: 0.52, NO2: 0.43, CO:
0.24, Os: 0.09
tTsai et al. (2009)
Taipei, Taiwan
2000-2006
(n = 21,581
ED visits)
24-h avg: 3.93
75th percentile:
5.02
Max: 12.7
Citywide avg Arrhythmia ED visits (OR);
>23°C: 1.04 (0.88, 1.23)
<23°C: 1.04 (0.88, 1.27)
Correlations: PM10: 0.52, NO2: 0.43, CO:
0.24, Os: 0.09
tZhao et al. (2014)
Shanghai,
China
2010-2011
(n = 56,940
outpatient
visits)
24-h avg: 11.1
75th percentile:
14.1
Max: 49.6
Central Arrhythmia outpatient visits (percent
monitor change);
Lag 0: 1.06 (1.04, 1.07)
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Table 5-26 (Continued): Epidemiologic studies of arrhythmia and cardiac arrest.
Location and Mean and Upper
Years Concentration Exposure Selected Effect Estimates3
Study (Sample Size) SO2 (ppb) Assessment (95% CI)
tDennekamp et al. Melbourne,
(2010) Australia
2003-2006
(n = 8,434
OHCA)
24-h avg: 0.49 Central
75th percentile: monitor
0.76
OHCA (percent change);
Lag 0: -10.0 (-40.3, 64.0)
Lag 1: 6.9 (-34.9, 75.6)
Lag 2: 0.8 (-39.0, 66.7)
Lag 01: -0.7 (-34.9, 75.6)
tSilverman et al.
(2010)
New York City,
NY
2003-2006
(n = 8,216
OHCA)
24-h avg: 6.3 Citywide avg No quantitative results; results presented
(median) graphically. Null association between
75th percentile: OHCA and year-round SO2
9.6 concentrations. OHCA positively but
95th percentile: imprecisely (i.e., wide 95% CI) associated
18 with ambient SO2 during the warm season
tStranev et al.
(2014)
Perth,
Australia
2000-2010
(n = 8,551
OHCA)
1-h avg: 0.4
(median)
75th percentile:
0.9
95th: 3.5
Nearest OHCA (OR);
monitor Lag 0: 0.91 (0.71, 1.17)
tRosenthal et al.
(2013)
Helsinki,
Finland
1998-2006
(n = 2,134
OHCA)
24-h avg: 1.5
Citywide avg OHCA (OR);
Lag 0
Lag 1
Lag 2
Lag 3
0.93 (0.58, 1.44)
0.68 (0.42, 1.08)
1.08 (0.68, 1.66)
1.00 (0.63, 1.55)
Lag 03: 0.86 (0.42, 1.55)
tKanq et al. (2016)
Seoul,
South Korea
2006-2013
(n = 28,315
OHCA)
24-h avg: 2.1
75th percentile:
2.5
Max:
.1
No quantitative results; results presented
graphically. Positive, statistically
significant associations at single day lags
0 through 3. Null associations at lags 4
and 5.
BS = black smoke; CI = confidence interval; CO = carbon monoxide; ED = emergency department; ICD = implantable cardioverter
defibrillators; n = sample size; NO = nitric oxide; N02 = nitrogen dioxide; NOx = the sum of NO and N02; 03 = ozone;
OHCA = out-of-hospital cardiac arrhythmias; OR = odds ratio; PM10 = particulate matter with a nominal aerodynamic diameter less
than or equal to 10 |jm; PM2.5 = particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm;
PNC = particle number concentration; S02 = sulfur dioxide; S042" = sulfate.
All Lag times are in days, unless otherwise noted.
fStudies published since the 2008 ISA for Sulfur Oxides.
aEffect estimates are standardized to a 10-ppb or 40-ppb increase in S02 concentration for 24-h avg and 1-h max metrics,
respectively.
1 The majority of out-of-hospital cardiac arrests (OHCA) are due to cardiac arrhythmias.
2 Dennekamp et al. (2010) considered the association between ambient pollutants and
3 OHCA among 8,434 cases identified through the Victorian Cardiac Arrest Registry in
4 Melbourne, Australia and found null and/or imprecise associations (e.g., wide 95% CIs)
5 between SO2 concentrations and risk of OHCA. A similar approach was used by
6 Silverman etal. (2010) with data from 8,216 OHCAs in New York City. Quantitative
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results for SO2 were not provided, but graphs showed a null association between OHCA
and year-round SO2 concentrations. Silverman et al. (2010) also presented
season-specific analyses graphically, demonstrating that out-of-hospital cardiac arrests
were positively but imprecisely (i.e., wide 95% CI) associated with SO2 concentrations
during the warm season. Two additional case-crossover studies of OHCA in Perth,
Australia (Stranev et al.. 2014) and Helsinki, Finland (Rosenthal et al.. 2013) observed
null associations with ambient SO2. In contrast, Kang et al. (2016) observed an
association between 24-h avg SO2 and OHCA in Seoul, South Korea at individual lag
days 0 through 3 (no quantitative results; results presented graphically).
One animal toxicological study (Nadzieiko et al.. 2004) evaluated arrhythmia frequency
in rats following short-term SO2 exposure and reported no significant changes in
spontaneous arrhythmias (irregular, delayed, or premature beats).
In summary, studies of patients with implantable cardioverter defibrillators, hospital
admissions for arrhythmias, and out of hospital cardiac arrest do not provide evidence to
support the presence of an association between ambient SO2 concentrations and
arrhythmias. Most of these studies have been focused on other pollutants and therefore
have not explored whether such an association might exist in certain subgroups.
Additionally, the majority of studies used central site monitors to estimate ambient SO2
exposure, which have noted limitations in capturing spatial variation in S02that generally
lead to attenuation and loss of precision in the effect estimates (Section 3.4.4). One
toxicological study also found no evidence for arrhythmias following short-term SO2
exposure.
5.3.1.4 Cerebrovascular Diseases and Stroke
Results among the studies reviewed in the 2008 ISA for Sulfur Oxides were inconsistent
with regard to the association between ambient SO2 concentrations and hospital
admissions or ED visits for cerebrovascular diseases or stroke (a specific form of
cerebrovascular disease). Many additional studies are now available for consideration
(study details and results presented in Table 5-27 and Figure 5-13). In Edmonton, AB,
Szvszkowicz (2008) reported that risk of ED visits for ischemic stroke was linked to SO2
concentrations, but this association was observed only in subgroup analyses stratified by
sex, season, and age. A subsequent study in Vancouver, BC, found that SO2 was
associated with risk of ED visits for ischemic stroke in the population overall [OR: 2.09
(95% CI: 1.23, 3.52) per 10-ppb increase in SO2 at lag 3] (Szvszkowicz et al. 2012a).
The association was generally unchanged after adjustment for O3 in a copollutant model,
and attenuated, although still positive, after adjustment for CO [OR: 1.73 (95% CI: 1.00,
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3.10)]. Chen et al. (2014b) also observed an association between SO2 and ischemic stroke
at longer lags in Edmonton, AB. In Brazil, Costa Nascimento et al. (2012) observed a
7.8% (95% CI: 0.0, 16.5%) increase in risk of hospital admissions of stroke per 10-ppb
increase in 24-h avg SO2 at lag 0. Zheng et al. (2013) reported a small but precise
association between SO2 concentrations and risk of hospital admission for
cerebrovascular disease [1.7% increase (95% CI: 0.5, 2.8%) per 10-ppb increase in
24-h avg SO2 at lag 2] in Lanzhou, a heavily polluted city in China with a high observed
mean daily concentration of SO2 (30.19 ppb) over the 5-year study period.
The association was as strong, or stronger, after adjustment for PM10 [1.8% increase
(95% CI: 0.4, 3.2%)] orNC>2 [2.6% increase (95% CI: 1.4, 3.7%)] in copollutant models.
In central Japan, Turin et al. (2012) found that the risk of hemorrhagic stroke was
associated with SO2 concentrations, but found no association with other types of stroke.
However, the 95% CI for the hemorrhagic stroke association was wide, indicating an
imprecise association, and copollutant confounding was not considered.
In contrast to the studies that reported some evidence of an association between SO2
concentrations and cerebrovascular disease, a number of studies observed null or
imprecise associations. In an effort to reduce uncertainty related to the use of central site
monitors, Bell et al. (2008) estimated SO2 exposure over the entire Taipei, Taiwan area
(average of 13 monitors), within Taipei City only (average of 5 monitors), and using a
subset of monitors where all pairs of monitors had SO2 correlations greater than 0.75
(6 monitors). Using three exposure metrics, the authors did not observe an association
between SO2 and risk of hospital admission for cerebrovascular diseases. Contrary to
other studies that reported associations between SO2 concentrations and hospital
admissions and ED visits for stroke in Canada (Chen et al.. 2014b: Szvszkowicz et al..
2012a: Szvszkowicz. 2008). Villeneuve et al. (2012) reported null and/or imprecise
associations between SO2 and all stroke, ischemic stroke, and hemorrhagic stroke in
Edmonton, AB. Studies in Hong Kong (Thach et al.. 2010). Dijon, France (Henrotin et
al.. 2007). and Lyon, France (Mechtouff et al.. 2012) also observed null associations
between SO2 concentrations and rates of hospital admission for stroke.
Thus, findings for the association between SO2 and cerebrovascular diseases continue to
be inconsistent across studies. As for other outcomes, associations reported from single
pollutant models in some locations may be at least partly due to confounding by other
pollutants.
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Table 5-27
Mean and upper percentile concentrations of sulfur dioxide from
cerebrovascular disease and stroke-related hospital admission and
emergency department visit studies.
Study
Location
Years
Exposure
Assignment
Metric
Mean/Median
Concentration
PPb
Upper Percentile of
Concentrations
PPb
tZhena et al. (
'2013) Lanzhou. China
(2001-2005)
Average across
four monitoring
stations
24-h avg
Mean: 30.19
75th: 40.46
Max: 141.60
tThach et al. (2010) Hong Kong, Average across 24-h avg Mean: 6.79 NR
China eight monitoring
(1996-2002) stations
tBell et al. (2008) Taipei, Taiwan Average across 24-h avg Mean: 4.7 Max: 26.9
(1995-2002) 13 monitoring
stations; 5 within
city limits; or
6 with correlations
>0.75
tTurin et al. (2012) Takashima Nearest monitor 24-h avg Mean: 3.9 75th: 4.8
County, Japan to Takashima
(1988-2004) county (20 km)
Henrotin et al.
(2007)
Dijon, France
(1994-2004)
Central site
monitor
24-h avg
Mean:
2.63
75th:
Max:
3.44
24.81
tSzvszkowicz
(2008)
Edmonton, AB
(1992-2002)
Average across
three monitoring
stations
24-h avg
Mean:
2.6
NR
tSzvszkowicz et al.
(2012a)
Vancouver, BC
(1999-2003)
Average across
11 monitoring
stations
24-h avg
Mean:
2.5
NR
tMechtouff et al.
(2012)
Lyon, France
(2006-2007)
Average across
five monitoring
stations
24-h avg
Mean:
2.02
75th: 2.67
Max: 22.52
tChen et al.
(2014b)
Edmonton, AB
(1998-2002)
Average across
three monitoring
stations
1-h avg
Mean:
2.0
95th:
6.7
tVilleneuve et al.
(2012)
Edmonton, AB
(2003-2009)
Average across
three monitoring
stations
24-h avg
Mean:
1.5
75th:
1.9
tCosta Nascimento
et al. (2012)
Sao Paulo,
Brazil
(2007-2008)
Central site
monitor
24-h avg
NR
NR
NR = not reported.
fStudies published since the 2008 ISA for Sulfur Oxides.
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Study Outcome
tZheng et al. (2013) Cerebrovascular Disease
Lag
0-3
Notes
tThachet al. (2010)
tBelletal. (2008)
-(Turin etal. (2012)
tHenrotiri et al. (2007)
tSzyszkowicz et al. (2008)
tSzyszkowiczetal. (2012)
fMechtouffetal. (2012)
tChen et al. (2014)
•fVilleneuve et al. (2012)
Stroke
Cerebrovascular Disease
Stroke
Cerebral Infarction
Intracerebral Hemorrhage
Subarachnoid Hemorrhage
Ischemic Stroke
Hemorrhagic Stroke
Ischemic Stroke
Ischemic Stroke
Ischemic Stroke
Acute Ischemic Stroke
Acute Ischemic Stroke
Stroke
Ischemic Stroke
Transient Ischemic Stroke
tNascimentoet al. (2012) Stroke
0-1
0-3
0-3
0-3
0
0
0
0
1
1
0-2
0-2
0-2
All Monitors
City Monitors
Correlated Monitors
~
I
I
65-100 years; Cold Season
NR
1-24 h
25-28 h
-•-r-
~
All Year
Warm Days
Cool Days
-+-
0.5 1
Risk Odds Ratio (95% CI)
2.5
CI = confidence interval.
1 Note: Studies in red are recent studies. Studies in black were included in the 2008 ISA for Sulfur Oxides. All-year
2 associations = circles; summer/warm-days associations = diamonds; winter/cold-days associations = squares.
3 Relative risks are standardized to a 10-ppb or 40-ppb increase in sulfur dioxide for 24-h avg and 1-h max metrics,
4 respectively, but not standardized for other metrics [e.g., (Chenet al.. 2014b)l. Lag times are reported in days,
5 unless otherwise noted. Corresponding quantitative results are reported in Supplemental Table 5S-15 (U.S. EPA.
6 2016u). All results are from single pollutant models.
Figure 5-13 Results of studies of short-term sulfur dioxide exposure and
hospital admissions for cerebrovascular disease and stroke.
5.3.1.5 Blood Pressure and Hypertension
7 Based on the data available at the time, the 2008 ISA for Sulfur Oxides (U.S. EPA.
8 2008d) concluded that the overall evidence was insufficient to determine that SO2 has an
9 effect on blood pressure. Recent evidence provides limited and inconsistent evidence for
10 changes in blood pressure associated with short-term exposure to SO2.
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29
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36
Epidemiologic Studies
A number of longitudinal studies measured BP in subjects in Beijing before, during, and
after the 2008 Beijing Olympics when citywide air pollution control measures
substantially reduced ambient levels of most criteria pollutants. Huang et al. (2012)
measured blood pressure repeatedly on up to four occasions in 40 participants with
pre-existing cardiovascular disease in Beijing, including one measurement during the
2008 Beijing Olympics when citywide air pollution control measures reduced ambient
SO2 concentrations by up to 50%. Huang et al. (2012) found a small decrement in
diastolic blood pressure per IQR (NR) increase in prior 30-minute exposure to SO2
[-0.9 mm Hg (95% CI: -2.0, 0.2 mm Hg)], but observed a null association between
ambient SO2 and systolic blood pressure. Focusing on healthy young adults, Rich et al.
(2012) and Zhang et al. (2013) observed associations between SO2 and blood pressure in
repeated-measures studies conducted before, during, and after the 2008 Beijing Olympics
(no quantitative results; results presented graphically). Using the same protocol, Zhang et
al. (2013) and Rich et al. (2012) observed a positive association between 24-h avg SO2
and systolic blood pressure, but an inverse association between 24-h avg SO2 and
diastolic blood pressure. The negative association between SO2 and diastolic blood
pressure was relatively unchanged after adjustment for PM2 5, EC, or sulfate, while the
association between SO2 and systolic blood pressure was also robust to sulfate, but
attenuated, although still positive, after adjustment for PM2 5 or EC (Zhang et al.. 2013).
In another repeated measures study, Kim et al. (2016b) observed positive associations
between short-term SO2 concentrations and systolic blood pressure, diastolic blood
pressure, and mean arterial pressure among 560 older adults living in Seoul, South Korea.
A pair of cross-sectional studies reported conflicting evidence of an association.
Examining data from 7,578 participants in the Taiwanese Survey on Prevalence of
Hyperglycemia, Hyperlipidemia, and Hypertension, Chuang et al. (2010) concluded that
there is "no significant association" between SO2 concentrations and blood pressure (no
quantitative results presented). However, in a cross-sectional analysis of data from
9,238 participants in the Taiwan Community-based Integrated Screening program, Chen
et al. (2012d) found a 4.0 mm Hg (95% CI: 3.0 to 5.0 mm Hg) increase in diastolic blood
pressure per 10-ppb increase in SO2 concentrations 2 days earlier, and a 1.6 mm Hg (95%
CI: 0.15, 3.1 mm Hg) decrease in systolic blood pressure related to SO2 concentrations
3 days earlier.
In addition to longitudinal and cross-sectional studies, a few new studies examined ED
visits for hypertension. In Beijing, Guo etal. (2010) observed a 10.0% (95% CI: 1.1,
19.7%) increase in risk of ED visits for hypertension per 10-ppb increase in 24-h avg SO2
on the same day. The association was attenuated, but still positive, in a copollutant model
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28
adjusting for PMio [6.7% (95% CI: -3.4, 17.9%) increase at lag 0] and no longer present
in a copollutant model adjusting for NO2 [-0.8% (95% CI: -12.8, 13.0%) change at
lag 0]. Inconsistent results were reported in two studies of ED visits for hypertension in
Canada. In a case-crossover study in Calgary and Edmonton, Brook and kousha (2015)
reported positive associations between ED visits for hypertension and 24-h avg SO2
concentrations for males [OR: 2.50 (95% CI: 1.00, 5.87) per 10-ppb increase] and
females [OR: 2.59 (95% CI: 1.12, 5.61) per 10-ppb increase]. Conversely, in Edmonton,
Szvszkowicz et al. (2012b) observed that ED visits for hypertension were both positively
and negatively associated with SO2 depending on the lag time examined.
Experimental Studies
Several experimental studies examined hypertension and blood pressure following SO2
exposure. Study characteristics are summarized in Supplemental Table 5S-13 (U.S. EPA.
2016s). One controlled human exposure study reported no change in mean arterial
pressure following SO2 exposure (Routledge et al.. 2006). Two animal toxicological
studies have examined blood pressure following SO2 exposure (Halinen et al.. 2000b;
Halinen et al. 2000a'). In both studies, SO2 was administered intratracheally to
hyperventilated guinea pigs in cold, dry air. These studies reported increases in blood
pressure following cold, dry air exposure with and without SO2 and did not determine
whether there were any effects on blood pressure caused by SO2 that may not be
attributable to cold, dry air exposure.
Summary of Blood Pressure
In summary, epidemiologic studies evaluating the association between ambient SO2
concentrations and blood pressure remain inconsistent with most relying on central site
monitors and few examining the potential for copollutant confounding. Experimental
studies provide no additional evidence for S02-induced changes in blood pressure.
The most informative studies to date found no evidence of within-person changes in
blood pressure despite relatively large changes in SO2 concentrations during the Beijing
Olympics. Experimental studies do not demonstrate effects of SO2 on blood pressure. As
such, the current evidence does not support the presence of an association between
ambient SO2 and blood pressure.
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23
24
25
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28
5.3.1.6
Venous Thromboembolism
Venous thromboembolism (VTE) is a term that includes both deep vein thrombosis
(DVT) and pulmonary embolism (PE). DVT occurs when a blood clot develops in the
deep veins, most commonly in the lower extremities. A part of the clot can break off and
travel to the lungs, causing a PE, which can be life threatening.
There were no epidemiologic studies of VTE available for the 2008 ISA for Sulfur
Oxides. One recent study covering the metropolitan region of Santiago, Chile, found a
10.8% (95% CI: 3.3, 15.7%) and 8.5% (95% CI: 4.0, 13.2%) increased rate of hospital
admission for venous thrombosis and pulmonary embolism, respectively, per 10-ppb
increase in 24-h avg SO2 concentrations (Dales et al.. 2010). Copollutant models were not
evaluated. Given the limited epidemiologic evidence, the association between ambient
SO2 concentrations and venous thromboembolism is unclear.
Results among the studies reviewed in the 2008 ISA for Sulfur Oxides (U.S. EPA.
2008d) were inconsistent with regard to the association between ambient SO2
concentrations and hospital admissions or ED visits for heart failure. A small number of
additional studies are now available, including a multicity study of seven Canadian cities
(Stieb et al.. 2009). Stieb et al. (2009) observed an imprecise association (i.e., wide 95%
CI) between 24-h avg SO2 concentrations on the previous day and ED visits for heart
failure [3.0 % (95% CI: —1.9, 8.2%) increase in risk of ED visits per 10-ppb increase in
SO2]. Similarly, in Guangzhou, China, Yang et al. (2014a) observed a 14.5% increase
(95% CI: 6.1, 23.2%) in emergency ambulance dispatches for heart failure per 10-ppb
increase in 24-h avg SO2 concentrations on the same day. This association was slightly
attenuated, but still positive and statistically significant in copollutant models adjusting
for PM10 [13.1% (95% CI: 3.3, 23.4%)] andN02 [11.3% (95% CI: 1.7, 21.5%)]. In
contrast, Yang (2008) did not observe evidence of a positive association between ambient
SO2 exposure and heart failure in Taipei, Taiwan.
In summary, the available epidemiologic evidence is limited and inconsistent, and
therefore does not support the presence of an association between ambient SO2
concentrations and hospital admissions or ED visits for heart failure.
5.3.1.7
Heart Failure
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5.3.1.8 Aggregated Cardiovascular Disease
1 Many epidemiologic studies consider the composite endpoint of all cardiovascular
2 diseases, which typically includes all diseases of the circulatory system (e.g., heart
3 diseases and cerebrovascular diseases). This section summarizes the results of
4 epidemiologic studies evaluating the association between ambient SO2 concentrations
5 and ED visits or hospitalizations for all cardiovascular diseases. Table 5-28 presents
6 study details and air quality characteristics of the city, or across all cities, from the U.S.
7 and Canadian cardiovascular-related hospital admission and ED visit studies evaluated in
8 the 2008 ISA for Sulfur Oxides and those more recent.
Table 5-28 Mean and upper percentile concentrations of sulfur dioxide from
cardiovascular-related hospital admission and emergency
department visit studies: U.S. and Canadian studies from the 2008
ISA for Sulfur Oxides and recent studies.
Mean
Upper Percentile of
Location
Type of Visit
Concentration
Concentrations
Study
(Years)
(ICD 9/10)
Metric
PPb
PPb
U.S.
Gwvnn et al.
Buffalo and
Hospital admissions:
24-h avg
12.2
Max: 37.7
(2000)
Rochester, NY
circulatory (401-405,
(1988-1990)
410-417)
tlto et al.
New York City,
Hypertensive
24-h avg
7.4
(2011)
NY
diseases (402, 111);
(2000-2006)
Ml (410, 121-122);
IHD (414, I25);
dysrhythmias (427,
I48); heart failure
(428, I50); and stroke
(430-439, I60-I69)
Koken et al.
Denver, CO
Discharge data from
24-h avg
5.7
Max: 18.9
(2003)
(1993-1997)
Agency for
Healthcare Research
and Quality
database:
Acute Ml
(410.00-410.92),
atherosclerosis
(414.00-414.05),
pulmonary heart
failure (416.0-416.9),
dysrhythmia
(427.0-427.9), CHF
(428.0)
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Table 5-28 (Continued): Mean and upper percentile concentrations of sulfur
dioxide from cardiovascular related hospital admission
and emergency department visit studies: U.S. and
Canadian studies from the 2008 ISA for Sulfur Oxides and
recent studies.
Study
Location
(Years)
Type of Visit
(ICD 9/10)
Metric
Mean
Concentration
PPb
Upper Percentile of
Concentrations
PPb
Low et al.
(2006)
New York City,
NY
(1995-2003)
Ischemic stroke
(433-434),
undetermined stroke
(436); monitored
intake in 11 hospitals
(ED or clinic visits).
Excluded stroke
patients admitted for
rehabilitation
24 h avg
10.98
Max: 96.0
Metzaer et al.
Atlanta, GA
ED visits:
1-h max:
11.0 (median)
90th: 39
(2004)
(1993-2000)
IHD (410-414);
acute Ml (410);
dysrhythmias (427);
cardiac arrest
(427.5); CHF (428);
peripheral and
cerebrovascular
disease (433-437,
440, 443-444,
451-453);
atherosclerosis
(440); stroke (436)
Michaud et al.
Hilo, HI
ED visits
24-h avg
1.92 (all hourly
Max: 447 (all hourly
(2004)
(1997-2001)
Heart (410-414,
measurements)
measurements)
425-429)
Moolaavkar
Cook County,
Hospital admissions:
24-h avg
Cook: 6 (median)
Cook: Max: 36
(2003)
IL; Los
CVD (390-429);
Los Angeles: 2
Los Angeles: Max: 16
Moolaavkar
(2000)
Angeles
County, CA;
Maricopa
County, AZ
(1987-1995)
cerebrovascular
disease (430-448)
(median)
Maricopa:
2 (median)
Maricopa: Max: 14
Morris et al.
Los Angeles,
Hospital admissions:
1-h max
Los Angeles: 10
NR
(1995)
CA; Chicago,
IL;
Philadelphia,
CHF (428)
Chicago: 25
Philadelphia: 29
PA; New York
New York City: 32
City, NY;
Detroit: 25
Detroit, Ml;
Houston: 18
Houston, TX;
Milwaukee, Wl
Milwaukee: 17
(1986-1989)
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Table 5-28 (Continued): Mean and upper percentile concentrations of sulfur
dioxide from cardiovascular related hospital admission
and emergency department visit studies: U.S. and
Canadian studies from the 2008 ISA for Sulfur Oxides and
recent studies.
Mean
Upper Percentile of
Location
Type of Visit
Concentration
Concentrations
Study
(Years)
(ICD 9/10) Metric
PPb
PPb
Peel et al.
Atlanta, GA
ED visits: 1-h max
16.5 (17.1)
90th: 39
(2007)
(1993-2000)
IHD (410-414),
dysrhythmia (427),
CHF (428),
peripheral vascular
and cerebrovascular
disease (433-437,
440, 443, 444,
451-453)
Hospital Admissions: 24-h avg NR NR
transmural infarction
(410.0, 410.1, 410.2,
410.3, 410.4, 410.5,
410.6),
nontransmural
infarction (410.7)
Schwartz and Detroit, Ml Hospital discharge: 24-h avg 25.4 90th: 44.0
Morris (1995) (1986-1989) IHD (410-414), CHF
(428), dysrhythmia
(427)
Schwartz
Tuscon, AZ
Hospital discharge:
24-h avg
4.6
90th: 10.1
(1997)
(1988-1990)
CVD (390-429)
Tolbert et al.
Atlanta, GA
ED visits:
1-h max
14.9
Max: 149.0
(2007)
(1993-2004)
CVD (410-414, 427,
428, 433-437, 440,
443-445, 451-453)
Ulirsch et al.
Southeast
Hospital admissions
NR
3.0
90th: 7.9, 7.7
(2007)
Idaho
and medical visits:
Max: 30.3, 30.3
(1994-2000)
CVD (390-429)
(two time series
examined)
Wellenius et al.
Birmingham,
Hospital admissions:
24-h avg
6.22 (median)
90th: 16.17
(2005b)
AL; Chicago,
ischemic stroke,
IL; Cleveland,
primary diagnosis of
OH; Detroit,
acute but ill-defined
Ml;
cerebrovascular
Minneapolis,
disease or occlusion
MN; New
of the cerebral
Haven, CT;
arteries; HS, primary
Pittsburgh, PA;
diagnosis of
Seattle, WA
intracerebral
(1986-1999)
hemorrhage. (ICD
codes not provided)
tRich et al. New Jersey
(201°) (2004-2006)
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Table 5-28 (Continued): Mean and upper percentile concentrations of sulfur
dioxide from cardiovascular related hospital admission
and emergency department visit studies: U.S. and
Canadian studies from the 2008 ISA for Sulfur Oxides and
recent studies.
Mean
Upper Percentile of
Location
Type of Visit
Concentration
Concentrations
Study
(Years)
(ICD 9/10)
Metric
PPb
PPb
Wellenius et al.
Allegheny
Hospital admissions:
24-h avg
14.78 (9.88)
95th: 33.93
(2005a)
County, PA
CHF (428)
(1987-1999)
Canada
Burnett et al.
Metropolitan
Hospital discharge:
1-h max
7.9
Max: 26
(1997)
Toronto (East
IHD (410-414);
York,
cardiac
Etobicoke,
dysrhythmias (427);
North York,
heart failure (428);
Scarborough,
all cardiac (410-414,
Toronto, York)
427, 428)
(1992-1994)
Burnett et al.
Metropolitan
IHD (410-414);
24-h avg
5.35
Max: 57
(1999)
Toronto (East
cardiac dysrhythmias
York,
(427); CHF (428); all
Etobicoke,
cardiac (410-414,
North York,
427, 428)
Scarborough,
Toronto, York)
(1980-1994)
Funa et al.
Windsor, ON
CHF (428), IHD
1-h max
27.5 (16.5)
Max: 129
(2005)
(1995-2000)
(410-414),
dysrhythmias (427)
and all cardiac
Stieb et al.
Saint John, NB
ED visits:
24-h avg
6.7 (5.6)
95th: 18
(2000)
(1992-1996)
angina pectoris, Ml,
Max: 60
dysrhythmia/conducti
on disturbance, CHF,
all cardiac
tSzvszkowicz Edmonton, AB ED visits: 24-h avg 2.6 NR
(2008) (1992-2002) acute ischemic
stroke (434 and 436)
tSzvszkowicz Vancouver, BC ED visits (discharge 24-h avg 2.5 NR
etal. (2012a) (iggg-2003) diagnosis):
transient ischemic
attack,
cerebrovascular
incident, seizure
tSzvszkowicz Edmonton, AB ED visits: 24 h avg 2.6 Max: 16.3
et al. (2012b) (1992-2002) hypertension (401.9)
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5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Table 5-28 (Continued): Mean and upper percentile concentrations of sulfur
dioxide from cardiovascular related hospital admission
and emergency department visit studies: U.S. and
Canadian studies from the 2008 ISA for Sulfur Oxides and
recent studies.
Study
Location
(Years)
Type of Visit
(ICD 9/10)
Metric
Mean
Concentration
PPb
Upper Percentile of
Concentrations
PPb
Villeneuve et
al. (2006a)
Edmonton, AB
(1992-2002)
ED visits:
stroke
24-h avg
All year: 2.6 (1.9)
All year 75th: 4.0
CHF = congestive heart failure; CVD = cardiovascular disease; ED = emergency department; HS = hemorrhagic stroke;
ICD = International Classification of Diseases; IHD = ischemic heart disease; Ml = myocardial infarction; NR = not reported;
S02 = sulfur dioxide.
fStudies published since the 2008 ISA for Sulfur Oxides.
The majority of epidemiologic studies reviewed in the 2008 ISA for Sulfur Oxides (U.S.
EPA. 2008d) found a positive association between ambient SO2 concentrations and rates
of hospital admission or ED visits for all cardiovascular diseases. One prominent study
from the previous ISA was a study conducted in 14 cities across Spain, which observed a
3.5% (95% CI: 0.5, 6.7%) increased risk of hospital admission for all cardiovascular
diseases per 10-ppb increase in SO2 at lag 0-1 KBallester et al.. 2006) study details and
results for this and other studies in this section are presented in Table 5-29. and
Figure 5-141. The authors indicate (results not reported) that the association with SO2 was
attenuated after adjustment for CO or NO2 in copollutant models. Most studies published
since the 2008 ISA for Sulfur Oxides also observed positive associations between SO2
and ED visits or hospitalizations for all CVD, although only a few considered potential
copollutant confounding. For example, a case-crossover study in Beijing found that SO2
averaged over eight monitoring sites was associated with risk of ED visits for all
cardiovascular diseases in a single-pollutant model [OR: 1.04 (95% CI: 1.01, 1.06) per
10-ppb increase in SO2 on the same day] (Guo et al.. 2009). The association remained
comparable in copollutant models adjusting for either PM2 5 [OR: 1.03 (95% CI: 0.99,
1.06)] orN02 [OR: 1.03 (95% CI: 1.00, 1.07)]. Similarly, in Shanghai, Chen et al.
(2010b) reported a small, but precise increase in risk of hospital admissions for CVD per
10-ppb increase in 24-h avg SO2 at lag 5 [1.7% (95% CI: 0.5, 3.0%)] and lag 0-6 [1.3%
(5% CI: 0.0, 3.2%)]. The association at lag 5 was similar after adjusting for NO2 or PM10,
while copollutant models for lag 0-6 were not presented.
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Table 5-29 Mean and upper percentile concentrations of sulfur dioxide from
cardiovascular-related hospital admission and emergency
department visit studies.
Study
Location
(Years)
Exposure
Assignment
Metric
Mean/Median
Concentration
PPb
Upper Percentile
of Concentrations
PPb
tlto etal. (2011)
New York City,
NY
(2000-2006)
Average across
five monitoring
sites
24-h avg
Mean: 7.4
NR
Metzaer et al. (2004)
Atlanta, GA
(1993-2000)
Central site
monitor
1-h max
Median: 11
90th: 39
Moolaavkar (2003)
Los Angeles, CA
(1987-1995)
Central site
monitor
24-h avg
NR
NR
Schwartz (1997)
Tuscon, AZ
(1998-1990)
Central site
monitor
24-h avg
Mean: 4.6
75th: 5.9
90th: 10.1
Burnett et al. (1997)
Toronto
(summer
1992-1994)
Average across
four to six
monitoring sites
1-h max
Mean: 7.9
75th: 11
Max: 26
Sunver et al. (2003)
Seven European
cities
(1990-1996)
Central site
monitors in each
city
24-h avg
Median: 1.9-8.0
across cities
90th: 5.3-29.4
across cities
Ballester et al.
(2006)
14 Spanish
cities
(1995-1999)
Citywide average
for each city
24-h avg
Mean: 2.9-15.6
across cities
90th: 4.8-28.8
across cities
Atkinson et al.
(1999)
London,
England
(1992-1994)
Average across
five monitoring
sites
24-h avg
Mean: 8.1
90th: 11.8
Max: 31.4
Poloniecki et al.
(1997)
London,
England
(1987-1994)
Central site
monitor
24-h avg
Median: 6
90th: 21
Max: 114
Anderson et al.
(2001)
Birmingham,
England
(1994-1996)
Average across
five monitoring
sites
24-h avg
Mean: 7.2
90th: 12.3
Max: 59.8
Ballester et al.
(2001)
Valencia, Spain
(1994-1996)
Average across
14 monitoring
sites
24-h avg
Mean: 9.8
Max: 26.1
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Table 5-29 (Continued): Mean and upper percentile concentrations of sulfur
dioxide from cardiovascular related hospital admission
and emergency department visit studies.
Study
Location
(Years)
Exposure
Assignment
Metric
Mean/Median
Concentration
PPb
Upper Percentile
of Concentrations
PPb
Llorca et al. (2005)
Torrelavega,
Spain
(1992-1995)
Average across
three monitoring
sites
24-h avg
Mean:
5.1
NR
tFilho et al. (2008)
Sao Paulo,
Brazil
(2001-2003)
Average across
13 monitoring
sites
24-h avg
Mean:
5.3
Max: 16.4
tMartins et al.
(2006)
Sao Paulo,
Brazil
(1996-2001)
Average across
six monitoring
sites
24-h avg
Mean:
6.5
Max: 28.7
tZhena et al. (2013)
Lanzhou, China
(2001-2005)
Average across
four monitoring
sites
24-h avg
Mean:
30.2
75th: 40.5
Max: 141.6
tZhana et al.
(2015b)
Beijing, China
(2009-2011)
Average across
11 monitoring
stations
24-h avg
Mean:
10.7
75th: 13.4
Max: 89.5
+Guo et al. (2009)
Beijing, China
(2004-2006)
Average across
eight monitoring
sites
24-h avg
Mean:
18.8
75th: 23.7
Max: 111.8
tChen et al. (2010b)
Shanghai, China
(2005-2007)
Average across
six monitoring
sites
24-h avg
Mean:
21.4
75th: 27.5
Max: 89.7
Wona et al. (1999)
Hong Kong,
China
(1994-1995)
Average across
seven monitoring
sites
24-h avg
Median: 6.5
75th: 9.5
Max: 26.1
Chana et al. (2005)
Taipei, Taiwan
(1997-2001)
Average across
six monitoring
sites
24-h avg
Mean:
4.3
75th: 5.5
Max: 14.6
Jalaludin et al.
(2006)
Sydney,
Australia
(1997-2001)
Average across
14 monitoring
sites
24-h avg
Mean:
1.07
75th: 1.39
Max: 3.94
Petroeschevskv et
al. (2001)
Brisbane,
Australia
(1987-1994)
Average across
two monitoring
sites
24-h avg
Mean:
13.9
Max: 49.7
NR = not reported.
fStudies published since the 2008 ISA for Sulfur Oxides.
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Study
Outcome Lag
Notes
tlto etal. (2011) CVD
Metzger et al. (2004) CVD
Moolgavkar et al. (2003) CVD
Schwartz et al. (1997) CVD
Burnett etal. (1997) CVD
Sunyer etal. (2003) CVD
Ballesteretal. (2006) CVD
Atkinson et al. (1999) CVD
Poloniecki et al. (1997) CVD
Anderson et al. (2001) CVD
Ballester et al. (2001) CVD
Llorca et al. (2005) CVD
tZheng etal. (2013) CVD
tZhang etal. (2015) CVD
tGuo etal. (2009) CVD
tChen etal. (2010b) CVD
Wonget al. (1999) CVD
Chang et al. (2005) CVD
Jalaludinetal. (2006) CVD
Petroeschevsky et al.
(2001) CVD
0-2
0
0-2
0-3
0-1
0-1
0
1
0-1
2
0
0-3
0
0
5
0-1
0-2
Warm Season
Cold Season
All Ages
65+ Tears Old
All Ages
0-64 years Old
65+ Years Old
65+ ?ears Old
:
!-•-
!~
38= 8
All Ages
15-64 Years Ok
65+ Years Old
-+-
0.75 1 1.25
Risk or Odds Ratio (95% CI)
1.5
CI = confidence interval; CVD = cardiovascular disease.
Note: Studies in red are recent studies. Studies in black were included in the 2008 ISA for Sulfur Oxides. All-year
associations = circles; summer/warm-days associations = diamonds; winter/cold-days associations = squares. Relative risks are
standardized to a 10-ppb or 40-ppb increase in sulfur dioxide for 24-h avg and 1-h max metrics, respectively. Lag times are reported
in days, unless otherwise noted. Corresponding quantitative results are reported in Supplemental Table 5S-16 (U.S. EPA. 2016)cc
All results are from single pollutant models.
Figure 5-14 Studies of hospital admissions and emergency department visits
for all cardiovascular disease.
1 A number of other studies considering single-pollutant models also reported generally
2 consistent associations between SO2 concentrations and hospital admissions or ED visits
3 for CVD. A study in New York City (Ito et al.. 2011) observed an association between
4 SO2 concentrations that was stronger and more precise in the warm season [OR: 1.026
5 (95% CI: 1.021, 1.031) per 10-ppb increase in 24-h avg SO2] than in the cold season
6 [OR: 1.018 (95% CI: 0.998, 1.049)]. Two studies in Sao Paolo, Brazil (Filho et al.. 2008;
7 Martins et al.. 2006) also found associations in single pollutant models (no quantitative
8 results; results presented graphically). Another study found an increase in the risk of daily
9 hospital admissions per IQR increase in 24-h avg SO2 in the heavily polluted city of
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Lanzhou, China (Zheng et al.. 2013). However, this association was less clinically
relevant when standardized to a 10-ppb increase in 24-h avg SO2. In contrast, a large
study in Beijing, China reported that CVD ED visits were not associated with SO2
concentrations on the same day (Zhang et al.. 2015b). The authors also examined a
number of other single-day lags and cumulative lags and found little evidence of an
association.
Overall, consistent associations between ambient SO2 concentrations and rates of hospital
admissions or ED visits for all cardiovascular diseases have been observed. Although
associations are evident in single-pollutant models in many locations, there was limited
assessment of potential copollutant confounding. Therefore, this association may at least
partly be the result of confounding by correlated pollutants. Additionally, most studies
examined 24-h avg exposure metrics for SO2, which may not adequately capture the
spatial and temporal variability in SO2 concentrations (Section 5.2.1.2).
5.3.1.9 Cardiovascular Mortality
Studies evaluated in the 2008 SOx ISA that examined the association between short-term
SO2 exposure and cause-specific mortality found consistent positive associations with
cardiovascular mortality using a 24-h avg exposure metric. Across studies, there was
evidence that the magnitude of the SC>2-cardiovascular mortality relationship was similar
or slightly larger than total mortality. Recent multicity studies conducted in Asia (Chen et
al.. 2012b; Kan et al.. 2010b) and Italy (Bellini et al.. 2007). and a meta-analysis of
studies conducted in Asia (Atkinson et al.. 2012) provide evidence that is consistent with
those studies evaluated in the 2008 SOx ISA (Section 5.5.1.3. Figure 5-18).
The associations between short-term SO2 concentrations and cardiovascular mortality are
further supported by studies focusing on stroke mortality (Yang et al.. 2014b; Chen et al..
2013). In a study conducted in eight of the CAPES cities, Chen et al. (2013) reported
associations for SO2 and stroke similar to those for all cardiovascular mortality across all
of the CAPES cities (Section 5.5.1.3. Figure 5-18). The magnitude of the association for
stroke mortality observed in Chen et al. (2013) is supported by multiple systematic
reviews and meta-analyses of stroke mortality (Shah et al.. 2015; Yang et al.. 2014b).
Both studies reported similar results, with Yang etal. (2014b) reporting a 2.5% increase
in stroke mortality (95% CI: 1.8, 3.1) for a 10-ppb increase in 24-h avg SO2
concentrations in a meta-analysis of mortality studies conducted in Asia, Europe, and
North America and Shah et al. (2015) reporting a 2.2% increase in stroke mortality (95%
CI: 1.4, 3.1) for a 10-ppb increase in SO2 concentrations (averaging time was not
reported) in a meta-analysis of studies conducted worldwide. However, when interpreting
the results of Yang et al. (2014b). it is important to note that when examining regional
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associations in SCh-related stroke (i.e., Asia vs. Europe and North America), which
combined both mortality and hospital admission outcomes, the magnitude of the
association was much smaller, 0.8% (95% CI: -0.2, 1.7), than those observed in studies
conducted in Asia, 2.1% (95% CI: 1.2, 3.2). This could be attributed to the relatively low
variability and overall low SO2 concentrations observed in both Europe and North
America compared to Asia (Section 5.5.1.3. Table 5-39).
Previous studies evaluated in and prior to the 2008 SOx ISA that examined the
association between short-term SO2 exposures and cardiovascular mortality focused
exclusively on single-pollutant analyses. Therefore, questions arose with regard to the
independent effect of SO2 on cardiovascular mortality and whether associations remained
robust in copollutant models. A few recent multicity studies conducted in China (Chen et
al.. 2012b) and across Asia (Kan et al.. 2010b) examined both of these questions. Chen et
al. (2012b) found that the SC>2-cardiovascular mortality association was attenuated, but
remained positive in copollutant models with PM10 [1.0% (95% CI: 0.08, 1.9) for a
10-ppb increase in 24-h avg SO2 concentrations at lag 0-1] and NO2 [0.5% (95% CI:
-0.5, 1.4)]. These results are similar to those reported by Chen et al. (2012b) when
examining the SCh-total mortality association in models with NO2 (i.e., -80% reduction),
but a larger degree of attenuation was observed in models with PM10 for cardiovascular
mortality (i.e., -40% reduction for total mortality and 50% reduction for cardiovascular
mortality) (Section 5.5.1.4V Kan et al. (2010b). as part of the PAPA study, also examined
potential copollutant confounding (i.e., NO2, PM10, and O3) but only in each city
individually. The authors found that, although the SCh-cardiovascular mortality
association remained positive in copollutant models, there was evidence of an attenuation
of the association in models with PM10 and NO2 (Figure 5-19). In an analysis of stroke
mortality in eight of the CAPES cities, Chen et al. (2013) reported pattern of associations
similar to that of Chen et al. (2012b) and Kan et al. (2010b) in copollutant models with
PM10 and NO2. In single-pollutant models, the authors reported a 2.3% (95% CI: 1.4, 3.2)
increase in stroke mortality for a 10 ppb increase in 24-h avg SO2 concentrations at
lag 0-1. However, in copollutant models, Chen et al. (2013) observed that SC>2-stroke
mortality associations were attenuated in models with PM10, -40% reduction [1.9% (95%
CI: 0.3, 3.5)] and NO2, -80% reduction [0.0% (95% CI: -1.8, 1.9)]. Overall, the studies
that examined potential copollutant confounding on the SC>2-cardiovascular mortality
relationship report results consistent with what was observed for total mortality.
However, the overall assessment of copollutant confounding remains limited, and it is
unclear how the results observed in Asia translate to other locations, specifically due to
the unique air pollution mixture and higher concentrations observed in Asian cities.
Of the multicity studies evaluated, potential seasonal differences in S02-cardiovascular
mortality associations were only assessed in a study conducted in Italy (Bellini et al..
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2007) with additional information from U.S.-based single-city studies conducted in
Philadelphia (Sacks et al.. 2012) and New York City (Ito et al.. 2011). In a study of
15 Italian cities, Bellini et al. (2007) reported larger S02-cardiovascular mortality
associations in the summer [9.4% increase (April-September)], compared to both winter
[1.6% increase (October-March)] and all-year analyses (92.9% increase), which are
consistent with the pattern of associations observed for total and respiratory mortality.
These results are supported by Ito etal. (2011) in a study conducted in New York City
that found that when examining single-day lags of 0 to 3 days, the SCh-cardiovascular
mortality association was consistently positive during the warm season, ranging from a
1.2 to 3.5% increase across lags. The authors reported no evidence of an association in
winter and all-year analyses. Within this analysis, Ito etal. (2011) reported rather poor
monitor-to-monitor temporal correlations for SO2, which would indicate potential
exposure error and subsequently attenuation and imprecision in the risk estimate
(Section 3.4.2. Section 3.4.4). Sacks et al. (2012) provide additional support to the limited
evidence indicating differences in the seasonal pattern of SC>2-cardiovascular mortality
associations. However, as detailed in Section 5.5.1.4. Sacks et al. (2012) demonstrated
that across models that use various approaches to control for seasonality and the potential
confounding effects of weather, the magnitude of seasonal SC>2-cardiovascular mortality
associations may vary depending on the modeling approach employed. Therefore,
although Bellini et al. (2007) and Ito et al. (2011) provide initial evidence indicating
potentially larger cardiovascular mortality associations in the summer, the results of
Sacks et al. (2012) suggest that the evidence remains unclear whether the seasonal pattern
of SCh-cardiovascular mortality associations is consistent across statistical modeling
choices and study locations.
An uncertainty that often arises when evaluating studies that examine the relationship
between short-term air pollution exposures and cause-specific mortality is whether
analyses of statistical modeling parameters, the lag structure of associations, and the C-R
relationship provide results that are consistent with what is observed for total mortality.
Chen et al. (2013) examined each of these issues in a study of stroke mortality, with
additional supporting evidence from the full CAPES study (Chen et al.. 2012b). When
examining alternative approaches to controlling for seasonality, Chen et al. (2013) found
that increasing the df employed from 4 to 10 df per year did not substantially change the
SCh-stroke mortality association. However, Chen et al. (2012b) when altering the lag
structure of the temperature term included to control for the potential confounding effects
of weather, reported an attenuation of the association, although it did remain positive.
However, as detailed in Section 5.5.1.4. this could be the result of including only one
temperature term in the model.
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When examining the lag structure of associations, Chen et al. (2013) reported results for
stroke mortality that are consistent with those observed for all cardiovascular mortality.
As depicted in Figure 5-15 there is evidence of a steady decline in the SC^-stroke
mortality association at longer individual lag days, with the strongest association
occurring for a moving average of lag 0-1 days. A similar pattern of associations was
observed for cardiovascular mortality by Chen et al. (2012b) in the full CAPES study
(Figure 5-20). as well as the PAPA study (Kan et al.. 2010b) (Figure 5-20). These results
are further confirmed in a systematic review and meta-analysis of studies of stroke
mortality conducted by Yang et al. (2014b). which found the strongest associations at
lag 0 and 1 in a subgroup analysis of single-day lags of 0 to 2 days.
2.5 r
Hi
3 0.5
"E 0.0
0)
£ -0.5
Lag
-1.0
S02= sulfur dioxide.
Source: Adapted from Chen et al. (2013).
Figure 5-15 Percent increase in stroke mortality associated with a 10 |jg/m3
(3.62 ppb) increase in sulfur dioxide concentrations using
different lag structures.
Chen et al. (2013) also examined the shape of the SC>2-stroke mortality C-R relationship.
To examine the assumption of linearity, the authors fit both a linear and spline model to
the SCh-stroke mortality relationship. Chen et al. (2013) then computed the deviance
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between the two models to determine any evidence of nonlinearity. An examination of
the deviance did not indicate that the spline model improved the overall fit of the
SCh-stroke mortality relationship (Figure 5-16).
S02 = sulfur dioxide.
Note: The solid line represents the mean estimate and the dotted lines are 95% confidence intervals.
Source: Adapted from Chen et al. (2013).
Figure 5-16 Pooled concentration-response curves for sulfur dioxide and
daily stroke mortality in eight Chinese cities for a 10 |jg/m3
(3.62 ppb) increase in 24-h avg concentrations at lag 0-1 days.
Overall, recent multicity studies report evidence of consistent positive associations
between short-term SO2 concentrations and cardiovascular mortality, which is consistent
with those studies evaluated in the 2008 SOx ISA. Unlike studies evaluated in the 2008
SOx ISA, recent studies examined whether copollutants confound the relationship
between short-term SO2 concentrations and cardiovascular mortality. Overall, these
studies reported evidence that the S02-respiratory mortality association was attenuated in
models with NO2 and PM10, but the analyses are limited to Asian cities where the air
pollution mixture and concentrations are different than those reported in other areas of
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the world. A few studies examined potential seasonal patterns in associations, and found
initial evidence of larger SC>2-cardiovascular mortality associations in the summer/warm
season. However, seasonal associations may be influenced by study location and the
statistical modeling choice employed. Limited analyses of model specification, the lag
structure of associations, and the C-R relationship suggest that: (1) associations remain
robust when alternating the df used to control for seasonality; (2) associations are larger
and more precise within the first few days after exposure in the range of 0 and 1 days;
and (3) there is a linear, no threshold C-R relationship, respectively. However, for both
total and cause-specific mortality, the overall assessment of linearity in the C-R
relationship is based on a very limited exploration of alternatives.
5.3.1.10 Subclinical Effects Underlying Cardiovascular Effects
The following subsections review studies of subclinical effects that serve as useful
measures of physiological and biochemical responses that could provide mechanistic
evidence to describe a role for SO2 in the manifestation of cardiovascular diseases. These
subclinical effects are not widely validated markers of specific clinical cardiovascular
outcomes, but could potentially underlie the development, progression, or indication of
various clinical events and provide biological plausibility for multiple outcomes.
Heart Rate and Heart Rate Variability
The 2008 ISA for Sulfur Oxides concluded that the overall evidence available at the time
was insufficient to conclude that SO2 has an effect on cardiac autonomic control as
assessed by indices of HRV. HRV provides a noninvasive marker of cardiac autonomic
nervous system function. The rhythmic variation in the intervals between heart beats can
be quantified in either the time domain or the frequency domain (TFESC and NASPE.
1996). Common time-domain measures of HRV include the standard deviation of all
normal-to-normal intervals (SDNN, an index of total HRV) and the root-mean-square of
successive differences (rMSSD, an index influenced mainly by the parasympathetic
nervous system). In the frequency domain, HRV is usually divided into the high
frequency (HF) and low frequency (LF) components, as well as the ratio of the LF to HF
components (LF:HF) (TFESC and NASPE. 1996). Decreases in indices of HRV have
been associated with increased risk of cardiovascular events in prospective cohort studies
(La Rovere et al.. 2003; kikuva et al.. 2000; Tsuii et al.. 1996; Tsuii et al.. 1994).
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Epidemiology
A number of additional epidemiologic studies are now available for review. In a
cross-sectional study in South Korea, Min et al. (2009) reported negative associations
between ambient SO2 concentrations and indices of HRV (SDNN, and the LF and HF
components) among 256 smokers, but no association among the 767 nonsmokers (no
quantitative results; result presented graphically). In another cross-sectional study, Min et
al. (2008b) reported a -7.6% (95% CI: -14.7, 0.1%) change in SDNN and a -23.1%
(95% CI: -35.4, -6.5%) change in LF per 10-ppb increase in 24-h avg SO2 among
1,349 participants in South Korea. The amount of overlapping participants between these
two studies is unclear.
The above studies are limited by their cross-sectional approach that compares measures
of HRV across individuals assessed on different days. In contrast, longitudinal or
repeated-measure study provide an estimate of the average association between SO2 and
measures of HRV within individuals. Huang et al. (2012) measured HRV repeatedly in
40 participants with pre-existing cardiovascular disease in Beijing in the summer of 2007
and again in the summer of 2008, including one measurement period during the 2008
Beijing Olympics when citywide air pollution control measures substantially reduced
ambient concentrations of most criteria pollutants. In this study, SO2 concentrations
during the Olympics were reduced by nearly 30% versus the previous month and nearly
50% versus the same period the previous summer (Huang et al.. 2012). Despite these
large changes in SO2 concentrations, overall only small associations were observed
between SO2 concentrations and HRV indices, limited to a 4.8% reduction (95% CI:
-9.1, -0.3%) in the LF component and an unexpected 4.1% increase (95% CI: -2.2,
10.9%) in the HF component of HRV per interquartile range (NR) increase in SO2 in the
previous 12 hours (Huang et al.. 2012). In subgroup analyses, SDNN was significantly
positively associated with SO2 concentrations among those with higher levels of
C-reactive protein (CRP; a marker of inflammation), those with diabetes, and males.
These results are difficult to understand given that a higher SDNN is generally thought to
be associated with lower risk of cardiovascular events. The findings were also
inconsistent with another study that observed a negative association between SDNN and
ambient SO2 concentrations. A repeated measure study in Shanghai, China reported a
4.36% reduction (95% CI: -5.85, -2.86%) in SDNN per IQR increase (NR) in 4-hour
moving average exposure to SO2 (Sun et al.. 2015). This association was attenuated, but
still statistically significant in copollutant models adjusting for BC [-2.91% (95% CI:
-4.66, -1.13%)] and O3 [-3.24% (95% CI: -4.83, -1.62%)], and attenuated and no
longer statistically significant, but still negative in copollutant models adjusting for NO2
[-0.56% (95% CI: -2.38, 1.30%)] and CO [-1.25% (95% CI: -3.02, 0.55%)]. In another
study in Beijing before, during, and after the 2008 Olympics, Rich et al. (2012) observed
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small but statistically significant increases in heart rate associated with ambient SO2
concentrations on the previous day (no quantitative results; result presented graphically).
In expanded results from the same protocol, Zhang et al. (2013) found that the association
was similar in copollutants models adjusting for CO, NO2, O3, EC, or OC, but was
attenuated and no longer positive after adjustment for PM2 5 or SO42 . Zhang et al. (2013)
also reported a strong association between LF:HF and ambient SO2 concentrations on the
previous day. This association was relatively unchanged after adjustment for CO, NO2,
O3, EC, OC, or PM2 5 in copollutant models, and attenuated but still positive after
adjustment for SO42 . In contrast, a panel study in Taipei, Taiwan used Holter monitors to
continuously monitor HRV in 46 participants, and observed no associations between
ambient SO2 and SDNN, r-MSSD, LF component, orHF component (quantitative results
not reported) (Chuang et al.. 2007). Although new studies are available, findings are
mixed and they do not support the presence of an association between ambient SO2 and
measures of HRV.
Experimental Studies
Several experimental studies examined heart rate and HRV following SO2 exposure.
Study characteristics are summarized in Supplemental Table 5S-13. (U.S. EPA. 2016s)
Animal studies have reported no changes in heart rate following SO2 exposures of
1,000-5,000 ppb in guinea pigs and 1,200 ppb in rats (Nadzieiko et al.. 2004; Halinen et
al.. 2000b; Halinen et al.. 2000a).
Controlled human exposure studies have reported changes in heart rate following SO2
exposure but not during exposure. Tunnicliffe et al. (2001) reported no change in heart
rate in healthy adults or adults with asthma during exposure to 200 ppb SO2 for 1 hour at
rest. However, in a similar study design, Rout ledge et al. (2006) reported a decrease in
heart rate measured by the RR interval from electrocardiographic (ECG) recordings
4 hours after SO2 exposure in healthy adults. This change in heart rate was not observed
in S02-exposed older adults with stable angina and coronary artery disease during or
immediately after exposure. Both studies found no change in heart rate during or
immediately following similar exposure conditions. Tunnicliffe et al. (2001) did not
obtain ECG measures following exposure and thus may have been unable to capture the
decrease in heart rate reported by Routledge et al. (2006).
Tunnicliffe et al. (2001) and Routledge et al. (2006) reported changes in different
measures of HRV in adults following SO2 exposure. 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 (statistical significance only in total power in
healthy adults). The LF:HF ratios were unchanged in both groups. Routledge et al. (2006)
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reported a reduction in SDNN, rMSSD, percentage of successive RR interval differences
exceeding 50 ms (pNNso), and HF power (not statistically significant) in healthy adults
4 hours after SO2 exposure. Baroreflex sensitivity was also reduced 4 hours after SO2
exposure determined by changes in a-HF and a-LF. There were no changes in HRV
among the patients with coronary heart disease; however, this lack of response may be
due to a drug treatment effect because a large portion of these patients were taking
beta-blockers. The changes in HRV observed in Tunnicliffe et al. (2001) and Routledge
et al. (2006) indicate the potential for SO2 to affect the autonomic nervous system (see
Section 4.3.1).
Summary of Heart Rate and Heart Rate Variability
The current epidemiologic evidence does not support the presence of an association
between ambient SO2 and measures of HRV. No changes in heart rate were observed in
experimental animal studies while changes in HRV observed in human clinical studies
may indicate the potential for SO2 to affect the autonomic nervous system (see
Section 4.3.1). Overall, studies evaluating the effect of ambient SO2 concentrations and
measures of HRV and heart rate remain limited.
QT Interval Duration
The QT interval provides an electrocardiographic marker of ventricular repolarization.
Prolongation of the QT interval is associated with increased risk of life-threatening
ventricular arrhythmias. In an analysis of data from the Boston-area Normative Aging
Study, Baiaetal. (2010) observed a small and imprecise (i.e., wide confidence intervals)
association between heart-rate-corrected QT interval and 10-hour moving average of SO2
concentrations among older, generally white men (no quantitative results; result
presented graphically). The only prior study available for comparison from the 2008 ISA
for Sulfur Oxides (U.S. EPA. 2008d) also found that SO2 concentrations were positively
associated with increased QT interval duration amongst a small sample of 56 men in
Erfurt, Germany [3.75 ms increase (95% CI: 1.21, 6.28 ms) per 0.61-ppb increase in
24-h avg SO2] (Henneberger et al.. 2005). There was little variability between daily
measured SO2 concentrations, so the effect estimate is not standardized to prevent
inflation of the confidence interval.
The two reviewed studies provide limited evidence of association between short-term
SO2 exposure and markers of ventricular repolarization. Neither of these studies
evaluated potential copollutant confounding and coherence for an association between
SO2 exposure and arrhythmias is not provided by experimental studies (Section 5.5.1.3).
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Insulin Resistance
There were no epidemiologic studies of diabetes or insulin deficiency available for the
2008 ISA for Sulfur Oxides. Recent studies reported contrasting findings regarding
short-term associations between air pollutants and measures of insulin resistance and
fasting glucose, which play key roles in the development of Type II diabetes mellitus. In
a panel study of older adults in Korea, Kim and Hong (2012) observed 0.94 (95% CI:
-0.02, 1.88) and 0.94 (95% CI: 0.01, 1.81) mean increases in the homeostatic model
assessment index of insulin resistance [fasting insulin x (fasting glucose ^ 22.5)] per
10-ppb increase in 24-h avg SO2 at lags 3 and 4, respectively. There were imprecise
(i.e., wide 95% CI) or null associations at all other individual lag days examined, from 0
to 10. Another panel study, conducted in the heavily polluted Tangshan, China, reported
an association between 24-h avg SO2 concentrations and fasting glucose levels (Chen et
al.. 2015b). However, this association is unlikely to be clinically relevant when
standardized to a 10-ppb increase in 24-h avg SO2 [0.045 mmol/L (95% CI: 0.039, 0.050
mmol/L) increase at lag 0-3], Conversely, Kelishadi et al. (2009) reported the lack of an
association between 24-h avg SO2 and insulin resistance in a cross-sectional study of
374 Iranian children aged 10-18 years.
In summary, the available epidemiologic evidence is limited and inconsistent, and does
not support the presence of an association between ambient SO2 concentrations and
measures of insulin resistance.
Biomarkers of Cardiovascular Risk
Several epidemiologic and toxicological studies have explored the potential relationship
between SO2 and biomarkers of cardiovascular risk. In particular, markers of
inflammation have been evaluated in a number of epidemiologic and toxicological
studies published since the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) (Table 5-30).
Relatively few studies have evaluated the potential link between SO2 and other
circulating markers of cardiovascular risk, including markers of coagulation, vascular
injury, or lipid oxidation.
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Table 5-30 Epidemiologic studies of biomarkers of cardiovascular effects.
Location and
Years
Study (Sample Size)
tDubowskv et al
(2006)
tSteinvil et al. Tel Aviv, Israel
(2008) 2002-2006
(n = 3,659)
tThompson et al. Toronto, ON
(201°) 1999-2003
(n = 45)
tGandhi et al. Piscataway, NJ
(2014) 2005-2009
(n = 49)
Mean and Upper
Concentration SO2 Exposure
(ppb) Assessment
24-h avg: 2.8 Citywide avg
75th percentile: 3.5
24-h avg: 3.57 Central site
24 h avg: 2.4 Central site
75th percentile: 3.2
Max: 13.8
Selected Effect Estimates3
(95% CI)
CRP (percent change)
Lag 04: -36.1 (-65.2, -2.8)
IL-6 (percent change)
Lag 04: -16.5 (-38.7, 6.5)
White blood cells (cells/pL)
Lag 04: 10.0 (0.4, 19.6)
CRP (percent
change) men;
women
Lag 0:
0 (-38, 38);
-13 (56, 28)
Lag 1:
-19 (-50, 25);
-13 (-63, 38)
Lag 2:
6 (-38, 44);
-25 (-69, 31)
Fibrinogen
(mg/dL)
men; women
Lag 0:
-20.0 (-40.0, 0.6);
-23.8 (-51.3, 3.8)
Lag 1:
-21.3 (-42.5, 0.0);
-13.1 (-41.3,
14.4)
Lag 2:
-15.0 (-37.5, 6.9);
17.5 (-11.9, 46.9)
No quantitative results; results
presented graphically. Increase in
IL-6 associated with 4- and 5-d
moving avg SO2 concentrations. Null
association between SO2 and
fibrinogen
Correlations: CO: 0.43, NO2: 0.44,
Os: -0.19, PM2.5: 0.45
Change in plasma nitrate (nM):
Lag 0: 53.6 (-4.5, 111.4)
Lag 1: 45.0 (0.9, 90.9)
Lag 2: 48.2 (-13.2, 110.0)
St. Louis, MO 24-h avg: 6.7 Central site
Mar-Jun 2002 75th percentile: 7.4
Max: 27
n = 44)
WBC (cells/pL)
men; women
Lag 0:
231 (-419,
875);
-169 (-1,000,
656)
Lag 1:
44 (-631, 713);
-544 (-1,381,
294)
Lag 2:
-125 (-819,
563);
-481 (-1,356,
388)
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Table 5-30 (Continued): Epidemiologic studies of biomarkers of cardiovascular
effects.
Study
Location and
Years
(Sample Size)
Mean and Upper
Concentration SO2
(PPb)
Exposure
Assessment
Selected Effect Estimates3
(95% CI)
tLee etal. (2011b)
Allegheny
County, PA
1997-2001
(n = 1,696)
7-d avg: 8.4
75th percentile: 10.1
Max: 25.4
City wide avg
No quantitative results presented.
"...SO2... associations (with CRP)
were negligible for both the entire
population and nonsmokers only."
tHildebrandt et al.
(2009)
Erfurt,
Germany
2001-2002
(n = 38)
24-h avg: 1.35
Max: 14.2
Central site
No quantitative results presented.
"No significant associations"
between SO2 and inflammatory
(fibrinogen, E-selectin) or
coagulation (D-dimer, prothrombin)
markers.
Baccarelli et al.
(2007a)
Lombardia,
Italy
1995-2005
(n = 1,218)
24-h avg median:
2.4
75th percentile: 4.5
Max: 96.7
City wide avg
Effect estimates not provided. SO2
not correlated with anticoagulation
proteins (plasma fibrinogen,
functional AT, functional protein C,
protein C antigen, functional
protein S, or free protein S).
Baccarelli et al.
(2007b)
Lombardia,
Italy
1995-2005
(n = 1,213)
24-h avg Median:
2.4
75th percentile: 4.5
Max: 96.7
City wide avg
Homocysteine difference, fasting
(percent change).
Lag 24 h: 0.2 (-6.3, 6.7)
Lag 0-6 d: 0.2 (-4.3, 4.7)
Homocysteine difference,
post-methionine-load (percent
change)
Lag 24 h: 2.6 (-3.2, 8.6)
Lag 0-6 d: 2.6 (-1.5, 6.7)
Wellenius et al.
(2007)
Boston, MA
2002-2003
(n = 28)
24-h avg: 4.8
Citywide avg
No quantitative results presented.
"No significant associations were
observed between (NO2) and B-type
natriuretic peptide levels at any of
the lags examined."
tGoldbera et al.
(2008)
Montreal, QC
2002-2003
(n = 31)
NR
Central site
Oxygen saturation (mean difference)
Lag 0: -0.104 (-0.320, 0.110)
Lag 1: -0.277 (-0.497, -0.058)
Lag 0-2: -0.210 (-0.536, 0.116)
tBruske et al.
(2011)
Augsburg,
Germany
2003-2004
(n = 200)
24-h avg: 1.15
75th percentile:
1.26
Max: 2.4
Central site
No quantitative results; results
presented graphically. Inverse
associations were observed for SO2
with Lp-PLA2 at Lag days 2 and 3
and positive associations were
estimated with Lp-PLA2 Lag days 4
and 5.
Correlations: PNC: 0.77, PM2.5:
0.42, PM10: 0.43, CO: 0.63, NO2:
0.51, NO: 0.60, O3: -0.45.
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Table 5-30 (Continued): Epidemiologic studies of biomarkers of cardiovascular
effects.
Location and
Years
Study (Sample Size)
Mean and Upper
Concentration SO2 Exposure
(ppb) Assessment
Selected Effect Estimates3
(95% CI)
No quantitative results; results
presented graphically. Positive
association between SO2 and
fibrinogen (lag 6). Inverse
association between SO2 and WBC
count (lag 5).
tZhanq et al. (2013)
Beijing, China
Jun-Oct, 2008
(n = 125)
24-h avg
Before: 7.45
During: 2.97
After: 6.81
Central site
tLinetal. (2015)
Beijing, China
2007-2008
(n = 36 school
children)
NR
Monitor located
nearby school
Urinary 8-oxodG
(Geometric mean ratio by SO2
exposure percentile)
<30th (<2.1 ppb):
referent
30th-60th (2.1-6.4 ppb):
1.26 (0.93, 1.70)
60th-90th (6.4-49.1 ppb):
1.66 (1.15, 2.41)
>90th (>49.1 ppb):
2.31 (1.54, 3.46)
Urinary Malondialdehyde
<30th: referent
30th-60th: 1.21 (1.05, 1.40)
60th-90th: 1.40 (1.15, 1.69)
>90th: 1.40 (1.08, 1.83)
tKhafaie et al. Pune City,
(2013) India
2005-2007
(n = 1,392)
24-h avg: 8.3 Citywide avg
No quantitative results; results
presented graphically. SO2 was
associated with increases in CRP at
lags 0, 1, 2, 4, 5, 0-7, 0-14, and
0-30.
AT = atascadero; CI = confidence interval; CO = carbon monoxide; CRP = C-reactive protein; IL-6 = interleukin-6;
Lp-PLA2 = lipoprotein-associated phospholipase A;2; n = sample size; NO = nitric oxide; N02 = nitrogen dioxide; NR = not
reported; 03 = ozone; PM2.5 = particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm;
PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm; PNC = particle number concentration;
S02 = sulfur dioxide; WBC = white blood cell.
fStudies published since the 2008 ISA for Sulfur Oxides.
Note: All lag times are in days, unless otherwise noted.
aEffect estimates are standardized to a 10-ppb or 40-ppb increase in S02 concentration for 24-h avg and 1-h max metrics,
respectively.
Epidemiologic Studies
1 The epidemiologic data available for review by the 2008 ISA for Sulfur Oxides (U.S.
2 EPA. 2008d) did not suggest a consistent link between SO2 and biomarkers of
3 cardiovascular risk, including markers of inflammation and coagulation. Results from
4 more recent studies continue to be inconsistent. Dubowskv et al. (2006) investigated
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associations between ambient pollutants and markers of systemic inflammation in a panel
(repeated-measures) study of 44 seniors in St. Louis, MO and found that higher ambient
SO2 concentrations were associated with lower levels of CRP and white blood cells, but
not IL-6 (results for this study, and other studies in this section can be found in
Table 5-30). Similarly, during the Beijing Olympics, SO2 was inversely associated with
white blood cell counts, although positively associated with fibrinogen (Zhang ct al..
2013). The negative associations observed in these two studies are unexpected and
difficult to explain. In contrast, among 45 nonsmoking adults, Thompson et al. (2010)
found a positive association between SO2 and IL-6, but not fibrinogen. In another panel
study examining pollutant levels before, during, and after the Beijing Olympics, Lin et al.
(2015) reported positive associations between SO2 concentrations and urinary markers of
oxidative stress, malondialdehyde and 8-oxodG, in children.
In a cross-sectional analysis of data from a panel study of 49 young adults in New Jersey,
Gandhi et al. (2014) observed that plasma nitrite levels, a marker for endothelial
dysfunction, were associated with an increase in 24-h avg SO2 concentrations on the
same day. Khafaie et al. (2013) observed a positive association between SO2 and CRP in
a cross-sectional study of Type II diabetes patients in Pune City, India, whereas a study
of 1,696 pregnant women (Lee et al.. 201 lb), and one of 38 male patients with chronic
pulmonary disease (Hildebrandt et al.. 2009) observed null associations between SO2 and
CRP. In a cross-sectional analysis of 3,659 participants in Tel-Aviv, Steinvil et al. (2008)
observed inconsistent and/or imprecise associations between SO2 and CRP, white blood
cells, or fibrinogen among men and women. Observed associations were both positive
and negative depending on the length of the lags, making interpretation of the results
difficult.
Ambient SO2 concentrations are reportedly not associated with blood coagulation
(Baccarelli et al.. 2007a). plasma homocysteine (Baccarelli et al.. 2007b). markers of
vascular injury (Hildebrandt et al.. 2009). or markers of functional status in patients with
heart failure (Wellenius et al.. 2007). Conversely, SO2 concentrations were inversely
associated with blood oxygen saturation in patients with heart failure (Goldberg et al..
2008) and positively associated with lipoprotein-associated phospholipase A2 (Lp-PLA2)
in survivors of myocardial infarction (Briiske et al.. 2011).
Experimental Studies
Several experimental studies examined biomarkers of cardiovascular risk following SO2
exposure, including markers of inflammation, coagulation, and oxidative injury. A recent
study examined the effect of exposure to SO2 on the mitochondrial function of the heart.
Study characteristics are summarized in Supplemental Table 5S-13 (U.S. EPA. 2016s).
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No changes were reported in serum C-reactive protein or markers of coagulation
(fibrinogen, D-dimer, platelet aggregation, blood count, or differential white cell count)
in healthy humans and patients with stable angina and coronary artery disease exposed to
SO2 (Routledge et al.. 2006). An animal toxicological study examined the hematological
effects of short-term SO2 exposure on blood biomarkers. Acute exposure of rats to
870 ppb SO2 for 24 hours 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.
A recent study reported mitochondrial dysfunction in cardiac muscles following SO2
inhalation in adult rats exposed to 1,340 ppb and greater concentrations (2,670 and
5,340 ppb) of SO2 for 4 hours/day for 30 days (Qin et al.. 2016). Inhalation of SO2
(1,340 ppb) resulted in mitochondrial ultrastructural changes in cardiac myocytes,
including swollen mitochondria and reduced amounts of cristae. In addition to the
structural changes, SO2 exposure decreased cytochrome c oxidase activity, mitochondrial
membrane potential, ATP contents, mtDNA content, mRNA expression of subunits that
are synthesized in the mitochondria (complex IV and V), and mitochondrial transcription
factors (TFAM, NRF1, and PGC-la). Mechanistic studies conducted in vitro suggest
reactive oxygen species contribute to the mitochondrial dysfunction leading to the
observed decrease in cardiomyocyte energy status and metabolic activity. In addition to
this study in the heart, a study has reported similar changes in the brain (Qin et al.. 2012).
Further discussion of these mechanisms are found in Section 4.3.4.
Summary of Blood Markers of Cardiovascular Risk
There is inconsistent evidence regarding any potential link between SO2 and other
circulating markers of cardiovascular risk. Studies of markers of inflammation or
oxidative stress in experimental animals are limited. Overall, evidence from available
studies does not support an effect of ambient SO2 concentrations and markers of
cardiovascular disease including inflammation.
5.3.1.11 Summary and Causal Determination
Overall, the available evidence is inadequate to infer the presence or absence of a causal
relationship between short-term exposure to SO2 and cardiovascular health effects.
Multiple epidemiologic studies report positive associations between short-term ambient
SO2 concentrations and cardiovascular outcomes; however, uncertainty remains regarding
the biological plausibility of the effects observed in epidemiologic studies. The limited
experimental evidence in humans or animals is not coherent with the positive associations
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observed in the epidemiologic studies and fails to provide evidence to propose a potential
mode of action. The observed associations in epidemiologic studies are generally
attenuated after adjustment for copollutants, complicating the determination of an
independent SO2 effect.
This determination is consistent with that of the 2008 ISA for Sulfur Oxides (U.S. EPA.
2008d). The majority of epidemiologic studies reviewed in the 2008 ISA for Sulfur
Oxides examined hospital admissions or ED visits for aggregated categories of
cardiovascular disease or for mortality from cardiovascular causes. These studies
generally reported positive associations in single pollutant models but analyses designed
to assess copollutant confounding were limited. Relatively few studies evaluated specific
cardiovascular outcomes such as MI, arrhythmia, cerebrovascular disease, and heart
failure, and those that were available did not support an association with short-term SO2
exposure. Controlled human exposure studies demonstrated the potential for SO2
exposure to exert an effect on the autonomic nervous system but there was a lack of
supporting animal toxicological data. The available animal toxicological studies did not
report effects on HR, HRV, arrhythmia, or blood pressure following short-term SO2
exposures [Table 5S-6 (U.S. EPA. 2016m)I. In addition, limited and inconsistent
mechanistic evidence, including evidence pertaining to key events in a proposed mode of
action, failed to describe a role for SO2 in the triggering of cardiovascular diseases.
Although multiple epidemiologic studies add to the evidence available for the current
review, the additional studies do not substantially reduce uncertainties related to
copollutant confounding. Moreover, there continues to be a lack of experimental
evidence to provide biological plausibility to strengthen the inference of causality for
S02-related cardiovascular effects.
The evidence for cardiovascular effects, with respect to the causal determination for
short-term exposure to SO2 is detailed below using the framework described in the
Preamble to the ISAs KU.S. EPA. 2015b). Table I and Table II]. The key evidence,
supporting or contradicting, as it relates to the causal framework is summarized in
Table 5-31.
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Table 5-31 Summary of evidence, which is inadequate to infer a causal
relationship between short-term sulfur dioxide exposure and
cardiovascular effects.
Rationale for Causal
Determination3 Key Evidence13
Key References'3
SO2 Concentrations
Associated with
Effects0
Triggering a myocardial infarction
Although most
epidemiologic studies
examining Ml or all
CVD report positive
associations, results
are generally
attenuated after
adjustment for
copollutant
confounding.
Increases in hospital admissions and Section 5.3.1.2
ED visits for IHD, Ml, and all CVD in
adults in multiple studies, including
multicity studies
However, a number of studies report
associations with ED visits and
hospital admissions were attenuated
after adjustment with CO, NO2, or
PM10.
Section 5.3.1.8
Supplemental figures 5S-3,
5S-4, and 5S-5 (U.S. EPA.
2016b. c, d)
24-h avg: 1.2-15.6 ppb
24-h avg: 1.9-30.2 ppb
Uncertainty due to Lack of evidence from epidemiologic
lack of coherence with panel studies and experimental
other lines of evidence studies for clinical cardiovascular
effects
Lack of evidence to
identify key events in
the proposed mode of
action
Lack of mechanistic evidence for key Section 43
events leading to extrapulmonary
effects
Limited and inconsistent evidence of Section 5.3.1.10
increased systemic inflammation in
epidemiologic studies
Other cardiovascular effects
Inconclusive evidence
from epidemiologic,
controlled human
exposure and
toxicological studies
Epidemiologic studies report
generally null associations between
SO2 and risk of cardiac arrest and
arrhythmias. One experimental study
provides no evidence of arrhythmia.
Section 5.3.1.3
Inconsistent epidemiologic evidence Section 5.3.1.4 and
for an association between SO2 and Section 5.3.1.5
risk of cerebrovascular disease and
stroke, and increased blood
pressure and hypertension
Insufficient quantity of studies Section 5.3.1.6 and
evaluating decompensation of heart Section 5.3.1.7
failure and venous thrombosis and
pulmonary embolism
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Table 5-31 (Continued): Summary of evidence, which is inadequate to infer a
causal relationship between short term sulfur dioxide
exposure and cardiovascular effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations
Associated with
Effects0
Changes in HR and HRV reported in
controlled human exposure but
coherence with animal toxicological
and epidemiologic studies is lacking
Tunnicliffe et al. (2001)
Routledae et al. (2006)
Section 5.3.1.10
200 ppb, 1 h at rest
(humans)
Some evidence to
identify key events in
the proposed mode of
action
Some evidence for activation of
neural reflexes in humans leading to
altered HRV
Section 4.3.1
Fiaure 4-2
Cardiovascular mortality
Consistent
epidemiologic
evidence but
uncertainty regarding
SO2 independent
effect
Multicity studies consistently observe Section 5.3.1.9
24-h avg: 2.5-38.2
associations with cardiovascular
mortality, including stroke with
24-h avg SO2 at lags primarily of
0-1 d.
Analysis of potential confounding by
copollutants primarily limited to PM10
and NO2 reported evidence of
attenuation of associations. No
studies included copollutant
analyses with PM2.5.
Chen etal. (2012b)
Chen etal. (2013)
Kan etal. (2010b)
Bellini etal. (2007)
Atkinson et al. (2012)
CO = carbon monoxide; CVD = cardiovascular disease; ED = emergency department; HR = heart rate; HRV = heart rate
variability; IHD = ischemic heart disease; Ml = myocardial infarction; N02 = nitrogen dioxide; PM10 = particulate matter with a
nominal aerodynamic diameter less than or equal to 10 |jm; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Table I and Table II of the
Preamble to the ISAs (U.S. EPA. 2015b).
bDescribes the key evidence and references, supporting or contradicting, that contribute most heavily to causal determination.
References to earlier sections indicate where full body of evidence is described.
°Describes the S02 concentrations with which the evidence is substantiated.
Recent epidemiologic studies of specific cardiovascular outcomes add to the overall
evidence for the effect of short-term SO2 exposure on the cardiovascular system with a
number of these studies evaluating effects related to triggering an MI (Section 5.3.1.2V
Several recent epidemiologic studies of MI hospitalizations and ED visits consistently
report associations in single pollutant models but associations are not always robust in
copollutant models indicating that the associations may be due to confounding (Hsieh et
al.. 2010; Cheng et al.. 2009; Ballester et al.. 2006). The small number of studies based
on clinical MI data, rather than hospitalizations, report inconsistent evidence regarding
associations between ambient SO2 concentrations and risk of MI (Miloicvic et al.. 2014;
Turin et al.. 2012; Bhaskaran et al.. 201 1). The only study that examined the association
of hourly ambient SO2 concentrations prior to MI onset reported no association, although
there was some evidence of a positive association in a sensitivity analysis of older adults
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(Bhaskaran et al.. 2011). Although Chuang et al. (2008) reported an association between
short-term SO2 exposure and ST-segment changes, a nonspecific marker of myocardial
ischemia, in patients with a history of coronary heart disease that generally remained
unchanged after additional control for PM2 5 and BC in copollutant models; the evidence
overall, was not generally consistent.
Findings from recent studies of the association of short-term exposure to SO2 with
hospital admissions or ED visits for cerebrovascular diseases or stroke are inconsistent
and, associations reported from single pollutant models in some locations may be due to
confounding by copollutants (Section 5.3.1.4V Epidemiologic studies evaluating the
association between ambient SO2 concentrations and blood pressure remain inconsistent
with most relying on centrally located monitors that do not capture the spatial variability
of SO2 and few examining the potential for copollutant confounding (Section 5.3.1.5).
Although a small number of studies were conducted to examine the association of
short-term SO2 exposure with other clinical outcomes, including heart failure
(Section 5.3.1.7) and VTE (Section 5.3.1.6). findings from these studies do not support an
effect of short-term exposure to SO2. There is also a lack of epidemiologic evidence
supporting an effect of short-term SO2 exposure on arrhythmia (Section 5.3.1.3).
although associations between short-term SO2 exposure and markers of ventricular
repolarization abnormalities that are risk factors for arrhythmia have been observed (Baia
et al.. 2010; Henneberger et al.. 2005) (Section 5.3.1.10).
Consistently positive associations have been reported in epidemiologic studies of
short-term SO2 exposure and cardiovascular mortality (Section 5.3.1.9). These include
studies reviewed in the 2008 ISA for Sulfur Oxides and recent multicity studies that
generally report an association similar or slightly larger in magnitude for cardiovascular
mortality compared to total mortality. Studies that report results from copollutants models
generally report attenuation of the association between short-term SO2 exposure and
cardiovascular mortality after adjustment for PM10 and NO2.
Few experimental studies have evaluated the effects of SO2 exposure on the
cardiovascular system. There is some evidence from controlled human exposure studies,
for which copollutant confounding is not a concern, that short-term exposure to SO2 can
affect the autonomic nervous system of healthy adults and adults with asthma (Routledge
et al.. 2006; Tunnicliffe et al.. 2001) (Section 5.3.1.10). These studies report changes in
HR and HRV following SO2 exposure in adults. However, coherence with these findings
is not provided by epidemiologic or experimental animal studies, which have not
observed an effect of short-term SO2 exposure on HR or HRV. In addition, uncertainty
remains regarding a potentially biologically plausible mechanism for short-term exposure
to SO2 leading to cardiovascular disease. Cardiovascular effects following SO2 exposure
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could be mediated through activation of neural reflexes or oxidative stress; however,
uncertainty remains (Section 4.3V Diffusion of sulfite into the circulation and tissues
following exposure to SO2 has been reported and could play a role in the induction of
systemic effects; however, these studies generally involve prolonged exposure to SO2 at
concentrations higher than is typically found in ambient air (Section 4.3.4). Overall, the
limited evidence available from these experimental studies in humans and animals are not
coherent with the positive associations observed in the epidemiologic studies and do not
support a potential mode of action.
Despite numerous additional epidemiologic studies reporting positive associations
between short-term SO2 exposure and cardiovascular effects, a key uncertainty that
remains since the 2008 ISA for Sulfur Oxides is the potential for confounding by other
pollutants, specifically those from a common source that are highly correlated with SO2.
The majority of hospital admission or ED visit studies have not evaluated whether the
reported associations with SO2 are robust to adjustment for other pollutants. Those
studies that do examine associations with SO2 adjusted for PM [Figure 5S-3, (U.S. EPA.
2016b) and Table 5S-17 (U.S. EPA. 2016v)|. N02 [Figure 5S-4, (U.S. EPA. 2016c) and
Table 5S-18 (U.S. EPA. 2015g)l. or other correlated pollutants [Figure 5S-5; (U.S. EPA.
2016d) and Table 5S-19 (U.S. EPA. 2015h)1 report that, in general, associations were
either attenuated or no longer present after controlling for potential copollutant
confounding (Hsieh et al.. 2010; Cheng et al.. 2009; Ballester et al.. 2006). A limited
number of studies examined copollutant confounding on the S02-cardiovascular
mortality relationship, which included analyses on stroke mortality, and provided
evidence that the SO2 association was reduced in copollutant models with NO2 and PM10
(Chen et al.. 2013; Chen et al.. 2012b; Kan et al.. 2010b). Finally, while copollutant
models are a common statistical tool used to evaluate the potential for copollutant
confounding, their interpretation can be limited (Section 5.1.2). Without consistent and
reproducible experimental evidence that is coherent with the effects observed in
epidemiologic studies, uncertainty still exists concerning the role of correlated pollutants
in the associations observed with SO2. Thus, uncertainty remains regarding the extent to
which SO2 exposure is independently associated with cardiovascular outcomes or if SO2
is a marker for the effects of another correlated pollutant or mix of pollutants.
In conclusion, the evidence overall is inadequate to infer the presence or absence of a
causal relationship between short-term SO2 exposure and cardiovascular health effects.
This conclusion does not represent a change from the conclusion of the 2008 ISA for
Sulfur Oxides (U.S. EPA. 2008d). Multiple epidemiologic studies report positive
associations between short-term ambient SO2 concentrations and cardiovascular
outcomes, but these associations are generally attenuated after adjustment for
copollutants. There is limited experimental evidence in humans or animals evaluating
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exposure to SO2 and the results of these studies do not provide coherence for the positive
associations observed in the epidemiologic studies. Further, the available experimental
studies do not provide evidence to propose a potential mode of action; consequently,
uncertainty remains regarding the biological plausibility of effects observed in
epidemiologic studies. The combined evidence from epidemiologic and experimental
studies lacks coherence and is of insufficient consistency, and thus, is inadequate to infer
the presence or absence of a causal relationship between short-term SO2 exposure and
cardiovascular effects.
Studies of the effects of long-term exposure to SO2 on the cardiovascular system were not
available for inclusion in the 1982 AQCD (U.S. EPA. 1982a). The 2008 ISA for Sulfur
Oxides (U.S. EPA. 2008d) reviewed a limited body of toxicological and epidemiologic
studies published through 2006 and concluded that the available evidence was "too
limited to make any conclusions" between the effects of long-term exposure to SO2 and
cardiovascular health.
The 2008 ISA for Sulfur Oxides included one epidemiologic study, which reported an
increased risk of cardiovascular events in association with long-term exposure to SO2 in
post-menopausal women (50-79 years old) without previous CVD from 36 U.S.
metropolitan areas. In this study, Miller et al. (2007) found that PM2 5 was most strongly
associated with cardiovascular events (MI, revascularization, angina, CHF, CHD death),
compared to the other pollutants evaluated [hazard ratio (HR): 1.24 (95% CI: 1.04, 1.48)
per 10 (ig/m3], followed by SO2 [1.07 (95% CI: 0.95, 1.20) per 5 ppb]. Exposures to air
pollution were estimated by assigning the annual (for the year 2000) mean air pollutant
concentration measured at the monitor nearest to the subject's five-digit residential ZIP
code centroid. The effect estimate for SO2 was strengthened in a multipollutant model
that was adjusted for several other pollutants including PM2 5. However, correlations
among pollutants were not described and exposure measurement error may have
introduced a bias (Section 3.4.2). Consequently, the extent to which this study supports
an independent effect of SO2 on the cardiovascular system is limited. Several recent
epidemiologic studies of the association of long term SO2 exposure with subclinical and
clinical cardiovascular outcomes add to the available body of evidence. These recent
studies do not change the conclusion from the 2008 ISA for Sulfur Oxides (U.S. EPA.
5.3.2
Long-Term Exposure
5.3.2.1
Introduction
2008d).
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Experimental animal studies with long-term exposures below 2,000 ppb were not
available for inclusion in the 2008 ISA for Sulfur Oxides. Although a small number of
studies using exposures above 2,000 ppb were included, they did not contribute heavily
to conclusions because the concentrations of SO2 used in these studies were unlikely to
be relevant to ambient concentrations of SO2. No new toxicological studies in humans or
animals have been published since the 2008 ISA for Sulfur Oxides. Overall, the
biological plausibility and independence of the effects observed in epidemiologic studies
remains an important uncertainty.
This section reviews the published studies of the cardiovascular effects of long-term
exposure to S02(i.e., longer than 1 month). To clearly characterize the evidence
underlying causality, the discussion of the evidence is organized into groups of related
outcomes [ischemic heart disease and myocardial infarction (Section 5.3.2.2).
cerebrovascular disease and stroke (Section 5.3.2.3). hypertension (Section 5.3.2.4). other
cardiovascular effects (Section 5.3.2.5). and cardiovascular mortality (Section 5.3.2.6)1.
Evidence for subclinical effects (e.g., blood biomarkers of cardiovascular effects) of
long-term exposure to SO2 are discussed in Section 5.3.2.7 and serve to inform biological
plausibility across multiple clinical cardiovascular events and outcomes.
Similar to Section 5.3.1. studies examining cardiovascular effects of sulfite exposure (via
i.p., i.v., etc.) are not included in this section because these studies generally involve
exposures to sulfite that are higher than what is expected to occur following inhalation of
SO2 at ambient relevant concentrations. Studies in humans and animals suggest that
prolonged exposure to SO2 may result in measurable changes in the concentrations of
sulfite in plasma and tissues, but these changes would be expected to be far less following
concentrations of SO2 typically found in ambient air. The literature describing the
distribution and metabolism of sulfite is discussed in Section 4.2.3 and Section 4.2.4.
The potential role of sulfite in the induction of systemic effects, such as effects of the
cardiovascular system, is discussed in Section 4.2.4.
5.3.2.2 Ischemic Heart Disease and Myocardial Infarction
IHD generally develops due to a buildup of plaques in the arterial walls
(i.e., atherosclerosis) that impede the blood flow and oxygen delivery to the heart. This
restricted oxygen delivery or ischemia from excess plaque, plaque rupture and clot
formation can lead to an MI. Several epidemiologic studies provide evidence of a
relationship between long-term exposure to SO2 and ischemic heart disease and incident
or fatal MI (Table 5-32). However, uncertainty remains regarding the influence of
exposure measurement error on the effect estimates observed in epidemiologic studies
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1
2
3
(Section 3.4.2) and the ability of these studies to distinguish the independent effect of
long-term SO2 exposure from the effect of correlated copollutant exposures
(Section 3.4.3).
Table 5-32 Epidemiologic studies of the association of long-term exposure to
sulfur dioxide with cardiovascular disease.
Cohort,
Location, and Mean Exposure
Study Study Period ppb Assessment Effect Estimates (95% Cl)a
tLipsett et al. (2011)
California
Teachers Study
Cohort
N = 124,614
California
Jun1996-
Dec 2005
S02
Geocoded
IQR: 0.43
residential address
mean:
linked to pollutant
1.72
surface developed
using IDW (fixed
site monitors
concentrations
from 1995-2005
used to model
exposure as a
time-dependent
function)
Correlation of SO2
with: ozone,
r= -0.17
PM2.5, r= 0.02
PM10, r= 0.54
NO2, r= 0.67
CO, r= 0.80
IQR:
Annual average
0,83
SO2 concentration
mean
for 2002 at a 1 by
(SD):
1-km resolution
1.47
derived from
dispersion models
and linked to
residential post
codes
Correlation of SO2
with: NO2, r= 0.86
Ml incidence
SO2: HR 1.97 (0.07, 60)
Covariates: age, race,
smoking second-hand
smoke, BMI, lifetime physical
activity, nutritional factors,
alcohol, marital status,
menopausal status, hormone
replacement therapy,
hypertension medication and
aspirin, and family history of
Ml/stroke
Copollutant adjustment:
none
tAtkinson etal. (2013)
National GP
Patient Cohort
England
2003
Ml incidence
HR: 1.34 (1.13, 1.50)
Covariates: age, sex,
smoking BMI, diabetes,
hypertension, and index of
multiple deprivation
Copollutant adjustment:
none
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Table 5-32 (Continued): Epidemiologic studies of the association of long term
exposure to sulfur dioxide with cardiovascular disease.
Study
Cohort,
Location, and
Study Period
Mean
PPb
Exposure
Assessment
Effect Estimates (95% Cl)a
tRosenlund et al. (2006)
SHEEP cohort
n = 1,397 cases
and
1,870 controls
Stockholm,
Sweden
1992-1994
Cases
med: 9.6
5th—95th:
2.6-18.2
Controls
med: 9.3
5th—95th:
7.7-17.5
Dispersion models
to estimate SO2
from heating at
residential
address.
Residential history
available for 30 yr
exposure estimate.
Correlation of 30 yr
SO2 with:
30 yrN02,r= 0.73
30 yr CO, r= 0.49
First Ml
OR: 0.99 (0.9,1.1) per 5 ppb
Covariate adjustment: age,
sex, hospital catchment
area, smoking diabetes,
physical inactivity, and SES
Copollutant adjustment:
none
tAncona et al. (2015)
Rome, Italy
(SOx:
2001-2010;
follow-up:
2001-2010)
2.5 |jg/m3
SOx
SD: 0.9
Lagrangian particle
dispersion model
(SPRAY Ver. 5)
used SOx as
exposure marker
for petrochemical
refinery emissions
IHDb
HR men: 0.87 (0.74, 1.02)
HR women: 0.83 (0.64,1.07)
CVDb
HR men: 1.01 (0.93, 1.0)
HR women: 1.02(0.92, 1.12)
PM10: 0.81
H2S: 0.78
Miller etal. (2007)
WHI Cohort
U.S.
1994-1998
NR Annual avg (2000):
nearest monitor to
residence ZIP
code centroid
Cardiovascular events (Ml,
revascularization, angina,
CHF, CHD death)
HR: 1.07 (0.95, 1.20)
Covariates: age, ethnicity,
education, household
income, smoking, diabetes,
hypertension, systolic blood
pressure, BMI, and
hypercholesterolemia
HR: 1.13 (0.98, 1.30) after
simultaneous adjustment for
PM2.5, PM10-2.5, CO, NO2,
and O3
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Table 5-32 (Continued): Epidemiologic studies of the association of long term
exposure to sulfur dioxide with cardiovascular disease.
Cohort,
Location, and Mean Exposure
Study Study Period ppb Assessment Effect Estimates (95% Cl)a
tQin etal. (2015)
N = 24,845
Random
selection
(18-74 yr) from
households in
33 communities
in 11 districts of
northeastern
China
Mean: 3-yr avg
20.3 (2006-2008) SO2
IQR: 7.5 concentration for
each district
NO2, r= 0.38
O3, r= 0.87
PM10, r= 0.70
CVD
BMK25 kg/m2
1.11 (0.97, 1.27)
BMK25 kg/m2
1.12 (0.99, 1.25)
Note: sex-stratified analyses
also presented
Covariate adjustment: age,
race education, income,
smoking drinking, exercise,
diet, sugar, family history of
CVD or stroke, district
Copollutant adjustment:
none
tDonq et al. (2013a)
N = 24,845
Random
selection
(18-74 yr) from
households in
33 communities
in 11 districts of
northeastern
China
Mean:
20
med: 18
IQR: 7.5
3-yr avg
(2006-2008) SO2
concentration for
each district
NO2, r= 0.38
O3, r= 0.87
PM10, r= 0.70
CHD, Ml, orCHF
OR: 1.08 (0.93, 1.26)
Note: associations stronger
among males
Covariate adjustment: age,
sex, educational level,
occupation, family income,
BMI, hypertension, family
history of stroke, family
history of CVD, smoking
status, drinking, diet, and
exercise
Copollutant adjustment:
none
BMI = body mass index; CHF = congestive heart failure; CHD = coronary heart disease; CI = confidence interval; CO = carbon
monoxide; CVD = cardiovascular disease; GP = general practice; HR = heart rate; HS = hemorrhagic stroke; IDW = inverse
distance weighting; IQR = interquartile range; Ml = myocardial infarction; n = sample size; N = population number; N02 = nitrogen
dioxide; NR = not reported; OR = odds ratio; 03 = ozone; PM25 = particulate matter with a nominal aerodynamic diameter less than
or equal to 2.5 |jm; PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm;
PM10-2.5 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm and greater than a nominal 2.5 |jm;
r= correlation coefficient; RR = relative risk; SD = standard deviation; SES = socioeconomic status; SHEEP = Stockholm Heart
Epidemiology Programme; S02 = sulfur dioxide; SOx = sulfur oxides; WHI = Women's Health Initiative.
aEffect estimates are standardized per 5-ppb increase in S02 concentrations.
bEffect estimate per 2.88 |jg/m3 increase in SOxconcentration (as reported by author in original publication).
fStudies published since the 2008 ISA for Sulfur Oxides.
1 Lipsett etal. (2011) analyzed the association of incident MI with long-term exposure to
2 SO2, other gases (NO2, CO, O3), and PM. These authors studied a cohort of California
3 public school teachers aged 20-80 years old (n = 124,614). Each participant's geocoded
4 residential address was linked to pollutant surfaces that were determined by IDW
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interpolation of pollutant concentrations measured at fixed site monitors during the
period 1996-2005. The average of monthly SO2 concentrations was modeled as a
time-dependent function for subjects with at least 12 months of exposure. Those living
outside the radial range for which the monitor was intended to provide representative data
were excluded from the analysis. This "representative range" was 3 km for neighborhood
SO2 monitors and 5 km for the urban/regional SO2. The association between SO2 and
incident MI was imprecise and standardization to an increase in SO2 concentration of
5 ppb (as opposed to the IQR of 0.43) affected the stability of the estimate. An increased
risk of 1.20 (1.02, 1.41) was observed per 10 (ig/m3 per PM2 5. Fewer observations were
available for the SO2 compared to PM analyses because the requirements for the
participants' proximity to the monitor were more stringent for SO2 (residing within 5 km
as opposed to 20 km for PM).
Atkinson et al. (2013) examined the association of incident cardiovascular disease with
SO2. These authors studied patients (aged 40-89 years) registered with 205 general
practices across England. The authors report that approximately 98% of the population is
registered with a general practitioner minimizing the potential for selective participation.
Predicted annual average SO2 concentrations within 1 x 1-km grids, estimated using
dispersion models, were assigned to participants based on their residential postal code.
Cardiovascular disease outcomes included in the analysis were MI, stroke, arrhythmias,
and heart failure. Authors reported an association of SO2 with MI in a fully adjusted
model [HR: 1.34 (95% CI: 1.13, 1.50) per 5 ppb]. The performance of the dispersion
model used to estimate SO2 concentration was characterized as moderate to poor
depending on the study year. Failure of the model to capture the spatial variability of SO2
could lead to bias toward or away from the null (Section 3.4.4.2). Associations of other
pollutants (i.e., PM10, NO2, ozone) with MI were also observed in this study.
Rosenlund et al. (2006) conducted a population case-control study to examine the
association of first MI with long-term exposure to air pollution in Stockholm, Sweden. In
this study residential histories were used to estimate 30-yr avg SO2 concentration from
residential heating sources using dispersion models. Although a positive association of
SO2 and other pollutants (NO2, CO, PM10) with fatal MI was observed in this study, no
association between nonfatal MI and long-term SO2 exposure was reported. Panascvich et
al. (2013) reported higher tumor necrosis factor alpha (TNF-a) levels among those with a
genetic polymorphism of a TNF-a gene (TNF308G/A) as well as an increased risk of MI
in the same population (Section 5.3.2.5).
Weak or inverse associations of both cardiovascular and ischemic heart disease were
reported in a study relying on a Lagrangian particle dispersion model (see
Section 3.3.2.4) to estimate SOx emissions (gaseous and particulate component) from a
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refinery (Ancona et al.. 2015). Exposure model performance statistics were not reported.
Null associations of cardiovascular hospitalizations with PMio, which was highly
correlated with SOx (/' = 0.81) in this study, were observed. Because SOx was used as a
marker for refinery emissions, which contains multiple toxics including VOCs, the study
was not designed to evaluate the independent effect of SO2. In addition to the study by
Miller et al. (2007). which was included in the previous review, two analyses examined
the association of long-term SO2 exposure with relatively broadly defined outcome that
included several cardiovascular diseases (Oin et al.. 2015; Dong et al.. 2013a). These
studies, which were conducted among Chinese adults, reported imprecise increases in the
risk of cardiovascular disease and results suggest the potential for age and body weight to
modify the association with long-term SO2 exposure. Neither of these analyses adjusted
for copollutant confounding, and the district-level SO2 concentrations used to indicate
exposure may not have adequately captured the spatial variability of long-term SO2
exposure.
Overall, these epidemiologic data do not provide support for an association of long-term
SO2 exposure with IHD or more broadly defined categories of cardiovascular disease.
There is uncertainty related the independent effect of SO2 on the cardiovascular system.
Comparable associations between concentrations of other pollutants (i.e. PM2 5 and PM10)
and long-term SO2 exposures were reported in most studies, which were generally not
designed to evaluate copollutant confounding. Further, the exposure assessment
techniques applied in the studies were subject to varying degrees of error depending on
the method. The uncertainties stemming from exposure measurement error were
potentially substantial (Section 3.4.2).
5.3.2.3 Cerebrovascular Diseases and Stroke
Lipsett et al. (2011) evaluated the association of incident stroke with long-term exposure
to SO2, other gases (NO2, NOx, CO, ozone), and PM (Table 5-33). The authors observed
an imprecise, although positive association between SO2 and incident stroke. Point
estimates for the association of other pollutants (PM10PM25, NO2, NOx, and ozone) with
incident stroke were also increased. A positive association of SO2 with incident stroke of
1.13 (95% CI: 1.00, 1.34) per 5 ppb was reported by Atkinson et al. (2013) in patients
across England (study methods in Section 5.3.2.2). Null associations with other pollutants
(PM10, NO2, and ozone) were observed.
Two analyses of a random selection of adults (n = 24,845) ranging from 18 to 74 years
old from households in 33 Chinese communities were examined the association between
long-term SO2 exposure and stroke. Monitor concentrations within each district were
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1 used to derive 3-yr avg concentrations that were assigned to participants. The mean
2 concentration among study participants was 20 ppb. Dong et al. (2013a) reported an
3 increased risk of stroke [OR: 1.09 (1.01, 1.18) per 5 ppb] with the strongest associations
4 in males. Oin et al. (2015) further evaluated effect modification by obesity and reported
5 an increased risk of stroke among participants with BMI greater or equal to 25 kg/m2
6 [OR: 1.18(1.05, 1.32) per 5 ppb]. Neither of these studies considered copollutants
7 confounding and both reported associations with at least one of the other pollutants that
8 were evaluated (PMio, NO2, or ozone). The district level SO2 concentrations may not
9 have adequately captured the spatial variability of SO2.
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Table 5-33 Epidemiologic studies of the association of long-term exposure to
sulfur dioxide with stroke.
Study
Cohort, Location, and Mean Exposure
Study Period ppb Assessment
Effect Estimates (95%
CI)
tLipsett et al. (2011)
California Teachers
Study Cohort
N = 124,614
California
Jun1996-
Dec 2005
S02
Geocoded
IQR:
residential
0.43
address linked
mean:
to pollutant
1.72
surface
developed
using IDW
(fixed site
monitors
concentrations
from
1995-2005
used to model
exposure as a
time-dependent
function)
Correlation of
SO2 with:
ozone,
r= -0.17
PM2.5, r= 0.02
PM10, r= 0.54
NO2, r= 0.67
CO, r= 0.80
IQR:
Annual average
0,83
SO2
mean
concentration
(SD):
for 2002 at a 1
1.47
by 1 km
resolution
derived from
dispersion
models and
linked to
residential post
codes
Correlation of
S02with: NO2,
r= 0.86
Stroke incidence
SO2: HR 6.21 (0.4, 88)
Covariates: age, race,
smoking, second-hand
smoke, BMI, lifetime
physical activity,
nutritional factors,
alcohol, marital status,
menopausal status,
hormone replacement
therapy, hypertension
medication and aspirin,
and family history of
Ml/stroke
Copollutant adjustment:
none
tAtkinson etal. (2013)
National GP Patient
Cohort
England
2003
Stroke incidence
HR: 1.13 (1.00, 1.34)
Covariates: age, sex,
smoking, BMI, diabetes,
hypertension, and index
of multiple deprivation
Copollutant adjustment:
none
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Table 5-33 (Continued): Epidemiologic studies of the association of long term
exposure to sulfur dioxide with stroke.
Study
Cohort, Location, and
Mean
Exposure
Study Period
PPb
Assessment
N = 24,845
Mean:
3-yr avg
Random selection
20
(2006-2008)
(18-74 yr) from
med: 18
SO2
households in
IQR:
concentration
33 communities in
7.5
for each district
11 districts of
NO2, r= 0.38
northeastern China
O3, r= 0.87
PM10, r= 0.70
Effect Estimates (95%
CI)
tDong et al. (2013a)
Prevalent stroke
OR: 1.09 (1.01, 1.18)
Note: associations
stronger among males
Covariate adjustment:
age, sex, educational
level, occupation, family
income, BMI,
hypertension, family
history of stroke, family
history of CVD, smoking
status, drinking, diet, and
exercise
tQin et al. (2015)
N = 24,845
Random selection
(18-74 yr) from
households in
33 communities in
11 districts of
northeastern China
Mean:
20.3
IQR:
7.5
3-yr avg
(2006-2008)
SO2
concentration
for each district
NO2, r= 0.38
Os, r= 0.87
PM10, r= 0.70
Stroke
BMI <25 kg/m2:
OR: 1.03 (0.92, 1.14)
BMI 25 kg/m2:
OR: 1.18 (1.05, 1.32)
Sex-stratified analyses
also presented
Covariate adjustment:
age, race, education,
income, smoking,
drinking, exercise, diet,
sugar, family history of
CVD or stroke, district
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Table 5-33 (Continued): Epidemiologic studies of the association of long term
exposure to sulfur dioxide with stroke.
Study
Cohort, Location, and Mean Exposure Effect Estimates (95%
Study Period ppb Assessment CI)
tJohnson et al. (2010)
Edmonton, Alberta
Canada
Jan 2003-
Dec 2007
SO2 IDW average
mean: monitor SO2
1.3 concentration
Ecological analysis of
stroke incidence rates:
Stroke ED visits
Q1 RR: 1.0 (reference)
Q2 RR: 0.91 (0.83, 1.00)
Q3 RR: 0.89 (0.81, 0.98)
Q4 RR: 0.84 (0.73, 0.96)
Q5 RR: 0.93 (0.89, 0.98)
aResults for HS, non-HS,
and TIA also presented
Covariate adjustment:
age, sex, and household
income
Copollutant adjustment:
none
assigned at
postal code
centroid level
Correlation of
5-yr avg SO2
with:
NO2, r= 0.40
O3, r= 0.41
CO, r= -0.19
BMI = body mass index; CI = confidence interval; CO = carbon monoxide; CVD = cardiovascular disease; ED = emergency
department; GP = general practice; HR = heart rate; HS = hemorrhagic stroke; IDW = inverse distance weighting;
IQR = interquartile range; Ml = myocardial infarction; N = population number; N02 = nitrogen dioxide; non-HS = nonhemhorragic
stroke; Q1 = 1st quartile; Q2 = 2nd quartile; Q3 = 3rd quartile; Q4 = 4th quartile; Q5 = 5th quartile; OR = odds ratio; 03 = ozone;
PM2.5= particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm; PM10 = particulate matter with a
nominal aerodynamic diameter less than or equal to 10 |jm; r= correlation coefficient; RR = relative risk; SD = standard deviation;
S02 = sulfur dioxide; TIA = transient ischemic attack.
aEffect estimates are standardized per 5-ppb increase in S02 concentrations.
fStudies published since the 2008 ISA for Sulfur Oxides.
An inverse association between SO2 concentration and stroke incidence was observed in
an ecological analysis of long-term exposure to ambient pollution conducted in
Edmonton (Johnson et al.. 2010) while an association of SO2 with stroke prevalence was
observed in a study of 33 Chinese communities [OR: 1.21 (95% CI 1.01, 1.46)] (Dong et
al.. 2013a).
In summary, the epidemiologic studies do not provide evidence in strong support of an
effect of long-term SO2 exposure on stroke morbidity. Findings are not generally
consistent across studies and there are uncertainties related to the potential for exposure
measurement error and confounding by copollutants.
Several analyses conducted in China where the mean long-term SO2 concentration is
18.7 ppb report positive associations with hypertension and increased blood pressure.
Dong et al. (2013d) found increased risk of hypertension [OR: 1.17 (95% CI: 1.06, 1.28)
per 5-ppb increase in SO2 concentration] among adults greater than 55 years of age in
33 Chinese communities. The absolute change in diastolic and systolic blood pressure in
5.3.2.4 Blood Pressure and Hypertension
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the study population overall was 0.46 mmHg (95% CI: 0.15, 0.75) and 1.18 mmHg (95%
CI: 0.68, 1.69) per 5-ppb increase in SO2 concentration, respectively. Zhao et al. (2013)
reported a greater effect of SO2 on blood pressure among the overweight and obese in
this population. A similar trend was also observed with other pollutants (i.e., ozone and
NO2). In a study of children 5-17 years old from elementary schools in seven Chinese
cities, Dong et al. (2014) reported associations with arterial blood pressure hypertension
in males [OR: 1.17 (95% CI 1.08, 1.27)] and females [OR 1.19 (95% CI 1.10, 1.28)] per
5-ppb increase in 4-yr avg SO2 concentration. In an extended analysis of this cohort,
Dong et al. (2015) reported large risks associated with SO2 concentration in overweight
and obese children. Although an array of risk factors were considered in the analysis as
potential confounders (Table 5-34). no adjustment for copollutants was presented nor
were copollutant correlations reported. Associations of hypertension with the other
pollutants examined (i.e., PM10, ozone, CO, NO2) were also reported in these studies.
Table 5-34 Epidemiologic studies of the association of long-term exposure to
sulfur dioxide with hypertension.
Cohort, Location,
Mean
Exposure
Study
and Study Period
PPb
Assessment
Effect Estimates (95% CI)
tDona et al. (2013d)
N =24,845
Mean:
3-yr avg
OR: 1.07 (1.03, 1.12)
Random selection
20.3
(2006-2008)SO2
(18-74 yr) from
households in
IQR: 7.5
concentration for
each district
SBP: 0.21 mm Hg (0.07,
0.34)
33 communities in
11 districts of
NO2, r= 0.38
DBP: 0.53 mm Hg (0.31,
0.76)
Covariate adjustment: age,
race, education, income,
smoking, drinking, exercise,
diet, sugar, family history of
hypertension, district
northeastern China
Os, r= 0.87
PM10, r= 0.70
tZhao et al. (2013)
N =24,845
Mean:
3-yr avg
OR normal: 1.03
Random selection
20.3
(2006-2008)SO2
(0.99-1.08)
(18-74 yr) from
IQR: 7.5
concentration for
OR overweight: 1.10
households in
each district
(1.05-1.15)
33 communities in
OR obese: 1.10 (0.99-1.23)
11 districts of
northeastern China
NO2, r= 0.38
Os, r= 0.87
PM10, r= 0.70
Covariate adjustment: race,
education, income,
smoking, drinking, exercise,
diet, sugar, family history of
hypertension, district
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Table 5-34 (Continued): Epidemiologic studies of the association of long term
exposure to sulfur dioxide with hypertension.
Study
Cohort, Location, Mean
and Study Period ppb
Exposure
Assessment
Effect Estimates (95% CI)
tDong etal. (2014)
n = 9,354 Mean:
Children (5-17 yr) 18.7.
Seven cities
northeastern China
2012-2013
4-yr avg
concentration for
one central site
monitor within
1 km of
participant's home
Correlations NR
Hypertension in males:
OR 1.17(1.08, 1.27)
Hypertension in females:
OR 1.19 (1.10, 1.28)
per 5 ppb
DPB (all children)
0.43 (0.26, 0.61)
SBP (all children)
0.71 (0.50, 0.91)
per 5 ppb
Covariate adjustment: age,
sex, BMI, parental
education, LBW, premature
birth, income, passive
smoking exposure, home
coal use, exercise time,
area residence per person,
family history of
hypertension, and district
tDong et al. (2015) n = 9,354 Mean:
Children (5-17 yr) 18.7
Seven cities
northeastern China
2012-2013
per 5 ppb
Covariate adjustment: age,
sex, parental education,
LBW, premature birth,
breastfeeding, income,
passive smoking, home coal
use, exercise time, area
residence per person, family
history of hypertension,
distance from air pollution
monitor, temperature, and
district
4-yr avg
concentration for
one central site
monitor within
1 km of
participant's home
Correlations NR
Hypertension
Normal weight:
0.89 (0.83, 0.96)
Overweight:
1.36 (1.18, 1.56)
Obese:
BMI = body mass index; CI = confidence interval; DPB = diastolic blood pressure; IQR = interquartile range; LBW = low birth rate;
n = sample size; N = population number; N02 = nitrogen dioxide; NR = not reported; 03 = ozone; OR = odds ratio;
PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm; r= correlation coefficient;
SBP = systolic blood pressure; S02 = sulfur dioxide.
fStudies published since the 2008 ISA for Sulfur Oxides.
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5.3.2.5
Other Cardiovascular Effects
Few studies have evaluated other cardiovascular effects associated with long-term SO2
concentrations. Atkinson et al. (2013) examined the association of arrhythmias and heart
failure with long-term SO2 exposure. Study methods are described in Section 5.3.2.2.
Authors reported a positive association of SO2 with heart failure in a fully adjusted model
[HR: 1.27 (95% CI: 1.06-1.59) per 5 ppb] and with arrhythmia [HR: 1.13 (95% CI 1.00,
1.27)]. A similar pattern of findings was observed for the associations ofNCh and PM10
with which moderate correlations with SO2 were reported. No association of annual SO2
concentration with hospital admissions for heart failure was reported in a study of
county-level air pollution indicator concentrations (Bennett et al.. 2014).
5.3.2.6 Cardiovascular Mortality
The recent evidence for associations between long-term SO2 exposure and total mortality
(Section 5.5.2) is generally consistent with the evidence in the 2008 ISA for Sulfur
Oxides. Several studies report associations between long-term SO2 exposure and
cardiovascular mortality (Figure 5-27); however, there is no consistent trend toward
positive associations for cardiopulmonary or cardiovascular causes of death overall.
Additionally, confounding by copollutants is not ruled out (Section 3.4.3) and
uncertainties remain regarding the influence of exposure measurement error
(Section 3.4.2). Together, these uncertainties limit the interpretation of the causal nature
of the associations observed in the available epidemiologic studies of long-term
mortality.
5.3.2.7 Subclinical Effects Underlying Cardiovascular Diseases
Carotid intima-media thickness (cIMT) is a measurement of thickness of the inner layers
of the wall of the artery and can be used to indicate the presence of subclinical
atherosclerosis. Other markers of preclinical atherosclerosis include pulse wave velocity
and augmentation index, both of which indicate arterial stiffening. In an analysis of the
Atherosclerosis Risk in Young Adults study, which is a prospective cohort study (Lenters
et al.. 2010). no association of SO2 concentration with carotid intima-media thickness
(cIMT) was observed; however, there was a weak imprecise increase in aortic pulse wave
velocity reported. The other pollutants examined (NO2, PM2 5, black smoke) were also
not associated cIMT although associations between NO2 concentration and both pulse
wave velocity and augmentation index were observed. SO2 concentration at the home
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address for the year 2000 was assigned to participants of this study. The correlations of
SO2 with NO2, black smoke, and PM2 5 reported in this study were low, ranging from
r = 0.09 to 0.12. The correlation of SO2 with metrics of traffic intensity were also low
(r = -0.06 to 0.06). In another study, Weng et al. (2015) reported that annual average SO2
concentration was correlated with brachial-ankle pulse wave velocity in univariate
analyses but not after adjustment for PM10 and other potential confounders. This study
was based on data from 127 heart disease patients undergoing hemodialysis in Taoyuan,
Taiwan.
Inflammation and oxidative stress have been shown to play a role in the progression of
chronic cardiovascular disease. Forbes et al. (2009b) examined the association of
predicted annual average SO2 concentration with CRP and fibrinogen among the English
population. Multilevel linear regression models were used to determine pooled estimates
across three cross-sectional surveys conducted during different years. Each participant's
postal code of residence was linked to predicted annual average SO2 concentration
derived from dispersion models. SO2, PM10, O3, and NO2 were not associated with
increased CRP or fibrinogen in these data. A study conducted among men and women
(45-70 years) in Stockholm reported an association of 30-yr avg source-specific
heating-related SO2 concentration estimated using dispersion models with increases in
IL-6; however, SO2 was not associated with CRP, TNF-a, fibrinogen, or plasminogen
activator inhibitor-1 in this study (Panasevich et al.. 2009). Associations between
long-term NO2 concentration, which were moderately correlated with SO2 (r = 0.53), and
increased plasma IL-6 were also observed in this study. A study conducted among older
adults in Taiwan reported no changes in blood pressure, total cholesterol, fasting glucose,
hemoglobin Ale, IL-6 and neutrophils in association with increasing SO2 concentration
while associations between these endpoints and other pollutants were observed (Chuang
et al.. 2011).
Overall, the body of evidence is limited and there is no consistent positive trend in the
associations observed between SO2 and subclinical atherosclerosis or circulating markers
of inflammation. These findings are consistent with the general lack of mechanistic
evidence for key events in the proposed mode of action leading to extrapulmonary
effects.
5.3.2.8 Summary and Causal Determination
Overall, the evidence is inadequate to infer the presence or absence of a causal
relationship between long-term exposure to SO2 and cardiovascular health effects.
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Although a number of epidemiologic studies report positive associations between
long-term exposure to SO2 concentrations and cardiovascular disease and stroke
(Section 5.3.2.3). the evidence for any one outcome is limited and inconsistent. As
discussed in Section 3.4.2.2. centrally located monitors may not capture the spatial
variability in SO2 concentration. Dispersion models generally capture SO2 variability on
near-source spatial scales (up to tens of km) but exposure estimates from such models are
subject to other uncertainties (Section 3.3.2.4). Bias stemming from exposure
measurement error can be either direction (i.e. toward or away from the null) and no
studies corrected for such error, complicating the interpretation of findings from studies
of long-term exposure of SO2 (Section 3.4.4.2V There is also uncertainty regarding the
potential for copollutant confounding (Section 3.4.3). Primary pollutants such as NO2 and
CO typically show moderate to high correlations with SO2 (Table 5-32. Table 5-33. and
Table 5-34) and there is a lack of experimental evidence to provide coherence or
biological plausibility for an independent effect of SO2 on cardiovascular health. Several
epidemiologic studies evaluated the association between SO2 concentration and
subclinical atherosclerosis or circulating markers of inflammation; however, there is no
consistent positive trend in the associations observed between SO2 and these potential
key events in a mode of action.
The available evidence examining the relationship between long-term exposure to SO2
and cardiovascular effects was evaluated using the framework described in Table I and
Table II of the Preamble to the IS As (U.S. EPA. 2015b). The key evidence, supporting or
contradicting, as it relates to the causal framework is summarized in Table 5-35. In
conclusion, the evidence lacks coherence and is of insufficient consistency, and thus, is
inadequate to infer the presence or absence of a causal relationship between long-term
exposure to SO2 and cardiovascular health effects.
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Table 5-35 Summary of evidence, which is inadequate to infer a causal
relationship between long-term sulfur dioxide exposure and
cardiovascular effects.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations
Associated with
Effects0
Some epidemiologic studies
report positive associations
but results are not generally
consistent.
Positive associations of SO2
with Ml, CVD events, or
stroke events
Lipsett et al. (2011)
1.72 ppb (mean)
Atkinson et al. (2013)
1.47 ppb (mean)
Miller etal. (2007)
NR
Null/inverse associations
observed with Ml and stroke
Rosenlund et al. (2006)
9.6 ppb (med)
Johnson et al. (2010)
1.3 ppb (mean)
Limited coherence with
evidence for cardiovascular
mortality
No consistent positive trend
observed in long term
studies of cardiovascular
mortality.
Section 5.3.2.4
Uncertainty due to
confounding by correlated
pollutants
Correlations of SO2 with CO
and NO2 vary by location but
are generally moderate to
high.
Table 5-32
Table 5-33
Table 5-34
Uncertainty due to exposure
measurement error
Centrally located monitors
may not capture spatial
variability of SO2
concentrations.
Miller etal. (2007)
Section 3.4.2
SO2 estimates from
dispersion model show poor
to moderate agreement with
measured concentrations.
Atkinson et al. (2013)
Forbes et al. (2009a)
Exposure measurement
error can introduce bias
away from the null in studies
of long-term exposure
Section 3.4.4.2
Uncertainty due to lack of
coherence with other lines of
evidence
Lack of experimental human
or animal studies evaluating
cardiovascular effects of
long-term SO2 exposure
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Table 5-35 (Continued): Summary of evidence, which is inadequate to infer a
causal relationship between long term sulfur dioxide
exposure and cardiovascular effects.
SO2 Concentrations
Rationale for Causal
Associated with
Determination3
Key Evidence13
Key References'3
Effects0
Weak evidence to identify key
Lack of mechanistic
Section 4.3
events in the mode of action
evidence for key events
leading to extrapulmonary
effects
Limited and inconsistent
evidence of increased
subclinical atherosclerosis
and systemic inflammation
(e.g., IL-6, CRP) in
epidemiologic studies
Section 5.3.2.7
CO = carbon monoxide; CRP = C-reactive protein; CVD = cardiovascular disease; IL-6 = interleukin-6; Ml = myocardial infarction;
N02 = nitrogen dioxide; NR = not reported; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Table I and Table II of the
Preamble to the ISAs (U.S. EPA. 2015b).
bDescribes the key evidence and references, supporting or contradicting, that contribute most heavily to causal determination.
References to earlier sections indicate where full body of evidence is described.
°Describes the S02 concentrations with which the evidence is substantiated.
5.4 Reproductive and Developmental Effects
5.4.1 Introduction
This section covers studies of health endpoints with exposures to SO2 occurring during or
around pregnancy and/or the first years of life. This includes not only pregnancy and
birth outcomes (including infant mortality) occurring close in time to the exposure, but
also developmental outcomes potentially occurring years later. Exposures occurring in
pregnancy and early life may alter development, and have effects not immediately
identifiable but evident at later points. These studies are characterized in this section as
they contribute to the weight of evidence for effects of SO2 on reproductive health and
development. Evidence regarding fertility, reproduction, and pregnancy are discussed in
Section 5.4.2. with a series of birth outcomes [fetal growth (Section 5.4.3.1). preterm
birth (Section 5.4.3.2). birth weight (Section 5.4.3.3). birth defects (Section 5.4.3.4). fetal
mortality (Section 5.4.3.5). and infant mortality (Section 5.4.3.6)1 discussed in
Section 5.4.3. Studies of developmental outcomes are discussed in Section 5.4.4. with a
focus on respiratory developmental outcomes in Section 5.4.4.1.
Epidemiologic studies included in the 2008 SOx ISA (U.S. EPA. 2008d) examined
impacts on reproductive outcomes including preterm birth, birth weight, intra-uterine
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growth retardation, birth defects, infant mortality, and neonatal respiratory
hospitalizations. While positive associations were observed in the previous SOx ISA
(U.S. EPA. 2008d). there was little biologic plausibility for these associations provided
by supporting toxicological literature. Interpretation of those results was also limited by
the lack of control for potential confounding by copollutants, the small number of studies,
and uncertainty regarding exposure. The 2008 SOx ISA (U.S. EPA. 2008d) concluded the
evidence was inadequate to infer the presence or absence of a causal relationship with
reproductive and developmental effects.
The body of literature characterizing the reproductive health effects of exposure to SO2
has grown considerably since the 2008 SOx ISA (U.S. EPA. 2008d). with over 50 recent
epidemiologic studies. However, the number of studies for any particular outcome
remains relatively limited. Among the recent epidemiologic studies, birth outcomes
(e.g., small for gestational age, preterm birth, and birth weight) predominate. Several new
studies of congenital anomalies are now available in addition to the single study included
in the 2008 SOx ISA. Recent studies of other outcomes, such as fetal mortality, infant
mortality, fertility, and conditions related to pregnancy have also been published. Key
epidemiologic studies are summarized in Table 5-36. In toxicological research, a single
study published at relevant exposure levels (1,500 ppb or lower) investigated
reproductive and developmental changes in exposed female rats and their offspring,
finding altered estrus cyclicity with fewer cycles over time, altered birth outcomes of
increased litter size, and decreased postnatal body weight in offspring whose dams were
exposed to SO2. This study is summarized in Table 5-37. The majority of the remaining
animal toxicological evidence for reproductive and development effects is for exposure at
5,000 ppb or greater, doses which are beyond the scope of this document.
Several recent articles have reviewed methodological issues relating to the study of
outdoor air pollution and adverse birth outcomes (Chen et al.. 2010a; Woodruff et al..
2009; Ritz and Wilhelm. 2008; Slama et al.. 2008). Some of the key challenges to
interpretation of birth outcome study results include: (1) the difficulty in assessing
exposure as most studies use existing monitoring networks to estimate individual
exposure to ambient air pollution; (2) the need for detailed exposure data and potential
residential movement of mothers during pregnancy; (3) the inability to control for
potential confounders such as other risk factors that affect birth outcomes (e.g., smoking),
evaluating the exposure window (e.g., trimester) of importance; and (4) the limited
evidence on the physiological modes of action for these effects (Ritz and Wilhelm. 2008;
Slama et al.. 2008). An additional limitation is the failure for many studies of
reproductive and developmental outcomes to adjust for co-occurring air pollutants. As
ozone, PM2 5, and NOx have all been associated with reproductive and developmental
health outcomes, the lack of adjustment makes interpretation of isolated SO2 effects more
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difficult. Recently, an international collaboration was formed to better understand the
relationships between air pollution and adverse birth outcomes and to examine some of
these methodological issues through standardized parallel analyses in data sets from
different countries (Woodruff et al.. 2010). At present, no results for analysis of SO2 have
been reported from this collaboration.
Overall, the number of studies examining associations between exposure to ambient SO2
and reproductive and developmental outcomes has increased substantially since
publication of the 2008 ISA for Sulfur Oxides, yet evidence for an association with
individual outcomes remains relatively limited and key uncertainties have not been
reduced.
Table 5-36 Key reproductive and developmental epidemiologic studies for sulfur
dioxide.
Location Mean SO2 Exposure Selected Effect Estimates3
Study Sample Size ppb Assessment 95% CI
Fetal growth
Liu et al. (2003)
Vancouver
(n = 229,085)
4.9 Monitors at census
subdivision level
IUGR (those with birth weight fall
below the 10th percentile, by sex
and gestational week, of all
singleton live births in Canada
between 1986 and 1998, term)
M1: 1.07 (1.01, 1.13)
Last mo: 1.00 (0.94, 1.06)
T1
T2
T3
1.07 (1.00, 1.14)
0.98 (0.91, 1.04)
1.03 (0.96, 1.10)
Brauer et al. (2008) Vancouver
(n = 70,249)
2.2 Inverse distance
weighting of three
closest monitors
within 50 km, 14 SO2
monitors
SGA (those with birth weights below
the 10th percentile of the cohort,
stratified by sex, for each week of
gestation)
EP: 1.02 (1.00, 1.03)
Rich et al. (2009) NewJersey T1: 5.7 Nearest monitor VSGA (growth ratio <0.75)
(n = i78) T2: 5.6 (within 10 km) T1: 1.00 (0.92, 1.08)
T3: 5.5 T2: 1.04 (0.96, 1.13)
T3: 1.05 (0.97, 1.14)
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Table 5-36 (Continued): Key reproductive and developmental epidemiologic
studies for sulfur dioxide.
Study
Location
Sample Size
Mean SO2
PPb
Exposure
Assessment
Selected Effect Estimates3
95% CI
tLe etal. (2012)
Detroit, Ml
(n = 112,609)
5.8 Nearest monitor (ZIP
code within 4 km of
one of three
monitors)
SGA (infants whose birth weights
fell below the 10th percentile by sex
and gestational week, based on
study population's distribution, term)
T1, adjusted for CO, NO2, and PM10
Q1
Q2
Q3
Q4
ref
1.18 (0.92, 1.51)
1.01 (0.83, 1.23)
1.05 (0.87, 1.28)
T2, adjusted for CO, NO2, and PM10
Q1
Q2
Q3
Q4
ref
1.30 (1.01, 1.69)
1.12 (0.91, 1.37)
1.11 (0.90, 1.36)
T3, adjusted for CO, NO2, and PM10
Q1
Q2
Q3
Q4
ref
1.17 (0.94, 1.45)
1.24 (1.02, 1.50)
1.31 (1.06, 1.60)
Preterm birth
Liu et al. (2003)
Vancouver, BC
(n = 229,085)
4.9
Monitors at census
subdivision level
M1: 0.95 (0.88, 1.03)
Last mo: 1.09 (1.01, 1.19)
Saaiv et al. (2005)
Pennsylvania
(n = 187,997)
7.9
Monitors at county
level
Last 6 wk: 1.05 (1.00, 1.10)
3 d lag: 1.02 (0.99, 1.05)
tZhao etal. (2011)
Guangzhou, China
(n = 7,836 preterm
births)
20
City average from
monitors
Same day: 1.04 (1.02, 1.06)
1 d lag: 1.01 (0.99, 1.04)
2 d lag: 1.02 (0.99, 1.04)
3 d lag: 1.02 (0.99, 1.04)
tMendola et al.
U.S.
3.99 Modeled, CMAQ
Week 34
(2016a)
(n = 223,502)
Delivery hospital
Asthma: 1.32 (1.05, 1.70)
referral region
No asthma: 1.02 (0.90, 1.14)
Week 35
Asthma: 1.17 (1.02, 1.34)
No asthma: 0.98 (0.92, 1.05)
Last 6 wk of pregnancy
Asthma: 0.90 (0.81, 1.00)
No asthma: 0.81 (0.77, 0.92)
EP
Asthma: 0.93 (0.83, 1.03)
No asthma: 0.92 (0.87, 0.97)
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Table 5-36 (Continued): Key reproductive and developmental epidemiologic
studies for sulfur dioxide.
Study
Location
Sample Size
Mean SO2
PPb
Exposure
Assessment
Selected Effect Estimates3
95% CI
Low birth weight
Ha et al. (2001) Seoul, South Korea T1: 13 Monitors averaged to T1: 1.05 (1.02, 1.08)
(n = 276 763) T3: 12 city T1, adjusted for T3:
1.06 (0.98,1.13)
T3: 0.96 (0.92, 0.99)
T3, adjusted forT1:
1.02 (0.94, 1.10)
Lee et al. (2003)
Seoul, South Korea
(n = 388,105)
12.1
Monitors averaged to
city
EP: 1.02 (0.99, 1.05)
T1: 1.05 (1.02, 1.09)
T2: 0.97 (0.92, 1.00)
T3: 1.12 (1.03, 1.20)
Liu et al. (2003)
Vancouver, BC
(n = 229,085)
4.9
Monitors at census
subdivision level
M1: 1.11 (1.01, 1.22)
Last mo: 0.98 (0.89, 1.08)
Duaandzic et al.
(2006)
Nova Scotia
(n = 74,284)
10
Nearest monitor
(postcode within
25 km)
T1: 1.20 (1.05, 1.38)
T2: 0.99 (0.91, 1.09)
T3: 0.95 (0.86, 1.04)
tMorello-Frosch et
al. (2010)
California
(n = 3,545,177)
2.1
Nearest monitor
(census block
centroid within 3, 5,
or 10 km)
EP
3 km: 1.10 (0.95, 1.34)
5 km: 1.05 (0.95, 1.16)
10 km: 1.05 (1.00, 1.10)
tEbisu and Bell
(2012)
Northeastern and
mid-Atlantic U.S.
(n = 1,207,800)
6.1
County average from
monitors
EP: 1.05 (1.01, 1.09)
tKumar (2012)
Chicago, IL
(n = 398,120)
4.7
4.6
Nearest monitor
(census tract within
3 miles)
County average from
monitors
EP: 1.19 (0.90, 1.57)
EP: 1.05 (0.91, 1.20)
Birth Weight
tDarrow et al.
Atlanta, GA
M1: 10.7
Population weighted
M1: 0.625 (-2.625, 3.875)
(2011)
(n = 400,556)
T3: 9.5
spatial model based
T3: -6.500 (-12.500, -0.667)
Distributed lag,
on monitors,
Non-Hispanic white
1-h max SO2
five-county area,
T3: -8.667 (-15.333, -2.000)
1-h max
Non-Hispanic black
T3: -3.167 (-9.833, 3.667)
Hispanic
T3: -9.5 (-19.000, -0.167)
tGeeretal. (2012)
Texas
(n = 1,548,904)
2.3
County average from
monitors
EP: -15.594 (-25.344, -5.844)
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Table 5-36 (Continued): Key reproductive and developmental epidemiologic
studies for sulfur dioxide.
Location
Mean SO2
Exposure
Selected Effect Estimates3
Study
Sample Size
PPb
Assessment
95% CI
Fetal and infant mortality
tHwana et al.
Taiwan
5.7
Inverse distance
Among preterm deliveries
(2011)
(n = 9,325 cases)
weighting of monitors
EP: 1.16 (1.00, 1.34)
to township or district,
M1: 1.22 (1.00, 1.34)
72 monitors
M2: 1.22 (1.00, 1.34)
M3: 1.16 (1.00, 1.34)
Among term deliveries
EP: 0.95 (0.82, 1.10)
M1: 1.00 (0.90, 1.16)
M2: 1.00 (0.90, 1.16)
M3: 0.95 (0.86, 1.16)
tFaiz et al. (2012)
New Jersey
5.9
Nearest monitor
EP: 1.32 (0.95, 1.84)
(n = 994)
(within 10 km, 1 of
T1: 1.23 (1.02, 1.51)
16 monitors)
T2: 1.21 (0.89, 1.53)
T3: 1.47 (1.05, 1.69)
tFaiz et al. (2013)
New Jersey
5.8
Nearest monitor
2-d lag
(n = 1,277)
(within 10 km, 1 of
1.12 (1.02, 1.24)
16 monitors)
Adjusted PM2.5: 1.18 (1.00, 1.40)
Adjusted NO2: 1.15 (1.00, 1.32)
Adjusted CO: 1.05 (0.93, 1.20)
Woodruff et al.
U.S.
3
Monitors, averaged to
All causes
(2008)
(n = 6,639 cases)
(median)
county
0.93 (0.84, 1.04)
Exposures for 2 mo
Respiratory
after birth
1.09 (0.89, 1.36)
Adjusted PM10, CO, O3:
1.13 (0.79, 1.60)
Adjusted PM2.5, CO, O3:
1.21 (0.79, 1.84)
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Table 5-36 (Continued): Key reproductive and developmental epidemiologic
studies for sulfur dioxide.
Study
Location
Sample Size
Mean SO2 Exposure
ppb Assessment
Selected Effect Estimates3
95% CI
Developmental
Dales et al. (2006) Atlanta, GA
(n = 8,586 cases)
4.3 Monitors, averaged to
city
Neonatal hospitalization for
respiratory disease
2-d lag
2.59 (1.05, 4.39)
Adjusted for O3, NO2, CO
1.95 (0.54, 3.68)
Adjusted for O3, NO2, CO, PM10
1.57 (0.25, 3.29)
2 Inverse distance Asthma
weighting 3 nearest EP: 1.45 (1.28, 1.84)
monitors (of 14) 1st year of life: 1.45(1.28, 1.84)
within 50 km
CI = confidence interval; CMAQ = Community Multiscale Air Quality; CO = carbon monoxide; EP = entire pregnancy;
IUGR = intra-uterine growth restriction; M1 = Month 1; M2 = Month 2; M3 = Month 3; n = sample size; N02 = nitrogen dioxide;
03 = ozone; PM2.5= particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm; PM10 = particulate matter
with a nominal aerodynamic diameter less than or equal to 10 |jm; PM10-2.5 = particulate matter with a nominal aerodynamic
diameter less than or equal to 10 |jm and greater than a nominal 2.5 |jm; Q1 = 1st quartile; Q2 = 2nd quartile; Q3 = 3rd quartile;
Q4 = 4th quartile; SGA = small for gestational age; S02 = sulfur dioxide; T1 = 1st trimester; T2 = 2nd trimester; T3 = 3rd trimester;
VSGA = very small for gestational age.
aRelative risk per 5-ppb change in S02, unless otherwise noted.
fStudies published since the 2008 ISA for Sulfur Oxides.
tClark et al. (2010) British Columbia
(n = 3,482 cases)
Table 5-37 Study specific details from animal toxicological studies of the
reproductive and developmental effects of sulfur dioxide.
Study and Species
Concentration SO2 Exposure
Measured Outcome(s)
Mamatsashvili (1970b)
0.057 or 1.5 ppm for 72 d
Estrus cyclicity duration (F0 and F1),
Rat
litter size, offspring growth (body
weight)
5.4.2 Fertility, Reproduction, and Pregnancy
1 Infertility affects approximately 11% of all women ages 15-44 in the U.S. (Chandra et
2 al.. 2013). and can have negative psychological impacts and affect quality of life;
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infertility and subfertility may also potentially signal poorer physiological health. Those
with fertility problems are at higher risk for adverse pregnancy and birth outcomes if they
do become pregnant (Hansen et al.. 2005; Helmerhorst et al.. 2004; Jackson et al.. 2004).
Outcomes studied in this area include fecundity (the ability to conceive frequently,
quantified as length of time to pregnancy) and fertility (the ability to have a live birth).
Studies in this area frequently use populations undergoing assisted reproductive
treatment, as these populations have a large amount of data collected on them during
treatment and defined menstrual cycles and start points. In cohorts recruited from the
general population, exact timing can be difficult to determine due to reliance on
participant recall, particularly if they are surveyed well after initiation of pregnancy
attempts. Many pregnancies are unplanned, which also adds a level of complication to
quantifying fertility. Researchers may also investigate potential mechanistic links
between pregnancy conditions and biomarkers and later birth outcomes; such as
pregnancy-related hypertension, which is a leading cause of perinatal and maternal
mortality and morbidity (Lee et al.. 2012).
Four recent studies have examined the effects of SO2 on measures of fertility; all use
different populations and outcomes and observed mainly null effects for SO2 exposures.
Recent studies examined semen quality parameters in cohorts of men from Chongqing,
China (Zhou et al.. 2014) and Poland (Radwan et al.. 2015) and observed decreases in
normal morphology with increases in SO2 exposure; however, all other quality metrics
showed null associations. Slama et al. (2013) examined fecundity rate ratios (FRs) with
SO2 exposures before and after the initiation of unprotected intercourse in a Czech
Republic population. Exposures prior to intercourse initiation (long-term, -30 or 60 days)
had slightly reduced FRs; however, SO2 was highly correlated with PM2 5 and NO2 in this
population and stronger reductions in fertility were observed with those pollutants. Legro
et al. (2010) examined odds of live birth in a population undergoing in vitro fertilization
and observed null associations for SO2 with all exposure windows from medication start
to birth (short-term windows during in vitro fertilization, long term from transfer to
pregnancy).
Mixed effect estimates are observed with SO2 exposure across other pregnancy-related
outcomes. Recent studies examined increased blood pressure during pregnancy or
pregnancy-related hypertensive disorders, including pre-eclampsia. Several studies
observed no associations between SO2 exposure during the first trimester and changes in
late pregnancy blood pressure (Lee et al.. 2012) or hypertensive disorders (Michikawa et
al.. 2015); however, a study in Florida observed increased hypertension with higher SO2
exposure during the first trimester (Xu et al.. 2014). Mendola et al. (2016b) observed a
positive association between pre-eclampsia and SO2 exposure among people with asthma,
but not among people without asthmas; the interaction between exposure to SO2 and
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asthma was statistically significant for the first trimester exposure window. A small
Iranian study found no association between pre-eclampsia and SO2 above versus below
median concentrations (Nahidi etal.. 2014). Assibev-Mensah et al. (2015) observed no
effect of SO2 on hypertensive disorders in Beijing comparing 2008 Olympic period with
same calendar days in 2009. In fact, there was an inverse relationship between SO2
exposure in the third trimester and hypertensive disorders.
In other pregnancy-related outcomes, no associations were observed in the Allegheny
County, PA population for short-term near-birth exposures and C-reactive protein, an
inflammatory biomarker linked to increased risk of preterm birth (Lee et al.. 201 lb).
Michikawa et al. (2016) observed positive associations with SO2 exposure and placenta
previa in a Japanese population, although the associations were smaller and less
consistent than those observed for ozone or suspended particulate matter. Increases in
SO2 exposure during the preconception period and the first trimester were associated with
increased odds of gestational diabetes mellitus (Robledo etal.. 2015). Assibev-Mensah et
al. (2015) examined other fetal-placental conditions, and observed no associations with
SO2 exposure in the first or second trimester, but reported a positive association with
fetal-placental conditions and third trimester SO2 exposures in Beijing comparing 2008
Olympic period with same calendar days in 2009. Wallace et al. (2016) observed positive
associations between premature rupture of membranes and SO2 exposure averaged over
the whole pregnancy, but not for shorter exposure windows (i.e., days or hours before
rupture).
No recent animal studies evaluating fertility and pregnancy were identified. An older
study in laboratory animals exposed to SO2 demonstrated reproductive toxicity in adult
female rodents and their offspring. Adult female albino rats were exposed to either
0.057 ppm or 1.5 ppm SO2 by inhalation for 72 days (Mamatsashvili. 1970b). During the
first month of treatment at 1.5 ppm, substantial alterations in stages of the estrus cycle
were seen including significant decreases in duration of diestrus and metastrus. During
the 2nd and 3rd month of exposure, prolongation of estrus cyclicity was found with
exposure to 1.5 ppm SO2, leading to fewer estrus cycles during the study period. This
change was not permanent as by 7 months after exposure ceased, estrus cyclicity returned
to normal. Exposure of adult female rodents to SO2 caused disruption of estrus cyclicity
that was not permanent as it returned to normal after cessation of SO2 exposure.
While studies of fertility, reproduction, and pregnancy are limited in number, generally,
SO2 exposures appear to have no association with these outcomes. A group of studies
examining hypertensive disorders during pregnancy report inconsistent results, with the
majority observing no association with SO2 exposure. Similarly, studies examining
endpoints related to fertility and other pregnancy conditions are generally inconsistent,
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with the majority observing no association, and few studies examining any one specific
outcome. Additionally, these studies do not provide evidence to help reduce uncertainty
related to exposure measurement error, copollutant confounding, or biological
mechanism by which SO2 could cause these effects. These studies are summarized in
Supplemental Table 5S-20 (U.S. EPA. 2015i).
This section discusses several categories of birth outcomes, including fetal growth
(Section 5.4.3.1). preterm birth (Section 5.4.3.2). birth weight (Section 5.4.3.3). birth
defects (Section 5.4.3.4). fetal mortality (Section 5.4.3.5). and infant mortality
(Section 5.4.3.6).
Fetal growth can be difficult to quantify; typically, small for gestational age (SGA) or
intra-uterine growth restriction (IUGR) are used. These designations, often used
interchangeably, are defined as infants with a birth weight below the 10th percentile for
gestational age, usually with consideration for sex and race as well. There are a number
of limitations in using SGA/IUGR as a metric of poor fetal growth. One is that a
percentile-based measure will always quantify a certain percentage of the infant
population as growth restricted whether or not this is truly the case (Wollmann. 1998).
For example, in term infants, it is unlikely that 10% are actually growth restricted.
Whereas in preterm infants, it is likely that more than 10% are growth restricted;
therefore, SGA cases would be overestimated in term infants and underestimated in
preterm infants. In addition, exact definitions shift between studies and some studies use
alternate definitions of SGA/IUGR. For example, some studies use the birth weight
distribution of their study population for defining SGA, which will naturally not be
identical for every study population, and others use country standards, likely to be more
stable overtime (Le et al.. 2012; Brauer et al.. 2008; Liu et al.. 2003). An alternate
approach to categorizing growth restriction is to use ultrasound images during gestation
(Woodruff et al.. 2009). This approach has the benefit of examining all fetuses with
ultrasounds, being less subjective to population definition, and distinguishing true growth
restriction from merely small-sized infants. However, not all women receive prenatal care
and ultrasounds, leading to the possibility of selection bias.
Several studies report positive associations between fetal growth and SO2, although
timing of exposure is inconsistent. A recent study conducted in Australia examined
5.4.3
Birth Outcomes
5.4.3.1
Fetal Growth
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ultrasound measures in midgestation in association with SO2 exposures during early
pregnancy (Hansen et al.. 2008). Hansen et al. (2008) observed decreases in biparietal
diameter and abdominal circumference with increases in SO2 during the first 4 months of
pregnancy [5-ppb SO2 increase in 1st month: -4.25 mm (-6.81, -1.69) biparietal
diameter; -9.31 mm (-19.31, 0.69) abdominal circumference]. Recent studies using the
traditional definition of SGA/IUGR had mixed results. In Vancouver, increases in ORs
for SGA were observed with entire pregnancy exposures (Brauer et al.. 2008) and with
1st month and 1st trimester exposures (Liu et al.. 2003). Rich et al. (2009) used an
alternate definition of SGA—having a growth ratio (infant birth weight divided by
median study cohort birth weight) below 0.75 for very SGA (VSGA), and between
0.75-0.85 for SGA—and observed elevated ORs with 1st trimester exposures for SGA,
and 2nd and 3rd trimester exposures for VSGA. Other studies did not observe positive
associations between fetal growth and SO2. In a study conducted in Italy, (Capobussi et
al.. 2016) observe a null association for SGA when SO2 exposure was estimated for the
entire pregnancy, but modest positive associations when exposure was averaged across
the first or second trimester. Whereas a study conducted in Calgary, Edmonton, and
Montreal, Liu et al. (2007) found lowered ORs for IUGR with exposures in months 1 to 5
of pregnancy and no associations in months 6 to 9. Of the two recent studies in the U.S.,
Le etal. (2012) observed generally null associations for SGA and 1st and last month
exposures; ORs with trimester exposure windows were null, although ORs became
elevated for the 2nd and 3rd trimesters after adjustment for CO, NO2, and PM10.
No recent animal studies evaluating fetal growth were identified.
In summary, there is inconsistent evidence for increased odds of fetal growth restriction
with exposure to SO2 during pregnancy, and the evidence lacks consistency in fetal
growth definition/metric and in exposure timing. Mean SO2 exposures for these studies
are generally low, although all studies examine average daily SO2 concentrations.
Additionally, these studies do not provide evidence to help reduce uncertainty related to
exposure measurement error, copollutant confounding, or the biological mechanism by
which SO2 could cause these effects. Studies examining the association between SO2 and
fetal growth can be found in Supplemental Table 5S-21 (U.S. EPA. 2015i).
5.4.3.2 Preterm Birth
Preterm birth (PTB), delivery that occurs before 37 weeks of completed gestation, is a
marker for fetal underdevelopment and a risk factor for further adverse health outcomes
(e.g., infant mortality, neurodevelopmental problems, growth issues) (Mathews and
MacDorman. 2010; Saigal and Dovle. 2008; IOM. 2007; Gilbert et al.. 2003). PTB is
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characterized by multiple etiologies (spontaneous, premature rupture of membranes, or
medically induced), and identifying exact causes of PTB is difficult. It is likely that some
mechanistic pathways are shared between the three groups; however, isolated causes are
also likely to exist. Few, if any, studies distinguish between these three groups in
examining associations between air pollution and PTB.
Given the uncertainty surrounding modes of action leading to PTB, many of the studies
reviewed here consider both short- and long-term exposure periods. For example,
exposure across all of gestation or during a particular trimester for long-term exposure
windows, or weeks or days leading up to birth for short-term exposure windows. With
near-birth exposure periods development will be at different points for term and preterm
infants (e.g., exposure 2 weeks before birth is at 34 weeks for a 36-week PTB, and
38 weeks for a 40-week term birth), which suggests the possibility of different modes of
action for increases in risk observed with near-birth exposures compared to exposures in
specific periods of fetal development.
There is evidence supporting a relationship between SO2 and preterm birth, primarily
with exposure near-birth and including both older and newer studies. Among a U.S. birth
cohort, Mendola et al. (2016a) examined PTB and exposure to SO2 during different
periods before and during pregnancy, observing generally null results among both women
with and without asthma, except for when exposure was limited to weeks near birth
(specifically weeks 34 and 36) for which positive associations were observed among
women with asthma, but not for women without asthma. Studies in Europe and Asia
report increased ORs/RRs of PTB with exposures across pregnancy, although not
consistently between studies (Dibben and Clemens. 2015; Zhao etal.. 2011; Leem et al..
2006; Bobak. 2000; Xu et al.. 1995). In a recent time-series analysis, Zhao etal. (2011)
found increased RRs with SO2 exposure days 0-3 lagged from birth, but SO2 was also
highly correlated with PM10 (Pearson correlation coefficient = 0.75) and NO2 (Pearson
correlation coefficient = 0.84) in the study area. Dibben and Clemens (2015) used a
pollution-climate model to assign SO2 concentrations with high spatial resolution as well
as incorporating daily activity data into the exposure and observed null associations with
PTB and modest, positive associations with VPTB among births in Scotland. Qian et al.
(2015) observed weak negative or null associations between SO2 exposures and PTB
across a range of different exposure windows among a birth cohort in Wuhan, China.
In the U.S. and Canada, studies of SO2 and PTB in Pennsylvania (Sagiv et al.. 2005) and
Vancouver (Liu et al.. 2003) found increased ORs with near-birth exposures rSagiv et al.
(2005): 6 week prebirth RR = 1.05 (1.00, 1.10); Liu et al. (2003): last month OR = 1.09
(1.01, 1.19) per 5-ppb increase]. More recently, in a Detroit, MI cohort, Le et al. (2012)
found similar associations for exposures in the last month of pregnancy [OR 4th to 1st
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quartile: 1.07 (1.01, 1.14)]. Another Vancouver cohort, examining entire pregnancy
exposure, only observed increases [OR= 1.03 (0.93, 1.15) per 5-ppb SO2 increase] with
PTB <30 weeks (Brauer et al.. 2008). Recent time-series and case-crossover studies in
Atlanta, GA and Brisbane, Australia observed null associations for both 1st month and
near-birth exposures using 1-h max SO2 [exposure during last week of pregnancy RR per
5-ppb increase = 0.99 (0.98, 1.01)] (Darrow et al.. 2009) and SO2 concentrations
24-48 hours preceding the onset of labor (Li et al.. 2016). Finally, a cross-sectional study
of PTB across the U.S. reported that SO2 showed "nonsignificant" effects with PTB for
exposures during the month of birth (Trasande et al.. 2013). In contrast, a recent study
conducted in Italy observed negative associations between SO2 exposure averaged across
the entire pregnancy as well as each trimester and PTB, suggesting the SO2 exposure was
associated with longer gestation (Capobussi et al.. 2016).
No recent animal studies evaluating preterm birth were identified.
In summary, there is some evidence for an association between exposure to SO2 and
preterm birth particularly with near-birth exposure windows. Studies examining PTB
primarily used average daily SO2. The one study that examined 1-h max SO2 found no
associations for PTB. Recent studies do not provide evidence to help reduce uncertainty
related to exposure measurement error, copollutant confounding, or the biological
mechanism by which SO2 could cause preterm birth. Studies are characterized in
Supplemental Table 5S-22 (U.S. EPA. 2015k).
5.4.3.3 Birth Weight
Birth weight is a measure of fetal growth and an important indicator of future infant and
child health. Birth weight is determined by gestational age and intra-uterine growth, as
well as maternal, placental, fetal, and environmental factors. Vulnerability to
environmental insults affecting birth weight may occur throughout pregnancy.
Implantation or formation of the placenta may be disrupted in the earliest weeks of
pregnancy, leading to decreased fetal nutrition throughout pregnancy; or inflammation
might result in constriction of the umbilical cord during the later trimesters resulting in
poor fetal nutrition. As the largest gains in birth weight occur during the last weeks of
gestation, this may be a particularly vulnerable period for birth weight outcomes.
Information on birth weight is routinely collected for vital statistics; given that measures
of birth weight do not suffer the same uncertainties as gestational age or growth
restriction, it is one of the most studied outcomes within air pollution and reproductive
health. Birth weight may be examined as a continuous outcome or dichotomous outcome
as low birth weight (LBW) (less than 2,500 g or 5 lbs, 8 oz).
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Studies examining LBW have found elevated ORs with exposures in the first trimester or
first month (Dugandzic et al.. 2006; Lee et al.. 2003; Liu et al.. 2003; Ha et al.. 2001) and
with entire pregnancy exposures (Capobussi et al.. 2016; Dibben and Clemens. 2015;
Yorifuii et al.. 2015a; Ebisu and Bell. 2012; Kumar. 2012; Morello-Frosch et al.. 2010).
In the two studies that examined distance to monitor, using concentrations from closer
monitors lead to stronger effect estimates (Kumar. 2012; Morello-Frosch et al.. 2010).
Some studies examining entire pregnancy exposure have also observed null associations
between SO2 and LBW (Brauer etal.. 2008; Bell et al.. 2007).
Studies examining continuous birth weight (Ag) have inconsistent results. In a northeast
U.S. population, Bell et al. (2007) observed no association with change in birth weight
for entire pregnancy exposure [-2.711 g (-13.253, 7.831) per 5 ppb SO2], including in a
stratified analysis of white and black mothers. Kumar (2012) reported results that shifted
around the null based on distance from monitor in Chicago; some effects were positive,
and some negative but all had wide confidence intervals. And, in a cross-sectional study
across the county, Trasande et al. (2013) reported only "nonsignificant" effects for SO2.
One recent California cohort study reported increases in birth weight with increases in
SO2 exposure in entire pregnancy and first trimester, although effects were reduced with
use of closer monitors (Morello-Frosch et al.. 2010). A recent Texas study observed
decreases in birth weight with county average SO2 exposure for the entire pregnancy
[-15.594 g (-25.344, -5.844)] (Geer et al.. 2012). A study in Beijing during the summer
Olympics of 2008 found increased SO2 in the 8th month of pregnancy associated with
decrements in birth weight; however, SO2 was highly correlated with PM2 5 and CO,
which showed similar patterns of effect (Rich et al. 2015). Finally, a recent study in
Atlanta found decreases in birth weight with increases in 3rd trimester 1-h max SO2
(Darrow et al.. 2011). This effect was stronger in non-Hispanic white and Hispanic
mothers than non-Hispanic black mothers (Darrow et al.. 2011).
No recent animal studies evaluating birth weight-related outcomes were identified. In
laboratory animals from an older study, exposure to SO2 affected birth outcomes in adult
female rodents and their offspring. Adult female albino rats were exposed to either
0.057 ppm or 1.5 ppm SO2 by inhalation for 72 days (Mamatsashvili. 1970b). At birth,
litter sizes were significantly increased in number from dams that were exposed to SO2
versus control dams (Table 5-37).
In summary, there is some evidence that LBW may be associated with SO2, while
evidence for an association with change in birth weight is inconsistent. Overall, the
results of studies of LBW and birth weight remain inconsistent and these do not provide
evidence to help reduce uncertainty related to exposure measurement error, copollutant
confounding, or the biological mechanism by which SO2 could cause these effects.
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Studies for both LBW and change in birth weight can be found in Supplemental
Table 5S-23 (U.S. EPA. 20151).
5.4.3.4 Birth Defects
Birth defects are structural and functional abnormalities that can cause physical disability,
intellectual disability, and other health problems. They are a leading cause of infant
mortality and developmental disability in the U.S. (Mai et al.. 2016). Since 2008, there
have been several studies examining birth defects and SO2 during pregnancy, particularly
during weeks 3-8 of gestation, which is thought to be highly vulnerable to insults
resulting in birth defects. Because birth defects as a whole are rare and specific birth
defects are rarer, these studies often have effect estimates with very wide confidence
intervals. Individual studies often look at different types of birth defects, meaning the
body of work examining any one birth defect may still be limited. Cardiac birth defects
and oral cleft defects are the most commonly studied anomalies. However, results (even
for these defects) are inconsistent across studies. For example, odds of ventricular septal
defects have been found to be increased (Gianicolo et al.. 2014; Stingone et al.. 2014;
Agav-Shav et al.. 2013; Gilboa et al.. 2005). decreased (Hwang et al.. 2015b; Dadvand et
al.. 2011a. b; Rankin et al.. 2009). and null (Strickland et al.. 2009) with increases in SO2
exposure. Odds of cleft lip with or without cleft palate have been found to be increased
(Zhu et al.. 2015). decreased (Hwang and Jaakkola. 2008; Gilboa et al.. 2005). or null
(Polk et al.. 2010; Rankin et al. 2009) with increases in SO2 exposure. A single study of
limb deformities found increased odds with exposure to SO2 during weeks 9-12 of
pregnancy (Lin etal.. 2014). Two studies examining repeating chromosomal defects
found no association or correlation between trisomy 21 or any sperm disomy and SO2
(Chung et al.. 2014; Jurewicz et al. 2014). Studies of any congenital anomaly in Israel
and China have reported inverse associations with increasing SO2 (Farhi et al.. 2014;
Liang et al.. 2014).
No recent animal studies evaluating birth defects were identified.
In summary, results for birth defects are either inconsistent across studies or limited in
number of studies. Studies of birth defects and SO2 are characterized in Supplemental
Table 5S-24 (U.S. EPA. 2015m).
5.4.3.5 Fetal Mortality
Fetal mortality or stillbirth is the intra-uterine death of a fetus. In most areas fetal deaths
are only reported after 20 weeks of completed gestation; this leads to potential bias, as the
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population at risk of fetal death is any conception but the actual measured population is
only those fetuses reaching at least 20 weeks gestational age. A single recent case-control
study of spontaneous abortion occurring before 14 weeks of gestation found no
associations with SO2 exposures determined by time-weighted concentrations for
residence and workplace (Moridi et al.. 2014). A recent large California cohort found no
associations between stillbirth and increasing SO2 exposure (Green et al.. 2015V In recent
studies of a New Jersey population examining both long-term and short-term exposure
windows, ORs for fetal death were elevated with a 2-day lag [OR per 5-ppb increase in
SO2: 1.12 (1.02, 1.24)] and with exposures across pregnancy and in each trimester,
particularly the 3rd trimester [OR per 5-ppb increase in SO2: 1.47 (1.05, 1.69)] (Faiz et
al.. 2013; Faiz et al.. 2012). Hwang et al. (2011) examined fetal mortality among term
and preterm deliveries in Taiwan, finding elevated associations for exposures during the
1st trimester only among preterm deliveries. Other studies have also found increased
associations between SO2 and fetal mortality, although mean SO2 concentrations were
higher in these studies (Hon et al.. 2014; Pereira et al.. 1998). Pereira et al. (1998)
observed elevated RRs in a Sao Paulo, Brazil time series with short-term exposure.
A recent study by Enkhmaa et al. (2014) found very strong correlations between seasonal
SO2 and fetal death, and Hou et al. (2014) found elevated ORs with long-term exposures
around the time of conception. Although Hou et al. (2014)"s models were unadjusted for
confounding factors and confidence intervals were very wide. In the study by Enkhmaa et
al. (2014). other pollutants also showed very strong correlations and were highly
correlated with one another.
No recent animal studies evaluating fetal mortality were identified.
In summary, although few in number, studies of fetal mortality and SO2 show elevated
associations for both short- and long-term exposures. However, these studies are limited
by the uncertainties associated reproductive and developmental outcomes identified in the
2008 SOx ISA. Studies are characterized in Supplemental Table 5S-25 (U.S. EPA.
2015n).
5.4.3.6 Infant Mortality
Studies of infant mortality and SO2 are limited in number. In a U.S. study, Woodruff et
al. (2008) observed increased ORs for respiratory-related post-neonatal infant mortality
with long-term (2 months) exposure increases in county-level SO2 concentrations
[OR = 1.09 (0.89, 1.36) per 5-ppb increase]. This association remained after adjusting for
other pollutants. A time-series study in Seoul, South Korea observed increased RRs for
all cause post-neonatal infant mortality with short-term SO2 exposure, although exact
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timing of exposure was unclear (Son et al.. 2008). No recent animal studies evaluating
postnatal mortality were identified. Studies are characterized in Supplemental
Table 5S-25 (U.S. EPA. 2015n).
5.4.4 Developmental Outcomes
5.4.4.1 Respiratory Outcomes
Recent studies examined asthma onset in association with early life exposure to SO2.
Clark et al. (2010). Liu et al. (2016). Deng et al. (2015b). and Deng et al. (2015a)
observed elevated ORs for asthma with SO2 exposure during pregnancy and the first year
of life. Nishimura et al. (2013) observed elevated ORs for asthma with SO2 exposure in
the first 3 years of life, but not the first year of life alone. Asthma onset is covered in
further detail in Section 5.2.1.2.
In a time-series study, Dales et al. (2006) investigated neonatal hospitalizations due to
respiratory causes in Atlanta, GA; they observed elevated ORs with 2-day lagged SO2
exposure. After adjustment for gaseous copollutants, confidence intervals for associations
with gaseous pollutants and PM10 were very large, but effect estimates remained elevated.
Hospitalizations due to respiratory causes are covered in Section 5.2.1.6.
In summary, there is some evidence for an association between gestational and early-life
exposure to SO2 and respiratory health effects later in life, although evidence is limited
and exposure windows are uncertain. Key studies are summarized in Table 5-36.
5.4.4.2 Other Developmental Effects
Studies examining other developmental exposures are limited in number. A recent study
examined SO2 exposure with apnea and bradycardia in a subpopulation of infants in
Atlanta, and observed no association for either health outcome (Peel et al.. 2011). Huang
et al. (2015a) observed no associations between prenatal and early life SO2 exposures and
atopic dermatitis among infants in Taiwan. Poursafa et al. (2016) examined the
association between SO2 exposure during pregnancy and markers of endothelial
disfunction (i.e., ICAM-1, V-CAM-1, endothelin-1) in cord blood. They observed a
positive association with endothelin-1, but not for other markers of endothelial
disfunction. Among a Japanese cohort, prenatal exposure to SO2 was associated with
verbal and fine motor delays assessed at ages 2.5 and 5.5 years (Yorifuii et al.. 2015b). In
an older study from the animal toxicology literature, adult female albino rats were
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exposed to either 0.057 ppm or 1.5 ppm SO2 by inhalation, 12 hours/day for 72 days
(Mamatsashvili. 1970b'). Changes in offspring postnatal growth or body weight over time
were reported with 1.5-ppm exposure.
Sulfur dioxide-dependent synaptic injury was measured in adolescent male rats exposed
to 1.24 ppm SO2 for 6 hours/day for 90 days (Yun et al. 2013). Nonsignificant
morphological changes were seen in the hippocampal synaptic junctions using
transmission electron microscopy. In the hippocampus, the synaptic vesicle membrane
protein synaptophysin (SYP) was significantly downregulated as was ERK1/2
phosphorylation. Phosphorylation is an important contributor to synaptic plasticity. Thus,
SO2 exposure in the adolescent rat contributes to downregulation of synaptic vesicle
protein SYP and decreased ERK1/2 phosphorylation, indicative of disruption at the
hippocampal synapse.
5.4.5 Summary and Causal Determination
Overall the evidence is inadequate to infer a causal relationship between exposure to SO2
and reproductive and developmental outcomes. This is consistent with the 2008 ISA for
Sulfur Oxides, which also concluded the evidence was inadequate to infer the presence or
absence of a causal relationship with reproductive and developmental effects. All
available evidence, including more than 50 recent studies, examining the relationship
between exposure to SO2 and reproductive and developmental effects was evaluated
using the framework described in the Preamble to the ISAs (U.S. EPA. 2015b). The key
evidence as it relates to the causal framework is summarized in Table 5-38.
There are several well-designed, well-conducted epidemiologic studies, many described
in papers published since the previous ISA, that indicate an association between SO2 and
reproductive and developmental health outcomes; the bulk of the evidence exists for
adverse birth outcomes. For example, several high quality studies reported positive
associations between SO2 exposures during pregnancy and fetal growth metrics (Lc et al..
2012; Rich et al.. 2009; Brauer et al.. 2008; Liu et al.. 2003). preterm birth (Mendola et
al.. 2016a; Le et al.. 2012; Zhao etal. 2011; Sagiv et al.. 2005; Liu et al.. 2003). birth
weight (Ebisu and Bell. 2012; Darrow et al.. 2011; Morello-Frosch et al.. 2010; Liu et al..
2003). and fetal and infant mortality (Faiz et al.. 2012; Hwang etal.. 2011; Woodruff et
al.. 2008). However, the evidence is not entirely consistent, and has not substantially
reduced any of the uncertainties connected with the associations observed between
exposure to SO2 and birth outcomes that were identified in the previous ISA.
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Table 5-38 Summary of evidence inadequate to infer a causal relationship
between sulfur dioxide exposure and reproductive and
developmental effects.
Rationale for Causal SO2 Concentrations
Determination3 Key Evidence13 Key References'3 Associated with Effects0
Overall reproductive and developmental effects—inadequate to infer a causal relationship
Evidence from multiple
Consistent positive
Liu et al. (2003)
Mean:
4.9
ppb
epidemiologic studies of
associations observed with
preterm birth is generally
near-birth exposures to
Saaiv et al. (2005)
Mean:
7.9
ppb
supportive but key
SO2 and preterm birth after
uncertainties remain.
adjustment for common
tLe et al. (2012)
Mean:
5 8
ppb
potential confounders.
Associations not evaluated
in copollutant models.
tMendola etal. (2016a)
Mean:
4.0
ppb
Section 5.4.3.2
Limited and inconsistent Several studies show Section 5.4.3.1 Means: 4.9-5.8 ppb
epidemiologic evidence for positive associations with
other birth outcomes fetal growth metrics,
although definitions vary
across studies, and timing
of exposure is inconsistent.
Associations not evaluated
in copollutant models
Several high quality studies Section 5.4.3.3 Means: 2.1-13.2 ppb
show associations between
SO2 exposure and low birth
weight but not for change
in birth weight. Timing of
exposure is inconsistent
across studies. Only one
study uses 1-h max for
exposure determination.
Limited and inconsistent Section 5.4.3.4 Reported means: 1.9-6
epidemiologic evidence for
associations with various
birth defects
Limited number of studies Section 5.4.3.6 Mean: 5.7 ppb
of SO2 and fetal death, Mean: 5.8 ppb
positive associations Mean: 5.9 ppb
observed across studies, Mean: 3 ppb
although timing of
exposure and outcome
definitions are inconsistent
Limited evidence for an
association with SO2 in
respiratory related infant
mortality
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Table 5-38 (Continued): Summary of evidence inadequate to infer a causal
relationship between sulfur dioxide exposure and
reproductive and developmental effects.
Rationale for Causal SO2 Concentrations
Determination3 Key Evidence13 Key References'3 Associated with Effects0
Limited evidence for Section 5.4.4.1 Means: 2-4.3 ppb
positive associations
between prenatal/early life
exposures and childhood
respiratory outcomes
Limited evidence for key Altered menstrual function, Mamatsashvili (1970a) 57 or 1,427 ppb
events in proposed mode fetal growth, and birth
of action weight outcomes with
impaired postnatal growth
in in utero exposed pups
Lack of evidence from
epidemiologic studies to
support an association of
SO2 exposure with
detrimental effects on
fertility or pregnancy
A limited number of studies Section 5.4.4.1
on fertility and pregnancy
outcomes show no
associations with SO2.
Mean 8.4-59 ppb
Uncertainty regarding
Limited adjustment for
+(Faiz et al. (2013): Slama
potential confounding by
copollutants, with no clear
etal. (2013): Le et al.
copollutants
directionality or trends for
effect estimate shifts after
adjustment
(2012))
Uncertainty regarding
Central site monitors
Chapter 3
exposure measurement
subject to some degree of
Section 3.4.4.2
error
exposure error. Spatial and
temporal heterogeneity
may introduce exposure
error in long-term effects
and bias could be toward
or away from the null.
Uncertainty regarding
exposure timing for specific
outcomes.
Associations of exposure to
SO2 at particular windows
during pregnancy are
inconsistent between
studies and across
outcomes.
S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in the Preamble to the ISAs
(U.S. EPA. 2015b).
bDescribes the key evidence and references contributing most heavily to causal determination and where applicable to
uncertainties and inconsistencies. References to earlier sections indicate where full body of evidence is described.
°Describes the S02 concentrations with which the evidence is substantiated (for experimental studies, below 2,000 ppb).
fStudies published since the 2008 ISA for Sulfur Oxides.
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One uncertainty is timing of exposure, wherein associations remain inconsistent among
studies and across outcomes. For example, some studies observe the strongest
associations when exposure is averaged over the entire pregnancy, while others observe
the strongest association when exposure is averaged over either the first, second, or third
trimester. As an exception to this, studies of PTB generally observed positive associations
between near-birth exposures (e.g., last month of gestation, same, or 3-day lag from birth)
(Mendola et al. 2016a; Le et al.. 2012; Zhao etal.. 2011; Sagiv et al.. 2005; Liu et al.
2003V
Another uncertainty centers on spatial and temporal variability in SO2 exposures. SO2 is a
temporally and spatially heterogeneous pollutant; it is difficult to accurately estimate for
"long-term" exposures, and there is the potential for exposure measurement error in
long-term SO2 exposures to bias estimates toward or away from the null (Section 3.5).
None of the epidemiologic studies made corrections or adjustments for exposure
measurement error or accounted for the potential for bias away from the null, the
potential for which has been demonstrated in simulation studies (see Section 3.4.4.2V
Current epidemiologic methods are not able to disentangle whether associations are due
to extended exposure to moderate concentrations of SO2 or repeated short-term exposure
to peaks in SO2 concentration.
Potential confounding by copollutants may explain some of the observed associations and
cannot be ruled out. SO2 is part of a mix of ambient air pollution; SO2 shares sources with
particulate matter and is chemically linked to sulfate. Few studies evaluate or provide
information that would inform the independent effect of SO2 in the context of the greater
air pollution mixture, and of those that do, no clear trends for the effects of copollutant
adjustment are apparent (Faiz et al.. 2013; Slama et al.. 2013; Le et al. 2012).
There is insufficient information on potential modes of action of SO2 on reproductive
outcomes at relevant exposure levels for this ISA (Chapter 4). In a single older study
from Mamatsashvili (1970a). SO2 inhalation exposure in laboratory rodents demonstrated
reproductive changes in exposed females and their offspring, altered birth outcomes, and
developmental effects. The specific outcomes affected after SO2 exposure included
altered estrus cycle length of F0 and F1 generations, decrements in offspring body weight
gain or growth after in utero exposure, and changes in litter size. The majority of the
remaining animal toxicological evidence for reproductive and developmental effects is
for exposure at 5,000 ppb or greater, doses which are beyond the scope of this document.
Since the 2008 ISA for Sulfur Oxides, researchers have begun evaluating more health
outcomes, including fertility, effects on pregnancy (e.g., pre-eclampsia, gestational
diabetes), and developmental effects. For each of these individual outcomes the literature
base is small, but new studies are quickly accumulating. However, at present there is little
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coherence or consistency among epidemiologic and toxicological studies for these
outcomes. In general, it is challenging to synthesize study findings on the wide variety of
health outcomes collected under the reproductive and developmental effects heading.
Given the wide variety of potential mechanisms or adverse outcome pathways that could
affect this breadth of outcomes, coherence is unlikely to be reached given the limited
literature base.
The state of California, under the auspices of Proposition 65, the California Safe
Drinking Water and Toxic Enforcement Act of 1986, has listed sulfur dioxide as a
chemical known to cause developmental toxicity based on evidence from laboratory
animal studies and epidemiologic studies, with the strongest evidence from IUGR. SO2 is
not listed as a reproductive toxicant under Proposition 65; much of this evidence is from
toxicological studies with exposure to SO2 at 5,000 ppb or greater (beyond the scope of
this ISA). Effects seen at the higher doses include male reproductive effects on sperm and
fecundity, as well as oxidative damage to the male reproductive organs, changes in birth
weight or litter size, delayed reflexes in early life, and aberrant behavior of pups after in
utero exposure. Epidemiologic evidence used for this listing is also evaluated under
differing criteria than are employed for the ISA.
Overall, many uncertainties remain when evaluating the evidence for these health
endpoints; therefore, the evidence is inadequate to infer a causal relationship between
exposure to SO2 and reproductive and developmental outcomes.
5.5 Mortality
5.5.1 Short-Term Exposure
5.5.1.1 Introduction
Earlier studies that examined the association between short-term SOx exposure, mainly
SO2, and total mortality were limited to historical data on high air pollution episodes
(U.S. EPA. 1982a). These studies were unable to decipher whether the associations
observed were due to particle pollution or SO2. Additional studies evaluated in the 1986
Second Addendum to the 1982 AQCD (U.S. EPA. 1986b) further confirm the findings of
these initial studies, but were still unable to address uncertainties and limitations related
to examining the effect of SO2 exposure on mortality, especially at lower concentrations.
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In the 2008 SOx ISA (U.S. EPA. 2008(1). a larger body of literature was available to
assess the relationship between short-term SO2 exposures and mortality; however, these
studies were still limited in that they primarily focused on PM, with SO2 only being
examined in single-pollutant models. These studies found that excess risk estimates for
total mortality due to short-term SO2 exposure from multicity studies and meta-analyses
generally ranged from 0.4 to 2.0% for a 10-ppb increase in 24-h avg SO2 concentrations.
These associations were primarily observed at mean 24-h avg SO2 concentrations
<15 ppb. Studies that examined cause-specific mortality found evidence of risk estimates
larger in magnitude for respiratory and cardiovascular mortality compared to total
mortality with the largest associations for respiratory mortality. The larger
SCh-respiratory mortality associations observed in the epidemiologic literature were
coherent with the scientific evidence providing stronger support for SO2 effects on
respiratory morbidity compared to cardiovascular morbidity (U.S. EPA. 2008d).
An examination of potential copollutant confounding of the S02-mortality relationship
was sparse. Studies evaluated in the 2008 SOx ISA found that S02-mortality risk
estimates from copollutant models were robust, but imprecise. An additional study that
examined the potential interaction between copollutants [i.e., SO2 and black smoke (BS)]
did not find evidence of interaction when stratifying days by high and low concentrations
of BS (katsouvanni et al.. 1997). Of the studies evaluated only the Air Pollution and
Health: A European Approach (APHEA) study examined seasonality and potential effect
modifiers of the S02-mortality relationship, and provided initial evidence of mortality
effects being larger during the warm season and that geographic location may influence
city-specific S02-mortality risk estimates, respectively (katsouvanni etal.. 1997).
The consistent, positive S02-mortality associations observed across studies were
supported by an intervention study conducted in Hong Kong that examined the health
impact of converting to fuel oil with low sulfur content and found evidence suggesting
that a reduction in SO2 concentrations leads to a reduction in mortality (Hedlev et al..
2002). Overall, the relatively sparse number of studies that examined the relationship
between short-term SO2 exposure and mortality along with the limited data with regard to
potential copollutant confounding resulted in the 2008 SOx ISA concluding that the
collective evidence is "suggestive" of a causal relationship between short-term SO2
exposure and mortality.
Since the completion of the 2008 SOx ISA (U.S. EPA. 2008d). there continues to be a
growing body of epidemiologic literature that has examined the association between
short-term SO2 exposure and mortality. However, similar to the collection of studies
evaluated in the 2008 SOx ISA (U.S. EPA. 2008d). most of the recent studies do not
focus specifically on the S02-mortality relationship, but instead on PM or O3. Of the
studies identified, a limited number have been conducted in the U.S., Canada, and
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Europe, with the majority being conducted in Asia due to the increased focus on
examining the effect of air pollution on health in developing countries. Although these
studies are informative when evaluating the collective evidence, the interpretation of
these studies in the context of results from studies conducted in the U.S., Canada, and
Western Europe requires caution. This is because studies conducted in Asia encompass
cities with meteorological, outdoor air pollution (e.g., concentrations, mixtures, and
transport of pollutants), and sociodemographic (e.g., disease patterns, age structure, and
socioeconomic variables) (Chen et al.. 2012b; Kan et al.. 2010a; Wong et al.. 2008b')
characteristics that differ from cities in North America and Europe, potentially limiting
the generalizability of results from studies of Asian cities to other cities.
As detailed in previous ISAs [e.g., U.S. EPA (2013c)l. this section focuses primarily on
multicity studies because they examine the association between short-term SO2 exposure
and mortality over a large geographic area using a consistent statistical methodology,
which avoids the potential publication bias often associated with single-city studies (U.S.
EPA. 2008d). However, where applicable single-city studies are evaluated that
encompass a long study-duration, provide additional evidence indicating that a specific
population or lifestage is at increased risk of SCh-related mortality, or address a limitation
or uncertainty in the SCh-mortality relationship not represented in multicity studies.
The remaining studies identified are not evaluated in this section due to issues associated
with study design or insufficient sample size, and are detailed in Supplemental
Table 5S-26 (U.S. EPA. 2015o).
The organization of the material on short-term SO2 exposure and mortality is as follows.
Section 5.5.1.2 evaluates studies that examined the association between short-term SO2
exposure and mortality, with the remaining sections addressing key limitations and
uncertainties in the SCh-mortality relationship that were evident at the completion of the
2008 SOx ISA (U.S. EPA. 2008d). Subsequent sections evaluate whether there is
evidence of: confounding (i.e., copollutants and seasonal/temporal) (Section 5.5.1.3).
effect modification (i.e., sources of heterogeneity in risk estimates across cities or within
a population) (Section 5.5.1.4). modification of the S02-mortality association including
seasonal heterogeneity (Section 5.5.1.5). and the SCh-mortality C-R relationship and
related issues, such as the lag structure of associations (Section 5.5.1.5).
5.5.1.2 Associations between Short-Term Sulfur Dioxide Exposure and Mortality in
All-Year Analyses
Multicity studies and meta-analyses evaluated in the 2008 SOx ISA reported consistent,
positive associations between short-term SO2 exposure and total mortality in all-year
analyses (U.S. EPA. 2008d). Although only a small number of multicity studies have
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1 been conducted since the completion of the 2008 SOx ISA, these studies, as well as a
2 meta-analysis of studies conducted in Asia (Atkinson et al.. 2012). build upon and
3 provide additional evidence for an association between short-term SO2 exposure and total
4 mortality (Figure 5-17). Air quality characteristics and study specific details for the
5 studies evaluated in this section are provided in Table 5-39.
Table 5-39 Air quality characteristics of multicity studies and meta-analyses
evaluated in the 2008 SOx ISA and recently published multicity
studies and meta-analyses.
Study
Location
Years
Mortality
Outcome(s)
Averaging
Time
Mean
Concentration
PPb
Upper
Percentile
Concentrations
PPb
North America
Dominici et al.
(2003)
72 U.S. cities
(NMMAPSf
1987-
1994
Total
24-h avg
0.4-14.2
—
Burnett et al. (2004)
12 Canadian
cities
1981-
1999
Total
cardiovascular
respiratory
24-h avg
0.9-9.6
—
tMoolaavkar et al.
(2013)
85 U.S. cities
(NMMAPSf
1987-
2000
Total
24-h avg
...
...
Europe
Katsouvanni et al.
(1997)
12 European
cities
(APHEA-1)
1980-
1992
Total
24-h avg
5.0-28.2b
90th:
17.2-111.8
Biaaeri et al. (2005)
Eight Italian
cities
(MISA-1)
1990-
1999
Total
cardiovascular
respiratory
24-h avg
2.5-15.6
95th: 6.0-50.1
Max: 7.1-111.0
Hoek (2003)
Netherlands
1986-
1994
Total
cardiovascular
respiratory
24-h avg
3.5-5.6
—
tBeralind et al.
(2009)
Five European
cities'
1992-
2002
Total
24-h avg
1.0-1.69
...
tBellini et al. (2007)
15 Italian
cities
(MISA-2)
1996-
2002
Total
cardiovascular
respiratory
24-h avg
—
—
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Table 5-39 (Continued: Air quality characteristics of multicity studies and meta
analyses evaluated in the 2008 SOx ISA and recently
published multicity studies and meta analyses.
Study
Location
Years
Mortality
Outcome(s)
Averaging
Time
Mean
Concentration
PPb
Upper
Percentile
Concentrations
PPb
Asia
tKan et al. (2010b):
Wona et al. (2008b):
Wona et al. (2010)
Four Asian
cities (PAPA)
1996-
2004h
Total
cardiovascular
respiratory
24-h avg
5.0-17.1
75th: 6.0-21.5
Max: 23.4-71.7
tChen et al. (2012b)
17 Chinese
cities
(CAPES)
1996-
2010'
Total
cardiovascular
respiratory
24-h avg
6.1-38.2
75th: 6.5-56.1
Max: 25.2-298.5
tChen et al. (2013)
Eight Chinese
cities
1996-
2008'
Stroke
24-h avg
6.1-32.1
...
tMena et al. (2013)
Four Chinese
cities
1996-
2008k
COPD
24-h avg
6.8-19.1
...
Meta-analyses
Stieb et al. (2003)
Meta-analysis
1958-
1999e
Total
24-h avg
0.7-75.2
...
HEI (2004) Meta-analysis 1980- Total 24-h avg ~10->200
(South Korea, 2003d
China,
Taiwan, India,
Singapore,
Thailand,
Japan)
tAtkinson et al. Meta-analysis 1980- Total 24-h avg
(2012) (Asia) 2007j cardiovascular
respiratory
COPD
tShah et al. (2015) Meta-analysis 1948-Jan Stroke NR 6.2C Max: 30.2
2014
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Table 5-39 (Continued: Air quality characteristics of multicity studies and meta
analyses evaluated in the 2008 SOx ISA and recently
published multicity studies and meta analyses.
Upper
Mean Percentile
Mortality Averaging Concentration Concentrations
Study Location Years Outcome(s) Time ppb ppb
tYanq et al. (2014b) Meta-analysis 1996- Stroke
(Asia, Europe, 2013
and North
America)
24-h avg Asia: 11.4b 75th: Asia: 18.6
Europe: 5.2b Europe: 2.3
North America: North America:
4.2b 7.6
APHEA = Air Pollution and Health: A European Approach study; CAPES = China Air Pollution and Health Effects Study;
COPD = chronic obstructive pulmonary disease; ISA = Integrated Science Assessment; MISA = Meta-analysis of the Italian
studies on short-term effects of air pollution; NMMAPS = The National Morbidity Mortality Air Pollution Study; NR = not reported;
PAPA = Public Health and Air Pollution in Asia; SOx = sulfur oxides.
aOf the 90 cities included in the NMMAPS analysis only 72 had S02 data.
bMedian concentration.
The mortality time series of studies included in the meta-analysis spanned these years.
dStudies included within this meta-analysis were published during this time period.
eOf the 108 cities included in the analyses using NMMAPS data, only 85 had S02 data.
fS02 data was not available for Barcelona; therefore, the S02 results only encompass four cities.
9Median concentrations.
The study period varied for each city, Bangkok: 1999-2003, Hong Kong: 1996-2002, and Shanghai and Wuhan: 2001-2004.
'Study period varied for each city and encompassed 2 to 7 yr. Hong Kong was the only city that had air quality data prior to 2000.
'Year defined represent the year in which studies were published that were included in the meta-analysis.
kStudy period varied from 2 to 7 yr. Hong Kong was the only city that had air quality data prior to 2001.
f = Studies published since the 2008 SOx ISA.
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Study
Location
Age
Dominici etal. (2003)
72 U.S. cities (NMMAPS)
All
Burnett et al. (2004)
12 Canadian cities
All
Katsouyanni et al. (1997)
12 European cities (APHEA1)
All
Biggeri et al. (2005)
8 Italian cities (MISA-1)
All
Hoek et al. (2003)
Netherlands
All
Stieb etal. (2002)
Meta-analysis (Worldwide)
All
HEI (2004)a
Meta-analysis (Asia)
All
Lag
Variable (0-3 days) ~
0-1 *
0-6 •
Variable
Variable —9—
fMoolgavkaretal. (2013) 85 U.S. cities (NMMAPS) All 1
•fBerglind et al. (2009)b 5 European cities 35-74 0-1
•fBellini et al. (2007) 15 Italian cities (MISA-2) All 0-1
fChen etal. (2012) 17 Chinese cities (CAPES) All 0-1
•fKan et al. (2010)c 4 Asian cities (PAPA) All 0-1
•f Atkinson et al. (2012) Meta-analysis (Asia) All Variable
< • ~
•
-5.0 0.0 5.0 10.0
% Increase (95% Confidence Interval)
APHEA = Air Pollution and Health: A European Approach study; CAPES = China Air Pollution and Health Effects Study; MISA = Meta-analysis of the Italian studies on short-term
effects of air pollution; NMMAPS = The National Morbidity Mortality Air Pollution Study; PAPA = Public Health and Air Pollution in Asia.
Note: f = studies published since the 2008 ISA for Sulfur Oxides;
a = Meta-analysis of Asian cities: South Korea, China, Hong Kong, Taipei, India, Singapore, Thailand, Japan (HEI. 2004):
b = Study was of myocardial infarction survivors therefore only included individuals 35+ (Beralind et al.. 2009):
c = Kan et al. (2010b) reported results that were also found in (Wong et al.. 2010; Wong et al. (2008b)).
Corresponding quantitative results are reported in Supplemental Table 5S-27 (U.S. EPA. 2016l)bb.
Figure 5-17 Percent increase in total mortality from multicity studies and meta-analyses evaluated in the 2008
ISA for Sulfur Oxides (black circles) and recently published multicity studies (red circles) for a
10-ppb increase in 24-h avg sulfur dioxide concentrations.
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When focusing on specific causes of mortality, some studies evaluated in the 2008 SOx
ISA reported similar risk estimates across mortality outcomes [e.g., (Zmirou et al. (1998);
Katsouvanni et al. (1997))!. while others indicated larger risk estimates for respiratory
mortality (Figure 5-18). However, a study conducted in the Netherlands by Hoek (2003)
suggested that specific cardiovascular mortality outcomes have larger risk estimates
compared to all cardiovascular, total, and respiratory-related mortality outcomes. Recent
multicity mortality studies provide additional support indicating larger risk estimates for
respiratory mortality compared to total and cardiovascular mortality. Additionally, the
results from the studies depicted in Figure 5-18 lend additional support to the body of
evidence indicating SC>2-induced respiratory effects presented in the 2008 SOx ISA, as
well as Section 5_2 of this ISA. Unlike the results reported in Hoek (2003). recent studies
do not provide evidence indicating associations larger in magnitude for SCh-related
cardiovascular mortality compared to other mortality outcomes.
5.5.1.3 Potential Confounding of the Sulfur Dioxide-Mortality Relationship
A limitation of the studies evaluated in the 2008 SOx ISA, was the relatively sparse
analyses of the potential confounding effects of copollutants on the S02-mortality
relationship (U.S. EPA. 2008d). The 2008 SOx ISA specifically stated that the "potential
confounding and lack of understanding regarding the interaction of SO2 with
copollutants" was one of the major limitations of the scientific literature that contributed
to the conclusion that the evidence is "suggestive of a causal relationship" between
short-term SO2 exposures and mortality. Copollutant analyses conducted in recent studies
further attempt to identify whether SO2 has an independent effect on mortality. In
addition to examining potential copollutant confounding, some studies have also
examined whether the covariates included in statistical models employed to examine
short-term SO2 exposures and mortality adequately control for the potential confounding
effects of season/temporal trends and weather.
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Study
Katsouyanni et al. (1997)
Zmirou et al. (1998)a
Katsouyanni et al. (1997)
Zmirou et al. (1998)
Biggeri et al. (2005)
Hoek et al. (2003)
"I"Bellini et al. (2007)
"I" Atkinson et al. (2012)
tChen et al. (2012)
tChen et al. (2013)
tMengetal. (2013)
tKanetal. (2010)d
Location
7 W. European Cities (APHEA1)
5 W. European Cities (APHEA1)
5 C. European Cities (APHEA1)
5 C. European Cities (APHEA1)
8 Italian cities (MISA-1)
Netherlands
15 Italian cities (MISA-2)
Meta-analysis (Asia)
17 Chinese cities (CAPES)
17 Chinese cities (CAPES)
8 Chinese cities (CAPES)b
17 Chinese cities (CAPES)
4 Chinese citiesc
4 Asian cities (PAPA)
Mortality
Total
Cardiovascular
Respiratory
Total
Cardiovascular
Respiratory
Total
Cardiovascular
Respiratory
Total
Cardiovascular
Heart Failure
Thrombosis-related
COPD
Pneumonia
Total
Cardiovascular
Respiratory
Total
Cardiovascular
Respiratory
COPD
Total
Cardiovascular
Stroke
Respiratory
COPD
Total
Cardiovascular
Respiratory
Lag
Variable (0-3 days)
Variable (0-3 days)
0-1
0-6
0-1
hA-
Variable
0-1
0-1
—I 1 1 1—
-6 -4 -2 0 2 4 6 8 10 12 14 16 18 20
% Increase (95% Confidence Interval)
APHEA = Air Pollution and Health: A European Approach study; CAPES = China Air Pollution and Health Effects Study; COPD = chronic obstructive pulmonary disease;
MISA = Meta-analysis of the Italian studies on short-term effects of air pollution; PAPA = Public Health and Air Pollution in Asia.
Note: f = studies published since the 2008 ISA for Sulfur Oxides; total mortality = circle; cardiovascular-related mortality = triangle; and respiratory-related mortality = diamond,
a = Zmirou et al. (1998) reported on only five of the seven cities included in Katsouyanni et al. (1997), which had cause-specific mortality data and were included in the analysis;
b = Chen et al. (2012b) examined stroke only in the China Air Pollution and Health Effects Study cities that had stroke data;
c = Menq et al. (2013) was not part of CAPES, but the four cities included had data for the same years as the CAPES study;
d = Kan et al. (2010b) reported results which were also presented in Wong et al. (2008b) and Wong et al. (2010).
Corresponding quantitative results are reported in Supplemental Table 5S-28 (U.S. EPA. 2016w).
Figure 5-18 Percent increase in total, cardiovascular, and respiratory mortality from multicity studies
evaluated in the 2008 ISA for Sulfur Oxides (black) and recently published multicity studies (red)
for a 10-ppb increase in 24-h avg sulfur dioxide concentrations.
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35
Examination of Potential Copollutant Confounding
In the 2008 SOx ISA (U.S. EPA. 2008(1). the analysis of potential copollutant
confounding was limited to studies conducted by Dominici et al. (2003) within the U.S.
as part of the National Morbidity Mortality Air Pollution Study (NMMAPS),
Katsouvanni et al. (1997) in Europe as part of the Air Pollution and Health: A European
Approach (APHEA-1) study, Hoek (2003) in the Netherlands, and Burnett et al. (2004) in
12 Canadian cities. Copollutant models in these studies focused on the effect of PMio, BS
or NO2 on the SCh-mortality relationship. The SCh-mortality risk estimate was found to
either increase (Hoek. 2003) or slightly attenuate (Dominici et al.. 2003; Katsouvanni et
al.. 1997) in models with BS or PM10; while risk estimates were reduced, but still
remained positive in models with NO2 (Burnett et al.. 2004). Additionally, there was
limited evidence from Burnett et al. (2000) of attenuation of the SO2 association when
PM2 5 was included in the model. Recent multicity studies conducted in the U.S. and Asia
have also examined whether there is evidence of copollutant confounding; however,
similar to the literature base considered in the 2008 SOx ISA (U.S. EPA. 2008d). the
evaluation of copollutant confounding on the SCh-mortality relationship has remained
limited.
In a study of 108 U.S. cities using data from the NMMAPS for 1987-2000 (of which 85
had SO2 data), Moolgavkar et al. (2013) used a subsampling approach where a random
sample of 4 cities were removed from the 108 cities over 5,000 bootstrap cycles to
examine associations between short-term air pollution concentrations and total mortality.
This approach was used instead of the two-stage Bayesian hierarchical approach
employed in the original NMMAPS analysis, which assumes that city-specific risk
estimates are normally distributed around a national mean (Dominici et al.. 2003). In a
single-pollutant model using 100 df (~7 df/year, which is consistent with NMMAPS) to
control for temporal trends, Moolgavkar et al. (2013) found a 1.5% (95% CI: 1.1, 1.7)
increase in total (nonaccidental) mortality at lag 1 for a 10-ppb increase in 24-h avg SO2
concentrations. In a copollutant analysis, the S02-mortality risk estimate remained robust
and was similar in magnitude to the single pollutant result upon the inclusion of PM10
[1.3% (95% CI: 0.4, 2.0)]. An analysis of the influence of NO2 on S02-mortality risk
estimates was not conducted. The results of Moolgavkar et al. (2013) provide additional
support for an SCh-mortality association, as observed in Dominici et al. (2003). through
an analysis that included more cities and used a different statistical approach than
previously employed in multicity studies.
Additional multicity studies in Asia, conducted more extensive analyses of potential
copollutant confounding by examining the effect of gaseous pollutants, in addition to
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PMio, on the SCh-mortality relationship. In a study of 17 Chinese cities as part of the
CAPES, (Chen et al.. 2012b) examined associations between short-term SO2 exposures
and multiple mortality outcomes. The potential confounding effects of other pollutants on
the SCh-mortality relationship was assessed in copollutant models with PM10 and NO2.
Within the cities examined, SO2 was found to be moderately correlated with PM10
(r = 0.49) and NO2 (r = 0.65), respectively. The results from copollutant models
(Table 5-40) indicate that although SO2 risk estimates remained positive, they were
attenuated by approximately 39-54% in models with PM10 and 65-79% in models with
NO2. These results are consistent with those observed in Chen et al. (2013). which
focused on stroke mortality in a subset of the CAPES cities (i.e., eight cities) and also
reported a similar reduction in SO2 risk estimates in models with PM10 and NO2.
Table 5-40 Percent increase in total, cardiovascular, and respiratory mortality
for a 10-ppb increase in 24-h avg sulfur dioxide concentrations at lag
0-1 in single and copollutant models.
Total Mortality Cardiovascular Mortality Respiratory Mortality
Copollutant % Increase (95% CI) % Increase (95% CI) % Increase (95% CI)
SO2
1.98 (1.24, 2.69)
2.19 (1.24, 3.15)
3.31 (2.05, 4.59)
+PM10
1.10 (0.45, 1.76)
1.00 (0.08, 1.92)
2.03 (0.89, 3.17)
+NO2
0.42 (-1.56, 1.00)
0.47 (-0.47, 1.42)
1.16 (-0.03, 2.37)
CI = confidence interval; N02 = nitrogen dioxide; PM10 = particulate matter with a nominal aerodynamic diameter less than or equal
to 10 |jm.
Source: Adapted from Chen et al. (20126).
Kan et al. (2010b) examined the association between short-term SO2 exposures and
mortality within four Asian cities as part of the PAPA study. Although the authors did not
examine copollutant models in a combined four-city analysis, they did on a city-to-city
basis. Similar to Chen et al. (2012b). in single pollutant models across cities and
mortality outcomes, there was evidence of a consistent positive association (Figure 5-19).
Of note is the highly imprecise estimate for Bangkok, but it is speculated that the
variability in risk estimates for Bangkok could be attributed to the lack of variability in
SO2 concentrations in this city compared to the Chinese cities (standard deviation in SO2
concentrations of 1.8 ppb; Chinese cities: 4.6-9.7 ppb) (Kan et al.. 2010b). Across
mortality outcomes and cities, S02-mortality risk estimates were attenuated, and in many
cases null in copollutant models with NO2. However, only in Shanghai and Wuhan were
SO2 correlations with NO2 greater than 0.60 (r = 0.64 and 0.76, respectively). Similarly,
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SO2 was also found to be moderately correlated with PMio in Shanghai (r = 0.67) and
Wuhan (r = 0.65), but SO2 mortality risk estimates, although attenuated, remained
positive across cities. In copollutant models with O3, SO2 mortality risk estimates were
almost unchanged compared to single-pollutant results.
Recent multicity studies add to the limited number of studies that have examined the
potential confounding effects of copollutants on the S02-mortality relationship. Within
the only recent U.S. study, Moolgavkar et al. (2013) reported that SO;-mortality risk
estimates remained robust in copollutant models with PM10, which is consistent with
Dominici et al. (2003). but these studies did not evaluate potential confounding by
gaseous pollutants. Studies that examined gaseous pollutants, including Chen et al.
(2012b) and Kan et al. (2010b) along with Burnett et al. (2004). found that in models
with NO2, SO2 risk estimates were reduced to a large extent, but remained positive.
However, the overall assessment of copollutant confounding remains limited, and it is
unclear how the results observed in Asia translate to other locations, specifically due to
the unique air pollution mixture and higher concentrations observed in Asian cities.
Modeling Approaches to Control for Weather and Temporal Confounding
Mortality risk estimates may be sensitive to model specification, which includes the
selection of weather covariates to include in statistical models to account for the potential
confounding effects of weather in short-term exposure studies. As such, some recent
studies have conducted sensitivity analyses to examine the influence of alternative
approaches to control for the potential confounding effects of weather on mortality risk
estimates.
As part of the CAPES study, Chen et al. (2012b) examined the influence of alternative
lag structures for controlling the potential confounding effects of temperature on the
SCh-mortality relationship by varying the lag structure of the temperature variable
(i.e., lag 0, lag 0-3, or lag 0-7). The authors found that although the SCh-mortality
associations remained positive and statistically significant across alternative lag
structures, risk estimates were attenuated as the number of lag days specified increased.
The attenuation observed when using a temperature variable lagged from 0-3 to 0-7 days
could be due to Chen et al. (2012b) only including one temperature term in the statistical
model. This approach differs from that used in some of the seminal multicity studies
(e.g., NMMAPS, APHEA) that include a temperature term averaged over multiple days
(e.g., average of lag 1-3 days). A second temperature term is often included in models, in
addition to a same-day temperature term, to account for (1) the potential delayed effects
of temperature on mortality and (2) potential residual confounding due to temperature.
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Total Mortality
BK
HK
SH
i.ll !111
jiL.il
WH
Cardiovascular Mortality
6 -
4
2 -
0
-2 -I
-4
-I
SC1C2C3 SC1C2C3 SC1C2C3 SC1C2C3
BK
HK
SH
WH
Ijllj4.11 Iiii
Respiratory Mortality
-1—1—1—1 1—1—1—1 1—1—1—1 1—1—1—1—
S C1C2C3 S C1C2C3 S C1C2C3 S C1C2C3
e -
6 ¦
4.
2
0 ¦
-2 ¦
-4.
BK
HK
SH
JilLilli
WH
S C1C2C3 S C1C2C3 SC1C2C3 S C1C2C3
BK = Bangkok; HK = Hong Kong; SH = Shanghai; WH = Wuhan.
Note: S = single-pollutant model; C1 = sulfur dioxide + nitrogen dioxide; C2 = sulfur dioxide + PM10; C3 = sulfur dioxide + ozone.
Source: Figure adapted from Kan et al. (201 Ob).
Figure 5-19 Percent increase in total, cardiovascular, and respiratory mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-h avg sulfur dioxide concentrations, lag 0-1, in single and copollutants
models in Public Health and Air Pollution in Asia cities.
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Temporal
In addition to examining the influence of model specification on mortality risk estimates
through the use of alternative weather covariates, recent studies have also examined
whether air pollution-mortality risk estimates are sensitive to the df per year employed to
control for temporal trends.
Within the CAPES study, Chen et al. (2012b) examined the influence of increasing the
number of degrees of freedom per year (i.e., 4, 6, 8, and 10 df per year) to control for
temporal confounding on SCh-mortality risk estimates. The authors found that as the
number of df per year increased the percent increase in both total and cause-specific
mortality attributed to SO2 was slightly attenuated, but remained positive across the range
of df examined (Figure 5-20.)
ai
to
ra
(D
b
*-*
c
(U
o
L_
(1)
Q.
2.0
1.5 -
- 1.0
0.5
0.0
10
10
10
-0.5 -
Total mortality
Cardiovascular mortality
Respiratory mortality
Source: (Chen et al.. 2012b).
Figure 5-20 Percent increase in daily mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-h avg sulfur dioxide concentrations at
lag 0-1 days using various degrees of freedom per year for time
trend, China Air Pollution and Health Effects Study cities,
1996-2008.
The results of Chen et al. (2012b) are consistent with those reported by Kan et al. (2010b)
in an analysis of each individual city within the PAPA study. In models using 4, 6, 8, 10,
or 12 df per year, the authors reported relatively similar SC^-mortality risk estimates
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across cities. However, as depicted in Figure 5-20. and in some cities in Figure 5-21.
using 4 df per year likely leads to inadequate control for temporal trends based on the
higher risk estimate observed compared to increasing the degrees of freedom.
All natural causes, all ages, S02
Mill
Mil
DF per year
BK = Bangkok; CI = confidence interval; df = degrees of freedom; HK = Hong Kong; SH = Shanghai; WH = Wuhan.
Source: (Kan et al.. 20106).
Figure 5-21 Percent increase in total mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-h avg sulfur dioxide concentrations at
lag 0-1 in Public Health and Air Pollution in Asia cities, using
different degrees of freedom per year for time trend.
Unlike Chen et al. (2012b) and Kan et al. (2010b). which conducted a systematic analysis
of the influence of increasing the df per year to control for temporal trends on the
SCh-mortality relationship, Moolgavkar et al. (2013) only compared models that used
50 df (-3.5 df per year) or 100 df (~7 df per year). Similar to both Chen et al. (2012b) and
Kan et al. (2010b). the authors reported relatively similar SC^-mortality risk estimates in
both models [1.6% (95% CI: 0.9, 1.9) for a 10-ppb increase in 24-h avg SO2
concentrations at lag 1 in the 50-df model and 1.5% (95% CI: 1.1, 1.7) in the 100 df
model].
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Overall, the studies that examined the effect of alternative approaches to control for the
potentially confounding effects of weather and temporal trends report relatively
consistent SCh-mortality risk estimates across models. The results of these studies are
further supported by an analysis conducted by Sacks et al. (2012). which examined
whether the different modeling approaches (to control for both weather and temporal
trends) used in a number of multicity studies (e.g., NMMAPS, APHEA) resulted in
similar risk estimates when using the same data set. In all-year analyses focusing on
cardiovascular mortality, SCh-mortality risk estimates remained relatively stable across
models using different weather covariates and a varying number of df per year (ranging
from 4 to 8 df per year across models) to control for temporal trends. Although the results
of Sacks et al. (2012) are consistent with Chen et al. (2012b). Kan et al. (2010b). and
Moolgavkar et al. (2013) in all-year analyses, seasonal analyses indicate that differences
in model specification may be more important when examining effects by season for
some pollutants, such as SO2.
5.5.1.4 Modification of the Sulfur Dioxide-Mortality Relationship
Individual- and Population-Level Factors
To date, a limited number of studies have examined potential factors that may increase
the risk of SCh-related mortality. In the 2008 SOx ISA (U.S. EPA. 2008d). only
Katsouvanni et al. (1997) examined potential effect measure modifiers and within the
APHEA-2 study reported that geographic location may influence city-specific
SCh-mortality risk estimates. Similar to the 2008 SOx ISA, only few recent multicity
studies [i.e., (Chen et al. (2012b); Berglind et al. (2009); Wong et al. (2008b))1 conducted
extensive analyses of potential effect measure modifiers of the S02-mortality relationship
as detailed in Chapter 6. These studies along with some single-city studies focusing on
SO2 and mortality provide limited evidence for potential differences in the risk of
S02-related mortality by lifestage, sex, and socioeconomic status (SES).
Season and Weather
A limited number of studies have examined whether there is evidence of seasonal
differences or that certain weather patterns modify in the S02-mortality relationship. In
the 2008 SOx ISA, only Zmirou et al. (1998) examined whether there are seasonal
differences in S02-mortality risk associations in a subset of the APHEA-1 cities.
The authors found some indication of larger associations in the summer months
compared to the winter months.
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Since the completion of the 2008 SOx ISA, only a few recent studies have examined
whether there are seasonal differences in SCh-mortality associations, and these studies
reported results consistent with Zmirou et al. (1998). In a study of 15 Italian cities
(MISA-2), Bellini et al. (2007) is the only multicity study that examined whether there
were seasonal differences in SCh-mortality risk estimates. The authors found a similar
pattern of associations across mortality outcomes with SCh-mortality risk estimates being
larger in the summer compared to the winter (total mortality: summer 3.2% vs. winter
1.4%; respiratory mortality: summer 12.0% vs. winter 4.1%; cardiovascular mortality:
summer 9.4% vs. winter 1.6%). These results are consistent, with the only U.S.-based
study that examined seasonal patterns in SCh-mortality associations. In a study conducted
in New York City focusing on cardiovascular mortality, Ito et al. (2011) reported larger
risk estimates in the warm season [2.9% (95% CI: -1.2, 7.1)] compared to the cold
season [0.0% (95% CI: -1.7, 1.8)] for a 10-ppb increase in 24-h avg SO2 concentrations.
Instead of examining whether only specific seasons modify the SCh-mortality
association, Vanos et al. (2013) focused on weather patterns, referred to as synoptic
weather types, in a study of 10 Canadian cities. Distinct weather types were identified by
combining a number of variables including temperature, dew point temperature, sea level
pressure, cloud cover, and wind velocity. Across the nine different synoptic weather
types examined, for SO2 Vanos et al. (2013) reported that mortality risk estimates in all
age analyses tended to be larger in magnitude for dry versus moist weather types,
particularly in warmer seasons.
Overall, the limited number of studies that conducted seasonal analyses reported initial
evidence indicating larger S02-mortality associations during the summer season.
Additionally, there is preliminary evidence that specific weather patterns in combination
with certain seasons may modify the S02-mortality association.
5.5.1.5 Sulfur Dioxide-Mortality Concentration-Response Relationship and Related
Issues
Lag Structure of Associations
Of the studies evaluated in the 2008 SOx ISA, the majority selected lag days a priori and
did not extensively examine the lag structure of associations for short-term SO2
exposures and mortality. These studies primarily focused on single- or multiday lags
within the range of 0-3 days. However, in a study in the Netherlands, Hock (2003)
conducted more extensive analyses to examine whether there was evidence of immediate
or delayed S02-mortality effects. The authors provided preliminary evidence of larger
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SCh-mortality risk estimates at a multiday lag of 0-6 days compared to a single-day lag
(i.e., lag 1 day). Recent multicity studies have conducted additional analyses further
examining the lag structure of associations for short-term SO2 exposures and mortality.
Chen et al. (2012b). within the CAPES study, examined individual lag days (lag day 0 to
7) and a multiday lag of 0-1 days. As depicted in Figure 5-22. the authors found evidence
of immediate SO2 effects on mortality that slowly declined overtime with the multiday
lag of 0-1 days exhibiting the largest risk estimate across mortality outcomes.
2.0
1.5 -
1.0
o
£
c
CO
03
0J
b
.E 0.5
££
0)
CL
0.0
-0.5
"Hun
01
Total mortality
01
Cardiovascular mortality
01
Respiratory mortality
Source: (Chen et al.. 2012b).
Figure 5-22 Percent increase in daily mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-h avg sulfur dioxide concentrations,
using various lag structures for sulfur dioxide in the China Air
Pollution and Health Effects Study cities, 1996-2008.
Kan et al. (2010b) also examined the lag structure of associations for the SC^-mortality
relationship within the PAPA study, but did not examine an extensive number of
alternative lags, instead focusing on lag 0 and moving averages of 0-1 and 0-4 days
(Figure 5-23). Unlike Chen et al. (2012b). which focused on the combined risk estimate
across all cities, Kan et al. (2010b) examined the lag structure of associations both within
individual cities and in a combined analyses across all PAPA cities. The results of both
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the individual city and combined analyses are consistent with those observed by Chen et
al. (2012b) in the CAPES study (i.e., the effect largest in magnitude across the lag days
examined occurred primarily at lag 0-1 days) (Figure 5-22).
BK
HK
SH
WH
Fixed -
effect
Combined
0
I
Random-
effect
Combined
II ;I
~I 1 1
0 0-1 0-4
1 1 1—
0 0-1 0-4
H 1 1
0 0-1 0-4
n 1 1—
0 0-1 0-4
n 1 1—
0 0-1 0-4
n 1 1—
0 0-1 0-4
BK = Bangkok; HK = Hong Kong; SH = Shanghai; WH = Wuhan.
Source: Kan et al. (20106).
Figure 5-23 Percent increase in total mortality associated with a 10 |jg/m3
(3.62 ppb) increase in 24-h avg sulfur dioxide concentrations for
different lag structures in individual Public Health and Air
Pollution in Asia cities and in combined four city analyses.
Bellini et al. (2007) took a slightly different approach to examining the lag structure of
associations in a study of 15 Italian cities (MISA-2) by focusing on whether there was
evidence of mortality displacement. The authors reported larger SC^-mortality effects at
lag 0-15 days (3.8% for a 10-ppb increase in 24-h avg SO2 concentrations) compared to a
lag of 0-1 days (1.6%), which supports no evidence of mortality displacement.
Additional information on the lag structure can be observed by examining the percent
increase in mortality associated with short-term SO2 exposures at each individual lag day
of the lag 0-15-day model. The individual lag day results remained positive up to
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approximately lag day 10, which is consistent with the results from Chen et al. (2012b)
(Figure 5-22). However, examining associations at single-day lags over a week, such as
10 days, may be uninformative due to potential inadequate control for weather variables
at these longer durations. Additionally, these longer lags may not be biologically
plausible due to controlled human exposure and animal toxicological studies
demonstrating that effects attributed to SO2 exposure are rather immediate
(Section 5.2.1.2).
Overall, the limited analyses that have examined the lag structure of associations for
short-term SO2 exposures and mortality suggest that the greatest effects occur within the
first few days after exposure (lag 0-1). However, the studies evaluated indicate that
positive associations may persist longer although the magnitude of those effects
diminishes overtime.
Concentration-Response Relationship
The studies evaluated in the 2008 SOx ISA (U.S. EPA. 200Sd). as well as prior
assessments, have not conducted formal analyses of the SCh-mortality C-R relationship.
Although limited in number, a few recent studies published since the completion of the
2008 SOx ISA have conducted analyses to examine the shape of the S02-mortality C-R
relationship and whether a threshold exists in the combined C-R relationship across
multiple cities, or in an evaluation of single-city C-R relationships in the context of a
multicity study. However, these studies have not conducted extensive analyses examining
alternatives to linearity in the shape of the S02-mortality C-R relationship.
Using a subsampling approach, Moolgavkar et al. (2013) examined the shape of the C-R
relationship between short-term air pollution exposures and mortality in the NMMAPS
data set by applying a nonlinear function (i.e., natural splines with 6 df) to each pollutant.
As demonstrated in Figure 5-24. the analysis conducted by Moolgavkar et al. (2013)
provides support for a linear, no threshold relationship between short-term SO2 exposures
and total mortality.
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0.06
a
.*•
0.04
y
CC
cc
S)
o
0.02
/ ^
'V
-0.02
0 10 20 30 40 50 60
Lag-1 S02
S02 = sulfur dioxide; RR = relative risk.
Note: Pointwise means and 95% confidence intervals adjusted for size of the bootstrap sample (d = 4).
Source: Reprinted from Environmental Health Perspectives; Moolqavkar et al. (2013).
Figure 5-24 Flexible ambient concentration-response relationship between
short-term sulfur dioxide (ppb) exposure (24-h avg
concentrations) and total mortality at lag 1.
In the four-city PAPA study, Kan et al. (2010b) also examined the SC^-mortality C-R
relationship, but only focused on the shape of the C-R curve in each individual city.
The C-R curve for the SC^-mortality relationship was assessed by applying a natural
spline smoother with 3 dfto SO2 concentrations. To examine whether the SC^-mortality
relationship deviates from linearity, the deviance between the smoothed (nonlinear)
pollutant model and the unsmoothed (linear) pollutant model was examined. When
examining the deviance, the authors only reported evidence for potential nonlinearity in
Hong Kong. However, across the cities, there is evidence of a linear, no threshold,
relationship within the range of SO2 concentrations where the data density is the highest,
specifically within the IQR (Figure 5-25). The linear relationship is most pronounced in
Shanghai and Wuhan, with evidence of an inverted U-shape for Bangkok and Hong
Kong. It should be noted, there is an overall lack of confidence in the shape of the C-R
curve at the high end of the distribution of SO2 concentrations in Bangkok and Shanghai
due to the lower data density within this range of concentrations observed in both cities.
A difficulty apparent in comparing the results across cities within Kan et al. (2010b) is
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the drastically different range of SO2 concentrations in Bangkok and Hong Kong
compared Shanghai and Wuhan. However, the cities with similar distributions of SO3
concentrations also have similar shapes to their respective SO:-mortaIity C-R curves.
Bangkok
10 20 30 40 50
S02 concentration (fig/m3)
Shanghai
0.3
0.2
0.1
0.0
-0.1
Hong Kong
ILU LL
0 20 40 60 80 100
S02 concentration (^g/m3)
Wuhan
n 1 ?' in i
50 100 150 50 100 150
S02 concentration (jig/m3) S02 concentration (^g/m3)
S02 = sulfur dioxide.
Note: x-axis is the average of lag 0-1 24-h avg S02 concentrations (pg/m3). Solid lines indicate the estimated mean percent change
in daily mortality, and the dotted lines represent twice the standard error. Thin vertical lines represent the interquartile range of SO?
concentrations within each city, while the thin vertical bar represents the World Health Organization guideline of 20 pg/m3 for a
24-h avg time of S02.
Source: Reprinted from Environmental Health Perspectives; (Wong et al.. 2008b').
Figure 5-25 Concentration-response curves for total mortality (degrees of
freedom = 3) for sulfur dioxide in each of the four Public Health
and Air Pollution in Asia cities.
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Both Moolgavkar et al. (2013) and Kan et al. (2010b) examined the shape of the
SCh-mortality C-R relationship by focusing on all-cause (total) mortality. Additional
information on the shape of the C-R curve can be assessed in studies that focused on
cause-specific mortality as discussed in Section 5.2.1.8 (respiratory mortality) and
Section 5.3.1.9 (cardiovascular mortality). In studies of multiple Chinese cities, Meng et
al. (2013) and Chen et al. (2013) examined the shape of the C-R relationship for mortality
and short-term air pollution exposures on COPD and stroke mortality, respectively. In
both studies the authors conducted similar analyses of linearity by examining the
deviance between linear and spline models. Meng et al. (2013) and Chen et al. (2013)
both found no evidence of a deviation in linearity in the SO2-COPD mortality and
SCh-stroke mortality relationship, respectively (Figure 5-11 and Figure 5-16).
To date studies have conducted a rather limited exploration of potential alternatives to
linearity when examining the shape of the C-R relationship, which in combination with
the spatial and temporal variability in SO2 concentrations, complicates the interpretation
of the S02-mortality C-R relationship (Section 3.4.2.2. and Section 3.4.2.3.). With these
limitations in mind, studies that examined the C-R relationship provide evidence that
indicates a linear, no threshold relationship between short-term SO2 concentrations and
mortality, specifically within the range of SO2 concentrations where the data density is
highest. Some differences in the shape of the curve were observed on a city-to-city basis,
which is consistent with the mortality C-R results that have been reported for other
criteria air pollutants.
5.5.1.6 Summary and Causal Determination
Recent multicity studies evaluated since the completion of the 2008 SOx ISA continue to
provide consistent evidence of positive associations between short-term SO2 exposures
and total mortality. Although the body of evidence is larger, key uncertainties and data
gaps still remain, which contribute to the conclusion that the evidence for short-term SO2
exposures and total mortality is suggestive of, but not sufficient to infer, a causal
relationship. This conclusion is consistent with that reached in the 2008 SOx ISA (U.S.
EPA. 2008d). Recent multicity studies evaluated have further informed key uncertainties
and data gaps in the S02-mortality relationship identified in the 2008 SOx ISA including
confounding, modification of the S02-mortality relationship, potential seasonal
differences in S02-mortality associations, and the shape of the S02-mortality C-R
relationship. However, questions remain regarding whether SO2 has an independent
effect on mortality, which can be attributed to: (1) the limited number of studies that
examined potential copollutant confounding, (2) the relative lack of copollutant analyses
with PM2 5, (3) and the evidence indicating attenuation of S02-mortality associations in
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copollutant models with NO2 and PM10. Additionally, all of the studies evaluated
averaged SO2 concentrations over multiple monitors and used a 24-h avg exposure metric
when assigning exposure, which may not adequately capture the spatial and temporal
variability in SO2 concentrations (Section 3.4.2.2. and Section 3.4.2.3V While
correlations between 24-h avg and 1-h max SO2 concentrations are high (r > 0.75) at
most monitors, lower correlations may occur at some monitors and in individual studies
which can add uncertainty to the ability of 24-h avg metrics to capture peak SO2
concentrations. This section describes the evaluation of evidence for total mortality, with
respect to the causal determination for short-term exposures to SO2 using the framework
described in Table II of the Preamble to the IS As (U.S. EPA. 2015b). The key evidence,
as it relates to the causal framework, is summarized in Table 5-41.
Table 5-41 Summary of evidence, which is suggestive of, but not sufficient to
infer, a causal relationship between short-term sulfur dioxide
exposure and total mortality.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2
Concentrations
Associated with
Effects0
Consistent epidemiologic
evidence from multiple,
high quality studies at
relevant SO2
concentrations
Increases in mortality in multicity studies
conducted in the U.S., Canada, Europe, and
Asia
Section 5.5.1.2
Fiaure 5-15
Mean 24-h avg:
U.S., Canada,
South America,
Europe:
0.4-28.2e ppb
Asia:
0.7->200 ppb
Table 5-39
The magnitude of SO2 associations remained Section 5.5.1.3
positive, but were reduced in copollutant Section 3 4 3
models with PM10 and NO2. No studies
examined copollutant models with PM2.5. SO2
generally exhibits low to moderate
correlations with other NAAQS pollutants at
collocated monitors, and attenuation of SO2-
mortality association may be a reflection of
spatial variability among the pollutants.
U.S. studies that examine the association Section 3.4.2.2
between short-term SO2 exposures and
mortality rely on single or the average of
multiple monitors in an area and SO2
generally has low to moderate spatial
correlations across urban geographical
scales.
Uncertainty regarding
potential confounding by
copollutants
Uncertainty regarding
exposure measurement
error
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Table 5-41 (Continued): Summary of evidence, which is suggestive of, but not
sufficient to infer, a causal relationship between short
term sulfur dioxide exposure and total mortality.
so2
Concentrations
Rationale for Causal Associated with
Determination3 Key Evidence13 Key References'3 Effects0
Uncertainty due to limited
coherence and biological
plausibility with
cardiovascular and
respiratory morbidity
evidence
Generally supportive, but not entirely Section 5.3.1.11
consistent epidemiologic evidence for Table 5-31
ischemic events such as triggering a
myocardial infarction. Inconclusive
epidemiologic and experimental evidence for
other cardiovascular endpoints. Uncertainties
with respect to the independent effect of SO2
on cardiovascular effects contributing to
limited coherence and biological plausibility
for S02-related cardiovascular mortality,
which comprises -35% of total mortality.d
Consistent evidence of asthma exacerbations Section 5.2.1.8
from controlled human exposure studies Table 5-21
demonstrating respiratory effects
(i.e., respiratory symptoms and decreased
lung function) in response to typically
5-10-min exposures, with generally
supportive evidence from short-term SO2
exposure epidemiologic studies
demonstrating asthma-related morbidity,
specifically hospital admissions and ED
visits. Uncertainty as to the biological
mechanism that explains the continuum of
effects leading to SCh-related respiratory
mortality, which comprises -8% of total
mortality.d
ED = emergency department; NAAQS = National Ambient Air Quality Standards; N02 = nitrogen dioxide; PM10 = particulate matter
with a nominal aerodynamic diameter less than or equal to 10 |jm; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Table I and Table II of the
Preamble to the ISAs (U.S. EPA. 2015b).
bDescribes the key evidence and references, supporting or contradicting, contributing most heavily to causal determination and,
where applicable, to uncertainties or inconsistencies. References to earlier sections indicate where full body of evidence is
described.
°Describes the S02 concentrations with which the evidence is substantiated.
Statistics taken from American Heart Association (2011).
eThe value of 28.2 represents the median concentration from Katsouvanni et al. (1997).
1 Collectively, the evidence from recent multicity studies of short-term SO2 exposures and
2 mortality consistently demonstrate positive SCh-mortality associations in single-pollutant
3 models. In the limited number studies that conducted copollutant analysis, correlations
4 between SO2 and other pollutants were low (r < 0.4) to moderate (r = 0.4-0.7). Although
5 SCh-mortality associations remain positive in copollutant models with PM10 and NChthey
6 were often attenuated to a large degree, questioning the independent effect of SO2 on
7 mortality. However, SO2 is more spatially variable than other pollutants as reflected in
8 the generally low to moderate spatial correlations across urban geographical scales
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(Section 3.4.2.2); therefore, the attenuation in SO2 associations in copollutant models
may be a reflection of the different degree of exposure error across pollutants
(Section 3.4.3). It is important to note, the majority of recent studies that examined
potential copollutant confounding have been conducted in Asian countries where
correlations between pollutants may be higher, possibly limiting the generalizability of
results to other study areas where SO2 concentrations along with the concentrations of
other air pollutants are much lower. This is reflected in the results of Moolgavkar et al.
(2013) in a U.S. multicity study where there was very little evidence of attenuation of the
SCh-mortality association in copollutant models with PM10; whereas, the multicity studies
conducted in Asian cities showed a rather pronounced reduction in SO2 associations. In
addition to copollutant analyses, recent studies examined the influence of the extent of
temporal adjustment and the lag structure for weather covariates on the S02-mortality
association. When examining, the extent of temporal adjustment, multiple studies
reported similar S02-mortality associations across a range of degrees of freedom per year.
Only Chen et al. (2012b) examined the lag structure for weather covariates, specifically
temperature, and found evidence of a difference in S02-mortality associations as the
number of lag days increased, but this could be attributed to the analysis being based on
only one covariate for temperature.
An examination of factors that may contribute to increased risk of SC>2-related mortality,
as discussed in Chapter 6. found evidence indicating that older adults (>65 years of age)
may be at increased risk with very limited evidence of potential differences by sex and
socioeconomic status. In the 2008 SOx ISA, initial evidence suggested potential seasonal
differences in S02-mortality associations, particularly in the summer months. A recent
multicity study conducted in Italy along with single-city studies conducted in the U.S.
add to this initial body of evidence suggesting larger associations during the summer or
warm months. Preliminary evidence indicates that not only season, but season in
combination with specific weather patterns may modify the S02-mortality association.
Additionally, an examination of different modeling approaches provides evidence that the
magnitude of the seasonal association may depend on the modeling approach employed
to control for the potential confounding effects of weather (Sacks et al.. 2012).
Those studies that examined the lag structure of associations for the S02-mortality
relationship generally observed that there is evidence of an immediate effect (i.e., lag 0 to
1 days) of short-term SO2 exposures on mortality. Multicity studies conducted in the U.S.
and Asia have examined the shape of the C-R relationship and whether a threshold exists
in both a multi- and single-city setting. These studies have used different statistical
approaches and consistently demonstrated a linear relationship with no evidence of a
threshold within the range of SO2 concentrations where the data density is highest.
The evidence of linearity in the SCh-mortality C-R relationship is further supported by
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studies of cause-specific mortality as detailed in Section 5.2.1.8 (respiratory mortality)
and Section 5.3.1.9 (cardiovascular). However, to date, studies have not conducted
extensive analyses exploring alternatives to linearity when examining the shape of the
SCh-mortality C-R relationship.
Overall, recent epidemiologic studies build upon and support the conclusions of the 2008
SOx ISA for total mortality. However, the biological mechanism that could lead to
mortality as a result of short-term SO2 exposures has not been clearly characterized. This
is evident when evaluating the underlying health effects (i.e., cardiovascular effects in
Section 53. and respiratory effects in Section 5.2) that could lead to cardiovascular
(-35% of total mortality) and respiratory (-9% of total mortality) mortality, the
components of total mortality most thoroughly evaluated (Hovert and Xu. 2012V For
cardiovascular effects the evidence is "inadequate to infer a causal relationship" with
exposure to short-term SO2 concentrations. An evaluation of epidemiologic studies that
examined the relationship between short-term SO2 exposure and cardiovascular effects
found a number positive associations but the evidence was not entirely consistent. Within
the collective body of evidence for cardiovascular effects, important uncertainties remain
especially regarding disentangling whether there is an independent effect of SO2 on
cardiovascular effects, which is the same uncertainty in total mortality studies. Overall,
this evidence complicates the interpretation of the relationship between SO2 and
cardiovascular mortality." For respiratory effects the evidence indicates a causal
relationship for short-term SO2 exposures. The strongest evidence for respiratory effects
is from studies examining S02-related asthma exacerbations, specifically controlled
human exposure studies demonstrating respiratory effects (i.e., respiratory symptoms and
decreased lung function) (Section 5.2.1.2) in people with asthma in response to short
term, generally 5-10-minutes, SO2 exposures. The results from controlled human
exposure studies are generally supported by epidemiologic studies reporting
respiratory-related morbidity including hospital admissions and ED visits, specifically for
asthma. However, the biological mechanism that explains the continuum of effects that
could lead to respiratory-related mortality remains unclear. Additionally, it is important
to note epidemiologic studies that examine the association between short-term SO2
exposures and mortality rely on single or the average of multiple monitors over an area to
assign exposure. Therefore, the exposure assessment approach used in the mortality
studies may contribute to exposure measurement error and underestimate associations
observed due to the spatially heterogeneous distribution of SO2 concentrations over a
wide area (Section 3.4.2.2). In conclusion, the consistent positive associations observed
across various multicity studies is limited by the uncertainty due to whether SO2 is
independently associated with total mortality, the representativeness of monitors and the
24-h avg SO2 exposure metric in capturing the spatial and temporal variability in
exposure to SO2 (Section 3.4.2.2 and Section 3.4.2.3). and the uncertainty in the
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biological mechanism that could lead to SCh-induced mortality (Section 4.3).
Collectively, this body of evidence is suggestive, but not sufficient to conclude there is a
causal relationship between short-term SO2 exposure and total mortality.
5.5.2 Long-Term Exposure
In past reviews, a limited number of epidemiologic studies have assessed the relationship
between long-term exposure to SO2 and mortality in adults. The 2008 SOx ISA
concluded that the scarce amount of evidence was "inadequate to infer a causal
relationship" (U.S. EPA. 2008d). The 2008 SOx ISA identified concerns as to whether
the observed associations were due to SO2 alone, or if sulfate or other particulate SOx,
such as H2SO4, or PM indices could have contributed to these associations.
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, could not be ruled out. Overall, a lack of
consistency across studies, inability to distinguish potential confounding by copollutants,
and uncertainties regarding the geographic scale of analysis limited the interpretation of
the causal relationship between long-term exposure to SO2 and mortality.
This section includes a review of the evidence for an association between long-term
exposure to SO2 and mortality, integrating evidence presented in previous NAAQS
reviews with evidence that is newly available to this review. The evidence in this section
will focus on epidemiologic studies because experimental studies of long-term exposure
and mortality are generally not conducted. However, this section will draw from the
morbidity evidence presented for different health endpoints across the scientific
disciplines (i.e., animal toxicological, controlled human exposure studies, and
epidemiology) to support the association observed for cause-specific mortality. Studies
are discussed by geographic region, with U.S. studies discussed in Section 5.5.2.1.
European studies in Section 5.5.2.2. and Asian studies in Section 5.5.2.3. Section 5.5.2.4
describes studies that evaluated the S02-mortality relationship over small geographic
scales. A brief summary of the studies included in these sections can be found in
Table 5-42.
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Table 5-42 Summary of studies of long-term exposure and mortality.
Location
Study Years
tHartetal. (2011) U.S.
(SO2:
1985-2000;
follow-up:
1985-2000)
Krewski et al. (2000) U.S.
HSC:
(SO2:
1977-1985;
follow-up:
1974-1991)
ACS:
(SO2: 1980;
follow-up:
1982-1989)
Pope et al. (2002) U.S.
(SO2:
1982-1998;
follow-up:
1982-1998)
tLipfert et al. (2009) U.S.
(SO2: 1999;
follow-up:
1976-2001)
tKrewski et al. (2009) U.S.
(SO2: 1980;
follow-up:
1982-2000)
Mean SO2
ppb Exposure Assessment
4.8 Annual average
exposures based on
residential address from
Correlation Selected Effect
with Other Estimates
Pollutants (95% Cl)a
All cause:
1.09 (1.03, 1.15)
Respiratory:
1.10 (0.89, 1.35)
COPD:
0.93 (0.71, 1.22)
Lung cancer:
1.11 (0.98, 1.27)
All cause:
HSC:
1.05 (1.02, 1.09)
ACS:
1.06 (1.05, 1.07)
Lung cancer: HSC:
1.03 (0.91, 1.16)
All cause:
1.03 (1.02, 1.05)
Subject- All cause:
weighted: 1.02 (1.01,1.03)
EC: 0.68
NOx: 0.65
SO42": 0.79
All cause:
1.02 (1.02, 1.03)
Lung cancer:
1.00 (0.98, 1.02)
model using spatial
smoothing and GIS-based
covariates; current
calendar year and
long-term average from
1985-2000
HSC: HSC: mean levels from
1.6-24.0 central site monitors
ACS: 9.3 ACS: City-specific annual
mean
6.7-9.7 Average across
monitoring stations in
each metropolitan area for
each study year using
daily average
(i.e., 24-h avg)
concentrations, averaged
over 1 yr (1980) and the
entire study period
(1982-1998)
4.3 County-level estimates
from AER plume-in-grid
air quality model; based
on 1999 emissions
inventory from point and
area sources for
36 x 36-km grid squares
9.6 City-specific annual mean
HSC:
PM2.5: 0.85
SO4: 0.85
NO2: 0.84
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Table 5-42 (Continued): Summary of studies of long term exposure and mortality.
Study
Location
Years
Mean SO2
PPb
Exposure Assessment
Correlation
with Other
Pollutants
Selected Effect
Estimates
(95% Cl)a
Lipfert et al. (2006a) U.S.
(SO2:
1999-2001;
follow-up:
1997-2001)
16.3 County-level "peak"
concentrations
Subject-
weighted:
PM2.5: 0.71
NO2: 0.41
Peak O3: 0.21
Peak CO:
0.41
SO42": 0.77
OC: 0.34
EC: -0.13
All cause:
0.99 (0.97, 1.01)
Abbey et al. (1999)
U.S.
5.6
ZIP code-level mo
Mean
All cause:
(S02:
IQR: 3.7
averages cumulated and
concentration:
Men:
1966-1992;
averaged overtime
PM10: 0.31
1.07 (0.92, 1.25)
follow-up:
Os: 0.09
Women:
1977-1992)
SO4: 0.68
1.00 (0.88, 1.14)
When
Lung cancer:
exceeding
Men:
100 ppb (Os)
2.52 (1.34, 4.77)
or 100 |jg/m3
Women:
(PM10)
4.40 (2.34, 8.33)
PM10: -0.05
Os: 0.13
Beelen et al. (2008b) Netherlands
(SO2:
1976-1985,
1987-1996;
follow-up:
1987-1996)
5.2 IDW to regional
3D- 1 g background monitors at
baseline residential
address
All cause:
0.94 (0.80, 1.10)
Respiratory:
0.92 (0.64, 1.31)
Lung cancer:
0.99 (0.73, 1.35)
Nafstad et al. (2004)
Norway
(SO2:
1974-1995;
follow-up:
1972-1998)
3.6 Model results (per square
kilometer) for some
year/urban locations,
supplemented with
background monitoring
data
All cause:
0.97 (0.95, 1.01)
Respiratory:
1.04 (0.91, 1.19)
Lung cancer:
1.00 (0.91, 1.11)
Filleul et al. (2005) France
(SO2:
1974-1976;
follow-up:
1974-2000)
3.0-8.2 3-yr mean concentrations BS: 0.29
for 24 areas in TSP: 0.17
seven different cities NO -0.01
NO2 -0.10
All cause:
1.01 (0.99, 1.04)
Lung cancer:
0.99 (0.90, 1.09)
tBentaveb et al.
(2015)
France
(SO2:
1989-2008;
follow-up:
1989-2013)
2.3 Annual concentrations
from CHIMERE
chemical-transport model
Os: -0.13
PM2.5: 0.58
PM10: 0.57
PM10-2.5: 0.30
NO2: 0.56
All cause: 1.23
(0.98, 1.52)
Respiratory: 0.76
(0.43, 1.33)
CVD: 0.85 (0.44,
1.67)
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Table 5-42 (Continued): Summary of studies of long term exposure and mortality.
Study
Location
Years
Mean SO2
PPb
Exposure Assessment
Correlation
with Other
Pollutants
Selected Effect
Estimates
(95% Cl)a
tHansell et al. (2016)
England
(SO2: 1971,
1981, 1991;
follow-up:
1971-2009)
1971
1981
1991
32.4
16.4
11.2
LUR models for annual
concentrations in 1971,
1981 and 1991
1991
All cause: 1.09
(1.05, 1.15)
Resp: 1.20 (1.09,
1.33)
COPD: 1.43 (1.23,
1.66)
Lung cancer: 1.29
(1.12, 1.47)
CVD: 1.05 (0.99,
1.13)
tCarev et al. (2013)
England 1.5
(SO2: 2002; SD: 0.8
follow-up: |QR. 0 8
2003-2007)
Annual mean for 1-km PM10: 0.45
grid cells from air NO2: 0.37
dispersion models (poor O3: -0.41
validation results for SO2)
All cause:
1.26 (1.19, 1.34)
Respiratory:
1.67 (1.42, 1.97)
Lung cancer:
1.34 (1.06, 1.58)
tAncona et al. (2015) Rome, Italy
(SOx:
2001-2010;
follow-up:
2001-2010)
2.5 |jg/m3
SOx
SD: 0.9
Lagrangian particle PM10: 0.81
dispersion model (SPRAY H2S: 0.78
Ver. 5) used SOx as
exposure marker for
petrochemical refinery
emissions
All cause:
Men:
1.04 (0.92, 1.18)
Women:
0.93 (0.81, 1.07)
CVD:
Men:
1.08 (0.89, 1.31)
Women:
1.00 (0.81, 1.25)
IHD:
Men:
1.05 (0.79, 1.41)
Women:
1.25 (0.89, 1.75)
Respiratory:
Men:
1.31 (0.88, 1.95)
Women:
0.64 (0.32, 1.28)
tCao et al. (2011) China 27.7
(SO2:
1991-2000;
follow-up:
1991-2000)
1.04 (1.02, 1.06)
Lung cancer:
1.06 (1.03, 1.08)
Annual average by linking All cause:
fixed site monitoring data 1.02 (1.02, 1.03)
with residential ZIP code
CVD:
1.02 (1.00, 1.03)
Rpcniratnrv
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Table 5-42 (Continued): Summary of studies of long term exposure and mortality.
Study
Location
Years
Mean SO2
PPb
Exposure Assessment
Correlation
with Other
Pollutants
Selected Effect
Estimates
(95% Cl)a
tChen etal. (2016)
China 25.5
(S02: 1998-
2009; follow-
up: 1998-
2009)
1-yr avg and time-varying
exposure from monitoring
stations calculated from
24-h avg
Lung cancer:
1.02 (1.01, 1.03)
tDong etal. (2012)
China
(SO2:
1998-2009;
follow-up:
1998-2009)
23.9
SD: 5.7
1-yr avg from five
monitors
Respiratory:
1.05 (0.96, 1.16)
tZhanq etal. (2011)
Shenyang,
China
(SO2:
1998-2009;
follow-up:
1998-2009)
23.9 1-yr avg and yearly
deviations in each of five
monitoring stations
calculated from 24-h avg
All cause:
0.93 (0.90, 0.99)
tKatanoda et al.
Japan 2.4-19.0
Annual mean Pearson:
Respiratory:
(2011)
(SO2:
1974-1983;
concentrations from SPM: 0.47
1.20 (1.15, 1.24)
monitoring station near
COPD:
follow-up:
each of eight study areas
1.15 (0.94, 1.41)
1983-1995)
Pneumonia:
1.20 (1.16, 1.25)
Lung cancer:
1.12 (1.03, 1.22)
Elliott et al. (2007)
Great Britain
(SO2:
1966-1970,
1990-1994;
follow-up:
1982-1986,
1994-1998)
12.2-41.4 4-yr exposure windows
from annual average
concentrations from
monitoring sites located in
residential areas
All cause:
1.02 (1.02, 1.02)
Respiratory:
1.06 (1.06, 1.07)
Lung cancer:
1.00 (0.99, 1.01)
tBennett et al. (2014)
Warwickshire, NR
U.K.
(SO2: 2010;
mortality
data:
2007-2012)
Single recorded level for
each ward from 2010
Heart failure:
1.11 (0.988, 1.22)
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Table 5-42 (Continued): Summary of studies of long term exposure and mortality.
Correlation Selected Effect
Location Mean SO2 with Other Estimates
Study Years ppb Exposure Assessment Pollutants (95% Cl)a
tWanq et al. (2009) Brisbane, 5.4 1-h max from Cardiopulmonary:
Australia 13 monitoring stations 1.26(1.03,1.54)
(302- aggregated to annual
1996-2004' means used with IDW
follow-up:
1996-2004)
tWanq et al. (2014a) China 46.31 Annual average across Life expectancy:
(302- monitoring stations in 10-|jg/m3 increase in
2004-2010' 85 city regions SO2 correlated with
life table: 0.28-0.47 yr
2010) decrease in life
expectancy
ACS = American Cancer Society; AER = Atmospheric and Environmental Research; BS = black smoke; CHI MERE = regional
chemistry transport model; CI = confidence interval; CO = carbon monoxide; COPD = chronic obstructive pulmonary disease;
CVD = cardiovascular disease; EC = elemental carbon; GIS = geographic information systems; H2S = hydrogen sulfide;
HSC = Harvard Six Cities; IDW = inverse distance weighting; IHD = ischemic heart disease; IQR = interquartile range; LUR = land
use regression; NO = nitric oxide; N02 = nitrogen dioxide; NOx = the sum of NO and N02; NR = not reported; 03 = ozone;
OC = organic carbon; PM25 = particulate matter with a nominal aerodynamic diameter less than or equal to 2.5 |jm;
PM10 = particulate matter with a nominal aerodynamic diameter less than or equal to 10 |jm; PM10-2.5 = particulate matter with a
nominal aerodynamic diameter less than or equal to 10 |jm and greater than a nominal 2.5 |jm; SD = standard deviation;
S02 = sulfur dioxide; S04 = sulfate; S042" = sulfate; SOx = oxides of sulfur; SPM = suspended particulate matter; TSP = total
suspended solids.
aEffect estimates are standardized per 5-ppb increase in S02 concentrations.
fStudies published since the 2008 ISA for Sulfur Oxides.
5.5.2.1 U.S. Cohort Studies
A number of longitudinal cohort studies have been conducted in the U.S. and have found
small, statistically significant positive associations between long-term exposure to SO2
and total mortality (Hart et al.. 2011; Lipfert et al.. 2009; Pope et al.. 2002; Krewski et
al.. 2000). The body of evidence is smaller and less consistent when these studies
examine cause-specific mortality, although Hart et al. (2011) observed positive, yet
imprecise associations with respiratory, lung cancer, and cardiovascular mortality. In the
Trucking Industry Particle Study, Hart et al. (2011) used the work records for over
50,000 men employed in four U.S. trucking companies to identify all-cause and
cause-specific mortality. Occupational exposures were assigned based on job title, while
exposure to ambient air pollution (i.e., PM10, SO2, and NO2 averaged over the study
period) were determined using spatial smoothing and geographic information system
(GlS)-based covariates based on residential address. All three pollutants were
independently associated with all-cause mortality, with central estimates the highest for
the association with NO2 and lowest for the association with PM10. Both NO2 and SO2
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were positively associated with lung cancer, cardiovascular disease, and respiratory
disease mortality, and negatively associated with COPD mortality. Correlation
coefficients between SO2 and other measured air pollutants were not reported, making it
difficult to evaluate for the potential of copollutants confounding on the associations
attributed to SO2. There was no evidence of confounding by occupational exposures
(based on job-title).
The Harvard Six Cities study is a prospective cohort study of the effects of air pollution
with the main focus on PM components in six U.S. cities and provides limited evidence
for an association between mortality and exposure to SO2. 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. Dockerv 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 etal. (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, observing positive associations between SO2 and total mortality and
cardiopulmonary deaths. In this data set SO2 was highly correlated with PM2 5 (r = 0.85),
sulfate (r = 0.85), and NO2 (r = 0.84), making it difficult to attribute the observed
associations to an independent effect of SO2.
Pope et al. (1995) investigated associations between long-term exposure to PM and the
mortality outcomes in the ACS cohort and provides limited evidence for an association
between exposure to SO2 and mortality. 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. Gaseous pollutants were not analyzed in the original
analysis. Extensive reanalysis of the ACS data, augmented with additional gaseous
pollutants data, showed positive associations between mortality and SO2, but not for the
other gaseous pollutants (Jerrett et al.. 2003; Krewski et al.. 2000). 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 study (Pope et al. 1995). Both PM2 5 and
SO2 were associated with all the mortality outcomes, although only SO2 was associated
with the deaths attributable to "all other causes." The association of SO2 with mortality
for "all other causes" makes it difficult to interpret the effect estimates due to a lack of
biological plausibility for this association. More recently, Krewski et al. (2009)
conducted an extended reanalysis of the study conducted by Pope et al. (2002). including
examination of ecologic covariates (e.g., education attainment, housing characteristics,
income) and evaluation of exposure windows. The inclusion of ecologic covariates
generally resulted in increased risk estimates, with the greatest effect on mortality from
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IHD. The authors also evaluated individual time-dependent exposure profiles to examine
whether there is a critical exposure time window most strongly associated with mortality
from ambient air pollution. The time window immediately preceding death (1-5 years)
produced the strongest effects for mortality associated with exposure to SO2, while later
time windows (6-10 years and 11-15 years) generally showed null associations between
SO2 and mortality.
Lipfert et al. (2000a) 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) and provides scant evidence for an
association between exposure to SO2 and mortality. This cohort was 35% black and 57%
of the cohort 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 (Pb) were examined in
these analyses. 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 as part of their preliminary screening were generally null. Lipfert et al. (2000a) noted
that Pb 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. In a subsequent analysis, Lipfert et al. (2006b) examined
associations between traffic density and mortality in the same cohort, extending the
follow-up period to 2001. As in their previous study (Lipfert et al.. 2000a). four exposure
periods were considered but included more recent years, and 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 al. (2006a) further
extended analysis of the veterans' cohort data to include the U.S. EPA's Speciation
Trends Network (STN) data, which collected chemical components of PM2 5. They
analyzed the STN data for 2002, again using county-level averages. PM2 5 and gaseous
pollutants data for 1999 through 2001 were also analyzed. As in the previous study
(Lipfert et al.. 2006b'). traffic density was the most important predictor of mortality, but
associations were also observed for elemental carbon, vanadium, nickel, and nitrate.
Ozone, NO2, and PM10 also showed positive but weaker associations. Once again, no
associations were observed between long-term exposure to SO2 and mortality. Lipfert et
al. (2009) re-examined these associations, this time averaging the exposure variables over
the entire follow-up period (1976-2001). For this exposure period, they observed positive
associations between SO2 and mortality. When the data set was stratified by county-level
traffic density, the SO2 association with mortality was stronger in the counties with high
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density traffic, and attenuated to near null in the counties with lower traffic density.
The fact that the association between long-term exposure to SO2 and mortality is only
observed in areas where traffic density has been characterized as high, along with the
moderate to strong correlations between SO2 and other traffic-related pollutants
(e.g., PM2 5, NO2, NOx, EC) in these analyses, makes it difficult to discern whether these
associations are truly attributable to SO2, or could be due to some other traffic-related
pollutant or mixture of pollutants.
Abbey et al. (1999) investigated associations between long-term ambient concentrations
of PM10, sulfate, SO2, O3, and NO2 and mortality 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 was positively
associated with total mortality for males but not for females. Generally, null associations
were observed for cardiopulmonary deaths and respiratory mortality for both males and
females.
Overall, the majority of the limited evidence informing the association between long-term
exposure to SO2 and mortality from U.S. cohort studies was included in the 2008 SOx
ISA. A recent cohort study of male truck drivers (Hart et al.. 2011) provided some
additional evidence for an association between long-term exposure to SO2 and both
respiratory mortality and total mortality, while updates to the ACS (Krewski et al.. 2009)
and Veterans (Lipfert et al.. 2009) cohort studies provides some limited evidence for an
association with total mortality, although none of these recent studies help to resolve the
uncertainties identified in the 2008 SOx ISA related to copollutant confounding or the
geographic scale of the analysis.
5.5.2.2 European Cohort Studies
A number of European cohort studies examined the association between both total
mortality and cause-specific mortality and SO2 concentrations, and found generally
inconsistent results. Beelen et al. (2008b) analyzed data from the Netherlands Cohort
Study on Diet and Cancer with 120,852 subjects. Traffic-related pollutants (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
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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 observed for SO2.
Several studies noting declining SO2 concentrations during the follow-up period (from
the mid-1970s through the mid-1990s) did not observe positive associations with
mortality. Nafstad et al. (2004) linked data from 16,209 males (aged 0 to 49 years) living
in Oslo, Norway with data from the Norwegian Death Register and with estimates of the
average 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. In this study, 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) linked
daily measurements of SO2, TSP, BS, NO2, and NO with data on mortality for
14,284 adults who resided in 24 areas from seven French cities enrolled in the Air
Pollution and Chronic Respiratory Diseases survey in 1974. Models were run before and
after exclusion of six area monitors influenced by local traffic as determined by a
NO:N02 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 or
acidimetric measurements. In this study, SO2 levels declined by a factor of two to three
(depending on the city) between the 1974 through 1976 period and the 1990 through
1997 period. The changes in air pollution levels over the study period complicate
interpretation of reported effect estimates.
Carev et al. (2013) examined the associations between long-term exposure to ambient air
pollutants and total and cause-specific mortality in a national English cohort
(n = 835,607). The authors used air dispersion models to estimate annual mean air
pollution concentrations for 1-km grid cells for a single year prior to the follow-up
period. Model validation using national air quality monitors and networks demonstrated
good agreement for NO2 and O3, moderate agreement for PM10 and PM2 5, but relatively
poor agreement for SO2 (R2 = 0-0.39). The authors observed positive associations with
total mortality for all of the air pollutants, and these associations were stronger for PM2 5,
NO2, and SO2 and respiratory and lung cancer mortality. Associations were generally not
observed with cardiovascular mortality and any of the pollutants. Although the authors
observed positive associations between SO2 and mortality (especially respiratory
mortality), these associations are difficult to interpret due to the poor validation of the
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dispersion model for SO2. Ancona et al. (2015) used a Lagrangian particle dispersion
model (see Section 3.3.2.4 for details) to estimate annual means of SOx (as an exposure
marker for emissions from a petrochemical refinery) in Rome, Italy and associations with
all-cause and cause-specific mortality among men and women. The authors did not
present any validation results for their dispersion model. Predicted concentrations of SOx
were highly correlated with predicted concentrations of PM10 (r = 0.81), and because SOx
was used as an exposure marker for petrochemical refinery emissions, it would likely be
correlated with other stack or fugitive refinery emissions, including PM2 5 and VOCs.
The authors observed associations for all-cause mortality and CVD mortality that were
near the null value for both men and women. When restricted to IHD mortality, the
association remained near the null value for men, but was elevated among women.
Conversely, slightly increased risks were observed for respiratory mortality and mortality
due to digestive diseases among men, while the risks for these were attenuated among
women. Due to the unknown validity of the dispersion model and the high correlations
with additional copollutants, it is difficult to interpret these associations.
Overall, the results of the European cohort studies provide very little evidence for an
association between long-term exposure to SO2 and mortality. The majority of these
studies were included in the 2008 SOx ISA (Beelen et al.. 2008b; Filleul et al.. 2005;
Nafstad et al.. 2004). Only the study by Carey et al. (2013) provided new evidence for
this review. None of the studies used copollutant models or accounted for potential
confounding or effect measure modification by other ambient air pollutants, including
sulfate. The study by C'arev et al. (2013) had the potential to inform uncertainties related
to the geographic scale of the exposure assessment; however, the poor validation results
of the dispersion model used to estimate the SO2 concentrations for 1-km grid cells
makes it difficult to interpret these results.
5.5.2.3 Asian Cohort Studies
Four recent cohort studies have been conducted in China to examine the association
between long-term exposure to SO2 and mortality (Chen et al.. 2016; Dong et al.. 2012;
Cao etal.. 2011; Zhang et al.. 2011) and observed inconsistent results. Each of these
studies used annual area-wide average concentrations from fixed site monitoring stations
to assign exposure. Notably, the mean SO2 concentrations in these study areas was much
higher than concentrations observed in other locations (see Table 5-42). Cao et al. (2011)
observed generally modest positive associations with all-cause, respiratory and lung
cancer mortality. Chen et al. (2016) observed a positive association with lung cancer
mortality, though the correlation between SO2 and PM10 was high (r > 0.94), and it is
possible that copollutant confounding could at least partially explain this relationship.
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Dong et al. (2012) observed a modest, positive association with respiratory mortality,
while Zhang etal. (2011) observed modest negative associations with all-cause mortality.
Katanoda et al. (2011) conducted a cohort study in Japan investigating the association
between long-term exposure to PM2 5, NO2, and SO2 and lung cancer and respiratory
mortality. The authors used annual mean concentrations from fixed site monitoring
stations near each of eight study areas. The authors observed positive associations
between long-term exposure to PM2 5, NO2, and SO2 and lung cancer and respiratory
mortality, with the strongest effect observed for the SO2 associations.
Overall, these recent Asian cohort studies provide some new evidence of an association
between long-term exposure to SO2 and mortality; however, they generally report similar
associations for other ambient air pollutants, and do not evaluate for potential bias due to
copollutant confounding (using copollutants models, reporting correlation coefficients
between SO2 and other measured pollutants, or other methods). Generally, these recent
studies do not help to resolve the uncertainties identified in the 2008 SOx ISA related to
copollutant confounding or the geographic scale of the analysis.
5.5.2.4 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), Elliott et al. (2007)
compared the mortality rates and air pollution concentrations 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. 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 individual risk factors, but the study did adjust for socioeconomic status
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 positive
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.
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 etal. (2000) reanalysis of the ACS study. Similarly, Bennett et al. (2014)
observed a positive association between ward-level SO2 concentrations measured in 2010
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and ward-level data on heart failure mortality from 2007-2012 in Warwickshire, U.K.
Stronger associations were observed for estimated benzene exposure in this population,
while estimated PM exposure was inversely associated with heart failure mortality. These
analyses are 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.
In a recent cross-sectional analysis, Wang et al. (2009) examined the long-term exposure
to gaseous air pollutants (i.e., NO2, O3, and SO2) and cardiorespiratory mortality in
Brisbane, Australia. Pollutant concentrations were estimated for small geographic units,
statistical local areas, using IDW. The authors observed a positive association between
cardio-respiratory mortality and SO2, but generally null associations for NO2 and O3.
The results of these cross-sectional studies are inconsistent, with much higher mortality
effects attributed to SO2 in Brisbane, Australia (Wang et al.. 2009) and Warwickshire,
U.K. (Bennett et al.. 2014) than in Great Britain (Elliott et al.. 2007). While each of these
studies took a geospatial approach to their analyses, the cross-sectional nature of the
study designs and the lack of control for potential bias due to copollutant confounding
limit the interpretation of their results.
5.5.2.5 Summary and Causal Determination
Figure 5-26 presents total mortality effect estimates associated with long-term exposure
to SO2. The overall range of effects spans 0.93 to 1.26 per 5-ppb increase in the annual
(or longer period) average SO2 concentration. 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 effect estimates of 1.02 to 1.06,
while the effect estimate from the recent cohort study of truck drivers was 1.09. Note that
each of the U.S. cohort studies 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. Because 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 the more general population. The evidence from the cohort studies
conducted in Europe and Asia is generally similar to that observed from the U.S. cohort
studies. That is, the magnitude of the effect estimates is generally similar, although there
is greater inconsistency in the direction of the association. Also, the effect estimate
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observed by Carey et al. (2013) is much higher than that observed in any of the other
studies. Generally, these results are consistent with a recent study (Wang et al.. 2014a')
that evaluated the correlation between life expectancy and SO2 concentrations in 85
major city regions in China. After accounting for a surrogate for socioeconomic status,
they observed that city regions with higher SO2 concentrations were correlated with
lower life expectancies.
Figure 5-27 presents the cause-specific mortality effect estimates associated with
long-term exposure to SO2. The overall range of effects spans 0.93 to 4.40 per 5-ppb
increase in the annual (or longer period) average SO2 concentration. Generally, there was
a trend toward more positive associations for respiratory and lung cancer mortality
compared to cardiopulmonary, cardiovascular, and other causes of death. Specifically,
recent studies examining respiratory mortality provide some evidence that this cause of
death may be more consistently associated with long-term exposure to SO2 than other
causes of death. This is consistent with both the short- and long-term exposure to SO2
that are associated with respiratory effects.
Overall, the majority of the limited evidence informing the association between long-term
exposure to SO2 and mortality was included in the 2008 SOx ISA. The 2008 SOx ISA
identified concerns regarding the consistency of the observed associations, whether the
observed associations were due to SO2 alone, or if sulfate or other particulate SOx or PM
indices could have contributed to these associations, and the geographic scale of the
exposure assessment. Specifically, the 2008 SOx ISA noted the possibility that the
observed effects may not be due to SO2, but other co-occurring pollutants 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, could not be ruled out. None of the epidemiologic
studies made corrections or adjustments for exposure measure measurement error, or
accounted for the potential for bias away from the null, the potential for which has been
demonstrated in simulation studies (see Section 3.4.4.2). Overall, a lack of consistency
across studies, inability to distinguish potential confounding by copollutants, and
uncertainties regarding the geographic scale of analysis limited the interpretation of the
causal relationship between long-term exposure to SO2 and mortality.
The recent evidence is generally consistent with the evidence in the 2008 SOx ISA.
The biggest notable difference is in the improved consistency in the association between
long-term exposure to SO2 and both respiratory and total mortality that comes from the
inclusion of recent cohort studies. However, none of these recent studies help to resolve
the uncertainties identified in the 2008 SOx ISA related to copollutant confounding or the
geographic scale of the analysis. All available evidence for mortality due to long-term
exposure to SO2 was evaluated using the framework described in Table II of the
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Preamble to the IS As (U.S. EPA. 2015b). The key evidence as it relates to the causal
framework is summarized in Table 5-43. The overall evidence is inadequate to infer a
causal relationship between long-term exposure to SO2 and total mortality among adults.
Study
Location
Mean
(PPb)
Notes
tHartetal. 2011
USA
4.8
Men
Krewski et al. 2000
USA
1.6-24.0 HSC
Krewski et al. 2000
USA
9.3
ACS
Pope et al. 2002
USA
6.7-9.7
ACS
+Krewski et al. 2009
USA
9.6
ACS
Lipfert et al. 2006
USA
16.3
Men
+Lipfert et al. 2009
USA
4.3
Men
Abbey et al. 1999
USA
5.6
Men
Abbey et al. 1999
USA
5.6
Women
Nafstad et al. 2004
Norway
3.6
Men
Beelen et al. 2008
The Netherlands
5.2
Case-cohort -
Filleul et al. 2005
France
3.0-8.2
+Bentayeb et al. 2015
France
2.3
+Carey et al. 2013
England
1.5
+Hansell et al. 2016
England
32.4
1971
+Hansell et al. 2016
England
16.4
1981
+Hansell et al. 2016
England
11.2
1991
Elliott et al. 2007
Great Britain
12.2-41.4
tCao et al. 2011
Eastern China
27.7
+Zhang et al. 2011
Shenyang, China
23.9
0.8 0.9 1 1.1 1.2 1.3 1.4
Relative Risk (95% Confidence Interval)
ACS = American Cancer Society Study; HSC = Harvard Six Cities Study.
Note: studies in red are recent studies. Studies in black were included in the 2008 ISA for Sulfur Oxides. Relative risks are
standardized to a 5-ppb increase in sulfur dioxide concentrations. Corresponding quantitative results are reported in Supplemental
Table 5S-29 (U.S. EPA. 2016x).
Figure 5-26 Relative risks (95% confidence interval) of sulfur
dioxide-associated total mortality.
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Study
tHartetal. 2011
Nafstad et al. 2004
Beelen et al. 2008
Elliott et al. 2007
tBentayeb et al. 2015
tHansell et al. 2016
tHansell et al. 2016
tHansell et al. 2016
+Cao et al. 2011
tCarey et al. 2013
+Dong et al. 2012
tKatanoda et al. 2011
tHartetal. 2011
tKatanoda et al. 2011
tKatanoda et al. 2011
tHartetal. 2011
Krewski et al. 2000
tKrewski et al. 2009
Abbey et al. 1999
Abbey et al. 1999
Nafstad et al. 2004
Beelen et al. 2008
Filleul et al. 2005
tCarey et al. 2013
tHansell et al. 2016
tHansell et al. 2016
tHansell et al. 2016
Elliott et al. 2007
tCao et al. 2011
tKatanoda et al. 2011
Krewski et al. 2000
tKrewski et al. 2009
Abbey et al. 1999
Abbey et al. 1999
Filleul et al. 2005
Elliott et al. 2007
tWang et al. 2009
tHartetal. 2011
Beelen et al. 2008
Elliott et al. 2007
tBentayeb et al. 2015
tHansell et al. 2016
tHansell et al. 2016
tHansell et al. 2016
tCao et al. 2011
tZhang et al. 2011
tHartetal. 2011
tKrewski et al. 2009
Nafstad et al. 2004
tCarey et al. 2013
Nafstad et al. 2004
tZhang et al. 2011
Beelen et al. 2008
Elliott et al. 2007
tKrewski et al. 2009
Location
USA
Norway
The Netherlands
Great Britain
France
England
England
England
Eastern China
England
China
Japan
USA
Japan
Japan
USA
USA
USA
USA
USA
Norway
The Netherlands
France
England
England
England
England
Great Britain
Eastern China
Japan
USA
USA
USA
USA
France
Great Britain
Brisbane, Australia
USA
The Netherlands
Great Britain
France
England
England
England
Eastern China
Shenyang, China
USA
USA
Norway
England
Norway
Shenyang, China
The Netherlands
Great Britain
USA
Mean
Notes
(PPb)
4.8
Men
3.6
Men
5.2
12.2-41.4
2.3
32.4
1971
16.4
1981
11.2
1991
27.7
1.5
23.9
2.4-19.0
4.8
COPD - Men -
2.4-19.0
COPD
2.4-19.0
Pneumonia
4.8
Men
HSC
9.6
ACS
5.6
Men
5.6
Women
3.6
Men
5.2
1.5
32.4
1971
16.4
1981
11.2
1991
12.2-41.4
27.7
2.4-19.0
9.6
5.6
5.6
12.2-41.4
5.4
HSC
ACS
Men
Women
4.8
5.2
12.2
2.3
32.4
16.4
11.2
27.7
23.9
4.8
9.6
3.6
1.5
3.6
23.9
Men
41.4
1971
1981
1991
IHD- Men
IHD
IHD -Men
Circulatory
Cerebrovascular -Men
Cerebrovascular —
5.2
12.2-41.4
9.6
Respiratory
Lung Cancer
2.52 (1.34,4.77)
4.40 (2.34,8.33)
Cardiopulmonary
Cardiovascular
Other
0.6 0.8 1 1.2 1.4 1.6 1.8 2
Relative Risk (95% Confidence Interval)
ACS = American Cancer Society Study; COPD = chronic obstructive pulmonary disease; HSC = Harvard Six Cities Study;
IHD = ischemic heart disease.
Note: Studies in red are recent studies. Studies in black were included in the 2008 ISA for Sulfur Oxides. Relative risks are
standardized to a 5-ppb increase in sulfur dioxide concentrations. Corresponding quantitative results are reported in Supplemental
Table 5S-30 (U.S. EPA. 2016V).
Figure 5-27 Relative risks (95% confidence interval) of sulfur
dioxide-associated cause-specific mortality.
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Table 5-43 Summary of evidence, which is inadequate to infer a causal
relationship between long-term sulfur dioxide exposure and total
mortality.
Rationale for
Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations
Associated with
Effects0
Some
epidemiologic
studies report
positive
associations but
results are not
entirely consistent.
Small, positive associations between
long-term exposure to SO2 and mortality
in the HSC cohort, the ACS cohort, and
the Veterans cohort, even after
adjustment for common potential
confounders
Krewski et al. (2000) Mean: 1.6-24.0 ppb
tKrewski et al. (2009)
Jerrett et al. (2003)
Krewski et al. (2000)
City-specific annual
¦ mean: 9.3-9.6 ppb
tLipfert et al. (2009) County-level mean from
air quality model: 4.3 ppb
Recent cohort studies in the U.S. observe tHart et al. (2011) Annual average at
increases in total mortality and mortality residential address from
due to lung cancer and cardiovascular model: 4.8 ppb
and respiratory disease, but exposure
assessment and statistical methods were
not adequate for study of SO2.
Some
epidemiologic
studies report no
associations.
No association observed in European
cohort studies for total, respiratory, or
cardiovascular mortality
Beelen et al. (2008b) IDW to regional monitors:
5.2 ppb
Nafstad et al. (2004)
Model/monitor hybrid:
3.6 ppb
Filleul et al. (2005) 3-yr mean: 3.0-8.2 ppb
Uncertainty due to
potential
confounding from
correlated
pollutants
When reported, correlations with
copollutants were generally moderate
(0.4-0.7) to high (>0.7). Confounding of
observed associations by other pollutants
or pollutant mixtures cannot be ruled out.
Table 5-42
Uncertainty
regarding how
exposure
measurement error
may influence the
results
SO2 has low (<0.4) to moderate (0.4-0.7)
spatial correlations across urban
geographical scales. The geographical
scale for estimating exposure used in
these studies may be too large for a
highly spatially heterogeneous pollutant
such as SO2.
Section 3.4.2
Exposure measurement error in long-term Section 3.4.4.2
SO2 exposure can lead to bias toward or
away from the null.
No evidence for long-term exposure and Section 5.2.2.4
respiratory health effects in adults to
support the observed associations with
respiratory mortality
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Table 5-43 (Continued): Summary of evidence, which is inadequate to infer a
causal relationship between long term sulfur dioxide
exposure and total mortality.
Rationale for
Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations
Associated with
Effects0
No coherence with No evidence for long-term exposure and Section 5.3.2.4
evidence for cardiovascular health effects in adults to
respiratory and support the observed associations with
cardiovascular cardiovascular mortality
morbidity
ACS = American Cancer Society; HSC = Harvard Six Cities; IDW = inverse distance weighting; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Table I and Table II of the
Preamble to the ISAs (U.S. EPA. 2015b).
bDescribes the key evidence and references contributing most heavily to causal determination and, where applicable, to
uncertainties or inconsistencies. References to earlier sections indicate where full body of evidence is described.
°Describes the S02 concentrations with which the evidence is substantiated (for experimental studies, below 5,000 ppb).
fStudies published since the 2008 ISA for Sulfur Oxides.
5.6 Cancer
5.6.1 Introduction
The body of literature characterizing the carcinogenic, genotoxic, and mutagenic effects
of exposure to SO2 has grown since the 2008 SOx ISA (U.S. EPA. 2008d). The cancer
section of the ISA characterizes epidemiologic associations between SO2 exposure and
cancer incidence or cancer mortality, as well as the animal toxicology carcinogenicity
studies (Section 5.6.2). Subsections discuss the evidence relating to lung cancer
(Section 5.6.2.1). bladder cancer (Section 5.6.2.2). and other cancers (Section 5.6.2.3).
Laboratory studies of mutagenicity or genotoxicity are discussed in Section 5.6.3.
The 2008 SOx ISA summarized the literature on SO2 concentrations and lung cancer as
"inconclusive" (U.S. EPA. 2008d). Multiple studies across the U.S. and Europe
investigated the relationship between SO2 concentrations and lung cancer incidence and
mortality. Many studies reported generally null associations, but some studies
demonstrated positive associations. However, some studies were limited by a small
number of cancer cases. The following summaries add to the previous knowledge on SO2
concentrations and cancer incidence and mortality. The sections below describe studies
investigating lung cancer, bladder cancer, and other cancers. Supplemental Tables
provide detailed summaries of the respective new epidemiologic [Table 5S-3KU.S. EPA.
2016z)I and genotoxic/mutagenic [Table 5S-32 (U.S. EPA. 2016! )aal literature.
The animal toxicology literature of SO2 exposure is dominated by studies of SO2 acting
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as a cocarcinogen or tumor promoter, with one study of SO2 inhalation associated with an
increased rate of lung tumor formation in lung tumor-susceptible female rodents.
Genotoxicity and mutagenicity studies show mixed results with null studies in a
Drosophila model and positive micronuclei findings in a mouse inhalation model of SO2
exposure.
5.6.2 Cancer Incidence and Mortality
5.6.2.1 Lung Cancer Incidence and Mortality
International studies exploring the associations between SO2 concentrations and lung
cancer incidence have provided inconsistent results. No recent studies on SO2
concentration and lung cancer incidence in the U.S. have been published. Large studies
conducted using the Netherlands Cohort Study on Diet and Cancer examined the
association between SO2 concentration and lung cancer incidence (Brunekreef et al..
2009; Beelen et al.. 2008a). Null associations were reported in both analyses of the full
cohort and a case-cohort design. None of the analyses adjusted for copollutants. An
ecological study in Israel examining lung cancer incidence among men also reported null
results for the association with SO2 concentrations (Eitan et al.. 2010). Results were
relatively unchanged when adjusting for PM10. No association was observed between SO2
concentrations and lung cancer hospitalizations among men or women in southern France
in an ecological study that did not control for copollutants (Pascal et al.. 2013). However,
an ecological analysis performed among women in Taiwan demonstrated a positive
association between SO2 concentration and lung cancer incidence (Tseng et al.. 2012).
This positive association remained in a regression model adjusted for other pollutants
(CO, NO2, NO, O3, and PM10; none of these air pollutants exhibited an association with
lung cancer incidence). The association was present in analyses for both types of lung
cancer examined, adenocarcinomas and squamous cell carcinomas. Thus, overall,
multiple ecologic studies have been performed examining SO2 concentrations and lung
cancer incidence with inconsistent findings, and analyses using a large cohort study
reported no association between SO2 concentrations and lung cancer incidence but had no
control of copollutant confounders. Each of these studies used SO2 concentrations
measured at central site monitors to assign exposure. Beelen et al. (2008a) and
Brunekreef et al. (2009) used inverse distance weighting between the central site monitor
location and residential address, and combined this with the output of land use regression
(LUR) models for urban contributions. Eitan et al. (2010) generated spatially interpolated
surfaces for a 7-year period, while the other ecological studies relied on annual averages
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from the central site monitors. None of the studies corrected for exposure measurement
error.
Studies in the U.S. have reported inconsistent findings for the association between SO2
concentrations and lung cancer mortality (see Section 5.5.2 and Figure 5-27). No
association between SO2 concentrations and lung cancer mortality was present in a report
by Health Effect Institute (Krewski et al.. 2009). Estimates stratified by high school
education (less than high school education, high school education, or greater) were also
examined and no association was present in either subgroup. In addition to the entire time
period of the study, the researchers also examined 5-year increments, none of which
demonstrated an association. However, a recent study of men in the trucking industry
found a slight positive association between SO2 concentrations and lung cancer mortality
(Hart et al.. 2011). With the inclusion of PM10 and NO2 in the model, the 95% CI
included the null but the point estimate was in the positive direction and only slightly
attenuated.
Recent studies have also been performed in Asia and Europe examining the relationship
between SO2 and lung cancer mortality. In China, a positive association was observed
between SO2 and lung cancer mortality (Chen et al.. 2016; Cao etal.. 2011). In the study
by Cao et al. (2011). this association was relatively unchanged with adjustment of either
TSP or NOx. A study in Japan also reported a positive association between SO2 and lung
cancer mortality (Katanoda et al.. 2011). However, the estimate was reduced when
additional potential confounders (smoking of parents during subjects' childhood,
consumption of nonyellow or nongreen vegetables, occupation, and health insurance)
were controlled for and no copollutant assessment was performed. Positive associations
were also observed for suspended PM, PM2 5, and NO2 concentrations. When examining
subgroups, the association was highest among male smokers. The point estimate was
similar to the overall estimate for male former smokers but the 95% confidence interval
was wide due to the small size of the study population. The estimate was lowest among
female never smokers. The number of male never smokers and female smokers were too
small to assess individually. A study in the U.K. also demonstrated a positive association
between SO2 concentration and lung cancer mortality (Carey et al.. 2013).
The association was slightly attenuated when education was included in the model
instead of income. However, a large study using the Netherlands Cohort Study on Diet
and Cancer reported no association between SO2 concentration and lung cancer mortality
(Brunekreef et al.. 2009). This study was mentioned above and also did not demonstrate
an association between SO2 concentration and lung cancer incidence. No copollutant
models were examined. In summary, consistent with studies conducted in the U.S.
examining SO2 concentrations and cancer mortality, recent studies performed in Asia and
Europe also had inconsistent findings. Many of these studies used SO2 concentrations
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measured at central site monitors to assign exposure, and none of the studies corrected for
exposure measurement error. Brunekreef et al. (2009) used inverse distance weighting
between the central site monitor location and residential address, and combined this with
the output of land use regression (LUR) models for urban contributions. Hart et al. (2011)
used spatial smoothing, and Carey et al. (2013) used a dispersion model constructed with
emissions data to assign exposure.
A study in Italy used a Lagrangian dispersion model to estimate SOx concentrations as a
marker for refinery plant emissions [exposure assessment technique summarized in
Section 3.3.2.4 (Ancona et al.. 2015)1. The relationship between these estimates and
cancer mortality and hospitalizations were investigated. No association was observed for
lung cancer among men or women; however, these results are difficult to interpret.
The estimated SOx concentrations were highly correlated with estimates of PMio, which
is expected as SOx was being treated as a marker for petrochemical refinery emissions.
This makes interpretation difficult as copollutant models were not shown for lung cancer
and additionally the validity of the model is unknown.
A recent meta-analysis (Chen et al.. 2015a) combined the results of five studies of SO2
and lung cancer and found an overall OR of 1.03 (95% CI: 1.02, 1.05), although one of
the five studies I (C'ao etal.. 2011); characterized above] accounted for nearly 80% of the
weight contributing to the overall OR and was the only study of the five to observe a
positive and statistically significant association between SO2 exposure and lung cancer.
Three of the remaining studies included in the meta-analysis observed null associations
between SO2 and lung cancer.
Sulfur Dioxide Lung Carcinogenesis, Cocarcinogenic Potential, and Tumor
Promotion in Laboratory Animal Models
The toxicological evidence for effects of sulfur dioxide in carcinogenicity, mutagenicity,
or genotoxicity is characterized below. Other regulatory agencies have characterized the
carcinogenic potential of sulfur dioxide and its metabolites. The International Agency for
Research on Cancer (IARC) has determined sulfur dioxide, sulfites, bisulfites, and
metabisulfites are not classifiable as to their carcinogenicity to humans (Group 3) and the
American Conference of Governmental Industrial Hygienists has rated sulfur dioxide as
not classifiable as a human carcinogen (A4).
Direct evidence of carcinogenicity was studied evaluating incidence of lung tumors in a
lung adenoma-susceptible mouse strain, (the LX mouse), with chronic exposure to sulfur
dioxide at 500 ppm, 5 minutes/day, 5 days/week for 2 years (Peacock and Spence. 1967).
S02-exposed female mice had an increase in the number of lung tumors subgrouped as
(1) adenomas and (2) primary carcinomas versus controls. Males had a smaller increase
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in adenomas versus controls and similar levels of primary carcinomas compared to
controls.
Evidence exists for SO2 to be a cocarcinogen (Pauluhn et al.. 1985); SO2 and
benzo[a]pyrene, B[a]P, coexposure increased the incidence of lung tumor formation in
rodents versus B[a]P exposure alone. Chronic coexposure to SO2 and B[a]P resulted in
increased incidence of upper respiratory tract neoplasia in rats (Laskin et al.. 1976) and
hamsters (Pauluhn et al.. 1985) over B[a]P exposure alone. SO2 exposure shortened the
induction period for spontaneous squamous cell lung tumor formation after B[a]P
exposure (Laskin et al.. 1976); rats were exposed 5 days a week, 6 hours/day for their
lifetime to 10 ppm SO2 alone via inhalation or 4 ppm SO2 + 10 mg/m3 B[a]P (1 hour
B[a]P/day). SO2 exposure also shortened the induction time for
methylcholanthrene-induced carcinogenesis.
Multiple studies explored SO2 as a cocarcinogen or promoter after particulate-induced
tumorigenesis. In a study of suspended particulate matter- (SPM-) induced tumorigenesis
(proliferative lesions of pulmonary endocrine cells) in the rat, SO2 did not exacerbate
SPM-dependent hyperplasia when rats were exposed to the mixture of SPM and SO2 (Ito
etal.. 1997). Adult male rats were exposed to SO2 for 11 months, 16 hours/day ± SPM
for 4 weeks, once/week by intratracheal injection. SO2 did not act as a tumor promoter or
cocarcinogen in this model. In a separate study of diesel exhaust particle- (DEP-)
dependent lung tumorigenesis, SO2 was able to promote DEP-dependent tumorigenesis
(Olnama et al.. 1999). Adult male rats were intratracheally instilled with diesel exhaust
particle extract-coated carbon black particles (DEcCBP) and exposed to 4 ppm SO2 for
10 months. Eighteen months after starting the experiment, the animals were examined for
respiratory tract tumors and DNA adducts were measured in lung tissue. Lung tumors and
DNA adducts were seen in animals with coexposure to SO2 and DEcCBP but not in
animals only exposed to DEcCBP. SO2 acted as a tumor promoter in animals exposed to
DEcCBP. In a separate investigation, hamsters were exposed to diesel engine exhaust
(separately with and without particles) and a mixture of SO2 and NO2 with or without
exposure to the carcinogen diethyl-nitrosamine to investigate the potential cocarcinogenic
effect of exposure to the dioxides mixture and diesel engine exhaust in the respiratory
tract (Heinrich et al.. 1989). These adult male hamster were exposed for 19 hours/day,
5 days/week for 6, 10.5, 15, or 18 months to diesel exhaust, filtered diesel exhaust
(without particles), a dioxide mixture of NO2 (5 ppm) and SO2 (10 ppm), or clean air.
Two exposure groups from each of the aforementioned test groups were also given a
single subcutaneous injection of diethylnitrosamine (DEN) (3 mg or 6 mg/kg body
weight). Exposure to the dioxide mixture by itself did not elevate tumor rate (tumor
induction), nor did it exacerbate DEN-dependent effects (tumor promotion) in the
hamster. In summary, a comparison of multiple studies of SO2 coexposure with particles
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reported mixed results in various models of carcinogenicity, cocarcinogenic potential, or
tumor promotion.
Oncogene and tumor suppressor genes also appear to be affected by SO2 exposure,
especially with coexposure to benzo[a]pyrene, B[a]P. Synergistic expression of c-fos and
c-jun with SO2 and B[a]P coexposure was observed in rodent lungs (Qin and Mcng.
2006). SO2 and B[a]P coexposure in male Wistar rats (26.5 ppm SO2 inhalation,
6 hours/day for 7 days; 3 mg B[a]P instilled) statistically significantly downregulated
expression of tumor suppressor genes pi 6 and myc, and increased expression of
oncogenes c-myc, H-ras, andp53. Others have reported that SO2 exposure alone could
induce p53 expression in rats (Bai and Meng. 2005).
5.6.2.2 Bladder Cancer Incidence and Mortality
Several studies on the relationship between SO2 concentrations and bladder cancer
incidence and mortality have been published since the 2008 SOx ISA (U.S. EPA. 2008d).
Positive associations were observed in studies of bladder cancer mortality but not bladder
cancer incidence. An ecological study in southern France reported on the relationship
between SO2 concentrations and hospitalizations for bladder cancer without examination
of copollutant models (Pascal et al.. 2013). Null associations were observed for men and
women. Another ecological study in Israel examining bladder cancer incidence also
reported sex-stratified results (Eitan et al.. 2010). Neither sex demonstrated an association
between SO2 concentrations and bladder cancer in models with and without adjustment
for PM10. However, an association was observed in a study examining the relationship
between SO2 and bladder cancer mortality (Liu et al.. 2009a). Liu et al. (2009a)
investigated the association between SO2 and bladder cancer mortality using controls with
mortality due to causes unrelated to neoplasm or genitourinary-related disease and
matched by sex, year of birth, and year of death. A positive association was observed
between SO2 concentration in the second and third tertiles of exposure and bladder cancer
mortality. For further investigations, the authors created a three-level exposure variable
combining NO2 and SO2 concentrations: the lowest tertile of SO2 and NO2 concentrations
(<4.32 ppb and <20.99 ppb, respectively), the highest tertile of SO2 and NO2
concentrations (>6.49 ppb and >27.33 ppb, respectively), and other
categorizations/combinations. The ORs were 1.98 (95% CI 1.36, 2.88) for the highest
level ofN02 and SO2 and 1.37 (95% CI 1.03, 1.82) for the middle level categorizations.
Although the point estimates are higher than those observed for SO2 alone (see
Supplemental Table 5S-31, (U.S. EPA. 2016z). the 95% confidence intervals overlap,
and therefore, conclusions that NO2 and SO2 combined contribute to higher odds of
mortality than either alone cannot be drawn. Finally, a study using SOx concentration
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estimated using a Lagrangian dispersion model reported no association between SOx
concentration and bladder cancer mortality or hospitalizations among men or women
(Ancona etal.. 2015). However, results of this study are difficult to interpret because of
unknown validity of the model (see Section 3.3.2.4) and high correlation with PMio and
H2S.
5.6.2.3 Incidence of Other Cancers
Recent studies of SO2 concentrations and other cancer types have been published since
the 2008 ISA for Sulfur Oxides (U.S. EP A. 2008d). but provide limited information on
associations with SO2. An ecological study in southern France investigated the
relationships between SO2 and hospitalizations for breast cancer, acute leukemia,
myeloma, and non-Hodgkin lymphoma (Pascal et al.. 2013). Null associations were
observed in sex-stratified analyses among men and women, with the exception of a
positive association between SO2 and acute leukemia among men. However, the authors
urge caution when interpreting the results due to a small number of male acute leukemia
cases. This study did not examine copollutant confounding. Another ecologic study used
Surveillance, Epidemiology, and End Results data to examine the correlation between
SO2 concentrations and breast cancer incidence (Wei et al. 2012). A positive relationship
was detected, but a there was no control for potential confounders of other air pollutants
(of which CO, NOx, and VOCs, but not PM10, also demonstrated a positive correlation
with breast cancer incidence). Both of these studies are limited by their ecologic nature
and the lack of individual-level data. A cross-sectional study was conducted in South
Korea that looked at the association between symptom scores for prostate cancer and
emissions data for SOx (measured in kg/year/person) and a number of other air pollutants
(Shim et al.. 2015). In logistic regression models adjusted for age, the authors observed
positive associations between men living in areas with greater emissions of SOx and
symptom scores for prostate cancer. Similar results were observed for NOx, CO, PM10,
VOCs and NH3. The lack of control for potential confounding by other air pollutants or
risk factors (e.g., smoking, SES) limit the interpretation of these results.
A cohort study examined the relationship between SOx concentrations, estimated using a
Lagrangian dispersion model, and hospitalizations and mortality for various cancer types
(Ancona etal.. 2015). No associations were found between SOx concentrations and either
hospitalizations or mortality due to cancers of the stomach, colon/rectum, liver, kidney,
brain, or breast. Positive associations were observed for SOx concentration and mortality
due to pancreatic and larynx cancers among women but not men. The 95% confidence
interval showed a large degree of imprecision in the estimates for cancer of the larynx.
The association with pancreatic cancer was not robust to adjustment with H2S or PM10.
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When examining the association between estimated SOx concentration and
hospitalizations, a positive, but imprecise, association was observed for cancer of the
larynx among women and an inverse association was noted for cancers of lymphatic and
hematopoietic tissue.
5.6.2.4 Summary of Cancer Incidence and Mortality
Similar to studies of SO2 concentrations and lung cancer in the previous ISA (U.S. EPA.
2008d). recent studies of SO2 concentrations and lung cancer have provided inconsistent
results (Carey et al.. 2013; Pascal et al.. 2013; Tseng et al.. 2012; Cao etal.. 2011; Hart et
al.. 2011; Katanoda et al.. 2011; Eitan et al.. 2010; Brunekreef et al.. 2009; Krewski et al..
2009; Beelen et al.. 2008a'). Studies of bladder cancer appear to find no association
between SO2 concentrations and bladder cancer incidence (Pascal et al.. 2013; Eitan et
al.. 2010). but a study of SO2 concentration and bladder cancer mortality reported a
positive association (Liu et al.. 2009a). Limited information is available regarding other
cancers. Animal toxicology models of SO2 inhalation exposure show SO2 acting as a
promoter or cocarcinogen, with one study showing increased lung tumor formation in a
lung tumor-prone animal model.
5.6.3 Genotoxicity and Mutagenicity
Multiple studies of genotoxicity or mutagenesis with SO2 in vivo or SO2 in vitro exposure
have been reported in the literature and are detailed below in Supplemental Table 5S-32
(U.S. EPA. 2016! )aa.
After inhalation exposure to SO2, mouse bone marrow micronuclei formation (MN) was
significantly elevated in both males and females after exposure to SO2 (5.4, 10.7, 21.4, or
32.1 ppm SO2, 4 hours/day for 7 days) (Meng et al.. 2002). The polychromatophilic
erythroblasts of the bone marrow (MNPCE) were formed in significantly increased
numbers with SO2 exposure. Another study recapitulated these findings; subacute
exposure to SO2 (10.7 ppm SChfor 5 day, 6 hours/days) induced a significant increase in
MNPCE with this effect attenuated by exogenous antioxidant SSO pretreatment (Ruanet
al.. 2003).
The rate of DNA single strand breaks induced by B[a]P exposure in fetal hamster lung
cells (50 ppm for 2 weeks) (Pool et al.. 1988b) and rat liver cells (2.5, 5, 9.9, or 19.9 ppm,
4 hours/day for 7 days) (Pool et al.. 1988a) was significantly attenuated by concomitant
exposure to SO2 (50 ppm for 2 weeks).
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Genotoxicity testing of Drosophila sperm for sex-linked recessive lethals after feeding
larvae 0.04 M or 0.08 M sodium sulfite in a 1% glucose solution was performed and no
increase was found above background. One caveat is that sulfite can interact with
glucose, making the exposure assessment more complicated.
Multiple studies of genotoxicity or mutagenesis with S02 in vivo or in vitro exposure
have been reported in the literature and are summarized in Supplemental Table 5S-32
(U.S. EPA. 2016! )aa. Mixed results of genotoxicity or mutagenicity have been reported
after SO2 exposure including positive associations with SO2 inhalation exposure in the
mouse MN assay.
5.6.4 Summary and Causal Determination
The overall evidence for long-term SO2 exposure and cancer is inadequate to infer a
causal relationship. This conclusion is based on the inconsistent evidence from
epidemiologic studies, as well as mixed evidence within the animal toxicology and mode
of action framework for mutagenesis and genotoxicity. In past reviews, a limited number
of epidemiologic studies had assessed the relationship between long-term SO2
concentrations and cancer incidence and mortality. The 2008 ISA for Sulfur Oxides
concluded that the evidence was "inconclusive" (U.S. EP A. 2008d). Recent studies
include evidence on lung cancer as well as new types of cancer, evaluating both
incidence and mortality. However the additional recent evidence has not informed any of
the uncertainties identified in the previous review, including uncertainties due to
exposure measurement error, potential copollutant confounding, and limited mechanistic
evidence or biological plausibility. All available evidence for cancer due to long-term
SO2 concentrations was evaluated using the framework described in Table II of the
Preamble to the IS As (U.S. EPA. 2015b). The key evidence as it relates to the causal
framework is summarized in Table 5-44.
American Conference of Governmental Industrial Hygienists has rated sulfur dioxide as
A4, not classifiable as a human carcinogen. The IARC has classified SO2 as a Group 3
substance, not classifiable as to its carcinogenicity to humans. The Registry of Toxic
Effects of Chemical Substances of National Institute for Occupational Safety and Health
lists SO2 as tumorigenic and cocarcinogenic by inhalation in rats and mice. The National
Toxicology Program of the National Institutes of Health and the U.S. Environmental
Protection Agency have not classified SO2 for its potential carcinogenicity. Overall, there
is inconsistent evidence for an association between long-term SO2 exposure and cancer
from epidemiologic and toxicological studies. Some of the epidemiologic studies
observed positive associations while others did not. Some of these studies with positive
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associations were relatively unchanged with the inclusion of various cofounders and
copollutants, although many did not evaluate the potential for copollutant confounding.
Cohort studies have reported null associations between SO2 concentrations and lung
cancer incidence. Similarly, some ecological studies also reported no associations;
although, an ecological study in Taiwan among women did report an association between
SO2 concentrations and lung cancer incidence that was relatively unchanged when
including other pollutants. Positive associations were also observed in a study of SO2
concentrations and bladder cancer mortality but not in ecological studies of bladder
cancer incidence. The study of bladder cancer mortality examined the relationship
between bladder cancer mortality and joint exposure to high levels of NO2 and SO2, but
no copollutant assessment was performed controlling for NO2 or other air pollutants.
None of the epidemiologic studies made corrections or adjustments for exposure measure
measurement error, or accounted for the potential for bias away from the null, the
potential for which has been demonstrated in simulation studies (see Section 3.4.4.2).
Animal toxicological studies employing SO2 exposure with other known carcinogens
provide some evidence, showing that inhaled SO2 can increase tumor load in laboratory
rodents. Toxicological data provided by a study in LX mice, lung adenoma susceptible
animals, showed evidence of the direct carcinogenic potential of SO2. Other studies in
animal models show SO2 as a cocarcinogen with B[a]P or as a tumor promoter with
particulate-induced tumorigenesis. Nonetheless, toxicological data provide no clear
evidence of SO2 acting as a complete carcinogen and not all epidemiologic studies report
positive associations.
Collectively, the inconsistent evidence from several toxicological and epidemiologic
studies is inadequate to infer a causal relationship between long-term exposure to SO2 and
cancer incidence and mortality.
Table 5-44 Summary of evidence, which is inadequate to infer a causal
relationship between long-term sulfur dioxide exposure and cancer.
Rationale for Causal SO2 Concentrations
Determination3 Key Evidence13 Key References'3 Associated with Effects0
Among a small body
Generally null associations Section 5.6.2
Means varied across studies
of evidence, evidence
from studies of cancer
including areas estimating mean
from epidemiologic
incidence, with some
concentrations of SO2 as low as
studies is inconsistent.
observed increases in lung
1.49 ppb to as high as 27.87 ppb.
cancer and bladder cancer
Associations observed with
mortality in studies conducted
bladder cancer mortality at levels
in the U.S., Europe, and Asia
as low as 4.39-6.09 ppb.
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Table 5-44 (Continued): Summary of evidence, which is inadequate to infer a
causal relationship between long term sulfur dioxide
exposure and cancer.
Rationale for Causal
Determination3
Key Evidence13
Key References'3
SO2 Concentrations
Associated with Effects0
Uncertainty due to
exposure
measurement error
Central site monitors used in
cancer studies may not
capture spatial variability of
SO2 concentrations.
Section 3.4.2.2
Exposure measure Section 3.4.4.2
measurement error in
long-term SO2 exposure
assessment can bias toward
or away from the null.
Uncertainty due to Correlations of SO2 with other Section 3.4.3
confounding by pollutants vary by study or
correlated copollutants are not examined. Some
pollutants are moderately to
highly correlated with SO2 but
are not always taken into
account as potential
confounders.
Uncertainty due to Studies in a Peacock and Spence 500,000 ppb
limited coherence with tumor-susceptible mouse (1967)
toxicological evidence model, females had
increased numbers of lung
adenomas and carcinomas.
Studies of facilitation of
metastasis and coexposures
with known carcinogens show
mixed SO2 related effects.
Laskin et al. (1976)
10,000 ppb
Pauluhn et al. (1985)
172,000 ppb
Ohvama et al. (1999)
4,000 ppb
Heinrich et al. (1989)
5,000 or 10,000 ppb
Ito et al. (1997)
4,000 ppb
Section 5.6.2.1
Some evidence
identifies key events
within the MOA from
mutagenesis and
genotoxicity.
Mixed evidence of
mutagenicity and genotoxicity
formation in animal cells
exposed to SO2
Menq et al. (2002).
Ruan et al. (2003).
Pool et al. (1988b)
Section 5.6.3
5,000, 10,700, 21,400,
32,100 ppb
MOA = mode of action; S02 = sulfur dioxide.
aBased on aspects considered in judgments of causality and weight of evidence in causal framework in Table I and Table II of the
Preamble to the ISAs (U.S. EPA. 2015b).
bDescribes the key evidence and references contributing most heavily to causal determination and, where applicable, to
uncertainties or inconsistencies. References to earlier sections indicate where full body of evidence is described.
°Describes the N02 concentrations with which the evidence is substantiated (for experimental studies, below 5,000 ppb).
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Annex for Chapter 5: Evaluation of Studies on Health Effects of
Sulfur Oxides
Table A-1 Scientific considerations for evaluating the strength of inference
from studies on the health effects of sulfur oxides.
Evaluation Factors
Study Design
Controlled Human Exposure:
Studies should clearly describe the primary and any secondary objectives of the study, or specific hypotheses being
tested. Study subjects should be randomly exposed without knowledge of the exposure condition. Preference is
given to balanced crossover (repeated measures) or parallel design studies that include control exposures (e.g., to
clean filtered air). In crossover studies, a sufficient and specified time between exposure days should be employed
to avoid carry over effects from prior exposure days. In parallel design studies, all arms should be matched for
individual characteristics such as age, sex, race, anthropometric properties, and health status. In studies evaluating
effects of disease, appropriately matched healthy controls are desired for interpretative purposes.
Animal Toxicology:
Studies should clearly describe the primary and any secondary objectives of the study, or specific hypotheses being
tested. Studies should include appropriately matched control exposures (e.g., to clean filtered air, time matched).
Studies should use methods to limit differences in baseline characteristics of control and exposure groups. Studies
should randomize assignment to exposure groups and where possible conceal allocation from research personnel.
Groups should be subjected to identical experimental procedures and conditions; animal care including housing,
husbandry, etc. should be identical between groups. Blinding of research personnel to study group may not be
possible due to animal welfare and experimental considerations; however, differences in the monitoring or handling
of animals in all groups by research personnel should be minimized.
Epidemiology:
Inference is stronger for studies that clearly describe the primary and any secondary aims of the study, or specific
hypotheses being tested.
For short-term exposure, time-series, case crossover, and panel studies are emphasized over cross-sectional
studies because they examine temporal correlations and are less prone to confounding by factors that differ
between individuals (e.g., SES, age). Studies with large sample sizes and conducted over multiple years are
considered to produce more reliable results. If other quality parameters are equal, multicity studies carry more
weight than single-city studies because they tend to have larger sample sizes and lower potential for publication
bias.
For long-term exposure, inference is considered to be stronger for prospective cohort studies and case-control
studies nested within a cohort (e.g., for rare diseases) than cross-sectional, other case-control, or ecologic studies.
Cohort studies can better inform the temporality of exposure and effect. Other designs can have uncertainty related
to the appropriateness of the control group or validity of inference about individuals from group-level data. Study
design limitations can bias health effect associations in either direction.
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Table A-1 (Continued): Scientific considerations for evaluating the strength of
inference from studies on the health effects of sulfur
oxides.
Evaluation Factors
Study Population/Test Model
Controlled Human Exposure:
In general, the subjects recruited into study groups should be similarly matched for age, sex, race, anthropometric
properties, and health status. In studies evaluating effects of specific subject characteristics (e.g., disease, genetic
polymorphism, etc.), appropriately matched healthy controls are preferred. Relevant characteristics and health
status should be reported for each experimental group. Criteria for including and excluding subjects should be
clearly indicated. For the examination of populations with an underlying health condition (e.g., asthma),
independent, clinical assessment of the health condition is ideal, but self reporting of physician diagnosis generally
is considered to be reliable for respiratory and cardiovascular disease outcomes.3 The loss or withdrawal of
recruited subjects during the course of a study should be reported. Specific rationale for excluding subject(s) from
any portion of a protocol should be explained.
Animal Toxicology:
Ideally, studies should report species, strain, substrain, genetic background, age, sex, and weight. Unless data
indicate otherwise, all animal species and strains are considered appropriate for evaluating effects of SO2 exposure.
It is preferred that the authors test for effects in both sexes and multiple lifestages, and report the result for each
group separately. All animals used in a study should be accounted for, and rationale for exclusion of animals or data
should be specified.
Epidemiology:
Confidence in results is greater in studies that recruit the study population from the target population and examine a
study population that is representative of the target population. Studies with high participation and low drop-out over
time that is not dependent on exposure or health status are considered to have low potential for selection bias.
Clear indication of criteria for including and excluding subjects can facilitate assessment of selection bias. For
populations with an underlying health condition, independent, clinical assessment of the health condition is valuable,
but self report of physician diagnosis generally is considered to be reliable for respiratory and cardiovascular
outcomes. Comparisons of groups with and without an underlying health condition are more informative if groups
are from the same source population. Selection bias can influence results in either direction or may not affect the
validity of results but rather reduce the generalizability of findings to the target population.
Pollutant
Controlled Human Exposure:
The focus is on studies testing SO2 exposure.
Animal Toxicology:
The focus is on studies testing SO2 exposure.
Epidemiology:
The focus is on studies testing SO2 exposure.
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Table A-1 (Continued): Scientific considerations for evaluating the strength of
inference from studies on the health effects of sulfur
oxides.
Evaluation Factors
Exposure Assessment or Assignment
Controlled Human Exposure:
For this assessment, the focus will be on studies that use SO2 concentrations less than or equal to 2 ppm
(Section 12). Studies that use higher exposure concentrations may provide information relevant to mode of action,
dosimetry, inter-species variation, or at-risk human populations. Controlled human exposure studies considering
short-term, (e.g. generally exposures from 5-10 min, to 0.2-0.6 ppm SO2, were emphasized) (Section 1.2).
Animal Toxicology:
For this assessment, the focus will be on studies that use SO2 concentrations less than or equal to 2,000 ppb
(Section 12). Studies that use higher exposure concentrations may provide information relevant to mode of action,
dosimetry, inter-species variation, or at-risk human populations. Studies should characterize pollutant concentration,
temperature, and relative humidity and/or have measures in place to adequately control the exposure conditions.
The focus is on inhalation exposure. Noninhalation exposure experiments may provide information relevant to mode
of action. In vitro studies may be included if they provide mechanistic insight or examine similar effects as in vivo,
but are generally not included. All studies should include exposure control groups (e.g., clean filtered air).
Epidemiology:
Of primary relevance are relationships of health effects with the ambient component of exposure to SO2. However,
information about ambient exposure rarely is available for individual subjects; most often, inference is based on
ambient concentrations. Studies that compare exposure assessment methods are considered to be particularly
informative. Inference is stronger when the duration or lag of the exposure metric corresponds with the time course
for physiological changes in the outcome (e.g., up to a few days for symptoms) or latency of disease (e.g., several
years for cancer).
Given the spatial heterogeneity in ambient SO2 and potentially variable relationships between personal exposures
and ambient concentrations (Section 3.4.2.2 and Section 3.4.1). validated methods that capture the extent of
variability for the particular study design (temporal vs. spatial contrasts) and location carry greater weight. Central
site measurements, whether averaged across multiple monitors or assigned from the nearest or single available
monitor, have well-recognized limitations in capturing spatial variation in air pollutants. Monitors impacted by large
SO2 sources are particularly subject to concentration fluctuations due to changes in emission rates and
meteorological conditions and may not fully represent population exposure. Results based on central site
measurements can be informative if correlated with personal exposures, closely located to study subjects, highly
correlated across monitors within a location, used in locations with well-distributed sources, or combined with
time-activity information.
In studies of short-term exposure, temporal variability of the exposure metric is of primary interest. Metrics that may
capture variation in ambient sulfur oxides and strengthen inference include concentrations in subjects'
microenvironments and individual-level outdoor concentrations combined with time-activity data. Atmospheric
models may be used for exposure assessment in place of or to supplement SO2 measurements in epidemiologic
analyses. Dispersion models (e.g., AERMOD) can provide valuable information on fine-scale temporal and spatial
variations (within tens of km) of SO2 concentrations, which is particularly important for assessing exposure near
large stationary sources. Alternatively, grid-scale models (e.g., CMAQ) that represent SO2 exposure over relatively
large spatial scales (e.g., typically greater than 4 * 4-km grid size) often do not provide enough spatial resolution to
capture acute SO2 peaks that influence short-term health outcomes. Uncertainty in exposure predictions from these
models is largely influenced by model formulations and the quality of model input data pertaining to emissions or
meteorology, which tends to vary on a study-by-study basis.
For long-term exposures, models that capture within-community spatial variation in individual exposure may be
given more weight for spatially variable ambient SO2.
Exposure measurement error often attenuates health effect estimates or decreases the precision of the association
(i.e., wider 95% CIs), particularly associations based on temporal variation in short-term exposure (Section 3.4.2.3).
However, exposure measurement error can bias estimates away from the null, particularly for long-term exposures.
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Table A-1 (Continued): Scientific considerations for evaluating the strength of
inference from studies on the health effects of sulfur
oxides.
Evaluation Factors
Outcome Assessment/Evaluation
Controlled Human Exposure:
Endpoints should be assessed in the same manner for control and exposure groups (e.g., time after exposure,
methods, endpoint evaluator) using valid, reliable methods. Blinding of endpoint evaluators is ideal, especially for
qualitative endpoints (e.g., histopathology). For each experiment and each experimental group, including controls,
precise details of all procedures carried out should be provided including how, when, and where. Time of the
endpoint evaluations is a key consideration that will vary depending on the endpoint evaluated. Endpoints should be
assessed at time points that are appropriate for the research questions.
Animal Toxicology:
Endpoints should be assessed in the same manner for control and exposure groups (e.g., time after exposure,
methods, endpoint evaluator) using valid, reliable methods. Blinding of endpoint evaluators is ideal, especially for
qualitative endpoints (e.g., histopathology). For each experiment and each experimental group, including controls,
precise details of all procedures carried out should be provided including how, when, and where. Time of the
endpoint evaluations is a key consideration that will vary depending on the endpoint evaluated. Endpoints should be
assessed at time points that are appropriate for the research questions.
Epidemiology:
Inference is stronger when outcomes are assessed or reported without knowledge of exposure status. Knowledge
of exposure status could produce artifactual associations. Confidence is greater when outcomes assessed by
interview, self reporting, clinical examination, or analysis of biological indicators are defined by consistent criteria
and collected by validated, reliable methods. Independent, clinical assessment is valuable for outcomes such as
lung function or incidence of disease, but report of physician diagnosis has shown good reliability.3 Outcomes
assessed at time intervals that correspond with the time course for physiological changes (e.g., up to a few days for
symptoms) are emphasized. When health effects of long-term exposure are assessed by acute events such as
symptoms or hospital admissions, inference is strengthened when results are adjusted for short-term exposure.
Validated questionnaires for subjective outcomes such as symptoms are regarded to be reliable,15 particularly when
collected frequently and not subject to long recall. For biological samples, the stability of the compound of interest
and the sensitivity and precision of the analytical method is considered.
If not based on knowledge of exposure status, errors in outcome assessment tend to bias results toward the null.
Potential Copollutant Confounding
Controlled Human Exposure:
Exposure should be well characterized to evaluate independent effects of SO2.
Animal Toxicology:
Exposure should be well characterized to evaluate independent effects of SO2.
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Table A-1 (Continued): Scientific considerations for evaluating the strength of
inference from studies on the health effects of sulfur
oxides.
Evaluation Factors
Epidemiology:
Not accounting for copollutant confounding can produce artifactual associations; thus, studies that examine
copollutant confounding carry greater weight. The predominant method is copollutant modeling, which is especially
informative when measurement error is comparable for copollutants and correlations are not high. Interaction and
joint effect models are examined to a lesser extent. Evaluating correlations between SO2 and copollutants and
comparing health associations between SO2 and copollutants in single-pollutant models can add to the analysis of
potential copollutant confounding, particularly when exposure measurement error is comparable among pollutants.
Studies that examine SO2 only in single-pollutant models provide minimal information on the potential for copollutant
confounding. Copollutant confounding is evaluated based on the extent of observed correlations and relationships
with health effects. Highly variable correlations have been observed between SO2 and other criteria pollutants at
collocated monitors (Section 3.4.3), ranging from negative to strong correlations, making evaluation of copollutant
confounding necessary on a study-specific, rather than a general, basis.
Other Potential Confounding Factors
Controlled Human Exposure:
Preference is given to studies using experimental and control groups that are matched for individual level
characteristics (e.g., body weight, smoking history, age) and time-varying factors (e.g., seasonal and diurnal
patterns).
Animal Toxicology:
Preference is given to studies using experimental and control groups that are matched for individual level
characteristics (e.g., body weight, litter size, food and water consumption) and time-varying factors (e.g., seasonal
and diurnal patterns).
Epidemiology:
Factors are considered to be potential confounders if demonstrated in the scientific literature to be related to health
effects and correlated with SO2. Not accounting for confounders can produce artifactual associations; thus, studies
that statistically adjust for multiple factors or control for them in the study design are emphasized. Less weight is
placed on studies that adjust for factors that mediate the relationship between SO2 and health effects, which can
bias results toward the null. In the absence of information linking health risk factors to SO2, a factor may be
evaluated as a potential effect measure modifier, but uncertainty is noted as to its role as a confounder.
Confounders vary according to study design, exposure duration, and health effect and may include, but are not
limited to, the following:
For time-series and panel studies of short-term exposure:
• Respiratory effects—meteorology, day of week, season, medication use, allergen exposure (potential
effect modifier)
• Cardiovascular effects—meteorology, day of week, season, medication use
• Total mortality—meteorology, day of week, season, long-term temporal trends
For studies of long-term exposure:
• Respiratory effects—socioeconomic status, race, age, medication use, smoking, stress
• Cardiovascular, reproductive, and development effects—socioeconomic status, race, age, medication use,
smoking, stress, noise
• Total mortality—socioeconomic status, race, age, medication use, smoking, comorbid health conditions
• Cancer—socioeconomic status, race, age, occupational exposure
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Table A-1 (Continued): Scientific considerations for evaluating the strength of
inference from studies on the health effects of sulfur
oxides.
Evaluation Factors
Statistical Methodology
Controlled Human Exposure:
Statistical methods should be clearly described and appropriate for the study design and research question
(e.g., correction for multiple comparisons). Generally, statistical significance is used to evaluate the findings of
controlled human exposure studies. Detection of statistical significance is influenced by a variety of factors
including, but not limited to, the size of the study, exposure and outcome measurement error, and statistical model
specifications. Sample size is not a criterion for exclusion; ideally, the sample size should provide adequate power
to detect hypothesized effects (e.g., sample sizes less than three are considered less informative). Because
statistical tests have limitations, consideration is given to both trends in data and reproducibility of results.
Animal Toxicology:
Statistical methods should be clearly described and appropriate for the study design and research question
(e.g., correction for multiple comparisons). Generally, statistical significance is used to evaluate the findings of
animal toxicology studies. Detection of statistical significance is influenced by a variety of factors including, but not
limited to, the size of the study, exposure and outcome measurement error, and statistical model specifications.
Sample size is not a criterion for exclusion; ideally, the sample size should provide adequate power to detect
hypothesized effects (e.g., sample sizes less than three are considered less informative). Because statistical tests
have limitations, consideration is given to both trends in data and reproducibility of results.
Epidemiology:
Multivariable regression models that include potential confounding factors are emphasized. However, multipollutant
models (more than two pollutants) are considered to produce too much uncertainty due to copollutant collinearity to
be informative. Models with interaction terms aid in the evaluation of potential confounding as well as effect
modification. Sensitivity analyses with alternate specifications for potential confounding inform the stability of
findings and aid in judgments of the strength of inference of results. In the case of multiple comparisons,
consistency in the pattern of association can increase confidence that associations were not found by chance alone.
Statistical methods that are appropriate for the power of the study carry greater weight. For example, categorical
analyses with small sample sizes can be prone to bias results toward or away from the null. Statistical tests such as
f-tests and Chi-squared tests are not considered sensitive enough for adequate inferences regarding
pollutant-health effect associations. For all methods, the effect estimate and precision of the estimate (i.e., width of
95% CI) are important considerations rather than statistical significance.
AERMOD = American Meteorological Society/U.S. EPA Regulatory Model; CI = confidence interval; CMAQ = Community
Multiscale Air Quality; SES = socioeconomic status; S02 = sulfur dioxide.
aToren et al. (1993); (Murqia et al. (2014); Weakley et al. (2013); Yang et al. (2011); Heckbert et al. (2004); Barr et al. (2002);
Muhaiarine et al. (1997)).
"Burnev et al. (1989).
°Many factors evaluated as potential confounders can be effect measure modifiers (e.g., season, comorbid health condition) or
mediators of health effects related to S02 (comorbid health condition).
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Chapter 6 Populations and Lifestages Potentially at
Increased Risk for Health Effects Related to
Sulfur Dioxide Exposure
6.1 Introduction
Interindividual variation in human responses to air pollution exposure can result in some
groups or lifestages being at increased risk for health effects in response to ambient
exposure to an air pollutant. The NAAQS are intended to protect public health with an
adequate margin of safety. Protection is provided for both the population as a whole and
those potentially at increased risk for health effects in response to exposure to a criteria
air pollutant (e.g., SO2) [see Preamble to the ISAs (U.S. EPA. 2015b)I. The scientific
literature has used a variety of terms to identify factors and subsequently populations or
lifestages that may be at increased risk of an air pollutant-related health effect, including
susceptible, vulnerable, sensitive, and at risk, with recent literature introducing the term
response-modifying factor (Vinikoor-lmler et al.. 2014) [see Preamble to the ISAs (U.S.
EPA. 2015bVI. Due to the inconsistency in definitions for these terms across the scientific
literature and the lack of a consensus on terminology in the scientific community, as
detailed in the Preamble to the ISAs (U.S. EPA. 2015b). this chapter focuses on
identifying those populations or lifestages potentially "at risk" of an SCh-related health
effect. This leads to a focus on the identification, evaluation, and characterization of
factors to address the main question of what populations and lifestages are at increased
risk of an SCh-related health effect. Some factors may lead to a reduction in risk, and
these are recognized during the evaluation process, but for the purposes of identifying
those populations or lifestages at greatest risk to inform decisions on the NAAQS, the
focus of this chapter is on characterizing those factors that may increase risk.
Individuals, and ultimately populations, can be at increased risk of an air pollutant-related
health effect in a number of ways. As discussed in the Preamble to the ISAs (U.S. EPA.
2015b). risk may be modified by intrinsic or extrinsic factors that act synergistically with
SO2 on a health endpoint (e.g., sociodemographic or behavioral factors), differences in
internal dose (e.g., due to variability in ventilation rates or exercise behaviors), or
differences in exposure to air pollutant concentrations (e.g., more time spent in areas with
higher ambient concentrations). The objective of this chapter is to identify, evaluate, and
characterize the evidence for factors that potentially increase the risk of health effects
related to exposure to SO2. Note also that although individual factors that may increase
the risk of an SC>2-related health effect are discussed in this chapter, it is likely in many
cases that portions of the population are at increased risk of an S02-related health effect
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due to a combination of factors [e.g., residential location and socioeconomic status
(SES)], but information on the interaction among factors remains limited. Thus, the
following sections identify, evaluate, and characterize the overall confidence that
individual factors potentially result in increased risk for SCh-related health effects [see
Preamble to the ISAs (U.S. EPA. 2015b)I.
6.2 Approach to Evaluating and Characterizing the Evidence
for At Risk Factors
The systematic approach used to evaluate factors that may increase the risk of a
population or specific lifestage to an air pollutant-related health effect is described in
more detail in the Preamble to the ISAs (U.S. EPA. 2015b). The evidence evaluated
includes relevant studies discussed in Chapter 5 of this ISA and builds on the evidence
presented in the 2008 ISA for Sulfur Oxides (U.S. EP A. 2008d). Based on the approach
developed in previous ISAs (U.S. EP A. 2016e. 2013b. c) evidence is integrated across
scientific disciplines, across health effects, and where available, with information on
exposure and dosimetry (Chapter 3 and Chapter 4). Greater emphasis is placed on those
health outcomes for which a "causal" relationship was concluded in Chapter 5 of this
ISA, while information from studies of health outcomes for which the causal
determination is "suggestive" is only used as supporting evidence where warranted.
Studies examining health outcomes for which an "inadequate" relationship was
concluded are not included in this chapter due to the uncertainty in the independent
association between exposure to SO2 and the health outcome; as a result, these studies are
unable to provide information on whether certain populations are at increased risk of
S02-related health effects. Conclusions are drawn based on the overall confidence that a
specific factor may result in a population or lifestage being at increased risk of an
S02-related health effect.
As discussed in the Preamble to the ISAs (U.S. EP A. 2015b). this evaluation includes
evidence from epidemiologic, controlled human exposure, and toxicological studies in
addition to considering relevant exposure-related information. With regard to
epidemiologic studies, the evaluation focuses on those studies that include stratified
analyses to compare populations or lifestages exposed to similar air pollutant
concentrations within the same study design along with consideration of the strengths and
limitations of each study. Other epidemiologic studies that do not stratify results but
instead examine a specific population or lifestage can provide supporting evidence for the
pattern of associations observed in studies that formally examine effect modification.
Similar to the characterization of evidence in Chapter 5. statistical significance is not the
sole criterion by which effect modification is determined; the greatest emphasis is placed
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on patterns or trends in results across studies. Experimental studies in human subjects or
animal models that focus on factors, such as genetic background or health status, are
evaluated because they provide coherence and biological plausibility of effects observed
in epidemiologic studies. Also evaluated are studies examining whether factors may
result in differential exposure to SO2 and subsequent increased risk of SCh-related health
effects.
The objective of this chapter is to identify, evaluate, and characterize the overall
confidence that various factors may increase the risk of an SCh-related health effect in a
population or lifestage, building on the conclusions drawn in the ISA with respect to SO2
exposure and health effects. The broad categories of factors evaluated in this chapter
include pre-existing disease/condition (Section 6.3). genetic factors (Section 6.4). and
sociodemographic and behavioral factors (Section 6.5). Formal conclusions are made
with respect to whether a specific factor increases the risk of an SCh-related health effect
based on the characterization of evidence framework detailed in Table 6-1. A summary of
the characterization of the evidence for each factor considered in this chapter is presented
in Section 6.6.
Table 6-1 Characterization of evidence for factors potentially increasing the
risk for sulfur dioxide-related health effects.
Classification Health Effects
Adequate There is substantial, consistent evidence within a discipline to conclude that a factor results in a
evidence population or lifestage being at increased risk of air pollutant-related health effect(s) relative to
some reference population or lifestage. Where applicable, this evidence includes coherence
across disciplines. Evidence includes multiple high-quality studies.
Suggestive The collective evidence suggests that a factor results in a population or lifestage being at
evidence increased risk of air pollutant-related health effect(s) relative to some reference population or
lifestage, but the evidence is limited due to some inconsistency within a discipline or, where
applicable, a lack of coherence across disciplines.
Inadequate The collective evidence is inadequate to determine whether a factor results in a population or
evidence lifestage being at increased risk of air pollutant-related health effect(s) relative to some reference
population or lifestage. The available studies are of insufficient quantity, quality, consistency,
and/or statistical power to permit a conclusion to be drawn.
Evidence of no There is substantial, consistent evidence within a discipline to conclude that a factor does not
effect result in a population or lifestage being at increased risk of air pollutant-related health effect(s)
relative to some reference population or lifestage. Where applicable, the evidence includes
coherence across disciplines. Evidence includes multiple high-quality studies.
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6.3
Pre-existing Disease/Condition
Individuals with pre-existing disease may be considered at greater risk for some air
pollution-related health effects because they are likely in a compromised biological state
depending on the disease and severity. The 2008 ISA for Sulfur Oxides (U.S. EPA.
2008d) concluded that those with pre-existing pulmonary conditions were likely to be at
greater risk for SCh-related health effects, especially individuals with asthma. Of the
recent epidemiologic studies evaluating effect modification of respiratory effects by
pre-existing disease, most focused on asthma (Section 6.3.1). Table 6-2 presents the
prevalence of asthma and other respiratory diseases according to the Centers for Disease
Control and Prevention's (CDC's) National Center for Health Statistics (Schiller et al.
2012). including the proportion of adults with a current diagnosis categorized by age and
geographic region. The large proportions of the U.S. population affected by many chronic
diseases indicates the potential public health impact, and thus, the importance of
characterizing the risk of S02-related health effects for affected populations.
Table 6-2 Prevalence of respiratory diseases among adults by age and region
in the U.S. in 2012.
Adults (18+)
Age (%f
Region (%)b
Chronic
Disease/Condition
N (in
Thousands)
<18°
18-44
45-64
65-74
75+
North-
east
Midwest
South
West
All (N, in thousands)
234,921
6,292
111,034
82,038
23,760
18,089
42,760
53,378
85,578
53,205
Selected respiratory diseases
Asthmad
24,009
8.6
8.1
8.4
7.8
6.0
9.2
8.1
7.3
7.8
COPD—chronic
bronchitis
8,658
-
2.5
4.7
4.9
5.2
3.2
4.4
3.9
2.4
COPD—emphysema
4,108
-
0.3
2.3
4.7
4.7
1.3
2.0
1.9
1.0
COPD = chronic obstructive pulmonary disease; N = population number.
Percentage of individual adults and children within each age group with disease, based on N (at the top of each age column).
Percentage of individual adults (18+) within each geographic region with disease, based on N (at the top of each region
column).
statistics for <18 category from http://www.cdc.gov/asthma/most recent data.htm, last updated March 2016; accessed on July
28, 2016.
dAsthma prevalence is reported for "still has asthma."
Source: Blackwell et al. (2014): National Center for Health Statistics: Data from Tables 1-4, 7, 8, 28, and 29 of the Centers for
Disease Control and Prevention report.
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6.3.1
Asthma
Approximately 8.0% of adults and 8.6% of children (age <18 years) in the U.S. currently
have asthma (Blackwell et al.. 2014; Bloom et al.. 2013). and it is the leading chronic
illness affecting children (Bloom et al.. 2013). Based on evidence from the 2008 ISA for
Sulfur Oxides (U.S. EPA. 2008d) and recent studies, Chapter 5 concludes that a causal
relationship exists between short-term SO2 exposure and respiratory effects, based
primarily on evidence from controlled human exposure studies demonstrating decrements
in lung function in individuals with asthma (Section 5.2.1.2 and Section 5.2.1.9). This is
nearly the same body of evidence evaluated in the 2008 ISA for Sulfur Oxides (U.S.
EPA. 2008d). which also concluded that individuals with asthma are more sensitive to
exposures to ambient SO2. Children with asthma may be particularly at risk compared to
adults with asthma due to (1) their increased responsiveness to methacholine, a potential
surrogate for SO2 (Section 5.2.1.2). relative to adults; (2) children's increased ventilation
rates relative to body mass compared to adults; and (3) the increased proportion of oral
breathing observed among children, particularly boys, relative to adults (Section 4.1.2).
In addition, children tend to spend more time outdoors (where SO2 levels are higher,
compared to indoor levels), and have the potential to be exposed to higher levels of SO2.
Such oral breathing allows greater SO2 penetration into the tracheobronchial region of the
lower airways than nasal breathing (Section 4.2.2). This section briefly describes
evidence from the experimental studies and supporting evidence from epidemiologic
studies (Table 6-3).
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Table 6-3 Controlled human exposure, epidemiology, and animal toxicology
studies evaluating pre-existing asthma and sulfur dioxide exposure.
Direction of Effect
Factor Reference Modification or Study
Evaluated Category Effect3 Outcome Population13 Study Details Study
Controlled human exposure
Asthma,
Healthy
f Decrements in
n = 9
1 ppm
Koenia et
adolescents
adults
Vmax75 and Vmax50
adolescents
SO2 + 1 mg/m3
al. (1980)
(14-18 yr)
(21-55 yr)
NaCI droplet,
Decrements in
1 mg/m3 NaCI
sRaw and FEVi
droplet for
60 min at rest
Asthma
Healthy
f Lung function
n = 4 normal
0.2, 0.4, 0.6 ppm
Linn et al.
(atopic)
(sRaw)
adults,
SO2 for 1 h with
(1987)
n = 21 atopic
exercise;
Mild asthma
T
adults
Exposures were
n = 16 adults
repeated eight
Moderate/
T
with mild
times
severe
asthma
asthma
n = 24 adults
with
Asthma
Healthy
f Lung function
moderate/
(atopic)
(FEVi)
severe
asthma
Mild asthma
T
Moderate/
T
severe
asthma
Asthma
Healthy
f Respiratory
(atopic)
symptoms during
exposure
Mild asthma
T
Moderate/
T
severe
asthma
Asthma
Healthy
f Lung function
n = 46 adults
0.5 ppm SO2 for
Maanussen
(sRaw)
with
10 min tidal
et al.
bronchial
breathing,
(1990)
asthma,
10 min of
12 healthy
isocapnic
adults
hyperventilation
(30 L/min);
Histamine
challenge
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Table 6-3 (Continued): Controlled human exposure, epidemiology, and animal
toxicology studies evaluating pre existing asthma and
sulfur dioxide exposure.
Factor
Evaluated
Reference
Category
Direction of Effect
Modification or
Effect3
Outcome
Study
Population13
Study Details Study
Asthma
Healthy
Lung function
n = 12 adults
0.2 ppm SO2 for
Tunnicliffe
LL
LU
o~
>
LL
>
LU
LL
with asthma,
1 h at rest
et al.
12 healthy
(2003)
adults
Epidemiology
With asthma
Without
Lung function (PEF)
n = 506
Guadeloupe
Amadeo et
n = 84
asthma
elementary
(French West
al. (2015)
n =422
school
Indies)
children
December
ages
2008-December
8-13 yr
2009
With asthma
Without
Oxidative stress
n = 36
Beijing, China
Lin et al.
n = 8
asthma
(8-oxo-7,8-dihydro-
elementary
June
(2015)
2 -deoxyguanosine
school
2007-September
n =28
and malondi-
children
2008
aldehyde)
(fourth
grade, mean
age 10.6 yr)
Toxicology
Rat asthma Normal
f AHR (metha-
Rats
2 ppm SO2 for Sona et al.
model (OVA rats
choline)
(Sprague-
4 h/d for 4 wk (2012)
sensitization)
¦ Dawley),
beginning at 15 d
T IL-4 in BALF
n = 10
males/group
IFN-y in BALF
(4 wk)
f Airway smooth
muscle cell stiffness
(in vitro)
f Airway smooth
muscle cell
contractility (in vitro)
AHR = airway hyperresponsiveness; BALF = bronchoalveolar lavage fluid; FE\A| = forced expiratory volume in 1 sec; FVC = forced
vital capacity; IFN-y = interferon gamma; IL-4 = interleukin 4; MMEF = maximum mid-expiratory flow; n = sample size;
NaCI = sodium chloride; OVA = ovalbumin; PEF = peak expiratory flow; S02 = sulfur dioxide; sRAW = specific airway resistance;
Vmax5o = maximal expiratory flow rate at 50%; Vmax75 = maximal expiratory flow rate at 75%.
aUp facing arrow (j) indicates that the effect of S02 is greater (e.g., larger lung function decrement, larger increase in airway
inflammation) in the group with the factor evaluated than in the reference group. Down facing arrow Q) indicates that the effect of
S02 is smaller in the group with the factor evaluated than in the reference group. A dash (-) indicates no substantial difference in
S02-related health effect between groups. In some studies, only a population with pre-existing disease was examined; therefore,
the arrow or dash represents the direction of the effect in that population after exposure to S02 relative to exposure to filtered air.
bUnless ages are indicated in the row for each study, the mean age or range was not reported in the study aside from indication of
adult subjects.
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Across experimental evidence, adults with asthma consistently have greater decrements
in lung function with SO2 exposure than those without asthma. Controlled human
exposure studies have evaluated respiratory outcomes among adults at SO2
concentrations ranging from 0.2 to 1 ppm and included exposures with and without
exercise. Linn et al. (1987) conducted an extensive study examining several
concentrations of SO2 with repeated exposures in healthy individuals, individuals with
mild asthma, individuals with atopic asthma, and individuals with moderate/severe
asthma and reported respiratory effects (airway resistance, FEVi, symptoms) with
increasing SO2 exposures according to clinical status, with individuals having moderate
and severe asthma showing the greatest SC>2-dependent effects. In addition, subject-level
characteristics other than clinical status did not influence response. Magnussen et al.
(1990) also reported greater decrements in sRaw in subjects with asthma relative to
healthy controls with SO2 exposures incorporating exercise; however, consistent
decrements in lung function were not observed in adults and adolescents with asthma
relative to healthy controls when exposed at rest (Tunnicliffe et al.. 2003; kocnig et al..
1980). It is important to note that these studies were limited by exposure design and small
sample sizes. In addition to controlled human exposure studies, a long-term exposure
study conducted in ovalbumin (OVA)-sensitized rats as an asthma model demonstrated
that 4 weeks of exposure to 2 ppm SO2 resulted in increased airway resistance compared
to normal rats (Song et al.. 2012).
Of the literature included in this ISA, two epidemiologic studies included stratification by
asthma status and did not find differences for short-term exposure to ambient SChwith
respiratory outcomes liable 6-3; (Amadeo et al.. 2015; Lin etal.. 2015)1. However,
evidence presented in Section 5.2.1.2 generally demonstrates consistent positive
associations between ambient SO2 concentrations and asthma-related hospitalizations and
ED visits. In addition, some evidence from recent panel studies and studies reviewed in
the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) indicates that children with asthma
experience respiratory symptoms associated with exposure to ambient SO2.
In conclusion, evidence from controlled human exposure studies and animal toxicology
studies is consistent in demonstrating decrements in lung function with SO2 exposures.
There is also clear biological plausibility, including key events contributing to the mode
of action (Section 4.3). supporting the observed effects from experimental studies.
Furthermore, epidemiologic studies report associations between SO2 exposure and
emergency department visits and hospital admissions due to asthma, and that individuals
with asthma experience respiratory symptoms associated with exposure to ambient SO2.
Overall, there is adequate evidence from multiple, high-quality studies and coherence
across scientific disciplines to conclude that people with pre-existing asthma are at
increased risk of S02-induced respiratory effects.
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29
30
31
6.4
Genetic Factors
Genetic variation in the human population is known to contribute to numerous diseases
and differential physiologic responses. The 2008 ISA for Sulfur Oxides (U.S. EPA.
2008d) discussed the biological plausibility of individuals with certain genotypes known
to result in reduced function in genes encoding antioxidant enzymes being at increased
risk for respiratory effects related to ambient air pollution. However, the evidence base
was limited to two studies demonstrating individuals with polymorphisms in GSTP1 and
tumor necrosis factor to be at increased risk for SC>2-related asthma and decrements in
lung function. A recently conducted study reviewed in this ISA examined effect measure
modification by genotype (Reddv et al.. 2012) and reported inconsistent results across
GSTM1 and GSTP1 genotypes in a relatively small sample of children in South Africa.
The GSTM1 null genotype and the GSTP1 Ilel05Ile and He 105 Val genotypes are
associated with reduced antioxidant enzyme function; however, effect measure
modification of these genotypes on SCh-associated intra-day variability of FEVi showed
conflicting results. Despite biological plausibility, the limited and inconsistent evidence
base is inadequate to determine whether genetic background contributes to increased risk
for SC>2-related health effects.
The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d) discussed some evidence for
increased risk of health effects related to SO2 exposure among different lifestages
(i.e., children and older adults). Lifestage refers to a distinguishable time frame in an
individual's life characterized by unique and relatively stable behavioral or physiological
characteristics associated with development and growth (U.S. EPA. 2014b). Differential
health effects of SO2 across lifestages theoretically could be due to several factors. With
regard to children, the human respiratory system is not fully developed until 18-20 years
of age, and therefore, children could plausibly have intrinsic risk for respiratory effects
due to potential perturbations in normal lung development (Finkelstein and Johnston.
2004). Older adults (typically considered those 65 years of age or greater) have weakened
immune function, impaired healing, decrements in pulmonary and cardiovascular
function, and greater prevalence of chronic disease I (Rosenthal and Kavic. 2004);
Table 6-21. which may contribute to or worsen health effects related to SO2 exposure.
Also, exposure or internal dose of SO2 may vary across lifestages due to varying
ventilation rates, increased oronasal breathing at rest, and time-activity patterns.
6.5
Sociodemographic and Behavioral Factors
6.5.1
Lifestage
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The following sections present the evidence comparing lifestages from the recent
literature, which builds on the evidence presented in the 2008 ISA for Sulfur Oxides
(U.S. EPA. 2008d).
6.5.1.1 Children
According to the 2010 census, 24% of the U.S. population is less than 18 years of age,
with 6.5% less than age 6 (Howden and Mover. 2011). The large proportion of children
within the U.S. demonstrates the public health importance of characterizing the risk of
SCh-related health effects among children. This is especially so because of the causal
relationship between ambient SO2 exposure and respiratory outcomes, with strong
evidence demonstrating lung function decrements in individuals with asthma, which
affects approximately 11% of children 5 years and older. The 2008 ISA for Sulfur Oxides
(U.S. EPA. 2008d) presented evidence indicating an increased risk of S02-related
respiratory outcomes in children compared to adults; however, recent evidence is not
entirely consistent with the evidence considered previously (Table 6-4). Although Son et
al. (2013) found children (0-14 years) to be at greater risk for S02-related asthma
hospital admissions, neither Ko et al. (2007b) nor Alhanti et al. (2016) observed
differences between children and adults when examining associations of ambient SO2 and
asthma hospitalizations or emergency department visits. When examining evidence for
different age groups of children, Jalaludin et al. (2008) observed that associations for
respiratory-related ED visits among children ages 1-4 years were greater than for
children ages 10-14 years; however, Samoli et al. (2011) and Villeneuve et al. (2007) did
not find stronger associations for asthma-related hospital admissions or ED visits among
younger children. Similarly, Dong et al. (2013c) did not find age-related differences
among children for S02-associated asthma, and Sahsuvaroglu et al. (2009) found children
ages 6-7 years had smaller S02-associated nonallergic asthma compared to adolescents
at 13-14 years.
Overall, the combined evidence from the previous and current ISA examining respiratory
outcomes across lifestages is suggestive of increased risk in children, given the
inconsistencies across epidemiologic studies and limited toxicological evidence to inform
plausibility. There are biological factors (e.g., increased ventilation rates relative to body
mass among children and increased oral breathing that lead to greater SO2 penetration
and bronchial surface doses) that could support increased risk to children. However,
recent evidence, mainly from epidemiologic studies of respiratory ED visits and hospital
admissions, does not consistently show increased risk among children (Table 6-4).
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Table 6-4 Epidemiologic studies evaluating childhood lifestage and sulfur
dioxide exposure.
Direction of
Factor
Reference
Effect
Study
Evaluated
Category
Modification3
Outcome
Population
Study Details
Study
Short-term exposure
Childhood
All ages
1
Hospital
14 hospitals
Hong Kong,
Wona et al. (2009)
ages 0-14 yr
n = 104.9/d
admissions
China
n = 60.1/d
for acute
1996-2002
respiratory
distress
Childhood
Adulthood
_
Asthma
15 hospitals
Hong Kong,
Ko et al. (2007b)
ages 0-14 yr
ages 15-65 yr
hospital
n = 69,176
China
n =23,596
n = 21,204
admissions
admissions
2000-2005
Childhood
Adulthood
T
Asthma
Database
Eight South
Son et al. (2013)
ages 0-14 yr
ages 15-64 yr
hospital
accounting for
Korean cities
n = 8.7/d
n = 4.3/d
admissions
48% of South
2003-2008
Korean
population
n = 19/d
Childhood
Childhood
_
Asthma
Three main
Athens,
Samoli et al. (2011)
ages 0-4 yr
ages 5-14 yr
hospital
children's
Greece
n = 72%
n = 28%
admissions
hospitals
2001-2004
approximately
85% of pediatric
beds of
metropolitan
area of Athens
n = 3,601
Childhood
Childhood
_
Asthma ED
Five hospitals
Edmonton,
Villeneuve et al.
ages 2-4 yr
ages 5-14 yr
visits
servicing more
Canada
(2007)
n = 7,247
n = 13,145
than 80% of the
1992-2002
metropolitan
area
n = 57,192
visits
Childhood
Childhood
T
Respiratory-
Daily number of
Sydney,
Jalaludin et al.
ages 1-4 yr
ages 10-14 yr
related ED
ED visits in
Australia
(2008)
n = 109/d
n = 25/d
visits
metropolitan
1997-2001
Sydney from
the New South
Wales Health
Department
n = 174/d
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Table 6-4 (Continued): Epidemiologic studies evaluating childhood lifestage and
sulfur dioxide exposure.
Direction of
Factor Reference Effect Study
Evaluated Category Modification3 Outcome Population Study Details Study
Childhood
ages 5-18
n = 59.6/d
Adulthood
yr ages 19-39
n = 41.1/d
yr
Asthma ED
visits
Daily number of
ED visits in
metropolitan
area
n = 62.8/d
(Atlanta)
n = 76.3/d
(Dallas)
n = 50.6/d (St.
Louis)
Three U.S.
cities (Atlanta,
GA
1993-2009;
Dallas, TX
2006-2009;
St. Louis, MO
2001-2007)
Alhanti et al. (2016)
Long-term exposure
Childhood
ages 2-5 yr
n = 7,508
Childhood
ages 6-14 yr
n = 23,541
Doctor-
diagnosed
asthma
Respiratory
symptoms
(cough,
phlegm,
current
wheeze)
n = 31,049
Children
ages 2-14 yr
Seven
northeastern
cities study,
Liaoning
Provence,
northeast
China
2008-2009
Dona et al. (2013c)
Younger Older children J, Non-allerg
children ages 13-14 yr asthma
ages 6-7 yr n = 549
n = 918
ic n~ 1,467 Hamilton, Sahsuvaroalu et al
Children grades Canada (2009)
1 (ages 6-7 yr) 1994-1995
and 8 (ages
13-14 yr)
ED = emergency department; n = sample size.
aUp facing arrow indicates that the effect of is greater (e.g., larger increase in hospital admission) in the group with the factor
evaluated than in the reference group. Down facing arrow indicates that the effect of sulfur dioxide is smaller in the group with the
factor evaluated than in the reference group. A dash indicates no substantial difference in sulfur dioxide-related health effect
between groups.
6.5.1.2 Older Adults
1 According to the 2008 National Population Projections issued by the U.S. Census
2 Bureau, approximately 12.9% of the U.S. population is age 65 years or older, and by
3 2030, this fraction is estimated to grow to 20% (Vincent and Velkoff. 2010). Thus, this
4 lifestage represents a substantial proportion of the U.S. population that is potentially at
5 increased risk for health effects related to SO2 exposure.
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25
26
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The 2008 ISA for Sulfur Oxides (U.S. EPA. 2008(1) indicated that compared with
younger adults, older adults (typically ages 65 years and older) may be at increased risk
for SC>2-related respiratory emergency department visits and hospitalizations, but limited
evidence was available to inform risk related to respiratory effects. Recently published
studies evaluating risk in older adults compared to younger adults are characterized in
Table 6-5 and generally support conclusions from the 2008 ISA for Sulfur Oxides (U.S.
EPA. 2008d). Villeneuve et al. (2007) and Son et al. (2013) both reported that
asthma-related ED visits and hospital admissions were more strongly associated with
short-term ambient SO2 exposure in individuals older than 75 years than adults
65-74 years or those younger than 65. However, the handful of recent studies evaluating
asthma and nonasthma respiratory admissions or ED visits in adults greater than 65 years
of age reported inconsistent results compared to the earlier literature (Alhanti et al.. 2016;
Son etal.. 2013; Arbex et al.. 2009; Wong et al.. 2009; Ko et al.. 2007b). In addition to
these studies of short-term SO2 exposure, Forbes et al. (2009c) found older adults (45-74
and older than 75 years) to have larger decrements in lung function compared to adults
aged 16-44. Additionally, Bravo et al. (2015). Chen et al. (2012c). and Wong et al.
(2008b) found evidence for increased risk of total mortality with short-term SO2
exposures in adults older than 75 years compared to other age groups, which is consistent
with age-specific evidence from respiratory studies. Evidence examining short-term SO2
exposure and total mortality is suggestive of, but not sufficient to infer, a causal
relationship (Section 5.5.1).
Taken together, the collective evidence builds on conclusions from the previous ISA and
is suggestive that older adults may be at increased risk for S02-related health effects.
The evidence from the current and previous ISA related to respiratory hospitalizations
and ED visits indicates that older adults, particularly those older than 75 years, may be at
increased risk for S02-related health effects, although this evidence is not entirely
consistent. Evidence is much more consistent for total mortality, demonstrating that older
adults (>65 or 75 years) are at greater risk than younger individuals, although there is
uncertainty in the independent association between short-term SO2 exposure and total
mortality.
6.5.2 Sex
A vast number of health conditions and diseases have been shown to differ by sex, and
there is some indication of differences by sex in the relationship between air pollution
and health effects. The 2010 U.S. census indicates an approximately equal distribution of
males and females in the U.S.: 49.2% male and 50.8% female (Howden and Mover.
2011). However, the distribution varies by age, with a greater prevalence of females
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above 65 years of age compared to males. Thus, the public health implications of
potential sex-based differences in air pollution-related health effects may vary among age
groups within the population.
Table 6-5
Epidemiologic studies evaluating older adult lifestage and sulfur
dioxide exposure.
Direction of
Factor
Reference
Effect
Study
Evaluated
Category
Modification3
Outcome
Study Population
Details
Study
Older
Younger
_
Asthma
15 hospitals
Hong Kong,
Ko et al. (2007b)
adulthood
adulthood
hospital
n = 69,176 admissions
China
ages >65 yr
ages
admissions
2000-2005
n =24,916
15-65 yr
n = 21,204
Older
Younger
_
Asthma ED
Five hospitals
Edmonton,
Villeneuve et al.
adulthood
adulthood
visits
n = 57,912 visits
Canada
(2007)
ages
ages
1992-2002
65-74 yr
15-64 yr
n =4,705
n = 32,815
Older
T
adulthood
ages >75 yr
n = 1,855
Adulthood
Adulthood
_
Asthma ED
Daily number of ED
Three U.S.
Alhanti etal. (2016)
ages 65+ yr
ages
visits
visits in metropolitan
cities
n = 4.7/d
19-39 yr
area
(Atlanta,
n = 41.1/d
n = 62.8/d (Atlanta)
GA
1993-2009;
n = 76.3/d (Dallas)
Dallas, TX
n = 50.6/d (St. Louis)
2006-2009;
St. Louis,
MO
2001-2007)
Older
Younger
T
COPDED
Sao Paulo Hospital,
Sao Paulo,
Arbex et al. (2009)
adulthood
adulthood
visits
daily records for
Brazil
ages >65 yr
ages
patients >40 yr
2001-2003
n = 789
40-64 yr
n = 1,769
n = 980
Older
Younger
_
Asthma and
Hospital admission
Eight South
Son etal. (2013)
adulthood
adulthood
allergic
database accounting
Korean
ages
ages
disease
for 48% of Korean
cities
65-74 yr
15-64 yr
hospital
population
2003-2008
n = 5.8/d
n = 8.8/d
admissions
n = 37.7/d
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Table 6-5 (Continued): Epidemiologic studies evaluating older adult lifestage and
sulfur dioxide exposure.
Direction of
Factor
Reference
Effect
Study
Evaluated
Category
Modification3
Outcome
Study Population
Details
Study
Older
Younger
T
adulthood
adulthood
ages >75 yr
ages
n = 5.8/d
15-64 yr
n = 8.8/d
Older
All ages
_
COPD
14 hospitals
Hong Kong,
Wona et al. (2009)
adulthood
n = 91.5
hospital
China
ages >65 yr
admissions
1996-2002
n = 59.6
Older
All ages
_
Respiratory
adulthood
n = 270.3
disease
ages >65 yr
hospital
n = 138.5
admissions
Older
Adulthood,
T
Total
Data from Municipal
17 Chinese
Chen et al. (2012c)
adulthood
childhood
mortality
Centers for Disease
cities
ages
ages
Control and
>65 yrb
5-64 yrb
Prevention
Older
All ages
T
Total
Data from the Ministry
Bangkok,
Wona et al. (2008b)
adulthood
(>65 yr)
mortality
of Public Health,
Thailand;
ages >75 yr
Bangkok; the Census
Hong Kong,
and Statistic
Shanghai,
Department, Hong
and
Kong; the Shanghai
Wuhan,
Municipal Center of
China
Disease Control and
1996-2004
Prevention, Shanghai;
and the Wuhan Centre
for Disease Prevention
and Control
Older
Ages 35-64
T
Mortality
N = 849,127
Sao Paulo,
Bravo et al. (2015)
adulthood
n = 315,435
Brazil
ages
May 1996-
65-74 yr
December
n = 194,202
2010
Older Ages 35-64 f
adulthood n = 315,435
ages >75 yr
n = 339,490
COPD = chronic obstructive pulmonary disease; ED = emergency department; n = sample size.
aUp facing arrow indicates that the effect of sulfur dioxide is greater (e.g., larger risk of hospital admission, larger decrement in
heart rate variability) in the group with the factor evaluated than in the reference group. Down facing arrow indicates that the effect
of sulfur dioxide is smaller in the group with the factor evaluated than in the reference group. A dash indicates no substantial
difference in sulfur dioxide-related health effect between groups.
bSample size not reported.
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There are a number of studies evaluating sex-based differences in SCh-associated health
effects, as detailed in Table 6-6. Studies of short-term SO2 exposures and respiratory
effects in children and adults did not consistently indicate differences by sex. Ishigami et
al. (2008) found adult females to have increased respiratory symptoms with ambient SO2
exposure compared to adult males; however, Son et al. (2013) found larger associations
for asthma or allergic disease hospitalizations for males compared to females. No
differences were found between men and women for S02-related COPD ED visits (Arbex
et al.. 2009). In children, S02-associated decrements in lung function were not different
between boys and girls (Linares et al.. 2010; Dales et al.. 2009). although Samoli et al.
(2011) found boys to have higher associations between ambient SO2 exposure and asthma
hospital admissions. In a long-term SO2 exposure study, Deng et al. (2015a) observed
stronger associations with asthma incidence among boys compared to girls.
The collective body of evidence does not clearly indicate that S02-related health effects
differ between males and females. Due to the inconsistent results demonstrated across
epidemiologic studies and a lack of experimental studies examining sex-based
differences, the evidence is inadequate to determine whether males or females may be at
increased risk for S02-related health effects.
6.5.3 Socioeconomic Status
SES is a composite measure that usually consists of economic status measured by
income, social status measured by education, and work status measured by occupation.
Generally, persons with lower SES have been found to have a higher prevalence of
pre-existing diseases, potential inequities in access to resources such as healthcare, and
possibly increased nutritional deficiencies, which may increase their risk to SC>2-related
health effects (Wong et al.. 2008a; WHO. 2006). According to U.S. census data, 15.9%
(approximately 48.5 million) of Americans lived below the poverty threshold in 2011 as
defined by household income, which is one metric used to define SES (Bishaw. 2012).
The wide array of SES factors that can be used to describe or assign SES can complicate
any synthesis of findings because definitions of SES vary across countries based on
population demographics, bureaucracy, and the local economy. As a result of these
complexities, the ability to draw conclusions regarding SES as a factor for increased risk
for health effects related to SO2 exposure can be difficult.
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Table 6-6 Epidemiologic studies evaluating effect modification by sex and
sulfur dioxide exposure.
Direction of
Factor
Reference
Effect
Study
Evaluated
Category
Modification3
Outcome
Population
Study Details
Study
Short-term exposure
Female
Male
T
Respiratory
Healthy adult
Miyakejima Island,
Ishiaami et al.
20%
80% person
symptoms
volunteers
Japan
(2008)
person h
h
(cough, scratchy working on an
2005
throat, sore
active
throat,
volcanic
breathlessness)
island after
the
evacuation
order was
lifted
n = 955
Female
Male
_
Lung function
Elementary
Windsor, Canada
Dales et al.
n = 39
n = 114
(FEVi)
school
October-December
(2009)
children with
2005
asthma (no
cigarette
smoking in
home)
n = 182
children (ages
9-14 yr)
Female
Male
_
Lung function
Children
Salamanca, Mexico
Linares et al.
n =235
n =229
(FEVi, FVC,
recruited from
2004-2005
(2010)
PEF,
two schools
FEVi/FVC)
with different
roadway
proximity
n = 464
(6-14 yr)
Female
Male
_
COPD ED visits
Sao Paulo
Sao Paulo, Brazil
Arbex et al.
n = 794
n = 875
Hospital, daily
2001-2003
(2009)
records for
patients
>40 yr
n = 1,769
Female
Male
1
Asthma hospital
Eight South Korean
Son et al. (2013)
n = 7.4
n = 8
admissions
cities
admissions/
admissions/
2003-2008
d
d
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Table 6-6 (Continued): Epidemiologic studies evaluating effect modification by
sex and sulfur dioxide exposure.
Direction of
Factor Reference Effect Study
Evaluated Category Modification3 Outcome Population Study Details Study
Female Male J, Allergic disease Database
n = 7 1 n = 8 hospital accounting for
admissions/ admissions/ admissions 48% of South
d d Korean
population
n = 19/d
Asthma hospital Three main Athens, Greece Samoli et al
admissions children's 2001-2004 (2011)
hospitals—
approximately
85% of
pediatric beds
of
metropolitan
area of
Athens
n = 3,601
Long-term exposure
Female Male J, Asthma Children from Changsha, China
n = 1 153 n = 1 337 incidence 36 different
kindergartens
n = 2,490
COPD = chronic obstructive pulmonary disease; ED = emergency department; FE\A| = forced expiratory volume in 1 sec;
FVC = forced vital capacity; n = sample size; PEF = peak expiratory flow.
aUp facing arrow indicates that the effect of S02 is greater (e.g., larger risk of hospital admission) in the group with the factor
evaluated than in the reference group. Down facing arrow indicates that the effect of sulfur dioxide is smaller in the group with the
factor evaluated than in the reference group. A dash indicates no substantial difference in sulfur dioxide-related health effect
between groups.
A single study (Cakmak et al.. 2016) evaluated the potential for SES (income or
education) to modify the effect of long-term exposure to SO2 on respiratory effects,
specifically measures of lung function. The authors observed greater decrements in lung
function for those in the lowest income and education groups when compared to those in
the highest. In addition, a study evaluated effect modification by education on
SCh-associated health outcomes. Chen et al. (2012c) found lower education to increase
risk for mortality with short-term SO2 exposure. Overall, the evidence for effect
modification by SES on SC>2-related health outcomes is limited to a single study of
respiratory health effects and one of mortality. Evidence examining short-term SO2
exposure and total mortality is suggestive of, but not sufficient to infer, a causal
relationship (Section 5.5.1). This limited evidence is inadequate to determine whether
low SES increases risk for SCh-related health effects.
Female Male
n = 1,332 n= 2,269
Deng et al
(2015a)
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6.5.4
Smoking
Smoking is a common behavior as indicated by the 2010 National Health Interview
Survey, which estimated that approximately 19.2% of the U.S. adult population report
being current smokers and 21.5% report being former smokers (Schiller et al.. 2012).
Smoking is a well-documented risk factor for many diseases, but it is unclear whether
smoking exacerbates health effects associated with air pollutant exposures, including
Dong et al. (2012). Forbes et al. (2009c). and Smith et al. (2016) investigated effect
modification of the relationship between long-term exposure to SO2 and respiratory
endpoints by smoking status. Dong et al. (2012) found that among the few respiratory
deaths included in their retrospective cohort study, associations with long-term ambient
SO2 were only present with current smoking. Smith et al. (2016) observed positive
associations between long-term average SO2 concentration and pulmonary tuberculosis
among ever smokers, but not with never smokers. Forbes et al. (2009c). on the other
hand, did not find current smoking to increase risk for lung function decrements with
long-term SO2 exposure compared to not smoking; however, former smoking did appear
to increase risk in this study.
Overall, the inconsistent evidence is inadequate to determine whether smoking
exacerbates SCh-related health effects. A limited number of long-term exposure studies
observed positive associations among current or former smokers, but not for never
smokers for various respiratory health endpoints, including respiratory mortality. No
studies evaluated smoking as an effect modifier of the relationship between short-term
exposure to SO2 and respiratory outcomes, for which there is the most confidence in the
causal nature of the relationship.
This chapter characterized factors that may result in populations and lifestages being at
increased risk for S02-related health effects; a summary of at-risk factors and resulting
evidence classifications is included in Table 6-7. The evaluation of each factor focused
on the consistency, coherence, and biological plausibility of evidence integrated across
scientific disciplines: specifically, epidemiologic, controlled human exposure, and
toxicological studies using the weight-of-evidence approach detailed in Table 6-1. In
evaluating and integrating evidence related to at-risk factors, it is important to consider
additional information including exposure concentrations, dosimetry, modes of action,
and/or the independence of relationships of SO2 exposure with health effects as detailed
S02.
6.6
Conclusions
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in Chapter 5. For many potential at-risk factors summarized in Table 6-7. the evidence
was limited with respect to ambient exposures to SO2.
Table 6-7 Summary of evidence for potential increased sulfur dioxide exposure
and increased risk of sulfur dioxide-related health effects.
Evidence Factor
Classification Evaluated
At-Risk Group
Rationale for Classification
Adequate
evidence
Pre-existing
disease
Individuals with Asthma (Section 6.3.1]
Consistent evidence for increased risk
for S02-related lung function
decrements in controlled human
exposure studies
Support provided by epidemiologic
studies of hospital admissions and ED
visits for respiratory causes
Suggestive
evidence
Lifestage
Children (Section 6.5.1.1)
Older adults (Section 6.5.1.2)
Evidence for increased risk among
children provided in previous ISA; older
studies provide biological plausibility;
recent epidemiologic studies provide
limited support, and are not entirely
consistent
Evidence for increased risk for older
adults provided in previous ISA; mixed
results in recent epidemiologic studies
for respiratory-related outcomes and
mortality
Inadequate
evidence
Genetic None identified
background
(Section 6.4)
Sex
None identified
(Section 6.5.2)
Socioeconomic
None identified
status
(Section 6.5.3)
Smoking
None identified
(Section 6.5.4)
Epidemiologic findings inconsistently
show differences in SC>2-related health
effects, show no difference, or are
limited in quantity
Uncertainty in independent relationships
with SO2 provides limited basis for
inferences about differential risk
Evidence of
no effect
None
ED = emergency department; ISA = Integrated Science Assessment; S02 = sulfur dioxide.
Consistent with observations made in the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d).
the evidence is adequate to conclude that people with asthma are at increased risk for
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SCh-related health effects. Most of the evidence for this conclusion was presented in the
previous ISA, but recent studies consistently indicate increased risk across studies.
Furthermore, the evidence is based on findings for short-term SO2 exposure and
respiratory effects (specifically lung function decrements), for which a causal relationship
exists (Section 5.2.1.9). There are a limited number of epidemiologic studies evaluating
SCh-related respiratory effects in people with asthma, but there is evidence for
asthma-related hospital admissions and emergency department visits (Section 5.2.1.2).
Further support for increased risk in individuals with asthma is provided by biological
plausibility drawn from modes of action.
There is suggestive evidence of an increased risk of S02-related respiratory effects in
children and older adults. Although the 2008 ISA for Sulfur Oxides (U.S. EPA. 2008d)
discussed several studies indicating stronger associations between SO2 and respiratory
outcomes for these lifestages, the evidence in the current ISA is less consistent. For
children, studies comparing SCh-associated respiratory outcomes reported mixed results,
but known age-related factors such as higher ventilation rates and time-activity patterns
provide plausibility for higher SO2 exposure and/or dose in children. For adults, recent
research generally finds similar associations for SCh-related respiratory outcomes or
mortality across age groups, although individuals over 75 years were more consistently at
increased risk. In addition, there was limited toxicological evidence to support
observations made across epidemiologic studies.
For all other at-risk factors considered based on information available in the studies
included in the current ISA, evidence was inadequate to determine whether those factors
result in increased risk for SCh-related health effects. Generally, there was a limited
number of studies available evaluating SES, genetic background, race/ethnicity, and
smoking. Many of these factors are interrelated and are known to impact health risks
related to air pollution in general, but the scientific evidence available in the published
literature specific to health effects associated with ambient SO2 exposure is inadequate to
determine whether these factors confer increased risk.
In conclusion, evidence is adequate to conclude that people with asthma are at increased
risk for SC>2-related health effects. Asthma prevalence in the U.S. is approximately
8-11% across age groups (Blackwell et al.. 2014; Bloom et al.. 2013). and thus,
represents a substantial fraction of the population that may be at risk for respiratory
effects related to ambient SO2 concentrations.
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exposure and other morbidity effects (i.e., eye irritation, effects on the nervous and gastrointestinal
systems).
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studies of S02 exposure and other morbidity effects (i.e., hematological and nervous system effects).
U.S. EPA (U.S. Environmental Protection Agency). (2015e). Table 5S-2. Study specific details of experimental
studies of S02 exposure and other morbidity effects (i.e., hematological and nervous system effects).
U.S. EPA (U.S. Environmental Protection Agency). (2015f). Table 5S-9. Summary of epidemiologic studies of
long-term exposure to S02 and respiratory morbidity.
U.S. EPA (U.S. Environmental Protection Agency). (2015g). Table 5S-18 Corresponding risk estimates of ambient
sulfur dioxide for hospital admissions for cardiovascular disease in studies conducting copollutants models
with N02 presented in Figure 5S-4.
U.S. EPA (U.S. Environmental Protection Agency). (2015h). Table 5S-19. Corresponding risk estimates of ambient
sulfur dioxide for hospital admissions for cardiovascular disease in studies conducting copollutants models
with PM presented in Figure 5S-5.
U.S. EPA (U.S. Environmental Protection Agency). (2015i). Table 5S-20. Summary of epidemiologic studies of
exposure to sulfur dioxide and fertility/pregnancy effects.
U.S. EPA (U.S. Environmental Protection Agency). (2015j). Table 5S-21. Summary of epidemiologic studies of
exposure to sulfur dioxide and fetal growth.
U.S. EPA (U.S. Environmental Protection Agency). (2015k). Table 5S-22. Summary of epidemiologic studies of
exposure to sulfur dioxide and preterm birth.
U.S. EPA (U.S. Environmental Protection Agency). (20151). Table 5S-23. Summary of epidemiologic studies of
exposure to sulfur dioxide and birth weight.
U.S. EPA (U.S. Environmental Protection Agency). (2015m). Table 5S-24. Summary of epidemiologic studies of
exposure to sulfur dioxide and birth defects.
U.S. EPA (U.S. Environmental Protection Agency). (2015n). Table 5S-25. Summary of epidemiologic studies of
exposure to sulfur dioxide and fetal and infant mortality.
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U.S. EPA (U.S. Environmental Protection Agency). (2015o). Table 5S-26. Summary of additional epidemiologic
studies of short-term sulfur dioxide exposure and mortality.
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online at https://www3.epa.gov/ttn/scram/llthmodconfpres.htm
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copollutant models of short term exposure to sulfur dioxide with and without CO or 03 and hospital
admissions for cardiovascular disease.
U.S. EPA (U.S. Environmental Protection Agency). (2016c). Figure 5S-4. Results of single pollutant and copollutant
models of short-term exposure to sulfur dioxide with and without N02 and hospital admissions for
cardiovascular disease.
U.S. EPA (U.S. Environmental Protection Agency). (2016d). Figure 5S-5. Results of single-pollutant and
copollutant models of short term exposure to sulfur dioxide with and without PM and hospital admissions
for cardiovascular disease.
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(final report) [EPA Report]. (EPA/600/R-15/068). Research Triangle Park, NC: U.S. Environmental
Protection Agency, National Center for Environmental Assessment.
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methods. Available online at https://www3.epa.gov/ttnamtil/files/ambient/criteria/reference-equivalent-
methods-listpdf
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term increases in ambient sulfur dioxide concentration and respiratory symptoms in adults with asthma.
U.S. EPA (U.S. Environmental Protection Agency). (2016h). Supplemental Figure 5S-2. Epidemiologic studies of
respiratory infection as self-reported by children and adults.
U.S. EPA (U.S. Environmental Protection Agency). (2016i). Supplemental Table 5S-3; Details and quantitative
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